7 citations · 12 across the 13 of their papers we have counts for
5 papers · 1 filter
DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT
Md Ashequr Rahman, Zitong Yu, Richard Laforest +3
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 i…
A list-mode multi-energy window low-count SPECT reconstruction method for isotopes with multiple emission peaks
Md Ashequr Rahman, Zekun Li, Zitong Yu +3
SPECT provides a mechanism to perform absorbed-dose quantification tasks for -particle radiopharmaceutical therapies (-RPTs). However, quantitative SPECT for -RPT is chall…
Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT
Zitong Yu, Md Ashequr Rahman, Richard Laforest +5
Artificial intelligence-based methods have generated substantial interest in nuclear medicine. An area of significant interest has been using deep-learning (DL)-based approaches fo…
Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT
Zitong Yu, Md Ashequr Rahman, Craig K. Abbey +2
Attenuation compensation (AC) is beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). However, tradi…
A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT
Md Ashequr Rahman, Zitong Yu, Barry A. Siegel +1
Deep-learning (DL)-based methods have shown significant promise in denoising myocardial perfusion SPECT images acquired at low dose. For clinical application of these methods, eval…