Patient-Specific Deep Learning Tracking Framework for Real-Time 2D Target Localization in Magnetic Resonance Imaging-Guided Radiation Therapy.

Purpose: We propose a tumor tracking framework for 2D cine magnetic resonance imaging (MRI) based on a pair of deep learning (DL) models relying on patient-specific (PS) training.

Methods And Materials: The chosen DL models are: (1) an image registration transformer and (2) an auto-segmentation convolutional neural network (CNN). We collected over 1,400,000 cine MRI frames from 219 patients treated on a 0.

Applications of Alpha Neurofeedback Processes for Enhanced Mental Manipulation of Unfamiliar Molecular and Spatial Structures.

This study explores a novel approach to enhancing cognitive proficiency by targeting neural mechanisms that facilitate science and math learning, especially mental rotation. The study specifically examines the relationship between upper alpha intensity and mental rotation skills. Although prior neurofeedback research for increasing upper alpha highlights this correlation, mostly with familiar objects, novel chemistry and math learning prompts envisioning unfamiliar objects which question the persistence of this correlation.

Stereotactic ultrahypofractionated MR-guided radiotherapy for localized prostate cancer - Acute toxicity and patient-reported outcomes in the prospective, multicenter phase II trial.

Background: Due to superior image quality and daily adaptive planning, MR-guided stereotactic body radiation therapy (MRgSBRT) has the potential to further widen the therapeutic window in radiotherapy of localized prostate cancer. This study reports on acute toxicity rates and patient-reported outcomes after MR-guided adaptive ultrahypofractionated radiotherapy for localized prostate cancer within the prospective, multicenter phase II trial.

Materials And Methods: A total of 69 patients with localized prostate cancer underwent MRgSBRT with daily online plan adaptation.