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1
Smith, Rhodri. "Motion correction in medical imaging." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/841883/.
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It is estimated that over half of current adults within Great Britain under the age of 65 will be diagnosed with cancer at some point in their lifetime. Medical Imaging forms an essential part of cancer clinical protocols and is able to furnish morphological, metabolic and functional information. The imaging of molecular interactions of biological processes in vivo with Positron Emission Tomography (PET) is informative not only for disease detection but also therapeutic response. The qualitative and quantitative accuracy of imaging is thus vital in the extraction of meaningful and reproducible information from the images, allowing increased sensitivity and specificity in the diagnosis and precision of image guided treatment. Furthermore the utilization of complementary information obtained via Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) in integrated PET-CT and PET-MR devices offers the potential for the synergistic effects of hybrid imaging to provide increased detection and precision of diagnosis with reduced radiation dose in a fully comprehensive single imaging examination. With the increasing sophistication in imaging technology respiratory organ motion during imaging has demonstrated itself to be a major degrading factor of PET image resolution. A modest estimate of respiratory motion amplitude of 5mm, results in PET system resolution degrading from ≈ 5mm to ≈8.5mm. This evidently has an impact on cancer lesion detectability. Therefore accurate and robust methods for respiratory motion correction are required for both clinical effectiveness and economic justification for purchasing state of the art hybrid PET scanners with high resolution capabilities. In addition the judicious use of imaging resources from hybrid imaging devices coupled with advanced image processing / acquisition protocols will allow optimization of data used for improving quantitative accuracy of PET images and those used for clinical interpretation. In essence it would prove impractical to use the MR scanner purely for monitoring respiratory motion. Numerous methods exist to attempt to correct PET imaging for respiratory motion. As presented in this thesis many methods demonstrate themselves to be ineffective in the clinical setting where the patients breathing patterns appear irregular in comparison to the idealized situation of regular periodic motion. Advanced respiratory motion correction techniques utilize hybrid PET/CT, PET/MR scanners coupled with an external source of information which serves as a surrogate to build a static correspondence to the estimated internal respiratory motion. Static models however are unable to adapt to their external environment and do not consider time dependent changes in the state of a system. A further confounding factor in the development and assessment of motion correction schemes for medical imaging data is the inability to acquire volumetric data with high contrast and high spatial and temporal resolution which serves as a ground truth for quantifying model accuracy and confidence. This thesis addresses both problems by analysing respiratory motion correspondence modelling under a manifold learning and alignment paradigm which may be used to consolidate many of the respiratory motion estimation models that exist today. A Bayesian approach is adopted in this work to incorporate a-priori information into the model building stage for a more robust, flexible adaptive respiratory motion estimation / correction framework. This thesis constructs and tests the first proposed adaptive motion model to correlate a surrogate signal with internal motion. This adaptive approach allows the relationship between external surrogate signal and internal motion to change dependent upon breathing pattern and system noise. The adaptive model was compared to a state-of the-art static model and allows more accurate motion estimates to be made when the patient is breathing with an irregular pattern. Testing performed on MRI data from 9 volunteers demonstrated the adaptive model was statistically more significant (p < 0.001) in the presence of irregular motion in comparison to a static model. The adaptive Kalman model on average reduced the error in motion by 30% in comparison to the static model. Utilizing the adaptive model during a typical PET study would theoretically result in ≈ 10% increase in PET resolution in comparison to relying on a static model alone for motion correction. The adaptive Kalman model has the capability to increase the performance of PET system resolution from ≈ 8.5mm to ≈ 5.8mm, ≈ 30%. A simulated PET study also demonstrated ≈ 30% increase in tumour uptake when using motion correction. Also demonstrated in the thesis is the first method to acquire volumetric imaging data from sparse MR samples during free breathing to allow the realization of high contrast, high resolution 4D models of respiratory motion using limited acquired data. The developed framework facilitates greater freedom in the acquisition of free breathing respiratory motion sequences which may be used to inform motion modelling methods in a range of imaging modalities as well as informing the development of generalizable models of human respiration. It is shown that the developed approach can provide equivalent motion vector fields in comparison to fully sampled 4D dynamic data. The incorporation of the manifold alignment step into the sparse motion model reduces the error in motion estimates by ≈ 16%. Example images of propagated motion are also presented as supplementary information. The thesis concludes by generalizing the concepts in this work and looking to utilize the developed methods to other problems in the medical imaging arena.
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Sklyar, Andrey V. "Testing SPECT Motion Correction Algorithms." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/419.
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Frequently, testing of Single Photon Emission Computed Tomography (SPECT) motion correction algorithms is done either by using simplistic deformations that do not accurately simulate true patient motion or by applying the algorithms directly to data acquired from a real patient, where the true internal motion is unknown. In this work, we describe a way to combine these two approaches by using imaging data acquired from real volunteers to simulate the data that the motion correction algorithms would normally observe. The goal is to provide an assessment framework which can both: simulate realistic SPECT acquisitions that incorporate realistic body deformations and provide a ground truth volume to compare against. Every part of the motion correction algorithm needs to be exercised: from parameter estimation of the motion model, to the final reconstruction results. In order to build the ground truth anthropomorphic numerical phantoms, we acquire high resolution MRI scans and motion observation data of a volunteer in multiple different configurations. We then extract the organ boundaries using thresholding, active contours, and morphology. Phantoms of radioactivity uptake and density inside the body can be generated from these boundaries to be used to simulate SPECT acquisitions. We present results on extraction of the ribs, lungs, heart, spine, and the rest of the soft tissue in the thorax using our segmentation approach. In general, extracting the lungs, heart, and ribs in images that do not contain the spine works well, but the spine could be better extracted using other methods that we discuss. We also go in depth into the software development component of this work, describing the C++ coding framework we used and the High Level Interactive GUI Language (HLING). HLING solved a lot of problems but introduced a fair bit of its own. We include a set of requirements to provide a foundation for the next attempt at developing a declarative and minimally restrictive methodology for writing interactive image processing applications in C++ based on lessons learned during the development of HLING.
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Sulikowska, Aleksandra. "Motion correction in high-field MRI." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33674/.
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The work described in this thesis was conducted at the University of Nottingham in the Sir Peter Mansfield Imaging Centre, between September 2011 and 2014. Subject motion in high- resolution magnetic resonance imaging (MRI) is a major source of image artefacts. It is a very complex problem, due to variety of physical motion types, imaging techniques, or k-space trajectories. Many techniques have been proposed over the years to correct images for motion, all looking for the best practical solution in clinical scanning, which would give cost- effective, robust and high accuracy correction, without decreasing patient comfort or prolonging the scan time. Moreover, if the susceptibility induced field changes due to head rotation are large enough, they will compromise motion correction methods. In this work a method for prospective correction of head motion for MR brain imaging at 7 T was proposed. It would employ innovative NMR tracking devices not presented in literature before. The device presented in this thesis is characterized by a high accuracy of position measurements (0.06 ± 0.04 mm), is considered very practical, and stands the chance to be used in routine imaging in the future. This study also investigated the significance of the field changes induced by the susceptibility in human brain due to small head rotations (±10 deg). The size and location of these field changes were characterized, and then the effects of the changes on the image were simulated. The results have shown that the field shift may be as large as |-18.3| Hz/deg. For standard Gradient Echo sequence at 7 T and a typical head movement, the simulated image distortions were on average equal to 0.5%, and not larger than 15% of the brightest voxel. This is not likely to compromise motion correction, but may be significant in some imaging sequences.
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Darwesh, Reem. "Motion correction in nuclear medicine imaging." Thesis, University of Nottingham, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.664310.
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Patient motion either internal (organ motion) or external (body movement) can produce artefacts that can adversely affect nuclear medicine imaging. Motion artefacts can impair diagnostic information and potentially affect the image findings and prognosis for patients. The goal of this work was to investigate the effect of motion on nuclear medicine imaging and to improve image quality, lesion detectability, and tumour volume delineation by applying motion correction techniques. To investigate the effects of motion under controlled simulated conditions, a three dimensional phantom drive system was designed and constructed suitable for use with planar, SPECT, PET and CT scanners. The system was used with a range of nuclear medicine phantoms for testing proof of principle with planar, SPECT and PET imaging prior to undertake further work involving patients. Planar phantom and patient 99mTc_DMSA studies demonstrated improvements in image quality by the application of motion correction techniques. A comparison between the motion correction software using dynamic frame and list mode data showed that "MOCO" software with the use of the list mode data produced the best quantification results with phantom data, whereas determining the best approach was more difficult with patient data. The potential of using list mode data as an improved method of combining data into frames for subsequent analysis was demonstrated. Motion correction techniques would appear to offer great potential in lung imaging. Respiratory gated SPECT phantom studies have been carried out to simulate the visualisation of small defects in the lung. The CNRs and alternative free response receiver operating characteristic (AFROC) analysis have demonstrated that summing the gated data after the application of motion correction software significantly improved image quality, observer confidence and small defect detectability (less than 20 mm, p=O.0002). The results of these studies have shown the promising role of "MCFLIRT" software as a motion correction tool with gated SPECT data. Tumour volume delineation was investigated on PET images both with and without motion. The accuracy and consistency of the gradient-based software method for segmentation in PET images, which is commercially available from Mimvista Ltd was investigated. The results of comparing the measured volumes to the true volumes indicated significant differences (p=O.0005). It was found that the Signal:Background ratio and registering the PET to the CT data have significant effects on volume measurements, whereas, the effect of using different grey scale and plane of orientation were not found to have significant effects on the volume measurement. Motion correction techniques also showed to be potentially beneficial in PET imaging. Improvement in volume measurement as a result of summing the motion corrected gated data was demonstrated. The results of these studies have also shown the promising role of "MCFLIRT" as a motion correction tool with gated PET data.
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Bannister, Peter R. "Motion correction for functional magnetic resonance images." Thesis, University of Oxford, 2004. http://ora.ox.ac.uk/objects/uuid:f01d9fcb-e8bc-44ae-99a5-cc3e0bc12956.
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This work addresses the distortions in Functional Magnetic Resonance Images (FMRI) caused by subject motion. FMRI is a non-invasive technique which shows great promise in providing researchers and clinicians with neurological information both about healthy subjects and clinical patients by mapping functional activation within the brain using Echo Planar Imaging (EPI). If reliable information is to be obtained from these images, motion correction must be carried out in order to remove or suppress the artefacts arising from subject movement. This work begins by using exploratory data techniques to describe these artefacts so that they can be characterised according to their origin and spatio-temporal manifestation. Based on testing of the accuracy and consistency of existing rigid-body motion correction methods on FMRI data, a new registration algorithm Motion Correction using the FMRIB Linear Image Registration Tool (MCFLIRT) has been developed. It is shown that while MCFLIRT is both more accurate and more robust than previous methods, rigid-body registration schemes in general cannot completely remove the distortions associated with motion and so subsequent analysis of the images may still be inaccurate. Furthermore, it is demonstrated that failure to use a sufficiently detailed model of subject motion in FMRI can in fact lead to degradation of the images through the use of existing motion correction algorithms. Based on these findings, alternative schemes including non-rigid registration and adaptive real-time methods are evaluated. Leading on from this investigation, a framework for Temporally-Integrated Geometric EPI Realignment (TIGER), incorporating both spatial and temporal information about the images, is proposed. An implementation based on this novel modality-specific model is developed and tested against existing rigid-body registration methods. Results show that this new approach is able to achieve significantly more accurate results than previous methods. The quality of correction provided by this new approach brings more subtle artefacts in the data to the fore, suggesting a number of avenues of further research in this area. These are outlined in the final chapter of the thesis.
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Dikaios, Nikolaos. "Respiratory motion correction for positron emission tomography." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609967.
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Schleyer, Paul. "Respiratory motion correction in PET/CT imaging." Thesis, King's College London (University of London), 2012. https://kclpure.kcl.ac.uk/portal/en/theses/respiratory-motion-correction-in-petct-imaging(001f09fd-b405-4cbf-9ff7-9ba6541f3dab).html.
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In dual modality PET-CT imaging, respiratory motion can introduce blurring in PET images and create a spatial mismatch between the PET and CT datasets. Attenuation correction errors can result from this mismatch, which can produce severe artefacts that potentially alter the clinical interpretation of the images. Various approaches of reducing these effects have been developed. Many involve respiratory gated acquisitions which generally require a measure of the respiratory cycle throughout imaging. In this work, a retrospective respiratory gating technique was devel¬oped for both PET and CT which extracts the respiratory cycle from the acquired data itself, removing the requirement for hardware that measures respiration. This data-driven gating method was validated with phantom and patient data, and compared with a hardware based approach of gating. Extensions to the method facilitated the gating of multi-bed position, 3D clinical PET scans. Finally, 60 Ammonia cardiac PET/CT images were used to compare several different ap¬proaches of reducing respiratory induced attenuation correction errors and motion blur. The data-driven respiratory gating method accurately substituted a hardware based approach, and no significant difference was found between images gated with either methods. Gating 11 clinical 3D whole body PET images validated the extended data-driven gating methods and demonstrated successful combination of separate PET bed-positions. All evaluated approaches to reduce respiratory motion artefacts in cardiac imaging demonstrated an average improvement in PET-CT alignment. However, cases were found where alignment worsened and artefacts resulted. Fewer and less severe cases were produced when the 4D attenuation correction data was created from a 3D helical CT and PET derived motion fields. Full motion cor¬rection produced a small effect on average, however in this case no detrimental effects were found.
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Bai, Wenjia. "Respiratory motion correction in positron emission tomography." Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:f73b144d-5287-4600-8b82-74229dc0eb31.
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In this thesis, we develop a motion correction method to overcome the degradation of image quality introduced by respiratory motion in positron emission tomography (PET), so that diagnostic performance for lung cancer can be improved. Lung cancer is currently the most common cause of cancer death both in the UK and in the world. PET/CT, which is a combination of PET and CT, providing clinicians with both functional and anatomical information, is routinely used as a non-invasive imaging technique to diagnose and stage lung cancer. However, since a PET scan normally takes 15-30 minutes, respiration is inevitable in data acquisition. As a result, thoracic PET images are substantially degraded by respiratory motion, not only by being blurred, but also by being inaccurately attenuation corrected due to the mismatch between PET and CT. If these challenges are not addressed, the diagnosis of lung cancer may be misled. The main contribution of this thesis is to propose a novel process for respiratory motion correction, in which non-attenuation corrected PET images (PET-NAC) are registered to a reference position for motion correction and then multiplied by a voxel-wise attenuation correction factor (ACF) image for attenuation correction. The ACF image is derived from a CT image which matches the reference position, so that no attenuation correction artefacts would occur. In experiments, the motion corrected PET images show significant improvements over the uncorrected images, which represent the acquisitions typical of current clinical practice. The enhanced image quality means that our method has the potential to improve diagnostic performance for lung cancer. We also develop an automatic lesion detection method based on motion corrected images. A small lung lesion is only 2 or 3 voxels in diameter and of marginal contrast. It could easily be missed by human observers. Our method aims to provide radiologists with a map of potential lesions for decision so that diagnostic efficiency can be improved. It utilises both PET and CT images. The CT image provides a lung mask, to which lesion detection is confined, whereas the PET image provides distribution of glucose metabolism, according to which lung lesions are detected. Experimental results show that respiratory motion correction significantly increases the success of lesion detection, especially for small lesions, and most of the lung lesions can be detected by our method. The method can serve as a useful computer-aided image analysing tool to help radiologists read images and find malignant lung lesions. Finally, we explore the possibility of incorporating temporal information into respiratory motion correction. Conventionally, respiratory gated PET images are individually registered to the reference position. Temporal continuity across the respiratory period is not considered. We propose a spatio-temporal registration algorithm, which models temporally smooth deformation in order to improve the registration performance. However, we discover that the improvement introduced by temporal information is relatively small at the cost of a much longer computation time. Spatial registration with regularisation yields similar results but is superior in speed. Therefore, it is preferable for respiratory motion correction.
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Weerasinghe, Arachchige Chaminda Perera. "Rotational Motion Artifact Correction in Magnetic Resonance Imaging." University of Sydney. Electrical and Information Engineering, 1999. http://hdl.handle.net/2123/357.
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The body motion of patients, during magnetic resonance (MR) imaging causes significant artifacts in the reconstructed image. Artifacts are manifested as a motion induced blur and ghost repetitions of the moving structures. which obscure vital anatomical and pathological detail. The techniques that have been proposed for suppressing motion artifacts fall into two major categories. Real-time techniques attempt to prevent the motion from corrupting the data by restricting the data acquisition times or motion of the patients, whereas the post-processing techniques use the information embedded in the corrupted data to restore the image. Most methods currently in widespread use belong to the real-time techniques, however with the advent of fast computing platforms and sophisticated signal processing algorithms, the emergence of post-processing techniques is clearly evident. The post-processing techniques usually demand an appropriate model of the motion. The restoration of the image requires that the motion parameters be determined in order to invert the data degradation process. Methods for the correction of translational motion have been studied extensively in the past. The subject of this thesis encompasses the rotational motion model and the effect of rotational motion on the collected MR data in the spatial frequency space (k-space), which is in general, more complicated than the translational model. Rotational motion artifacts are notably prevalent in MR images of head, brain and limbs. Post-processing techniques for the correction of rotational motion artifacts often involve interpolation and re-gridding of the acquired data in the k-space. These methods create significant data overlap and void regions. Therefore, in the past, proposed corrective techniques have been limited to suppression of artifacts caused by small angle rotations. This thesis presents a method of managing overlap regions, using weighted averaging of redundant data, in order to correct for large angle rotations. An iterative estimation technique for filling the data void regions has also been developed by the use of iterated application of projection operators onto constraint sets. These constraint sets are derived from the k-space data generated by the MR imager, and available a priori knowledge. It is shown that the iterative algorithm diverges at times from the required image, due to inconsistency among the constraint sets. It is also shown that this can be overcome by using soft. constraint sets and fuzzy projections. One of the constraints applied in the iterative algorithm is the finite support of the imaged object, marked by the outer boundary of the region of interest (ROI). However, object boundary extraction directly from the motion affected MR image can be difficult, specially if the motion function of the object is unknown. This thesis presents a new ROI extraction scheme based on entropy minimization in the image background. The object rotation function is usually unknown or unable to be measured with sufficient accuracy. The motion estimation algorithm proposed in this thesis is based on maximizing the similarity among the k-space data subjected to angular overlap. This method is different to the typically applied parameter estimation technique based on minimization of pixel energy outside the ROI, and has higher efficiency and ability to estimate rotational motion parameters in the midst of concurrent translational motion. The algorithms for ROI extraction, rotation estimation and data correction have been tested with both phantom images and spin echo MR images producing encouraging results.
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Maclaren, Julian Roscoe. "Motion Detection and Correction in Magnetic Resonance Imaging." Thesis, University of Canterbury. Electrical and Computer Engineering, 2007. http://hdl.handle.net/10092/1220.
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Magnetic resonance imaging (MRI) is a non-invasive technique used to produce high-quality images of the interior of the human body. Compared to other imaging modalities, however, MRI requires a relatively long data acquisition time to form an image. Patients often have difficulty staying still during this period. This is problematic as motion produces artifacts in the image. This thesis explores the methods of imaging a moving object using MRI. Testing is performed using simulations, a moving phantom, and human subjects. Several strategies developed to avoid motion artifact problems are presented. Emphasis is placed on techniques that provide motion correction without penalty in terms of acquisition time. The most significant contribution presented is the development and assessment of the 'TRELLIS' pulse sequence and reconstruction algorithm. TRELLIS is a unique approach to motion correction in MRI. Orthogonal overlapping strips fill k-space and phase-encode and frequency-encode directions are alternated such that the frequency-encode direction always runs lengthwise along each strip. The overlap between pairs of orthogonal strips is used for signal averaging and to produce a system of equations that, when solved, quantifies the rotational and translational motion of the object. Acquired data is then corrected using this motion estimation. The advantage of TRELLIS over existing techniques is that k-space is sampled uniformly and all collected data is used for both motion detection and image reconstruction. This thesis presents a number of other contributions: a proposed means of motion correction using parallel imaging; an extension to the phase-correlation method for determining displacement between two objects; a metric to quantify the level of motion artifacts; a moving phantom; a physical version of the ubiquitous Shepp-Logan head phantom; a motion resistant data acquisition technique; and a means of correcting for T2 blurring artifacts.
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Cavaturu, Raja Kalyan Ram. "Motion Correction Structured Light using Pattern Interleaving Technique." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_theses/551.
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Phase Measuring Profilometry (PMP) is the most robust scanning technique for static 3D data acquisition. To make this technique robust to the target objects which are in motion during the scan interval a novel algorithm called ‘Pattern Interleaving’ is used to get a high density single scan image and making Phase Measuring Profilometry insensitive to ‘z’ motion and prevent motion banding which is predominant in 3D reconstruction when the object is in motion during the scan time
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Larsson, Jonatan. "Implementation and evaluation of motion correction for quantitative MRI." Thesis, Linköpings universitet, Medicinsk informatik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-61331.
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Image registration is the process of aligning two images such that their mutual features overlap. This is of great importance in several medical applications. In 2008 a novel method for simultaneous T1, T2 and proton density quantification was suggested. The method is in the field of quantitative Magnetic Resonance Imaging or qMRI. In qMRI parameters are quantified by a pixel-to-pixel fit of the image intensity as a function of different MR scanner settings. The quantification depends on several volumes of different intensities to be aligned. If a patient moves during the data aquisition the datasets will not be aligned and the results are degraded due to this. Since the quantification takes several minutes there is a considerable risk of patient movements. In this master thesis three image registration methods are presented and a comparison in robustness and speed was made. The phase based algorithm was suited for this problem and limited to finding rigid motion. The other two registration algorithms, originating from the Statistical Parametrical Mapping, SPM, package, were used as references. The result shows that the pixel-to-pixel fit is greatly improved in the datasets with found motion. In the comparison between the different methods the phase based algorithm turned out to be both the fastest and the most robust method.
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Lewis, Peter. "Improving Peripheral Vision Through Optical Correction and Stimulus Motion." Doctoral thesis, Linnéuniversitetet, Institutionen för medicin och optometri (MEO), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-52286.
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The loss of central vision subsequent to macular disease is often extremely debilitating. People with central field loss (CFL) must use other peripheral areas of the retina in order to see; areas with inferior resolution capacity, which are also affected by off-axis optical errors. The overall aim of the work encompassed by this thesis was to identify and evaluate methods of improving vision for people with CFL; with focus on the effects of off-axis optical correction and stimulus motion on resolution acuity and contrast sensitivity. Off-axis optical errors were measured using a commercially-available COAS-HD VR open-view aberrometer. We used adaptive psychophysical methods to evaluate grating resolution acuity and contrast sensitivity in the peripheral visual field; drifting gratings were employed to measure the effect of motion on these two measures of visual performance. The effect of sphero-cylindrical correction and stimulus motion on visual performance in healthy eyes and in subjects with CFL was also studied; in addition, the effect of adaptive optics aberration correction was examined in one subject with CFL. The COAS-HD aberrometer provided rapid and reliable measurements of off-axis refractive errors. Correction of these errors gave improvements in low-contrast resolution acuity in subjects with higher amounts of oblique astigmatism. Optical correction also improved high-contrast resolution acuity in most subjects with CFL, but not for healthy subjects. Adaptive optics correction improved both high and low contrast resolution acuity in the preferred retinal locus of a subject with CFL. The effect of stimulus motion depended on spatial frequency; motion of 7.5 Hz improved contrast sensitivity for stimuli of low spatial frequency in healthy and CFL subjects. Motion of 15 Hz had little effect on contrast sensitivity for low spatial frequency but resulted in reduced contrast sensitivity for higher spatial frequencies in healthy subjects. Finally, high-contrast resolution acuity was relatively insensitive to stimulus motion in the periphery. This thesis has served to broaden the knowledge regarding peripheral optical errors, stimulus motion and their effects on visual function, both in healthy subjects and in people with CFL. Overall it has shown that correction of off-axis refractive errors is important for optimizing peripheral vision in subjects with CFL; the use of an open-view aberrometer simplifies the determination of these errors. In addition, moderate stimulus motion can have a beneficial effect on contrast sensitivity for objects of predominantly low spatial frequency.
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Bush, Michael. "Patient-specific prospective respiratory motion correction in cardiovascular MRI." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554979431869627.
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Sieberth, Till. "Motion blur in digital images : analys, detection and correction of motion blur in photogrammetry." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/20212.
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Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This thesis proves the negative affect that blurred images have on photogrammetric processing. It shows that small amounts of blur do have serious impacts on target detection and that it slows down processing speed due to the requirement of human intervention. Larger blur can make an image completely unusable and needs to be excluded from processing. To exclude images out of large image datasets an algorithm was developed. The newly developed method makes it possible to detect blur caused by linear camera displacement. The method is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of additional images. However, the calculated blur value named SIEDS (saturation image edge difference standard-deviation) on its own does not provide an absolute number to judge if an image is blurred or not. To achieve a reliable judgement of image sharpness the SIEDS value has to be compared to other SIEDS values of the same dataset. This algorithm enables the exclusion of blurred images and subsequently allows photogrammetric processing without them. However, it is also possible to use deblurring techniques to restor blurred images. Deblurring of images is a widely researched topic and often based on the Wiener or Richardson-Lucy deconvolution, which require precise knowledge of both the blur path and extent. Even with knowledge about the blur kernel, the correction causes errors such as ringing, and the deblurred image appears muddy and not completely sharp. In the study reported in this paper, overlapping images are used to support the deblurring process. An algorithm based on the Fourier transformation is presented. This works well in flat areas, but the need for geometrically correct sharp images for deblurring may limit the application. Another method to enhance the image is the unsharp mask method, which improves images significantly and makes photogrammetric processing more successful. However, deblurring of images needs to focus on geometric correct deblurring to assure geometric correct measurements. Furthermore, a novel edge shifting approach was developed which aims to do geometrically correct deblurring. The idea of edge shifting appears to be promising but requires more advanced programming.
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Gu, Songxiang. "Body Deformation Correction for SPECT Imaging." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-dissertations/319.
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"Single Photon Emission Computed Tomography (SPECT) is a medical imaging modality that allows us to visualize functional information about a patient's specific organ or body systems. During 20 minute scan, patients may move. Such motion will cause misalignment in the reconstruction, degrade the quality of 3D images and potentially lead to errors in diagnosis. Body bend and twist are types of patient motion that may occur during SPECT imaging and which has been generally ignored in SPECT motion correction strategies. To correct for these types of motion we propose a deformation model and its inclusion within an iterative reconstruction algorithm. One simulation and three experiments were conducted to investigate the applicability of our model. The simulation employed simulated projections of the MCAT phantom formed using an analytical projector which includes attenuation and distance-dependent resolution to investigate applications of our model in reconstruction. We demonstrate in the simulation studies that twist and bend can significantly degrade SPECT image quality visually. Our correction strategy is shown to be able to greatly diminish the degradation seen in the slices, provided the parameters are estimated accurately. To verify the correctness of our deformation model, we design the first experiment. In this experiment, the return of the post-motion-compensation locations of markers on the body-surface of a volunteer to approximate their original coordinates is used to examine our method of estimating the parameters of our model and the parameters' use in undoing deformation. Then, we design an MRI based experiment to validate our deformation model without any reconstruction. We use the surface marker motion to alter an MRI body volume to compensate the deformation the volunteer undergoes during data acquisition, and compare the motion-compensated volume with the motionless volume. Finally, an experiment with SPECT acquisitions and modified MLEM algorithm is designed to show the contribution of our deformation correction for clinical SPECT imaging. We view this work as a first step towards being able to estimate and correct patient deformation based on information obtained from marker tracking data."
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Jung-Ha, Kim. "The Estimation and Correction of Rigid Motion in Helical Computed Tomography." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/15353.
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X-ray CT is a tomographic imaging tool used in medicine and industry. Although technological developments have significantly improved the performance of CT systems, the accuracy of images produced by state-of-the-art scanners is still often limited by artefacts due to object motion. To tackle this problem, a number of motion estimation and compensation methods have been proposed. However, no methods with the demonstrated ability to correct for rigid motion in helical CT scans appear to exist. The primary aims of this thesis were to develop and evaluate effective methods for the estimation and correction of arbitrary six degree-of-freedom rigid motion in helical CT. As a first step, a method was developed to accurately estimate object motion during CT scanning with an optical tracking system, which provided sub-millimetre positional accuracy. Subsequently a motion correction method, which is analogous to a method previously developed for SPECT, was adapted to CT. The principle is to restore projection consistency by modifying the source-detector orbit in response to the measured object motion and reconstruct from the modified orbit with an iterative reconstruction algorithm. The feasibility of this method was demonstrated with a rapidly moving brain phantom, and the efficacy of correcting for a range of human head motions acquired from healthy volunteers was evaluated in simulations. The methods developed were found to provide accurate and artefact-free motion corrected images with most types of head motion likely to be encountered in clinical CT imaging, provided that the motion was accurately known. The method was also applied to CT data acquired on a hybrid PET/CT scanner demonstrating its versatility. Its clinical value may be significant by reducing the need for repeat scans (and repeat radiation doses), anesthesia and sedation in patient groups prone to motion, including young children.
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Segobin, Shailendra Hemun. "Development of a motion correction and partial volume correction algorithm for high resolution imaging in Positron Emission Tomography." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/development-of-a-motion-correction-and-partial-volume-correction-algorithm-for-high-resolution-imaging-in-positron-emission-tomography(d84967c5-422a-40ce-9c4b-a0e74c431005).html.
Full textAbstract:
Since its inception around 1975, Positron Emission Tomography (PET) has proved to be an important tool in medical research as it allows imaging of the brain function in vivo with high sensitivity. It has been widely used in clinical dementia research with [18F]2-Fluoro-2-Deoxy-D-Glucose (FDG) and amyloid tracers as imaging biomarkers in Alzheimer's Disease (AD). The high resolution offered by modern scanner technology has the potential to provide new insight into the interaction of structural and functional changes in AD. However, the high resolution of PET is currently limited by movement and resolution (even for high resolution dedicated brain PET scanner) which results in partial volume effects, the undersampling of activity within small structures. A modified frame-by-frame (FBF) realignment algorithm has been developed that uses estimates of the centroid of activity within the brain to detect movement and subsequently reframe data to correct for intra-frame movement. The ability of the centroid to detect motion was assessed and the added benefit of reframing data for real clinical scans with patient motion was evaluated through comparison with existing FBF algorithms. Visual qualitative analysis on 6 FDG PET scans from 4 blinded observers demonstrated notable improvements (ANOVA with Tukey test, p < 0.001) and time-activity curves were found to deliver biologically more plausible activity concentrations. A new method for Partial Volume Correction (PVC) is also proposed, PARtially-Segmented Lucy-Richardson (PARSLR),that combines the strength of image based deconvolution approach of the Lucy-Richardson (LR) Iterative Deconvolution Algorithm with a partial segmentation of homogenous regions. Such an approach is of value where reliable segmentation is possible for part but not all of the image volume or sub-volume. Its superior performance with respect to region-based methods like Rousset or voxel-based methods like LR was successfully demonstrated via simulations and measured phantom data. The approach is of particular importance for studies with pathological abnormalities where complete and accurate segmentation across or with a sub-volume of the image volume is challenging and for regions of the brain containing heterogeneous structures which cannot be accurately segmented from co-registered images. The developed methods have been shown to recover radioactivity concentrations from small structures in the presence of motion and limited resolution with higher accuracy when compared to existing methods. It is expected that they will contribute significantly to future PET studies where accurate quantitation in small or atrophic brain structures is essential.
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Klyuzhin, Ivan S. "Deformable motion correction and spatial image analysis in positron emission tomography." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/60277.
Full textAbstract:
Positron emission tomography (PET) is a molecular imaging modality that allows to quantitatively assess the physiological function of tissues in-vivo. Subject motion during imaging degrades the quantitative accuracy of the data. In small animal imaging, motion is minimized by the use of anesthesia, which interferes with the normal physiology of the brain. This can be circumvented by imaging awake rodents; however, in this case correction for non-cyclic motion with rigid and deformable components is required. In the first part of the thesis, the problem of motion correction in PET imaging of unrestrained awake rodents is addressed. A novel method of iterative image reconstruction is developed that incorporates correction for non-cyclic deformable motion. Point clouds were used to represent the imaged objects in the image space, and motion was accounted by using time-dependent point coordinates. The quantitative accuracy and noise characteristics of the proposed method were quantified and compared to traditional methods by reconstructing projection data from digital and physical phantoms. A digital phantom of a freely moving mouse was constructed, and the efficacy of motion correction was tested by reconstructing the simulated coincidence data from the phantom. In the second part of the thesis, novel approaches to PET image analysis were explored. In brain PET, analysis based on the tracer kinetic modeling (KM) may not always be possible due to the complexity of the scanning protocols and inability to find a suitable reference region. Therefore, the ability of KM-independent shape and texture metrics to convey useful information on the disease state was investigated, based on an ongoing Parkinson's disease study with radiotracers that probe the dopaminergic system. The pattern of the radiotracer distribution in the striatum was characterized by computing the metrics from multiple regions of interest defined using PET and MRI images. Regression analysis showed a significant correlation between the metrics and clinical disease measures (p<0.01). The effect of the region of interest definition and texture computation parameters on the correlation was investigated. Results demonstrate that there is clinically-relevant information in the tracer distribution pattern that can be captured using shape and texture descriptors.Science, Faculty of
Physics and Astronomy, Department of
Graduate
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McQuaid, S. "Characterisation and correction of respiratory-motion artefacts in cardiac PET-CT." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/19732/.
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Respiratory motion during cardiac Positron Emission Tomography (PET) Computed Tomography (CT) imaging results in blurring of the PET data and can induce mismatches between the PET and CT datasets, leading to attenuation-correction artefacts. The aim of this project was to develop a method of motion-correction to overcome both of these problems. The approach implemented was to transform a single CT to match the frames of a gated PET study, to facilitate respiratory-matched attenuation-correction, without the need for a gated CT. This is benecial for lowering the radiation dose to the patient and in reducing PETCT mismatches, which can arise even in gated studies. The heart and diaphragm were identied through phantom studies as the structures responsible for generating attenuation-correction artefacts in the heart and their motions therefore needed to be considered in transforming the CT. Estimating heart motion was straight-forward, due to its high contrast in PET, however the poor diaphragm contrast meant that additional information was required to track its position. Therefore a diaphragm shape model was constructed using segmented diaphragm surfaces, enabling complete diaphragm surfaces to be produced from incomplete and noisy initial estimates. These complete surfaces, in combination with the estimated heart motions were used to transform the CT. The PET frames were then attenuation-corrected with the transformed CT, reconstructed, aligned and summed, to produce motion-free images. It was found that motion-blurring was reduced through alignment, although benets were marginal in the presence of small respiratory motions. Quantitative accuracy was improved from use of the transformed CT for attenuation-correction (compared with no CT transformation), which was attributed to both the heart and the diaphragm transformations. In comparison to a gated CT, a substantial dose saving and a reduced dependence on gating techniques were achieved, indicating the potential value of the technique in routine clinical procedures.
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Wang, Jiali. "Motion Correction Algorithm of Lung Tumors for Respiratory Gated PET Images." FIU Digital Commons, 2009. http://digitalcommons.fiu.edu/etd/96.
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Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.
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Noonan, Philip John. "An investigation into motion correction schemes for high resolution 3D PET And PET/CT." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/an-investigation-into-motion-correction-schemes-for-high-resolution-3d-pet-and-petct(b52783fa-a5cf-4f85-a050-e57f8f775f45).html.
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Although motion correction in medical imaging is well established and has attracted much interest and research funding, a gap still exists in that there is a lack of reliable, low-cost hardware to enable such techniques to be widely adopted in healthcare. Motion correction of brain Positron Emission Tomography (PET) data for instance is an important step in realising the potential offered by modern high resolution PET scanners. Since it is not likely that subjects can remain stationary throughout the PET scan, which can last 60 minutes or more, accurate and reliable motion tracking is needed to correct the PET data for any observed motion. A commercially available marker based motion tracking system was evaluated and found to produce unreliable data. This was due to the possibility of the tracking tool slipping from the subject. This thesis describes the investigations into alternative and novel tracking techniques for use in PET. These included a markerless tracking system using the Microsoft Kinect (a low cost depth sensor) as well as a multiple target marker tracking system. The performance characteristics of both systems (low cost, high spatial and temporal accuracy, and real-time operation) were evaluated using phantom and clinical experiments. Investigations into using these two tracking techniques in whole body PET, specifically measuring the respiratory rate during lung imaging, were developed and compared against current commercially available solutions.
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Karlsson, Anette. "In-Plane Motion Correction in Reconstruction of non-Cartesian 3D-functional MRI." Thesis, Linköpings universitet, Datorseende, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72056.
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When patients move during an MRI examination, severe artifacts arise in the reconstructed image and motion correction is therefore often desired. An in-plane motion correction algorithm suitable for PRESTO-CAN, a new 3D functional MRI method where sampling of k-space is radial in kx-direction and kz-direction and Cartesian in ky-direction, was implemented in this thesis work. Rotation and translation movements can be estimated and corrected for sepa- rately since the magnitude of the data is only affected by the rotation. The data were sampled in a radial pattern and the rotation was estimated by finding the translation in angular direction using circular correlation. Correlation was also used when finding the translation in x-direction and z-direction. The motion correction algorithm was evaluated on computer simulated data, the motion was detected and corrected for, and this resulted in images with greatly reduced artifacts due to patient movements.När patienter rör sig under en MRI-undersökning uppstår artefakter i den rekonstruerande bilden och därför är det önskvärt med rörelsekorrigering. En 2D- rörelsekorrigeringsalgoritm som är anpassad för PRESTO-CAN har tagits fram. PRESTO-CAN är en ny fMRI-metod för 3D där samplingen av k-rummet är radiell i (kx,kz)-planet och kartesisk i ky-riktningen. Rotations- och translationsrörelser kan estimeras separat då magnituden av signalen bara påverkas av rotationsrörelser. Eftersom data är samplat radiellt kan rotationen estimeras genom att hitta translationen i vinkelled med hjälp av cirkulär korrelation. Korrelation används även för att hitta translationen i i x- och z-riktningen. Test på simulerat data visar att rörelsekorrigeringsalgoritmen både detekterar och korrigerar för rörelser vilket leder till bilder med mycket mindre rörelseartefakter.
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Flouri, Dimitra. "Tracer-kinetic model-driven motion correction with application to renal DCE-MRI." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/16485/.
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A major challenge of the image registration in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is related to the image contrast variations caused by the contrast agent passage. Tracer-kinetic model-driven motion correction is an attractive solution for DCE-MRI, but previous studies only use the 3-parameter modified Tofts model. Firstly, a generalisation based on a 4-parameter 2-compartment tracer-kinetic model is presented. A practical limitation of these models is the need for non-linear least-squares (NLLS) fitting. This is prohibitively slow for image-wide parameter estimations, and is biased by the choice of initial values. To overcome this limitation, a fast linear least-squares (LLS) method to fit the two-compartment exchange and -filtration models (2CFM) to the data is introduced. Simulations of normal and pathological data were used to evaluate calculation time, accuracy and precision of the LLS against the NLLS method. Results show that the LLS method leads to a significant reduction in the calculation times. Secondly, a novel tracer-kinetic model-driven motion correction algorithm is introduced which uses a 4-parameter 2-compartment model to tackle the problem of image registration in 2D renal DCE-MRI. The core architecture of the algorithm can briefly described as follows: the 2CFM is linearly fitted pixel-by-pixel and the model fit is used as target for registration; then a free-form deformation model is used for pairwise co-registration of source and target images at the same time point. Another challenge that has been addressed is the computational complexity of non-rigid registration algorithms by precomputing steps to remove redundant calculations. Results in 5 subjects and simulated phantoms show that the algorithm is computationally efficient and improves alignment of the data. The proposed registration algorithm is then translated to 3D renal dynamic MR data. Translation to 3D is however challenging due to ghosting artefacts caused by within-frame breathing motion. Results in 8 patients show that the algorithm effectively removes between-frame breathing motion despite significant within-frame artefacts. Finally, the effect of motion correction on the clinical utility has been examined. Quantitative evaluation of single-kidney glomerular filtration rate derived from DCE-MRI against reference measurements shows a reduction of the bias, but precision is limited by within-frame artefacts. The suggested registration algorithm with a 4-parameter model is shown to be a computational efficient approach which effectively removes between-frame motion in a series of 2D and 3D renal DCE-MRI data.
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Loktyushin, Alexander [Verfasser], and Bernhard [Akademischer Betreuer] Schölkopf. "Blind Retrospective Motion Correction of MR Images / Alexander Loktyushin ; Betreuer: Bernhard Schölkopf." Tübingen : Universitätsbibliothek Tübingen, 2016. http://d-nb.info/1164169130/34.
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Scott, Andrew David. "Magnetic resonance coronary vessel wall imaging with highly efficient respiratory motion correction." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/6978.
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There is a need for a noninvasive imaging technique for use in longitudinal studies of sub-clinical coronary artery disease. Magnetic resonance (MR) can be used to selectively and non-invasively image the coronary wall without the use of ionising radiation. However, high-resolution 3D studies are often time consuming and unreliable, as data acquisition is generally gated to a small window of diaphragm positions around end-expiration which results in inherently poor and variable respiratory efficiency. This thesis describes the development and application of a novel technique (beat-to-beat respiratory motion correction (B2B-RMC)) for correcting respiratory motion in 3D spiral MR coronary imaging. This technique uses motion of the epicardial fat surrounding the artery as a surrogate for the motion of the artery itself and enables retrospective motion correction with respiratory efficiency close to 100%. This thesis first describes an assessment of the performance of B2B-RMC using a purpose built respiratory motion phantom with realistic coronary artery test objects. Subsequently, MR coronary angiography studies in healthy volunteers show that the respiratory efficiency of B2B-RMC far exceeds that of conventional navigator gating, yet the respiratory motion correction is equally effective. The performance and reproducibility of 3D spiral imaging with B2B-RMC for assessment of the coronary artery vessel wall is subsequently compared to that of commonly used 2D navigator gated techniques. The results demonstrate the high performance, reproducibility and reliability of 3D spiral imaging with B2B-RMC when data acquisition is gated to alternate cardiac cycles. Using this technique, a further in-vivo study demonstrates thickening of the coronary vessel wall with age in healthy subjects and these results are shown to be consistent with outward remodelling of the vessel wall. Finally, the performance of B2B-RMC in a variety of coronary vessel wall applications, including in a small cohort of patients with confirmed coronary artery disease, is presented.
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Sun, Jing. "Correction of the respiratory motion of the heart from 4D myocardial images." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0015877.
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Yarach, Uten [Verfasser]. "Correction of residual artifacts in prospectively motion-corrected MR-data / Uten Yarach." Magdeburg : Universitätsbibliothek, 2016. http://d-nb.info/1128726424/34.
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Loktyushin, Alexander Verfasser], and Bernhard [Akademischer Betreuer] [Schölkopf. "Blind Retrospective Motion Correction of MR Images / Alexander Loktyushin ; Betreuer: Bernhard Schölkopf." Tübingen : Universitätsbibliothek Tübingen, 2016. http://d-nb.info/1164169130/34.
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Woods, Bronwyn Lewisia. "Toward a Processing Pipeline for Two-photon Calcium Imaging of Neural Populations." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/393.
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Two-photon calcium imaging (TPCI) is a functional neuroimaging technique that simultaneously reveals the function of small populations of cells as well as the structure of surrounding brain tissue. These unique properties cause TPCI to be increasingly popular for experimental basic neuroscience. Unfortunately, methodological development for data processing has not kept pace with experimental needs. I address this lack by developing and testing new methodology for several key tasks. Specifically, I address two primary analysis steps which are nearly universally required in early data processing: region of interest segmentation and motion correction. For each task I organize the sparse existing literature, clearly define the requirements of the problem, propose a solution, and evaluate it on experimental data. I develop MaSCS, an automated adaptable multi-class segmentation system that improves with use. I carefully define and describe the impact of motion artifacts on imaging data, and quantify the effects of standard and innovative motion correction approaches. Finally, I apply my work on segmentation and motion correction to explore one scientific target, namely discovering correlation-based cell clustering. I show that estimating such correlation-based clustering remains an open question, as it is highly sensitive to motion artifacts, even after motion correction techniques are applied. The contributions of this work include the organization of existing resources, methodological advances in segmentation, motion correction and clustering, and the development of prototype analysis software.
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Bhushan, Manav. "Motion correction and parameter estimation in DCE-MRI sequences : application to colorectal cancer." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:1793b0c1-7f2a-41cb-8427-c6927379056e.
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Cancer is one of the leading causes of premature deaths across the world today, and there is an urgent need for imaging techniques that can help in early diagnosis and treatment planning for cancer patients. In the last four decades, magnetic resonance imaging (MRI) has emerged as one of the leading modalities for non-invasive imaging of tumours. By using dynamic contrast-enhanced magnetic resonance imaging (DCEMRI), this modality can be used to acquire information about perfusion and vascularity of tumours, which can help in predicting response to treatment. There are many factors that complicate the analysis of DCE-MRI data, and make clinical predictions based on it unreliable. During data acquisition, there are many sources of uncertainties and errors, especially patient motion, which result in the same image position being representative of many different anatomical locations across time. Apart from motion, there are also other inherent uncertainties and noise associated with the measurement of DCE-MRI parameters, which contribute to the model-fitting error observed when trying to apply pharmacokinetic (PK) models to the data. In this thesis, a probabilistic, model-based registration and parameter estimation (MoRPE) framework for motion correction and PK-parameter estimation in DCE-MRI sequences is presented. The MoRPE framework is first compared with conventional motion correction methods on simulated data, and then applied to data from a clinical trial involving twenty colorectal cancer patients. On clinical data, the ability of MoRPE to discriminate between responders and non-responders to combined chemoand radiotherapy is tested, and found to be superior to other methods. The effect of incorporating different arterial input functions within MoRPE is also assessed. Following this, a quantitative analysis of the uncertainties associated with the different PK parameters is performed using a variational Bayes mathematical framework. This analysis provides a quantitative estimate of the extent to which motion correction affects the uncertainties associated with different parameters. Finally, the importance of estimating spatial heterogeneity of PK parameters within tumours is assessed. The efficacy of different measures of spatial heterogeneity, in predicting response to therapy based on the pre-therapy scan alone are compared, and the prognostic value of a new derived PK parameter the 'acceleration constant' is investigated. The integration of uncertainty estimates of different DCE-MRI parameters into the calculation of their heterogeneity measures is also shown to improve the prediction of response to therapy.
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Alhamud, Alkathafi Ali. "Implementation of anatomical navigators for real time motion correction in diffusion tensor imaging." Doctoral thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/10052.
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Includes bibliographical references.Prospective motion correction methods using an optical system, diffusion-weighted prospective acquisition correction, or a free induction decay navigator have recently been applied to correct for motion in diffusion tensor imaging. These methods have some limitations and drawbacks. This article describes a novel technique using a three-dimensional-echo planar imaging navigator, of which the contrast is independent of the b-value, to perform prospective motion correction in diffusion weighted images, without having to reacquire volumes during which motion occurred, unless motion exceeded some preset thresholds. Water phantom and human brain data were acquired using the standard and navigated diffusion sequences, and the mean and whole brain histogram of the fractional anisotropy and mean diffusivity were analyzed.
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Delgado, Reyes Lourdes Marielle. "Evaluating motion processing algorithms for use with fNIRS data from young children." Thesis, University of Iowa, 2015. https://ir.uiowa.edu/etd/5929.
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Motion artifacts are often a significant component of the measured signal in functional near-infrared spectroscopy (fNIRS) experiments. A variety of methods have been proposed to address this issue, including principal component analyses (PCA), Kalman filtering, correlation-based signal improvement (CBSI), wavelet filtering, spline interpolation, and autoregressive algorithms. The efficacy of these techniques has been compared using simulated data; however, our understanding of how these techniques fare when dealing with task-based cognitive data is limited. Recently, Brigadoi et al. (2014) quantitatively compared 6 motion correction techniques in a sample of adult data measured during a simple cognitive task. Wavelet filtering showed the most promise as an optimal technique for motion correction. Because fNIRS is often used with infants and young children, it is critical to evaluate the effectiveness of motion correction techniques directly with data from these age groups. Here we examined which techniques are most effective with data from young children. The efficacy of each technique was compared quantitatively using objective metrics related to the physiological properties of the hemodynamic response using two different sets of parameters to ensure maximum retention of included trials. Results showed that targeted PCA (tPCA) and CBSI retained a higher number of trials. These techniques also performed well in direct head-to-head comparisons with the other approaches using both quantitative metrics and a qualitative assessment. The CBSI technique corrected many of the artifacts present in our data; however, this technique was highly influenced by the parameters used to detect motion. The tPCA technique, by contrast, was robust across changes in parameters while also performing well across all comparison metrics. We conclude, therefore, that tPCA is an effective technique for the correction of motion artifacts in fNIRS data from young children.
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Robie, David Lee. "Error Correction and Concealment of Bock Based, Motion-Compensated Temporal Predition, Transform Coded Video." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7101.
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Error Correction and Concealment of Block Based, Motion-Compensated Temporal Prediction, Transform Coded Video
David L. Robie
133 Pages
Directed by Dr. Russell M. Mersereau
The use of the Internet and wireless networks to bring multimedia to the consumer continues to expand. The transmission of these products is always subject to corruption due to errors such as bit errors or lost and ill-timed packets; however, in many cases, such as real time video transmission, retransmission request (ARQ) is not practical. Therefore receivers must be capable of recovering from corrupted data. Errors can be mitigated using forward error correction in the encoder or error concealment techniques in the decoder. This thesis investigates the use of forward error correction (FEC) techniques in the encoder and error concealment in the decoder in block-based, motion-compensated, temporal prediction, transform codecs. It will show improvement over standard FEC applications and improvements in error concealment relative to the Motion Picture Experts Group (MPEG) standard. To this end, this dissertation will describe the following contributions and proofs-of-concept in the area of error concealment and correction in block-based video transmission. A temporal error concealment algorithm which uses motion-compensated macroblocks from previous frames. A spatial error concealment algorithm which uses the Hough transform to detect edges in both foreground and background colors and using directional interpolation or directional filtering to provide improved edge reproduction. A codec which uses data hiding to transmit error correction information. An enhanced codec which builds upon the last by improving the performance of the codec in the error-free environment while maintaining excellent error recovery capabilities. A method to allocate Reed-Solomon (R-S) packet-based forward error correction that will decrease distortion (using a PSNR metric) at the receiver compared to standard FEC techniques. Finally, under the constraints of a constant bit rate, the tradeoff between traditional R-S FEC and alternate forward concealment information (FCI) is evaluated. Each of these developments is compared and contrasted to state of the art techniques and are able to show improvements using widely accepted metrics. The dissertation concludes with a discussion of future work.
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Ullisch, Marcus Görge [Verfasser]. "A navigator-based rigid body motion correction for magnetic resonance imaging / Marcus Görge Ullisch." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2012. http://d-nb.info/1024349616/34.
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Burger, Ian H. "A control system approach to subject specific prospective respiratory motion correction in cardiac MRI." Doctoral thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/10220.
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Includes bibliographical references.Respiratory motion of the heart is a problem for high-resolution cardiac MRI. Diaphragmatic navigator gating with a 5mm acceptance window is most commonly used to address this but has an inherently low respiratory efficiency that is further compromised by respiratory drift. A novel method is presented that uses data from multiple navigators prior to the imaging segment as input for a control system to predict the diaphragm position throughout the imaging segment and correct the slice position in real time.
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Kraus, Martin [Verfasser], and Joachim [Gutachter] Hornegger. "Motion Correction and Signal Enhancement in Optical Coherence Tomography / Martin Kraus ; Gutachter: Joachim Hornegger." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2017. http://d-nb.info/113613316X/34.
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Kanj, Ali. "Flicker Removal and Color Correction for High Speed Videos." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1115/document.
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Le deflickering consiste à supprimer le scintillement présent dans les séquences d’images afin de réduire les variations lumineuses entre chacune des images de la vidéo. Il s’agit d’une tâche essentielle, nécessaire dans plusieurs applications, en particulier dans les séquences de films archivés, les vidéos comprimées et les vidéos time-lapse. Au cours de ces dernières années, avec le développement des technologies d’acquisition à haute vitesse, il y a eu un regain d’intérêt pour le problème de suppression de flicker, en particulier le flicker périodique. Dans ce contexte, le flicker correspond à des variations indésirables de luminosité et des couleurs dues à l’interaction entre la vitesse d’acquisition d’une part, et d’autre part le courant alternatif alimentant les sources lumineuses. La présente thèse formule le problème du déflickering périodique dans les vidéos à haute vitesse, étudie les propriétés physiques du flicker et propose à la fois des solutions théoriques et expérimentales pour sa suppression des séquences d’images. Enfin, une nouvelle approche est proposée permettant d’effectuer simultanément le suivi de mouvement et la correction des couleursDeflickering consists of removing rapid, artifactual changes of luminosity and colorimetry from image sequences and improving luminosity consistency between video frames. It is a necessary and fundamental task in multiple applications, for instance in archived film sequences, compressed videos and time-lapse videos. In recent years, there has been a renewal of interest for improving luminosity consistency acquisition technology in the flicker removal problem, in particular for periodic flickering. In this context, flicker corresponds to undesirable intensity and chroma variations due to the interaction between the acquisition frequencies on the one hand, and the alternating current powering the light sources on the other hand. The present thesis formulates the periodic deflickering problem in high speed videos, studies the physical properties of flicker and suggests both theoretical and experimental solutions for its removal from image sequences. Finally, a new flicker removal approach is proposed performing jointly motion tracking and color correction
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Torle, Petter. "Scene-based correction of image sensor deficiencies." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1752.
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This thesis describes and evaluates a number of algorithms for reducing fixed pattern noise in image sequences. Fixed pattern noise is the dominantnoise component for many infrared detector systems, perceived as a superimposed pattern that is approximately constant for all image frames.
Primarily, methods based on estimation of the movement between individual image frames are studied. Using scene-matching techniques, global motion between frames can be successfully registered with sub-pixel accuracy. This allows each scene pixel to be traced along a path of individual detector elements. Assuming a static scene, differences in pixel intensities are caused by fixed pattern noise that can be estimated and removed.
The algorithms have been tested by using real image data from existing infrared imaging systems with good results. The tests include both a two-dimensional focal plane array detector and a linear scanning one-dimensional detector, in different scene conditions.
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Hering, Jan [Verfasser], and Bernd [Akademischer Betreuer] Jähne. "Robust Motion and Distortion Correction of Diffusion-Weighted MR Images / Jan Hering ; Betreuer: Bernd Jähne." Heidelberg : Universitätsbibliothek Heidelberg, 2016. http://d-nb.info/1180737466/34.
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Barnett, Robert. "Improving the Accuracy of CT-derived Attenuation Correction in Respiratory-Gated PET/CT Imaging." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17710.
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The effect of respiratory motion on attenuation correction in Fludeoxyglucose (18F) positron emission tomography (FDG-PET) was investigated. Improvements to the accuracy of computed tomography (CT) derived attenuation correction were obtained through the alignment of the attenuation map to each emission image in a respiratory gated PET scan. Attenuation misalignment leads to artefacts in the reconstructed PET image and several methods were devised for evaluating the attenuation inaccuracies caused by this. These methods of evaluation were extended to finding the frame in the respiratory gated PET which best matched the CT. This frame was then used as a reference frame in mono-modality compensation for misalignment. Attenuation correction was found to affect the quantification of tumour volumes; thus a regional analysis was used to evaluate the impact of mismatch and the benefits of compensating for misalignment. Deformable image registration was used to compensate for misalignment, however, there were inaccuracies caused by the poor signal-to-noise ratio (SNR) in PET images. Two models were developed that were robust to a poor SNR allowing for the estimation of deformation from very noisy images. Firstly, a cross population model was developed by statistically analysing the respiratory motion in 10 4DCT scans. Secondly, a 1D model of respiration was developed based on the physiological function of respiration. The 1D approach correctly modelled the expansion and contraction of the lungs and the differences in the compressibility of lungs and surrounding tissues. Several additional models were considered but were ruled out based on their poor goodness of fit to 4DCT scans. Approaches to evaluating the developed models were also used to assist with optimising for the most accurate attenuation correction. It was found that the multimodality registration of the CT image to the PET image was the most accurate approach to compensating for attenuation correction mismatch. Mono-modality image registration was found to be the least accurate approach, however, incorporating a motion model improved the accuracy of image registration. The significance of these findings is twofold. Firstly, it was found that motion models are required to improve the accuracy in compensating for attenuation correction mismatch and secondly, a validation method was found for comparing approaches to compensating for attenuation mismatch.
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Pedroza, Moises. "MOBILE TRACKING SYSTEM “MOTION ON THE OCEAN” TEST." International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/608307.
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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, NevadaThe Transportable Range Augmentation and Control System (TRACS), Mobile Telemetry System (MTS), is a versatile system capable of supporting anywhere when called upon. The MTS is designed to operate anywhere on land. It is unknown how the system will perform on a floating platform without a stabilizing gimbal. The operation of a tracking system at sea generally require the use of a three-axis pedestal. The MTS is a two-axis pedestal. This paper is a report on how the MTS responds to simulated ocean-motion. Testing the system on a body of water is very expensive, especially out in the desert. The MTS was tested in the desert area of Las Cruces, New Mexico in the parking lot of EMI Technologies, prime contractor, using two forklifts to simulate ship motion in the pitch and yaw planes. The location is perfect for crossover dynamics tests. The tests conducted were for the purpose of determining if the MTS could auto-track a moving signal in space while it also moves due to “simulated ocean swells” that increase the generated tracking error signal levels in an opposite or in addition to the ones generated from the space vehicle. There is no gyroscopic correction. Successful results of the tests could preclude the use of a gyroscopically stabilized gimbaled platform necessary to keep the tracking system steady for auto-tracking a target during “6 degrees of freedom” disturbances. Several thousand dollars can be saved if the concept can be proven.
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Eschelbach, Martin [Verfasser], and Klaus [Akademischer Betreuer] Scheffler. "Head Motion Correction in Magnetic Resonance Imaging Using NMR Field Probes / Martin Eschelbach ; Betreuer: Klaus Scheffler." Tübingen : Universitätsbibliothek Tübingen, 2018. http://d-nb.info/1173699961/34.
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Webley, Peter William. "Atmospheric water vapour correction to InSAR surface motion measurements on mountains : case study on Mount Etna." Thesis, University of Reading, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405480.
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Hess, Aaron T. "Real-time motion and main magnetic field correction in MR spectroscopy using an EPI volumetric navigator." Doctoral thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/11359.
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In population groups where subjects do not lie still during Magnetic Resonance Spectroscopy (MRS) scans, real-time volume of interest (VOI), frequency, and main magnetic field (B0) shim correction may be necessary. This work demonstrates firstly that head movement causes significant B0 disruption in both single voxel spectroscopy and spectroscopic imaging.
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Polycarpou, Irene. "The impact of respiratory motion correction methods on tumour detection and quantification in Positron Emission Tomography." Thesis, King's College London (University of London), 2014. https://kclpure.kcl.ac.uk/portal/en/theses/the-impact-of-respiratory-motion-correction-methods-on-tumour-detection-and-quantification-in-positron-emission-tomography(2f38d893-4531-41df-a7fc-1fa7080cd63c).html.
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Respiratory motion degrades PET spatial resolution and image quality limiting the potential benefits from increased resolution. Motion correction is complicated by limitations of the poor statistical quality of PET data and there is still not a robust method available. Motion correction can be implemented at different stages of data processing either during or after reconstruction and may result in substantial improvements in image quality. The recent development of whole body PET-MRI scanners might provide a potential solution for motion correction since internal organ motion could be measured concurrently with PET using MRI. However, although there have been various proposed methods for motion correction, there is not sufficient evidence in the current literature to answer which method is better in clinical practice and investigating the impact of motion correction on lesion detectability. The aim of this thesis is to assess respiratory motion correction in terms of its quantitative accuracy and detectability performance to determine its potential for improved and early cancer diagnosis. This thesis is based on numerical 4D simulated PET data using real MRI data. Motion correction is investigated based on MRI-derived motion fields as could be obtained from a simultaneous PET-MRI acquisition. As a first step, this thesis aims to understand the behaviour of different approaches to motion-corrected image reconstruction in terms of convergence rate and the properties of the reconstructed images obtained. This thesis then deals with the impact of respiratory motion on lesion detectability. A comprehensive assessment is performed using different amplitudes for lesion displacement due to respiration and different respiration patterns derived from actual patient respiratory traces. The impact on the detectability is compared with that achievable by a higher resolution scanner in order to investigate the importance of correcting for motion to realise the benefit from the increased resolution of future PET scanners.
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Roujol, Sébastien. "MR-guided thermotherapies of mobile organs : advances in real time correction of motion and MR-thermometry." Thesis, Bordeaux 1, 2011. http://www.theses.fr/2011BOR14263/document.
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L'ablation des tissus par hyperthermie locale guidée par IRM est une technique prometteuse pour le traitement du cancer et des arythmies cardiaques. L'IRM permet d'extraire en temps réel des informations anatomiques et thermiques des tissus. Cette thèse a pour objectif d'améliorer et d'étendre la méthodologie existante pour des interventions sur des organes mobiles comme le rein, le foie et le coeur. La première partie a été consacrée à l'introduction de l'imagerie rapide (jusqu'à 10-15 Hz) pour le guidage de l'intervention par IRM en temps réel. L'utilisation de cartes graphiques (GPGPU) a permis une accélération des calculs afin de satisfaire la contrainte de temps réel. Une précision, de l'ordre de 1°C dans les organes abdominaux et de 2-3°C dans le coeur, a été obtenue. Basé sur ces avancées, de nouveaux développements méthodologiques ont été proposés dans une seconde partie de cette thèse. L'estimation du mouvement basée sur une approche variationnelle a été améliorée pour gérer la présence de structures non persistantes et de fortes variations d'intensité dans les images. Un critère pour évaluer la qualité du mouvement estimé a été proposé et utilisé pour auto-calibrer notre algorithme d'estimation du mouvement. La méthode de correction des artefacts de thermométrie liés au mouvement, jusqu'ici restreinte aux mouvements périodiques, a été étendue à la gestion de mouvements spontanés. Enfin, un nouveau filtre temporel a été développé pour la réduction du bruit sur les cartographies de température. La procédure interventionnelle apparaît maintenant suffisamment mature pour le traitement des organes abdominaux et pour le transfert vers la clinique. Concernant le traitement des arythmies cardiaques, les méthodes ont été évaluées sur des sujets sains et dans le ventricule gauche. Par conséquent, la faisabilité de l'intervention dans les oreillettes mais aussi en présence d'arythmie devra être abordéeMR-guided thermal ablation is a promising technique for the treatment of cancer and atrial fibrillation. MRI provides both anatomical and temperature information. The objective of this thesis is to extend and improve existing techniques for such interventions in mobile organs such as the kidney, the liver and the heart. A first part of this work focuses on the use of fast MRI (up to 10-15 Hz) for guiding the intervention in real time. This study demonstrated the potential of GPGPU programming as a solution to guarantee the real time condition for both MR-reconstruction and MR-thermometry. A precision in the range of 1°C and 2-3°C was obtained in abdominal organs and in the heart, respectively. Based on these advances, new methodological developments have been carried out in a second part of this thesis. New variational approaches have proposed to address the problem of motion estimation in presence of structures appearing transient and high intensity variations in images. A novel quality criterion to assess the motion estimation is proposed and used to autocalibrate our motion estimation algorithm. The correction of motion related magnetic susceptibility variation was extended to treat the special case of spontaneous motion. Finally, a novel temporal filter is proposed to reduce the noise of MR-thermometry measurements while controlling the bias introduced by the filtering process. As a conclusion, all main obstacles for MR-guided HIFU-ablation of abdominal organs have been addressed in in-vivo and ex-vivo studies, therefore clinical studies will now be realized. However, although promising results have been obtained for MR-guided RF-ablation in the heart, its feasibility in the atrium and in presence of arrhythmia still remains to be investigated
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Soultanidis, Georgios. "Methodology and phantom studies for the development and assessment of motion correction in simultaneous PET-MR." Thesis, King's College London (University of London), 2015. https://kclpure.kcl.ac.uk/portal/en/theses/methodology-and-phantom-studies-for-the-development-and-assessment-of-motion-correction-in-simultaneous-petmr(1d2e96a4-5e50-4b07-85f2-492f010abc24).html.
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Simultaneous PET-MR imaging has recently emerged into routine clinical use, with significant role in diagnosis and treatment. The high sensitivity of a PET scanner, combined with the anatomical and functional information given by MR can focus to a molecular level and provide simultaneous information about individual but correlated parameters. The combination of these modalities has also brought new opportunities. One of these is the potential for the MRI to capture and measure patient motion during PET scanning. This motion information, in the form of motion fields can be applied back to the PET and correct any motion artefacts created. For the success of this study many methodological problems have to be resolved. This thesis investigates the methodology for motion correction of PET data by appropriate use of MR motion information, which could in due course become a standard motion correction method for both pre-clinical and human studies. In particular, this study utilized prototype pre-clinical MR-compatible PET inserts. Following the phantom development and during the experimental procedures, various prototype simultaneous MR-compatible PET small-diameter scanners, in conjunction with a 3T clinical MR scanner, were used to validate the phantom and evaluate several different approaches to motion correction. All scanner combinations used were a combination of a removable PET insert operating within a clinical MR scanner so it was necessary to develop robust methods to ensure spatial alignment and temporal synchronization. The data obtained from simultaneous PET and MR acquisition was arranged into gates and reconstructed by deriving motion transformations from the MR data and using this to correct the PET data for the effects of motion using several PET reconstruction approaches. The experimental results obtained with various PET-MR systems demonstrate the feasibility of this approach and the degree of motion correction that can realistically be expected in pre-clinical PETMR investigations.
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Küstner, Thomas [Verfasser]. "Motion Correction of Positron Emission Tomography Data by a Magnetic Resonance Imaging derived Model / Thomas Küstner." München : Verlag Dr. Hut, 2018. http://d-nb.info/1153253984/34.
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Schulz, Eric Werner mathematics UNSW. "Air-sea flux parameterisations in a shallow tropical sea." Awarded by:University of New South Wales. mathematics, 2002. http://handle.unsw.edu.au/1959.4/18659.
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This thesis is a study of the air-sea fluxes of momentum, sensible heat and latent heat. Fluxes are estimated using the covariance, COARE2.6b bulk flux algorithm, and inertial dissipation methods. The bulk algorithm is validated against the covariance fluxes for the first time in a light-wind, shallow tropical sea, with strong atmospheric instability and low sea state conditions. The removal of ship motion contamination is investigated. This is the first study to quantify the errors associated with corrections for ship motion contamination, and the effects of motion contamination on the covariance calculated heat fluxes. Flow distortion is investigated. Bulk transfer coefficients and roughness lengths are computed and related to the sea state. Ship motion contamination is successfully removed in 86% of the runs. Error analysis of the motion removal algorithm indicates maximum uncertainties of 15% in the wind fluctuations, and 0.002 N/m/m for the wind stress. Motion correction changes the stress by more than 15% in half of the runs analysed. The ship is found to accelerate the mean air flow and deflect it above the horizontal. A correction is developed for the air flow acceleration. The scalar fluxes show good agreement on average for all the methods. As wind speed approaches zero, covariance wind stress is significantly larger than the bulk and inertial dissipation derived wind stress. The non-zero covariance wind stress is reflected in the drag coefficient, CdN10, and momentum roughness length, z0, which are much larger than the parameterisations used in the bulk algorithm. The MCTEX CdN10, wind speed (u10N) relation is 1000 x Cd10N = 1.03 + 7.88/(u10N)^2 0.8 < u10N < 7.5 m/s z0 is primarily a function of wind speed rather than sea state, with largest roughness lengths occurring as wind speed approaches zero. This relation is used in the bulk algorithm, yielding good agreement between covariance and bulk derived wind stress. A new parameterisation for the effects of gustiness, based on wind variance is developed. This brings the bulk wind stress into agreement with the covariance derived wind stress.