70 citations · 163 across the 18 of their papers we have counts for
18 papers
A Mixed Precision FFT with applications in MRI
Nikhil Deveshwar, Abhejit Rajagopal, Peder E. Z. Larson
A mixed precision Fast Fourier transform (FFT) implementation is presented. The procedure uses per-block microscaling (MX), a global power-of-two prescale, and prequantized low bit…
Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI
Abhejit Rajagopal, Antonio C. Westphalen, Nathan Velarde +6
Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >=…
Physics-driven Deep Learning for PET/MRI
Abhejit Rajagopal, Andrew P. Leynes, Nicholas Dwork +3
In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which ha…
Synthetic PET via Domain Translation of 3D MRI
Abhejit Rajagopal, Yutaka Natsuaki, Kristen Wangerin +7
Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the ne…
Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology
Abhejit Rajagopal, Ekaterina Redekop, Anil Kemisetti +5
Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and d…
Scan-specific Self-supervised Bayesian Deep Non-linear Inversion for Undersampled MRI Reconstruction
Andrew P. Leynes, Nikhil Deveshwar, Srikantan S. Nagarajan +1
Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling. Recently, supervised deep learning has emerged as a promising tech…