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Academic literature on the topic 'Water-fat MRI'

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Published: 25 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Water-fat MRI"

Schick, Fritz. "Fat and water selective MRI." Zeitschrift für Medizinische Physik 27, no. 1 (March 2017): 1–3. http://dx.doi.org/10.1016/j.zemedi.2017.01.003.

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Vasanawala, Shreyas S., Ananth J. Madhuranthakam, Ramesh Venkatesan, Arvind Sonik, Peng Lai, and Anja C. S. Brau. "Volumetric fat-water separated T2-weighted MRI." Pediatric Radiology 41, no. 7 (January 18, 2011): 875–83. http://dx.doi.org/10.1007/s00247-010-1963-5.

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Taviani, Valentina, Diego Hernando, Christopher J. Francois, Ann Shimakawa, Karl K. Vigen, Scott K. Nagle, Mark L. Schiebler, Thomas M. Grist, and Scott B. Reeder. "Whole-heart chemical shift encoded water-fat MRI." Magnetic Resonance in Medicine 72, no. 3 (November 1, 2013): 718–25. http://dx.doi.org/10.1002/mrm.24982.

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Andersson, Thord, Thobias Romu, Anette Karlsson, Bengt Norén, Mikael F. Forsgren, Örjan Smedby, Stergios Kechagias, et al. "Consistent intensity inhomogeneity correction in water-fat MRI." Journal of Magnetic Resonance Imaging 42, no. 2 (October 30, 2014): 468–76. http://dx.doi.org/10.1002/jmri.24778.

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Jacob, M., and B. P. Sutton. "Algebraic Decomposition of Fat and Water in MRI." IEEE Transactions on Medical Imaging 28, no. 2 (February 2009): 173–84. http://dx.doi.org/10.1109/tmi.2008.927344.

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Picaud, Julien, Guylaine Collewet, and Jérôme Idier. "Quantification of mass fat fraction in fish using water–fat separation MRI." Magnetic Resonance Imaging 34, no. 1 (January 2016): 44–50. http://dx.doi.org/10.1016/j.mri.2015.10.004.

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Salvati, Roberto, Eric Hitti, Jean-Jacques Bellanger, Hervé Saint-Jalmes, and Giulio Gambarota. "Fat ViP MRI: Virtual Phantom Magnetic Resonance Imaging of water–fat systems." Magnetic Resonance Imaging 34, no. 5 (June 2016): 617–23. http://dx.doi.org/10.1016/j.mri.2015.12.002.

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Joshi, Anand A., Houchun H. Hu, Richard M. Leahy, Michael I. Goran, and Krishna S. Nayak. "Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI." Journal of Magnetic Resonance Imaging 37, no. 2 (September 25, 2012): 423–30. http://dx.doi.org/10.1002/jmri.23813.

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Nardo, Lorenzo, Dimitrios C. Karampinos, Drew A. Lansdown, Julio Carballido-Gamio, Sonia Lee, Roberto Maroldi, C. Benjamin Ma, Thomas M. Link, and Roland Krug. "Quantitative assessment of fat infiltration in the rotator cuff muscles using water-fat MRI." Journal of Magnetic Resonance Imaging 39, no. 5 (September 24, 2013): 1178–85. http://dx.doi.org/10.1002/jmri.24278.

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Gifford, Aliya, Joel Kullberg, Johan Berglund, Filip Malmberg, Katie C. Coate, Phillip E. Williams, Alan D. Cherrington, Malcolm J. Avison, and E. Brian Welch. "Canine body composition quantification using 3 tesla fat-water MRI." Journal of Magnetic Resonance Imaging 39, no. 2 (April 17, 2013): 485–91. http://dx.doi.org/10.1002/jmri.24156.

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Dissertations / Theses on the topic "Water-fat MRI"

Cui, Chen. "MRI fat-water separation using graph search based methods." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5740.

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The separation of water and fat from multi-echo images is a classic problem in magnetic resonance imaging (MRI) with a wide range of important clinical applications. For example, removal of fat signal can provide better visualization of other signal of interest in MRI scans. In other cases, the fat distribution map can be of great importance in diagnosis. Although many methods have been proposed over the past three decades, robust fat water separation remains a challenge as radiological technology and clinical expectation continue to grow. The problem presents three key difficulties: a) the presence of B0 field inhomogeneities, often large in the state-of-the-art research and clinical settings, which makes the problem non-linear and ill-posed; b) the ambiguity of signal modeling in locations with only one metabolite (either fat or water), which can manifest as spurious fat water swaps in the separation; c) the computational expenditure in fat water separation as the size of the data is increasing along with evolving MRI hardware, which hampers the clinical applicability of the fat water separation. The main focus of this thesis is to develop novel graph based algorithms to estimate the B0 field inhomogeneity maps and separate fat water signals with global accuracy and computational efficiency. We propose a new smoothness constrained framework for the GlObally Optimal Surface Estimation (GOOSE), in which the spatial smoothness of the B0 field is modeled as a finite constraint between adjacent voxels in a uniformly discretized graph. We further develop a new non-equidistant graph model that enables a Rapid GlObally Optimal Surface Estimation (R-GOOSE) in a subset of the fully discretized graph in GOOSE. Extensions of the above frameworks are also developed to achieve high computational efficiency for processing large 3D datasets. Global convergence of the optimization formulation is proven in all frameworks. The developed methods have also been extensively compared to the existing state-of-the-art fat water separation methods on a variety of datasets with consistent performance of high accuracy and efficiency.

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Saputra, Michael Wijaya. "Water and Fat Image Reconstruction from MRI Raw Multi Coil Data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372138.

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n MRI, water and fat signal separation with robust techniques are often helpful in the diagnosis using MRI. Reliable separation of water and fat will help the doctor to get accurate diagnoses such as the size of a tumour. Moreover, fat images can also help in diagnosing the liver and heart condition. To perform water and fat separation, multiple echoes, i.e. measurements of the raw MR signal at different time points, are required. By utilizing the knowledge of the expected signal evolution, it is possible to perform the separation. A main magnetic field is used in MRI. This field is not perfectly homogeneous. Estimating the non-homogeneities is crucial for correcting the separation signal. This thesis used the method of "Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation" (IDEAL). The aims of the thesis are developed a method which reconstruct fat or water MRI images from raw multi-coil image data and evaluate the method’s accuracy and speed by comparing with an available, implemented reconstruction method. In particular, the stability to so called swap artefacts will be analysed. Estimated field maps or inhomogeneity fields are one important and essential step, but there exist multiple local minima. To avoid choosing the incorrect minima, the initial estimation of the field map had to be close to the actual field map value. Neighbouring pixels would have a similar field map values, since the inhomogeneity field was smoothly varying. As such, we carried out the combination of IDEAL algorithms with a region growing method. We implemented the method to do the water and fat separation from a raw image consisting of multi-coil data and multi- echo. The proposed method was tested and the region growing method shows a significantly improved separation of water and fat, when compared to the traditional method without region growing.

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Mendoza, Michael A. "Water Fat Separation with Multiple-Acquisition Balanced Steady-State Free Precession MRI." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/4304.

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Magnetic resonance imaging (MRI) is an important medical imaging technique for visualizing soft tissue structures in the body. It has the advantages of being noninvasive and, unlike x-ray, does not rely on ionizing radiation for imaging. In traditional hydrogen-based MRI, the strongest measured signals are generated from the hydrogen nuclei contained in water and fat molecules.Reliable and uniform water fat separation can be used to improve medical diagnosis. In many applications the water component is the primary signal of interest, while the fat component represents a signal which can obscure the underlying pathology or other features of interest. In other applications the fat signal is the signal of interest. There currently exist many techniques for water fat separation. Dixon reconstruction techniques take multiple images acquired at select echo times with specific phase properties. Linear combinations of these images produce separate water and fat images. In MR imaging, images with high signal-to-noise ratio (SNR), that can be generated in a short time, are desired. Balanced steady-state free precession (bSSFP) MRI is a technique capable of producing images with high SNR in a short imaging time but suffers from signal voids or banding artifacts due to magnetic field inhomogeneity and susceptibly variations. These signal voids degrade image quality. Several methods have been developed to remove these banding effects. The simplest methods combine images across multiple bSSFP image acquisitions. This thesis describes a technique in water fat separation I developed which combines the advantages of bSSFP with Dixon reconstruction in order to produce robust water fat decomposition with high SNR in a short imaging time, while simultaneously reducing banding artifacts which traditionally degrade image quality. This algorithm utilizes four phased-cycled bSSFP acquisitions at specific echo times. Phase sensitive post-processing and a field map are used to prepare the data and reduce the effects of field inhomogeneities. Dixon reconstruction is then used to generate separate water and fat images.

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Huang, Fangping. "Water and Fat Image Reconstruction in Magnetic Resonance Imaging." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1309791802.

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Mehemed, Taha Mohamed M. "Fat-Water Interface on Susceptibility-Weighted Imaging and Gradient-Echo Imaging: Comparison of Phantoms to Intracranial Lipomas." Kyoto University, 2014. http://hdl.handle.net/2433/193572.

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Salvati, Roberto. "Development of Magnetic Resonance Imaging (MRI) methods for in vivo quantification of lipids in preclinical models." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1B026/document.

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L'obésité est associée à une augmentation de la morbidité et de la mortalité liée à de nombreuses maladies, y compris le diabète de type 2, l'hypertension et des pathologies hépatiques menant à une surcharge lipidique d’origine non alcoolique. Récemment, l’imagerie par résonance magnétique (IRM) est devenue la méthode de choix pour la quantification non invasive de la graisse. Dans cette thèse, les méthodes d'IRM ont été étudiées sur un scanner préclinique de 4.7T in vitro (fantômes MR) et in vivo (souris). Deux algorithmes de quantifications de la graisse -la méthode de Dixon et l’algorithme IDEAL- ont été considérés. Les performances de l'algorithme IDEAL ont été analysées en fonction de propriétés des tissus (T2*, fraction de graisse et modèle spectral de la graisse), de paramètres d'acquisition IRM (temps d’écho, nombre d'échos) et de paramètres expérimentaux (SNR et carte de champ). Sur les fantômes, l'approche standard single-T2* IDEAL a montré certaines limites qui pourraient être surmontées en optimisant le nombre d'échos. Une nouvelle méthode, pour déterminer les valeurs de vérité terrain pour T2* de l'eau et pour T2* de la graisse, a été proposée. Pour les mesures in vivo, différentes analyses ont été effectuées en utilisant l'algorithme IDEAL sur le foie et les muscles. L'analyse statistique sur les mesures de ROI a montré que le choix optimal du nombre d'échos est égal à trois pour la quantification de la graisse et six ou plus pour la quantification du T2*. Les valeurs de la fraction de graisse, calculées avec l'algorithme IDEAL, étaient statistiquement comparables aux valeurs obtenues avec la méthode de Dixon. Enfin, un procédé pour générer des signaux de référence mimant les systèmes eau-graisse (Fat Virtual Phantom MRI), sans l'aide d'objets physiques, a été proposé. Ces fantômes virtuels, qui présentent des caractéristiques de bruit réalistes, représentent une alternative intéressante aux fantômes physiques pour fournir un signal de référence dans les mesures IRM
Obesity is associated with increased morbidity and mortality linked to many diseases, including type 2 diabetes, hypertension and disease nonalcoholic fatty liver. Recently, 1H magnetic resonance imaging (MRI) has emerged as the method of choice for non-invasive fat quantification. In this thesis, MRI methodologies were investigated for in vitro (MR phantoms) and in vivo (mice) measurements on a 4.7T preclinical scanner. Two algorithms of fat quantifications – the Dixon’s method and IDEAL algorithm – were considered. The performances of the IDEAL algorithm were analyzed as a function of tissue properties (T2*, fat fraction and fat spectral model), MRI acquisition parameters (echo times, number of echoes) and experimental parameters (SNR and field map). In phantoms, the standard approach of single-T2* IDEAL showed some limitations that could be overcome by optimizing the number of echoes. A novel method to determine the ground truth values of T2* of water and T2* of fat was here proposed. For in vivo measurements, different analyses were performed using the IDEAL algorithm in liver and muscle. Statistical analysis on ROI measurements showed that the optimal choice of the number of echoes was equal to three for fat quantification and six or more for T2* quantification. The fat fraction values, calculated with IDEAL algorithm, were statistically similar to the values obtained with Dixon’s method. Finally, a method for generating reference signals mimicking fat-water systems (Fat Virtual Phantom MRI), without using physical objects, was proposed. These virtual phantoms, which display realistic noise characteristics, represent an attractive alternative to physical phantoms for providing a reference signal in MRI measurements

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Palosaari, K. (Kari). "Quantitative and semiquantitative imaging techniques in detecting joint inflammation in patients with rheumatoid arthritis:phase-shift water-fat MRI method for fat suppression at 0.23 T, contrast-enhanced dynamic and static MRI, and quantitative ^99mTc-nanocolloid scintigraphy." Doctoral thesis, University of Oulu, 2008. http://urn.fi/urn:isbn:9789514288623.

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Abstract The purpose of this study was to evaluate the value of 0.23T low-field magnetic resonance imaging (MRI) and nanocolloid (NC) scintigraphy in assessing joint pathology associated with rheumatoid arthritis (RA). Fat suppression methods combined with contrast media enhancement aid in distinguishing enhancing inflamed tissue from the surrounding fat, especially in the imaging of arthritic joints. The feasibility and image quality of a phase-shift water-fat MRI method for fat suppression at low-field 0.23T open configuration MR scanner was evaluated. The technique was combined with contrast-enhanced imaging to assess the conspicuity of synovial hypertrophy in the joints of 30 RA patients. Improved conspicuity and delineation of synovitis was detected with this method. However, because of a great amount of manual post processing, future development is needed to make this method more feasible. Contrast-enhanced MRI and NC scintigraphy may provide objective and quantitative information about the inflammatory activity in arthritic joints. The value of quantitative and semiquantitative measures of inflammation derived from NC scintigraphy and low-field MRI of the wrist joint of 28 early RA patients was evaluated. Furthermore, it was investigated whether these parameters have predictive value of further erosive development during two years of follow-up. Strong correlations were detected between the NC scintigraphy and MRI measures, and these parameters were associated with laboratory markers of inflammation. During the two-year follow-up, the initial MRI and NC scintigraphy measures were closely related with the progression of wrist joint erosions. Small erosive-like bone defects can occasionally be found in wrist MRI of patients without clinically overt arthritis. The prevalence of these lesions was studied in bilateral wrist MRI examinations of 31 healthy persons. Small lesions resembling erosions were detected in 14 out of 31 subjects. Altogether 24 of the 930 wrist bones evaluated showed such lesions (3%). Thus small changes resembling erosions can be found in the wrist MRI of healthy subjects; the significance of these findings must always be interpreted with reference to the clinical picture. In conclusion, early RA patients with high local inflammatory activity, as detected by NC scintigraphy and MRI are at risk of developing further bone damage. Furthermore, in the follow-up of early RA patients, if clinically sustained response is not achieved, these methods help to identify patients who need more intensive drug treatment.

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Berglund, Johan. "Separation of Water and Fat Signal in Magnetic Resonance Imaging : Advances in Methods Based on Chemical Shift." Doctoral thesis, Uppsala universitet, Enheten för radiologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-158111.

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Magnetic resonance imaging (MRI) is one of the most important diagnostic tools of modern healthcare. The signal in medical MRI predominantly originates from water and fat molecules. Separation of the two components into water-only and fat-only images can improve diagnosis, and is the premier non-invasive method for measuring the amount and distribution of fatty tissue. Fat-water imaging (FWI) enables fast fat/water separation by model-based estimation from chemical shift encoded data, such as multi-echo acquisitions. Qualitative FWI is sufficient for visual separation of the components, while quantitative FWI also offers reliable estimates of the fat percentage in each pixel. The major problems of current FWI methods are long acquisition times, long reconstruction times, and reconstruction errors that degrade image quality. In this thesis, existing FWI methods were reviewed, and novel fully automatic methods were developed and evaluated, with a focus on fast 3D image reconstruction. All MRI data was acquired on standard clinical scanners. A triple-echo qualitative FWI method was developed for the specific application of 3D whole-body imaging. The method was compared with two reference methods, and demonstrated superior image quality when evaluated in 39 volunteers. The problem of qualitative FWI by dual-echo data with unconstrained echo times was solved, allowing faster and more flexible image acquisition than conventional FWI. Feasibility of the method was demonstrated in three volunteers and the noise performance was evaluated. Further, a quantitative multi-echo FWI method was developed. The signal separation was based on discrete whole-image optimization. Fast 3D image reconstruction with few reconstruction errors was demonstrated by abdominal imaging of ten volunteers. Lastly, a method was proposed for quantitative mapping of average fatty acid chain length and degree of saturation. The method was validated by imaging different oils, using gas-liquid chromatography (GLC) as the reference. The degree of saturation agreed well with GLC, and feasibility of the method was demonstrated in the thigh of a volunteer. The developed methods have applications in clinical settings, and are already being used in several research projects, including studies of obesity, dietary intervention, and the metabolic syndrome.

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Belbaisi, Adham. "Deep Learning-Based Skeleton Segmentation for Analysis of Bone Marrow and Cortical Bone in Water-Fat Magnetic Resonance Imaging." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297528.

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A major health concern for subjects with diabetes is weaker bones and increased fracture risk. Current clinical assessment of the bone strength is performed by measuring Bone Mineral Density (BMD), where low BMD-values are associated with an increased risk of fracture. However, subjects with Type 2 Diabetes (T2D) have been shown to have normal or higher BMD-levels compared to healthy controls, which does not reflect the recognized bone fragility among diabetics. Thus, there is need for more research about diabetes-related bone fragility to find other factors of impaired bone health. One potential biomarker that has recently been studied is Bone Marrow Fat (BMF). The data in this project consisted of whole-body water-fat Magnetic Resonance Imaging (MRI) volumes from the UK Biobank Imaging study (UKBB). Each subject in this data has a water volume and a fat volume, allowing for a quantitative assessment of water and fat content in the body. To analyze and perform quantitative measurements of the bones specifically, a Deep Learning (DL) model was trained, validated, and tested for performing fully automated and objective skeleton segmentation, where six different bones were segmented: spine, femur, pelvis, scapula, clavicle and humerus. The model was trained and validated on 120 subjects with 6-fold cross-validation and tested on eight subjects. All ground-truth segmentations of the training and test data were generated using two semi-automatic pipelines. The model was evaluated for each bone separately as well as the overall skeleton segmentation and achieved varying accuracy, performing better on larger bones than on smaller ones. The final trained model was applied on a larger dataset of 9562 subjects (16% type 2 diabetics) and the BMF, as well as bone marrow volume (BMV) and cortical bone volume (CBV), were measured in the segmented bones of each subject. The results of the quantified biomarkers were compared between T2D and healthy subjects. The comparison revealed possible differences between healthy and diabetic subjects, suggesting a potential for new findings related to diabetes and associated bone fragility.

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Johnson, David Herbert. "Phenotyping Rodent Models of Obesity Using Magnetic Resonance Imaging." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1250086728.

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Book chapters on the topic "Water-fat MRI"

Horowitz, Alfred L. "Fat and Water." In MRI Physics for Radiologists, 161–70. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-0785-6_19.

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Horowitz, Alfred L. "Fat and Water." In MRI Physics for Radiologists, 166–77. New York, NY: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4684-0428-9_17.

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Glocker, Ben, Ender Konukoglu, Ioannis Lavdas, Juan Eugenio Iglesias, Eric O. Aboagye, Andrea G. Rockall, and Daniel Rueckert. "Correction of Fat-Water Swaps in Dixon MRI." In Lecture Notes in Computer Science, 536–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46726-9_62.

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Zhao, Liang, Yiqiang Zhan, Dominik Nickel, Matthias Fenchel, Berthold Kiefer, and Xiang Sean Zhou. "Identification of Water and Fat Images in Dixon MRI Using Aggregated Patch-Based Convolutional Neural Networks." In Patch-Based Techniques in Medical Imaging, 125–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47118-1_16.

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Lee, Christine U., and James F. Glockner. "Case 11.8." In Mayo Clinic Body MRI Case Review, edited by Christine U. Lee and James F. Glockner, 544. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199915705.003.0286.

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50-year-old perimenopausal woman with intermittent mild abdominopelvic pain Fat, water, IP, and OP images from a 3D SPGR Dixon acquisition (Figure 11.8.1) reveal a posterior right adnexal lesion with a fluid-fluid level. The anterior nondependent layer is lipid, which appears bright on the fat image and dark on the water image. Notice also the chemical shift artifact appearing at the fat-fluid interface on the OP image....

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Lee, Christine U., and James F. Glockner. "Case 17.31." In Mayo Clinic Body MRI Case Review, edited by Christine U. Lee and James F. Glockner, 847–48. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199915705.003.0449.

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49-year-old woman with a history of invasive right breast cancer with extensive ductal carcinoma in situ; she has undergone mastectomy and reconstruction with a saline implant Axial water image (Figure 17.31.1A) from a T2-weighted FSE acquisition using a modified 3-point Dixon (IDEAL) method for fat suppression demonstrates water signal in a right breast implant. There is also a minimal amount of right pleural fluid. Axial FSE IR image with fat suppression and selective water suppression (silicone image) (...

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Lee, Christine U., and James F. Glockner. "Case 17.17." In Mayo Clinic Body MRI Case Review, edited by Christine U. Lee and James F. Glockner, 821–22. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199915705.003.0435.

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58-year-old woman with cirrhosis Axial precontrast (Figure 17.17.1) and arterial phase (Figure 17.17.2) and portal venous phase (Figure 17.17.3) postgadolinium water and fat images from a 3D SPGR Dixon acquisition. Notice that the phase and frequency directions have been swapped on the arterial phase acquisition and that there is a large geographic signal void in the middle of the liver on the water image, with the missing anatomy appearing on the corresponding fat image. All artifacts have been corrected on the portal venous phase images....

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Lee, Christine U., and James F. Glockner. "Case 15.8." In Mayo Clinic Body MRI Case Review, edited by Christine U. Lee and James F. Glockner, 741. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199915705.003.0391.

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46-year-old woman with a history of metachronous bilateral breast cancers status post bilateral mastectomies with implant reconstruction; her physician detected an area of palpable concern in the 12 o’clock position of the right breast Water images (Figure 15.8.1) from a T2-weighted FSE acquisition using a modified 3-point Dixon (IDEAL) method for fat suppression demonstrate features suggestive of dual-lumen implants. Corresponding T2-weighted IR images with water suppression (...

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Lee, Christine U., and James F. Glockner. "Case 15.6." In Mayo Clinic Body MRI Case Review, edited by Christine U. Lee and James F. Glockner, 739. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199915705.003.0389.

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55-year-old woman with bilateral augmentation mammoplasties 30 years ago, now with a palpable asymmetry in the right breast Sagittal 2D IR image with fat suppression and selective water suppression of the right breast (Figure 15.6.1) demonstrates a well-circumscribed area of silicone signal and the linguine sign. ...

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Lee, Christine U., and James F. Glockner. "Case 15.7." In Mayo Clinic Body MRI Case Review, edited by Christine U. Lee and James F. Glockner, 740. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199915705.003.0390.

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65-year-old woman with bilateral breast implants placed 25 years ago presents with increasing capsular contracture Axial (Figure 15.7.1) and bilateral sagittal (right, Figure 15.7.2A; left, Figure 15.7.2B) IR images with fat suppression and selective water suppression demonstrate silicone outside of the fibrous capsule bilaterally. Intracapsular rupture of both implants is indicated by the teardrop signs and subcapsular line signs in both breasts....

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Conference papers on the topic "Water-fat MRI"

Xu, Jing, Xiaofei Hu, Haiying Tang, Richard Kennan, and Karim Azer. "Water-Fat Decomposition by IDEAL-MRI With Phase Estimation: A Method to Determine Chemical Contents In Vivo." In ASME 2010 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2010. http://dx.doi.org/10.1115/sbc2010-19296.

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High-resolution Magnetic Resonance Imaging (MRI) of humans and animals in vivo is routine and non-invasive. Identifying and quantifying chemical composition of tissue from acquired images is a challenge. MR spectroscopy (MRS) may be used to identify chemical components accurately over a finite volume in the tissue. However, the temporal and spatial resolutions are limited. Multi-spectral MRI exploits the multiple modes of MR such as T1, T2 and proton density maps and classifies voxels into different tissue types, but the chemical identity of the tissue remains unknown. Many fat suppression methods were developed because the unwanted fat signal often compromises image interpretability in clinical MRI, but these techniques are sensitive to MR field inhomogeneity. Multi-point Dixon methods separate MR images into water and fat images and are less sensitive to field inhomogeneity [1] and IDEAL-MRI (iterative decomposition of water and fat with echo asymmetry and least-squares estimation) improved upon the Dixon methods by avoiding the problem of phase unwrapping [2]. However, special care has to be taken when estimating the field map to avoid erroneous solutions to the least-squares estimation problem which lead to artifacts such as swapping of water and fat. The use of region growing schemes (with a reliable seed) mitigates this problem as demonstrated in previous studies [3][4]. However, the seed is not always reliable and growing schemes can be sensitive to phase discontinuities. Moreover, although the technology was successfully demonstrated on many clinical scanners, only limited applications were found in preclinical scanners with high MR field where the field inhomogeneity can be far worse [5]. We developed a robust and accurate algorithm to compute water and fat content on an 11.7T small animal scanner by improving upon existing phase estimation methods through multiple starting pixels and consensus-based region growing. The method, after further validation, has the potential of providing a translatable assay to study disease progression and regression related to fat and water contents in various animal models, such as studying atherosclerotic plaque composition.

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Tisdall, M. Dylan, and M. Stella Atkins. "Fat/water separation in a single MRI image with arbitrary phase shift." In Medical Imaging, edited by Michael J. Flynn and Jiang Hsieh. SPIE, 2006. http://dx.doi.org/10.1117/12.655128.

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Shen, Chenfei, Huajun She, and Yiping Du. "Improved Robustness in Water-Fat Separation in MRI using Conditional Adversarial Networks." In ICBBE '20: 2020 7th International Conference on Biomedical and Bioinformatics Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3444884.3444891.

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Picaud, Julien, Guylaine Collewet, and Jerome Idier. "Correction of RF inhomogeneities for high throughput water and fat quantification by MRI." In 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2015. http://dx.doi.org/10.1109/ipta.2015.7367176.

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Pirogov, Yuri A., Nikolai V. Anisimov, and Leonid V. Gubski. "3D visualization of pathological forms from MRI data obtained with simultaneous water and fat signal suppression." In Medical Imaging 2003, edited by Martin J. Yaffe and Larry E. Antonuk. SPIE, 2003. http://dx.doi.org/10.1117/12.479767.

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Ding, J., PA Thompson, Y. Gao, MT Marron, BC Wertheim, MI Altbach, J.-P. Galons, et al. "Abstract P3-02-03: Accurate and reliable automated breast density measurements with no ionizing radiation using fat-water decomposition MRI." In Abstracts: 2016 San Antonio Breast Cancer Symposium; December 6-10, 2016; San Antonio, Texas. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.sabcs16-p3-02-03.

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Ong, Henry H., Corey D. Webb, Marnie L. Gruen, Alyssa H. Hasty, John C. Gore, and E. B. Welch. "Fat-water MRI is sensitive to local adipose tissue inflammatory changes in a diet-induced obesity mouse model at 15T." In SPIE Medical Imaging, edited by Barjor Gimi and Robert C. Molthen. SPIE, 2015. http://dx.doi.org/10.1117/12.2082333.

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Ding, J., PA Thompson, BC Wertheim, DJ Roe, MT Marron, MI Altbach, J.-P. Galons, et al. "Abstract P6-09-19: Breast density change at 6 months is associated with change at 12 months as measured by fat-water decomposition MRI in women on tamoxifen." In Abstracts: 2016 San Antonio Breast Cancer Symposium; December 6-10, 2016; San Antonio, Texas. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.sabcs16-p6-09-19.

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