no code implementations • 7 Jun 2023 • Md Ashequr Rahman, Zitong Yu, Richard Laforest, Craig K. Abbey, Barry A. Siegel, Abhinav K. Jha
There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects.
no code implementations • 3 Mar 2023 • Zitong Yu, Md Ashequr Rahman, Richard Laforest, Thomas H. Schindler, Robert J. Gropler, Richard L. Wahl, Barry A. Siegel, Abhinav K. Jha
Our objectives were to (1) investigate whether evaluation with these FoMs is consistent with objective clinical-task-based evaluation; (2) provide a theoretical analysis for determining the impact of denoising on signal-detection tasks; (3) demonstrate the utility of virtual clinical trials (VCTs) to evaluate DL-based methods.
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.
no code implementations • 10 Feb 2021 • Zitong Yu, Md Ashequr Rahman, Thomas Schindler, Richard Laforest, Abhinav K. Jha
The proposed method uses data acquired in the scatter window to reconstruct an initial estimate of the attenuation map using a physics-based approach.
Medical Physics
no code implementations • 5 Feb 2021 • Ziping Liu, Richard Laforest, Joyce Mhlanga, Tyler J. Fraum, Malak Itani, Farrokh Dehdashti, Barry A. Siegel, Abhinav K. Jha
In this study, we develop a stochastic and physics-based method to generate realistic oncological two-dimensional (2-D) PET images, where the ground-truth tumor properties are known.
Medical Physics Image and Video Processing
no code implementations • 29 Feb 2020 • Ziping Liu, Joyce C. Mhlanga, Richard Laforest, Paul-Robert Derenoncourt, Barry A. Siegel, Abhinav K. Jha
Conventional segmentation methods are typically designed to assign each voxel in the image as belonging to a certain tissue class.