Journal articles: 'MRI methods'

1

Kozaitis, Samuel. "Denoising Methods for MRI Imagery." American Journal of Engineering and Applied Sciences 10, no. 3 (March 1, 2017): 655–60. http://dx.doi.org/10.3844/ajeassp.2017.655.660.

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Piccolomini, E. Loli, F. Zama, G. Zanghirati, and A. Formiconi. "Regularization methods in dynamic MRI." Applied Mathematics and Computation 132, no. 2-3 (November 2002): 325–39. http://dx.doi.org/10.1016/s0096-3003(01)00196-5.

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Madelin, Guillaume, Jae-Seung Lee, Ravinder R. Regatte, and Alexej Jerschow. "Sodium MRI: Methods and applications." Progress in Nuclear Magnetic Resonance Spectroscopy 79 (May 2014): 14–47. http://dx.doi.org/10.1016/j.pnmrs.2014.02.001.

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Madhuranthakam, Ananth J., Qing Yuan, and Ivan Pedrosa. "Quantitative Methods in Abdominal MRI." Topics in Magnetic Resonance Imaging 26, no. 6 (December 2017): 251–58. http://dx.doi.org/10.1097/rmr.0000000000000145.

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Clarke, L. P., R. P. Velthuizen, M. A. Camacho, J. J. Heine, M. Vaidyanathan, L. O. Hall, R. W. Thatcher, and M. L. Silbiger. "MRI segmentation: Methods and applications." Magnetic Resonance Imaging 13, no. 3 (January 1995): 343–68. http://dx.doi.org/10.1016/0730-725x(94)00124-l.

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Bogachev, Yu V., A. V. Nikitina, and M. N. Shishkina. "Advanced Methods of Magnetic Resonance Theranostics." LETI Transactions on Electrical Engineering & Computer Science 16, no. 1 (2023): 5–24. http://dx.doi.org/10.32603/2071-8985-2023-16-1-5-24.

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The combination of high-resolution and high-sensitivity diagnostic methods with the therapy of socially significant diseases under the influence of various physical fields, methods or substances is the basis of new intensively developed areas of modern medicine. This review article analyzes advanced methods of magnetic resonance (MR) theranostics, such as MRI-guided laser therapy, MRI tracking in stem cell therapy, MRguided therapy using MRI-theranostic agents based on magnetic nanoparticles, transcranial magnetic stimulation combined with functional MRI (TMS/fMRI). The paper presents both research materials and examples of clinical applications of these methods.

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Sijens, P. E. "Combining New MRI Methods - MR Spectroscopy, Functional MRI, Diffusion Tensor Imaging." Imaging Decisions MRI 9, no. 1 (April 2005): 39–48. http://dx.doi.org/10.1111/j.1617-0830.2005.00039.x.

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Miller, Karla L., Rob HN Tijssen, Nikola Stikov, and Thomas W. Okell. "Steady-state MRI: methods for neuroimaging." Imaging in Medicine 3, no. 1 (February 2011): 93–105. http://dx.doi.org/10.2217/iim.10.66.

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Kanwal, Laraib, and Usman Shahid. "A Review on MRI Denoising Methods." Lahore Garrison University Journal of Life Sciences 3, no. 3 (April 22, 2020): 132–39. http://dx.doi.org/10.54692/lgujls.2019.030369.

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Many years ago, a lot of work has been done to enhance the resolution and signal-to-noise ratio of magnetic resonance images for accurate monitoring. However, artifacts and random noise still affects the quality of Magnetic Resonance images. In order to maintain the relevant image content, compensation between noise reduction and preservation of details have to be made. Therefore, noise reduction is a paramount challenge. A published literature review of each technique is discussed along with its advantages and limitations. After the introduction of magnetic resonance imaging (MRI) technology, the famous approaches for denoising MRI images are classified with overview of each method.

10

Borrero, Camilo G., James M. Mountz, and John D. Mountz. "Emerging MRI methods in rheumatoid arthritis." Nature Reviews Rheumatology 7, no. 2 (November 2, 2010): 85–95. http://dx.doi.org/10.1038/nrrheum.2010.173.

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Lenglet, C., J. S. W. Campbell, M. Descoteaux, G. Haro, P. Savadjiev, D. Wassermann, A. Anwander, et al. "Mathematical methods for diffusion MRI processing." NeuroImage 45, no. 1 (March 2009): S111—S122. http://dx.doi.org/10.1016/j.neuroimage.2008.10.054.

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Presciutti, O., G. P. Pelliccioli, R. Tarducci, P. Chiarini, and G. Gobbi. "New Processing Methods in Functional MRI." Rivista di Neuroradiologia 10, no. 3 (June 1997): 261–64. http://dx.doi.org/10.1177/197140099701000301.

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Two processing methods of fMRI data are proposed: vector orthogonalization and Fisher transformation of correlation coefficients (cc) distribution. The former is based on orthogonalization of vectors V (time course of signal intensity) and R (noise) to make V independent from R. The latter is based on Fisher transformation of cc distribution. From this distribution, by usual statistical tests, it is possible to detect the values of the cc that are significantly different from the mean in order to select activated pixels.

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Lewin, Jonathan S. "Functional MRI: An introduction to methods." Journal of Magnetic Resonance Imaging 17, no. 3 (February 19, 2003): 383. http://dx.doi.org/10.1002/jmri.10284.

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Lamb, Jennifer, and Jason P. Holland. "Advanced Methods for Radiolabeling Multimodality Nanomedicines for SPECT/MRI and PET/MRI." Journal of Nuclear Medicine 59, no. 3 (October 12, 2017): 382–89. http://dx.doi.org/10.2967/jnumed.116.187419.

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Rausch, Ivo, Andreas Zitterl, Neydher Berroterán-Infante, Lucas Rischka, Daniela Prayer, Matthias Fenchel, Reza A. Sareshgi, et al. "Dynamic [18F]FET-PET/MRI using standard MRI-based attenuation correction methods." European Radiology 29, no. 8 (January 11, 2019): 4276–85. http://dx.doi.org/10.1007/s00330-018-5942-9.

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Hunt, Ruskin H., and Kathleen M. Thomas. "Magnetic resonance imaging methods in developmental science: A primer." Development and Psychopathology 20, no. 4 (2008): 1029–51. http://dx.doi.org/10.1017/s0954579408000497.

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AbstractStructural and functional magnetic resonance imaging (MRI) are increasingly common research methods among investigators interested in typically and atypically developing populations. However, the effective use of these tools requires an understanding of the basis of the magnetic resonance signal, as well as some of the additional experimental complications that arise when collecting MRI data from developmental populations. This primer provides a foundation for investigators who wish to utilize MRI methods in their research and whose primary interest involves typically and atypically developing populations. The basic concepts of MRI physics are introduced, as well as the typical MRI scanner components and their role in MRI data acquisition. In addition, a variety of scan types (structural, functional, diffusion tensor) are discussed, along with a number of important experimental design factors that can impact the quality and utility of the data collected. Special consideration is given to working with pediatric and special populations.

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Larson, Peder Eric Zufall, and Jeremy W. Gordon. "Hyperpolarized Metabolic MRI—Acquisition, Reconstruction, and Analysis Methods." Metabolites 11, no. 6 (June 14, 2021): 386. http://dx.doi.org/10.3390/metabo11060386.

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Hyperpolarized metabolic MRI with 13C-labeled agents has emerged as a powerful technique for in vivo assessments of real-time metabolism that can be used across scales of cells, tissue slices, animal models, and human subjects. Hyperpolarized contrast agents have unique properties compared to conventional MRI scanning and MRI contrast agents that require specialized imaging methods. Hyperpolarized contrast agents have a limited amount of available signal, irreversible decay back to thermal equilibrium, bolus injection and perfusion kinetics, cellular uptake and metabolic conversion kinetics, and frequency shifts between metabolites. This article describes state-of-the-art methods for hyperpolarized metabolic MRI, summarizing data acquisition, reconstruction, and analysis methods in order to guide the design and execution of studies.

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Zhang, Bida, Weidong Wang, Xiaoyu Song, Donglin Zu, Guangfei Chen, and Shanglian Bao. "Parallel imaging methods for phased array MRI*." Progress in Natural Science 13, no. 4 (April 1, 2003): 307–10. http://dx.doi.org/10.1080/10020070312331343590.

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ZHANG, Bida. "Parallel imaging methods for phased array MRI." Progress in Natural Science 13, no. 4 (2003): 307. http://dx.doi.org/10.1360/03jz9055.

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Balafar, M. A., A. R. Ramli, M. I. Saripan, and S. Mashohor. "Review of brain MRI image segmentation methods." Artificial Intelligence Review 33, no. 3 (January 22, 2010): 261–74. http://dx.doi.org/10.1007/s10462-010-9155-0.

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Grucka, A., Francesco Sensi, Francesco Brun, M. Ukmar, A. Chincarini, and R. Longo. "Development of methods for MRI longitudinal studies." Physica Medica 32 (September 2016): 310. http://dx.doi.org/10.1016/j.ejmp.2016.07.175.

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22

., Swapnali Matkar. "IMAGE SEGMENTATION METHODS FOR BRAIN MRI IMAGES." International Journal of Research in Engineering and Technology 04, no. 03 (March 25, 2015): 263–66. http://dx.doi.org/10.15623/ijret.2015.0403045.

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N, Pradeepa, Renuga Devi A, and Saumiya Jose Thomas. "Survey of MRI Brain Image Segmentation Methods." International Journal of Electronics and Communication Engineering 2, no. 2 (February 25, 2015): 21–23. http://dx.doi.org/10.14445/23488549/ijece-v2i2p105.

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Vandsburger, Moriel H., and Frederick H. Epstein. "Emerging MRI Methods in Translational Cardiovascular Research." Journal of Cardiovascular Translational Research 4, no. 4 (March 31, 2011): 477–92. http://dx.doi.org/10.1007/s12265-011-9275-1.

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25

Wild, J. M., H. Marshall, M. Bock, L. R. Schad, P. M. Jakob, M. Puderbach, F. Molinari, E. J. R. Van Beek, and J. Biederer. "MRI of the lung (1/3): methods." Insights into Imaging 3, no. 4 (June 13, 2012): 345–53. http://dx.doi.org/10.1007/s13244-012-0176-x.

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Gilani, Irtiza Ali, and Raimo Sepponen. "Quantitative rotating frame relaxometry methods in MRI." NMR in Biomedicine 29, no. 6 (April 21, 2016): 841–61. http://dx.doi.org/10.1002/nbm.3518.

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27

Hormuth, David A., Anna G. Sorace, John Virostko, Richard G. Abramson, Zaver M. Bhujwalla, Pedro Enriquez‐Navas, Robert Gillies, et al. "Translating preclinical MRI methods to clinical oncology." Journal of Magnetic Resonance Imaging 50, no. 5 (March 29, 2019): 1377–92. http://dx.doi.org/10.1002/jmri.26731.

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Rao, Gaofeng, Hui Gao, Xiaoyang Wang, Jinchao Zhang, Miaoqing Ye, and Liyuan Rao. "MRI measurements of brain hippocampus volume in relation to mild cognitive impairment and Alzheimer disease: A systematic review and meta-analysis." Medicine 102, no. 36 (September 8, 2023): e34997. http://dx.doi.org/10.1097/md.0000000000034997.

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Background: This is the first meta-analysis conducted to compare the hippocampal volume measured by magnetic resonance imaging (MRI) in healthy normal subjects, mild cognitive impairment (MCI) and Alzheimer disease (AD), and to analyze the relationship between hippocampal volume changes and MCI and AD. Methods: English literatures published from January 2004 to December 2006 were extracted from PubMed, Embase, Wanfang Medical, and China National Knowledge Infrastructure databases. Statistical analysis was carried out with Stata/SE 16.0 software. Results: The smaller the volume of the hippocampus measured by MRI, the more severe the cognitive impairment or AD. Different MRI post-measurement correction methods have different measurement results: Left hippocampal volume measured by MRI Raw volume method is negatively correlated with MCI and AD (OR [odds ratio] = 0.58, 95%CI [confidence interval]: 0.42, 0.75) right hippocampal volume measured was not associated with MCI OR AD (OR = 0.87, 95%CI: 0.56, 1.18); left hippocampal volume measured by MRI total intracranial volume (TIV) Correction was not associated with MCI and AD (OR = 0.90, 95%CI: 0.62, 1.19), measured right hippocampal volume was not associated with MCI OR AD (OR = 0.81, 95%CI: 0.49, 1.12); left hippocampal volume measured by MRI TIV Correction was not associated with MCI and AD (OR = 0.90, 95%CI: 0.62, 1.19), measured right hippocampus volume was negatively associated with MCI and AD (OR = 0.49, 95%CI: 0.35, 0.62). Conclusion: The shrinkage of hippocampus volume is closely related to MCI and AD. MRI measurement of hippocampus volume is not only an auxiliary diagnostic tool for MCI and AD, but also a good prognosis assessment tool.

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Despotović, Ivana, Bart Goossens, and Wilfried Philips. "MRI Segmentation of the Human Brain: Challenges, Methods, and Applications." Computational and Mathematical Methods in Medicine 2015 (2015): 1–23. http://dx.doi.org/10.1155/2015/450341.

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Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation.

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Sarkar, Saradwata, and Sudipta Das. "A Review of Imaging Methods for Prostate Cancer Detection." Biomedical Engineering and Computational Biology 7s1 (January 2016): BECB.S34255. http://dx.doi.org/10.4137/becb.s34255.

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Imaging is playing an increasingly important role in the detection of prostate cancer (PCa). This review summarizes the key imaging modalities–multiparametric ultrasound (US), multiparametric magnetic resonance imaging (MRI), MRI-US fusion imaging, and positron emission tomography (PET) imaging–-used in the diagnosis and localization of PCa. Emphasis is laid on the biological and functional characteristics of tumors that rationalize the use of a specific imaging technique. Changes to anatomical architecture of tissue can be detected by anatomical grayscale US and T2-weighted MRI. Tumors are known to progress through angiogenesis–-a fact exploited by Doppler and contrast-enhanced US and dynamic contrast-enhanced MRI. The increased cellular density of tumors is targeted by elastography and diffusion-weighted MRI. PET imaging employs several different radionuclides to target the metabolic and cellular activities during tumor growth. Results from studies using these various imaging techniques are discussed and compared.

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Jain, Dhyanendra, Prashant Singh, and P. K. Bharti. "Deep Learning-Based Clinical Image Segmentation Methods With Different Modalities." Journal of Physics: Conference Series 2236, no. 1 (March 1, 2022): 012002. http://dx.doi.org/10.1088/1742-6596/2236/1/012002.

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Abstract This paper provides a evaluation of the furnished strategies for classifying picture of brain lump the Nuclear-MRI (MRI) and the computed tomography (CT) scanning. The separation of MRI picture of the mind and CT are extensively used as preoperative imaging, in-affected person studies and with inside the use of equipment, within side the area of scientific imaging processing. Separation of MRI and CT picture is a tough venture due to the similarities among the robust and tender institution of muscle anatomy with inside the mind photo. And many components of the identical area within side the image are separated via way of means of the separation of the photo and the direction. Selection of the ideal phase kind constructed at the photo element. This evaluation changed into carried out with the purpose of authorizing a mixture of kind separation of MRI and CT picture. These opinions are classified primarily based totally at the strategies taken into consideration for classification. imaging of MRI & CT also known as X-ray. The separation of MRI picture of the mind and X-ray are extensively used as preoperative imaging, in-affected person studies and withinside the use of equipment, withinside the area of scientific imaging processing. Separation of MRI and CT picture is a tough venture due to the similarities among the robust and tender institution of muscle anatomy withinside the mind photo. And many components of the identical area withinside the image are separated via way of means of the separation of the photo and the direction. Selection of the ideal phase kind constructed at the photo element. This evaluation changed into carried out with the purpose of authorizing a mixture of kind separation of MRI and CT picture. These opinions are classified primarily based totally at the strategies taken into consideration for classification.

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Zhao, Xiang, Tiejun Yang, and Bingjie Li. "A review on generative based methods for MRI reconstruction." Journal of Physics: Conference Series 2330, no. 1 (August 1, 2022): 012002. http://dx.doi.org/10.1088/1742-6596/2330/1/012002.

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Abstract Magnetic resonance imaging (MRI) is one of the most important methods for clinical diagnosis. However, the main drawback of MRI is the long imaging time, which will cause the moving artifact by patient movements. With the rapid development of the computing power of computer, deep learning is widely used in computer vision, natural language processing, visual recognition and so on. Meanwhile, a large number of reconstruction methods based on deep learning have also emerged. Recently, many generative models have been proposed to solve the perception quality problem that existed in fast MRI images. In this paper, we manage to survey the motivations and reconstruction strategies of generative-based methods published in journals and conferences over the past five years. First, the background and theoretical basis of MRI reconstruction are introduced. Secondly, the application of generative-based methods in MRI reconstruction field is comprehensively summarized and analyzed, including Generative Adversarial Network (GAN), Variational Autoencoder (VAE) and VAE-GAN. Then the advantages and disadvantages of the existing generative-based MRI reconstruction methods are discussed. Finally, several publicly available MR image datasets and evaluation metrics are presented, which can provide a reference for researchers and practitioners working in related domains. The conclusions and challenges are also given.

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Yang, Pin. "MRI View of Rehabilitation Methods to Relieve Anterior Cruciate Ligament Injury in Dancers." Scanning 2022 (September 14, 2022): 1–7. http://dx.doi.org/10.1155/2022/1544440.

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In order to solve the problem of the difference in the diagnostic effect of different sequences of magnetic resonance imaging (MRI) examinations for anterior and posterior cruciate ligament injuries, the author proposes an MRI rehabilitation method to relieve anterior cruciate ligament injury in dancers. This method retrospectively analyzed the clinical data of 60 patients with knee anterior and posterior cruciate ligament injuries in our hospital, and all patients were diagnosed with knee anterior and posterior cruciate ligament injuries. All patients underwent MRI 3D sequence and 2D sequence examination successively to compare anatomical measurements. This study aimed at comparing the measurements of the posterior cruciate ligament (PCL) and anterior cruciate ligament (ACL) between the two examination sequences; comparing the diagnosis; comparing the grading and judgment of the anterior and posterior cruciate ligament injuries of the knee joint between the two inspection sequences; and comparing the diagnostic coincidence rates of the two examination sequences in the complete tear of the anterior and posterior cruciate ligaments of the knee. Experimental results show that, in terms of PCL and ACL, the angle, thickness, and length of two-dimensional MRI examination were significantly different from those of MRI examination and anatomical measurement ( P < 0.05 ); for PCL and ACL, the angle, thickness, and length of 3D MRI were not significantly different from anatomical measurements ( P > 0.05 ). The diagnostic accuracy of 2D MRI was 83.33%, which was lower than 95.00% of 3D MRI ( P < 0.05 ). There was no significant difference in the grading of anterior and posterior cruciate ligament injuries between the two examination sequences ( P > 0.05 ). The diagnostic coincidence rates of 3D MRI and 2D MRI for complete tear of the anterior and posterior cruciate ligaments were 95.55% and 80.00%, respectively ( P < 0.05 ). In conclusion, three-dimensional MRI examination can obtain higher diagnostic value for patients with knee joint anterior and posterior cruciate ligament injury.

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Wafa, BOUKELLOUZ, and MOUSSAOUI Abdelouahab. "Classification of methods for generating pseudo-CT from MRI images for MRI-alone RT." Medical Technologies Journal 1, no. 3 (September 28, 2017): 54. http://dx.doi.org/10.26415/2572-004x-vol1iss3p54-54.

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Background: Since the last decades, research have been oriented towards an MRI-alone radiation treatment planning (RTP), where MRI is used as the primary modality for imaging, delineation and dose calculation by assigning to it the needed electron density (ED) information. The idea is to create a computed tomography (CT) image or so-called pseudo-CT from MRI data. In this paper, we review and classify methods for creating pseudo-CT images from MRI data. Each class of methods is explained and a group of works in the literature is presented in detail with statistical performance. We discuss the advantages, drawbacks and limitations of each class of methods. Methods: We classified most recent works in deriving a pseudo-CT from MR images into four classes: segmentation-based, intensity-based, atlas-based and hybrid methods. We based the classification on the general technique applied in the approach. Results: Most of research focused on the brain and the pelvis regions. The mean absolute error (MAE) ranged from 80 HU to 137 HU and from 36.4 HU to 74 HU for the brain and pelvis, respectively. In addition, an interest in the Dixon MR sequence is increasing since it has the advantage of producing multiple contrast images with a single acquisition. Conclusion: Radiation therapy field is emerging towards the generalization of MRI-only RT thanks to the advances in techniques for generation of pseudo-CT images. However, a benchmark is needed to set in common performance metrics to assess the quality of the generated pseudo-CT and judge on the efficiency of a certain method.

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Masuda, Seiichi, Mitsuhiro Kakuta, Yoshimasa Mizuno, Tsukasa Doi, Fumio Uenoyama, and Tamotsu Ochi. "500. Examination of image evaluation methods in MRI." Japanese Journal of Radiological Technology 49, no. 8 (1993): 1521. http://dx.doi.org/10.6009/jjrt.kj00003325084.

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Deng, Hongping, Lucio Mayer, Henrik Latter, Philip F. Hopkins, and Xue-Ning Bai. "Local Simulations of MRI turbulence with Meshless Methods." Astrophysical Journal Supplement Series 241, no. 2 (April 1, 2019): 26. http://dx.doi.org/10.3847/1538-4365/ab0957.

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Potts, Alain Segundo, Rodrigo Alvite Romano, and Claudio Garcia. "Enhancement in performance and stability of MRI methods." IFAC Proceedings Volumes 45, no. 16 (July 2012): 1484–89. http://dx.doi.org/10.3182/20120711-3-be-2027.00311.

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Finkelstein, Alan, Xiaozhi Cao, Congyu Liao, Giovanni Schifitto, and Jianhui Zhong. "Diffusion Encoding Methods in MRI: Perspectives and Challenges." Investigative Magnetic Resonance Imaging 26, no. 4 (2022): 208. http://dx.doi.org/10.13104/imri.2022.26.4.208.

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Niendorf, Thoralf, and Daniel K. Sodickson. "Parallel imaging in cardiovascular MRI: methods and applications." NMR in Biomedicine 19, no. 3 (2006): 325–41. http://dx.doi.org/10.1002/nbm.1051.

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Özcan, Alpay, Kenneth H. Wong, Linda Larson-Prior, Zang-Hee Cho, and Seong K. Mun. "Background and mathematical analysis of diffusion MRI methods." International Journal of Imaging Systems and Technology 22, no. 1 (February 14, 2012): 44–52. http://dx.doi.org/10.1002/ima.22001.

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Yang, Yihong, Venkata S. Mattay, Daniel R. Weinberger, Joseph A. Frank, and Jeff H. Duyn. "Localized echo-volume imaging methods for functional MRI." Journal of Magnetic Resonance Imaging 7, no. 2 (March 1997): 371–75. http://dx.doi.org/10.1002/jmri.1880070220.

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Jack, Clifford R., Matt A. Bernstein, Nick C. Fox, Paul Thompson, Gene Alexander, Danielle Harvey, Bret Borowski, et al. "The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods." Journal of Magnetic Resonance Imaging 27, no. 4 (2008): 685–91. http://dx.doi.org/10.1002/jmri.21049.

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Lundy, Paige, Emmanuel Vlastos, Joseph Domino, Grace S. Mitchell, Kristin Fickenscher, and Paul Grabb. "Ventricular size measurement methods in fetuses considered for prenatal closure of myelomeningocele." Journal of Neurosurgery: Pediatrics 28, no. 2 (August 2021): 147–51. http://dx.doi.org/10.3171/2020.12.peds20609.

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OBJECTIVE Prenatal closure of myelomeningocele is associated with a reduced rate of hydrocephalus treatment. This need for hydrocephalus treatment is positively correlated with fetal ventricular width. When ventricular width is 15 mm or greater, the benefits of prenatal closure, as a method to decrease hydrocephalus treatment, are reduced. Thus, fetal ventricular size is an important factor when counseling families who are considering intrauterine surgery with mitigation of hydrocephalus as the primary goal. This study sought to determine whether imaging modality (ultrasound [US] vs MRI) and interobserver variability were factors in any ventricular size disparity seen on imaging studies. METHODS The imaging studies of 15 consecutive fetuses who underwent prenatal myelomeningocele repair at Children’s Mercy Fetal Health Center, Kansas City, Missouri, were reviewed. All fetuses were imaged with US and fetal MRI; on average (range), procedures were performed 3.8 (0–20) days apart. Three comparisons were performed to analyze interobserver and intermodality variability in ventricular width measurements: 1) retrospective comparison of dictated ventricular widths measured with MRI and US by pediatric radiologists (PRs) and maternal-fetal medicine specialists (MFMs), respectively; 2) blinded measurements obtained with US by PRs versus initial US-based measurements by MFMs, and blinded measurements obtained with MRI by PRs versus initial MRI-based measurements by PRs; and 3) blinded measurements obtained with MRI by PRs versus those obtained with US. RESULTS Retrospective comparison showed that measurements with MRI by PRs were on average 2.06 mm (95% CI 1.43–2.69, p < 0.001) larger than measurements with US by MFMs. Blinded measurements with US by PRs were on average larger than dictated measurements obtained with US by MFMs, but by only 0.6 mm (95% CI 0.31–0.84, p < 0.001). When PRs measured ventricular size in a blinded fashion with both US and MRI, the mean width determined with MRI was significantly larger by 2.0 mm (95% CI 1.26–2.67, p < 0.0001). CONCLUSIONS The ventricular width of these fetuses was larger when measured with MRI than US by an amount that could impact recommendations for fetal surgery. Every center involved in counseling families about the risks and benefits of fetal intervention for spina bifida needs to be aware of these possible imaging-based disparities.

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Wang, Xiaoqing, Zhengguo Tan, Nick Scholand, Volkert Roeloffs, and Martin Uecker. "Physics-based reconstruction methods for magnetic resonance imaging." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2200 (May 10, 2021): 20200196. http://dx.doi.org/10.1098/rsta.2020.0196.

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Conventional magnetic resonance imaging (MRI) is hampered by long scan times and only qualitative image contrasts that prohibit a direct comparison between different systems. To address these limitations, model-based reconstructions explicitly model the physical laws that govern the MRI signal generation. By formulating image reconstruction as an inverse problem, quantitative maps of the underlying physical parameters can then be extracted directly from efficiently acquired k-space signals without intermediate image reconstruction—addressing both shortcomings of conventional MRI at the same time. This review will discuss basic concepts of model-based reconstructions and report on our experience in developing several model-based methods over the last decade using selected examples that are provided complete with data and code. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 1’.

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Kostenikov, N. A., A. V. Pozdnyakov, V. F. Dubrovskaya, O. Yu Mirolyubova, Yu R. Ilyushchenko, and A. A. Stanzhevsky. "MODERN METHODS OF DIAGNOSTIC IMAGING OF GLIOMAS." Diagnostic radiology and radiotherapy, no. 2 (August 4, 2019): 15–23. http://dx.doi.org/10.22328/2079-5343-2019-10-2-15-23.

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The review presents an analysis of the literature on the diagnosis of gliomas and the study of their structural and biological features based on implementation of new techniques in clinical practice of diagnostic imaging. These techniques include perfusion technologies for multislice spiral computed tomography (MSCT), magnetic resonance imaging (MRI), functional MRI, proton magnetic resonance spectroscopy (MRS), single photon emission computed tomography (SPECT), and positron emission tomography (PET) with various radiopharmaceuticals (RPHs), first and foremost, labeled amino acids, such as11C-L-methionine and18F-Fluoroethyltyrosine. There is presented that with the application of these two methods (MRI and PET), the most important biochemical processes underlying oncogenesis of malignant tumors might be studied by non-invasive way. The obtained data can be crucial for an early detection of tumor lesions, staging the pathological process, rationale for therapeutic tactics, personalization of treatment, evaluation of the efficiency of therapy at early stages and prognosis of the disease result.

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Zhang, Rong, Hongliang Luo, Weijie Chen, and Yongqiang Bai. "Review of deep learning-driven MRI brain tumor detection and segmentation methods." Advances in Computer, Signals and Systems 7, no. 8 (September 2023): 17–28. http://dx.doi.org/10.23977/acss.2023.070803.

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The application of deep learning in the field of medical imaging has become increasingly widespread, greatly promoting the advancement and development of Magnetic Resonance Imaging (MRI) brain tumor detection and segmentation techniques. Therefore, a comprehensive review of deep learning-based methods for MRI brain tumor detection and segmentation was conducted. This review introduces the basic concepts of brain tumors and MRI brain tumor detection and segmentation, discusses the specific applications and typical methods of deep learning in MRI brain tumor detection and segmentation, and analyzes and compares the performance and advantages and disadvantages of different methods. Additionally, representative brain tu-mor segmentation dataset (BraTS) and its evaluation metrics are introduced, upon which the performance of various deep learning-based brain tumor segmentation methods on the BraTS 2019-2022 dataset is compared. Lastly, the challenges and future development trends in deep learning-based MRI brain tumor detection and segmentation methods are summarized and anticipated.

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Piccardo, Arnoldo, Francesco Paparo, Riccardo Picazzo, Mehrdad Naseri, Paolo Ricci, Andrea Marziano, Lorenzo Bacigalupo, et al. "Value of Fused18F-Choline-PET/MRI to Evaluate Prostate Cancer Relapse in Patients Showing Biochemical Recurrence after EBRT: Preliminary Results." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/103718.

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Purpose. We compared the accuracy of18F-Choline-PET/MRI with that of multiparametric MRI (mMRI),18F-Choline-PET/CT,18F-Fluoride-PET/CT, and contrast-enhanced CT (CeCT) in detecting relapse in patients with suspected relapse of prostate cancer (PC) after external beam radiotherapy (EBRT). We assessed the association between standard uptake value (SUV) and apparent diffusion coefficient (ADC).Methods. We evaluated 21 patients with biochemical relapse after EBRT. Patients underwent18F-Choline-PET/contrast-enhanced (Ce)CT,18F-Fluoride-PET/CT, and mMRI. Imaging coregistration of PET and mMRI was performed.Results.18F-Choline-PET/MRI was positive in 18/21 patients, with a detection rate (DR) of 86%. DRs of18F-Choline-PET/CT, CeCT, and mMRI were 76%, 43%, and 81%, respectively. In terms of DR the only significant difference was between18F-Choline-PET/MRI and CeCT. On lesion-based analysis, the accuracy of18F-Choline-PET/MRI,18F-Choline-PET/CT, CeCT, and mMRI was 99%, 95%, 70%, and 85%, respectively. Accuracy, sensitivity, and NPV of18F-Choline-PET/MRI were significantly higher than those of both mMRI and CeCT. On whole-body assessment of bone metastases, the sensitivity of18F-Choline-PET/CT and18F-Fluoride-PET/CT was significantly higher than that of CeCT. Regarding local and lymph node relapse, we found a significant inverse correlation between ADC and SUV-max.Conclusion.18F-Choline-PET/MRI is a promising technique in detecting PC relapse.

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Rodgers, Zachary B., John A. Detre, and Felix W. Wehrli. "MRI-based methods for quantification of the cerebral metabolic rate of oxygen." Journal of Cerebral Blood Flow & Metabolism 36, no. 7 (April 18, 2016): 1165–85. http://dx.doi.org/10.1177/0271678x16643090.

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The brain depends almost entirely on oxidative metabolism to meet its significant energy requirements. As such, the cerebral metabolic rate of oxygen (CMRO2) represents a key measure of brain function. Quantification of CMRO2 has helped elucidate brain functional physiology and holds potential as a clinical tool for evaluating neurological disorders including stroke, brain tumors, Alzheimer’s disease, and obstructive sleep apnea. In recent years, a variety of magnetic resonance imaging (MRI)-based CMRO2 quantification methods have emerged. Unlike positron emission tomography – the current “gold standard” for measurement and mapping of CMRO2 – MRI is non-invasive, relatively inexpensive, and ubiquitously available in modern medical centers. All MRI-based CMRO2 methods are based on modeling the effect of paramagnetic deoxyhemoglobin on the magnetic resonance signal. The various methods can be classified in terms of the MRI contrast mechanism used to quantify CMRO2: T2*, T2′, T2, or magnetic susceptibility. This review article provides an overview of MRI-based CMRO2 quantification techniques. After a brief historical discussion motivating the need for improved CMRO2 methodology, current state-of-the-art MRI-based methods are critically appraised in terms of their respective tradeoffs between spatial resolution, temporal resolution, and robustness, all of critical importance given the spatially heterogeneous and temporally dynamic nature of brain energy requirements.

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Kopcik, Katarzyna. "DIAGNOSTIC METHODS IN KIENBOCK DISEASE." Issues of Rehabilitation, Orthopaedics, Neurophysiology and Sport Promotion – IRONS 42 (March 1, 2023): 7–13. http://dx.doi.org/10.19271/irons-000180-2023-42.

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ABSTRACT Introduction Kienbock disease is relatively rare condition of aseptic necrosis of the lunate, which can be diagnosed using X-ray scans, CT scans, MRI scans and diagnostic arthroscopy. Aim Main aim of the paper is to sum up available knowledge of diagnostic methods of Kienbock disease with staging classifications. Material and methods The paper is based on academic literature and scientific publications, which are available in PubMed database. After evaluation of article’s abstracts, articles were selected and analysed with the references citated. Conclusions There are different methods of radiological diagnostics of Kienbock disease. MRI scans and diagnostic arthroscopy appear to be the most detailed techniques, but X-ray scan should be ordered as primary radiological examination during diagnostic process. Ultrasonography is not useful while searching for a diagnosis.

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Lehman, Vance T., Petrice M. Cogswell, Lorenzo Rinaldo, Waleed Brinjikji, John Huston, James P. Klaas, and Giuseppe Lanzino. "Contemporary and emerging magnetic resonance imaging methods for evaluation of moyamoya disease." Neurosurgical Focus 47, no. 6 (December 2019): E6. http://dx.doi.org/10.3171/2019.9.focus19616.

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Numerous recent technological advances offer the potential to substantially enhance the MRI evaluation of moyamoya disease (MMD). These include high-resolution volumetric imaging, high-resolution vessel wall characterization, improved cerebral angiographic and perfusion techniques, high-field imaging, fast scanning methods, and artificial intelligence. This review discusses the current state-of-the-art MRI applications in these realms, emphasizing key imaging findings, clinical utility, and areas that will benefit from further investigation. Although these techniques may apply to imaging of a wide array of neurovascular or other neurological conditions, consideration of their application to MMD is useful given the comprehensive multidimensional MRI assessment used to evaluate MMD. These MRI techniques span from basic cross-sectional to advanced functional sequences, both qualitative and quantitative.The aim of this review was to provide a comprehensive summary and analysis of current key relevant literature of advanced MRI techniques for the evaluation of MMD with image-rich case examples. These imaging methods can aid clinical characterization, help direct treatment, assist in the evaluation of treatment response, and potentially improve the understanding of the pathophysiology of MMD.

Journal articles on the topic 'MRI methods'

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