Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI

no code implementations • 13 Dec 2022 • Abhejit Rajagopal, Antonio C. Westphalen, Nathan Velarde, Tim Ullrich, Jeffry P. Simko, Hao Nguyen, Thomas A. Hope, Peder E. Z. Larson, Kirti Magudia

To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5.

Physics-driven Deep Learning for PET/MRI

no code implementations • 11 Jun 2022 • Abhejit Rajagopal, Andrew P. Leynes, Nicholas Dwork, Jessica E. Scholey, Thomas A. Hope, Peder E. Z. Larson

In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer, neurological disorders, and heart disease.

MRI Reconstruction

Synthetic PET via Domain Translation of 3D MRI

1 code implementation • 11 Jun 2022 • Abhejit Rajagopal, Yutaka Natsuaki, Kristen Wangerin, Mahdjoub Hamdi, Hongyu An, John J. Sunderland, Richard Laforest, Paul E. Kinahan, Peder E. Z. Larson, Thomas A. Hope

Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT.

MRI Reconstruction Translation