Journal of magnetic resonance imaging : JMRI
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J Magn Reson Imaging · Dec 2019
Influence of temporal parameters of DCE-MRI on the quantification of heterogeneity in tumor vascularization.
Evaluating heterogeneity in tumor vascularization through texture analysis could improve predictions of patients' outcome and response evaluation. ⋯ 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1773-1788.
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J Magn Reson Imaging · Dec 2019
Observational StudyProton density fat fraction MRI of vertebral bone marrow: Accuracy, repeatability, and reproducibility among readers, field strengths, and imaging platforms.
Chemical shift-encoding based water-fat MRI is an emerging method to noninvasively assess proton density fat fraction (PDFF), a promising quantitative imaging biomarker for estimating tissue fat concentration. However, in vivo validation of PDFF is still lacking for bone marrow applications. ⋯ 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1762-1772.
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J Magn Reson Imaging · Dec 2019
Ultrashort echo time imaging of the lungs under high-frequency noninvasive ventilation: A new approach to lung imaging.
Although ultrashort echo time (UTE) sequences allow excellent assessment of lung parenchyma, image quality remains lower than that of computed tomography (CT). ⋯ 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1789-1797.
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J Magn Reson Imaging · Dec 2019
High-field mr diffusion-weighted image denoising using a joint denoising convolutional neural network.
Low signal-to-noise ratio (SNR) has been a major limiting factor for the application of higher-resolution diffusion-weighted imaging (DWI). Most of the conventional denoising models suffer from the drawbacks of shallow feature extraction and hand-crafted parameter tuning. Although multiple studies have shown the promising applications of image denoising using convolutional neural networks (CNNs), none of them have considered denoising multiple b-value DWIs using a multichannel CNN model. ⋯ 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1937-1947.
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J Magn Reson Imaging · Nov 2019
Accuracy of breast cancer lesion classification using intravoxel incoherent motion diffusion-weighted imaging is improved by the inclusion of global or local prior knowledge with bayesian methods.
Diffusion-weighted MRI (DWI) has potential to noninvasively characterize breast cancer lesions; models such as intravoxel incoherent motion (IVIM) provide pseudodiffusion parameters that reflect tissue perfusion, but are dependent on the details of acquisition and analysis strategy. ⋯ 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1478-1488.