Journal of magnetic resonance imaging : JMRI
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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.
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J Magn Reson Imaging · Nov 2019
IMPROD biparametric MRI in men with a clinical suspicion of prostate cancer (IMPROD Trial): Sensitivity for prostate cancer detection in correlation with whole-mount prostatectomy sections and implications for focal therapy.
Prostate MRI is increasingly being used in men with a clinical suspicion of prostate cancer (PCa). However, development and validation of methods for focal therapy planning are still lagging. ⋯ 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1641-1650.
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J Magn Reson Imaging · Nov 2019
Monoexponential, Biexponential, and stretched-exponential models using diffusion-weighted imaging: A quantitative differentiation of breast lesions at 3.0T.
Diffusion-weighted imaging (DWI) plays an important role in the differentiation of malignant and benign breast lesions. ⋯ 4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;50:1461-1467.
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J Magn Reson Imaging · Nov 2019
Uniform combined reconstruction of multichannel 7T knee MRI receive coil data without the use of a reference scan.
MR image intensity nonuniformity is often observed at 7T. Reference scans from the body coil used for uniformity correction at lower field strengths are typically not available at 7T. ⋯ 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1534-1544.
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J Magn Reson Imaging · Nov 2019
Data-driven synthetic MRI FLAIR artifact correction via deep neural network.
FLAIR (fluid attenuated inversion recovery) imaging via synthetic MRI methods leads to artifacts in the brain, which can cause diagnostic limitations. The main sources of the artifacts are attributed to the partial volume effect and flow, which are difficult to correct by analytical modeling. In this study, a deep learning (DL)-based synthetic FLAIR method was developed, which does not require analytical modeling of the signal. ⋯ 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1413-1423.