Journal of magnetic resonance imaging : JMRI
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J Magn Reson Imaging · Apr 2019
Fully automatic segmentation on prostate MR images based on cascaded fully convolution network.
Computer-aided diagnosis (CAD) can aid radiologists in quantifying prostate cancer, and MRI segmentation plays an essential role in CAD applications. Clinical experience shows that prostate cancer occurs predominantly in the peripheral zone (PZ) and there exist different evaluation criteria for different regions in the Prostate Imaging Reporting and Data System (PI-RADS). ⋯ 2 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2019;49:1149-1156.
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J Magn Reson Imaging · Apr 2019
Microstructural and Neurochemical Changes in the Rat Brain After Diffuse Axonal Injury.
Diffuse axonal injury (DAI) is one of the devastating types of traumatic brain injury, but is difficult to detect on conventional imaging in its early stages. ⋯ 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1069-1077.
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J Magn Reson Imaging · Apr 2019
ReviewDeep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. ⋯ Magn. Reson. Imaging 2019;49:939-954.
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J Magn Reson Imaging · Apr 2019
Comparative StudyQuantitative radiomic biomarkers for discrimination between neuromyelitis optica spectrum disorder and multiple sclerosis.
Precise diagnosis and early appropriate treatment are of importance to reduce neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) morbidity. Distinguishing NMOSD from MS based on clinical manifestations and neuroimaging remains challenging. ⋯ 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1113-1121.
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J Magn Reson Imaging · Apr 2019
View-Sharing Artifact Reduction With Retrospective Compressed Sensing Reconstruction in the Context of Contrast-Enhanced Liver MRI for Hepatocellular Carcinoma (HCC) Screening.
View-sharing (VS) increases spatiotemporal resolution in dynamic contrast-enhanced (DCE) MRI by sharing high-frequency k-space data across temporal phases. This temporal sharing results in respiratory motion within any phase to propagate artifacts across all shared phases. Compressed sensing (CS) eliminates the need for VS by recovering missing k-space data from pseudorandom undersampling, reducing temporal blurring while maintaining spatial resolution. ⋯ 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:984-993.