BACKGROUND AND PURPOSE:Quantitative susceptibility mapping allows overcoming several nonlocal restrictions of susceptibilityweighted and phase imaging and enables quantification of magnetic susceptibility. We compared the diagnostic accuracy of quantitative susceptibility mapping and R2* (1/T2*) mapping to discriminate between patients with Parkinson disease and controls.

The QSM value in GPl increases gradually with age, which allows for the identification of GPm in elderly PD subjects.

Although dose prediction for intensity modulated radiation therapy (IMRT) has been accomplished by a deep learning approach, delineation of some structures is needed for the prediction. We sought to develop a fully automated dose-generation framework for IMRT of prostate cancer by entering the patient CT datasets without the contour information into a generative adversarial network (GAN) and to compare its prediction performance to a conventional prediction model trained from patient contours.

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