Journal of magnetic resonance imaging : JMRI
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J Magn Reson Imaging · Sep 2020
Randomized Controlled TrialMultiparametric MRI-Based Radiomics Nomogram for Predicting Lymph Node Metastasis in Early-Stage Cervical Cancer.
Lymph node metastasis (LNM) is a critical risk factor affecting treatment strategy and prognosis in patients with early-stage cervical cancer. ⋯ 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:885-896.
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J Magn Reson Imaging · Sep 2020
Improving Spatial Normalization of Brain Diffusion MRI to Measure Longitudinal Changes of Tissue Microstructure in the Cortex and White Matter.
Fractional anisotropy (FA) and mean diffusivity (MD) are frequently used to evaluate longitudinal changes in white matter (WM) microstructure. Recently, there has been a growing interest in identifying experience-dependent plasticity in gray matter using MD. Improving registration has thus become a major goal to enhance the detection of subtle longitudinal changes in cortical microstructure. ⋯ 1 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:766-775.
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J Magn Reson Imaging · Sep 2020
Functional Alterations of White Matter in Chronic Never-Treated and Treated Schizophrenia Patients.
Schizophrenia is one of the most severe psychiatric disorders and dysfunction of gray matter (GM) has been usually investigated by resting-state functional (f)MRI. However, functional organization of white matter (WM) in chronic schizophrenia remains unclear. ⋯ 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:752-763.
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J Magn Reson Imaging · Sep 2020
Data-Driven Quantitative Susceptibility Mapping Using Loss Adaptive Dipole Inversion (LADI).
Quantitative susceptibility mapping (QSM) uses prior information to reconstruct maps, but prior information may not show pathology and introduce inconsistencies with susceptibility maps, degrade image quality and inadvertently smoothing image features. ⋯ 4 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:823-835.
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J Magn Reson Imaging · Sep 2020
Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study.
Preoperative differentiation between malignant and benign soft-tissue masses is important for treatment decisions. ⋯ 3 TECHNICAL EFFICACY: Stage 2 J. Magn. Reson. Imaging 2020;52:873-882.