Washington University

Olga Green

Sasa Mutic

Hua Li

Michael Altman

James Kavanaugh

Hasani Wooten

Chunjoo (Justin) Park

Hao Zhang

The present disclosure describes a computer-implemented method of transforming a low-resolution MR image to a high-resolution MR image using a deep CNN-based MRI SR network and a computer-implemented method of transforming an MR image to a pseudo-CT (sCT) image using a deep CNN-based sCT network. The present disclosure further describes a MR image-guided radiation treatment system that includes a computing device to implement the MRI SR and CT networks and to produce a radiation plan based in the resulting high resolution MR images and sCT images.

ML-based methods for pseudo-CT and HR MR image estimation

A system and method for validating the accuracy of delineated contours in computerized imaging using statistical data for generating assessment criterion that define acceptable tolerances for delineated contours, with the statistical data being conditionally updated and/or refined between individual processes for validating delineated contours to thereby adjust the tolerances defined by the assessment criterion in the stored statistical data, such that the stored statistical data is more closely representative of a target population. The present invention may be used to facilitate, as one example, on-line adaptive radiation therapy.

System and method for the validation and quality assurance of computerized contours of human anatomy

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St. Louis, MO, United States of America

Olga Green