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.
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.
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.