Journal of magnetic resonance imaging : JMRI
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J Magn Reson Imaging · Apr 2021
ReviewArtificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep-learning-based MR image reconstruction. ⋯ Recent efforts in these areas show that deep-learning-based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep-learning-based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.
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J Magn Reson Imaging · Jan 2021
ReviewQuantitative Susceptibility Mapping: Technical Considerations and Clinical Applications in Neuroimaging.
Quantitative susceptibility mapping (QSM) is a novel magnetic resonance imaging (MRI) technique for quantifying the spatial distribution of magnetic susceptibility within an object or tissue. Recently, QSM has been widely used to study various dominant magnetic susceptibility sources in the brain, including iron and calcium. ⋯ This review aims to summarize the physical concepts and potential clinical applications of QSM in neuroimaging. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.
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J Magn Reson Imaging · Nov 2020
ReviewCurrent state of the art MRI for the longitudinal assessment of cystic fibrosis.
Pulmonary MRI can now provide high-resolution images that are sensitive to early disease and specific to inflammation in cystic fibrosis (CF) lung disease. With specificity and function limited via computed tomography (CT), there are significant advantages to MRI. Many of the modern MRI techniques can be performed throughout life, and can be employed to understand changes over time, in addition to quantification of treatment response. ⋯ Magn. Reson. Imaging 2019.
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Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. ⋯ Magn. Reson. Imaging 2020;52:998-1018.
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Renal perfusion can be quantitatively assessed by multiple magnetic resonance imaging (MRI) methods, including dynamic contrast enhanced (DCE), arterial spin labeling (ASL), and diffusion-weighted imaging with intravoxel incoherent motion (IVIM) analysis. In this review we summarize the advances in the field of renal-perfusion MRI over the past 5 years. The review starts with a brief introduction of relevant MRI methods, followed by a discussion of recent technical developments. ⋯ Magn. Reson. Imaging 2020;52:369-379.