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DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT
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.
Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT
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.
A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT
However, while promising, studies have shown that these methods may have limited impact on the performance of clinical tasks in SPECT.
Observer study-based evaluation of a stochastic and physics-based method to generate oncological PET images
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
A Bayesian approach to tissue-fraction estimation for oncological PET segmentation
Conventional segmentation methods are typically designed to assign each voxel in the image as belonging to a certain tissue class.
Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospective multi-center cohort
Features were more reliable in PET with quantizationB, whereas quantizationW showed better results in CT. Conclusion: The test-retest repeatability of shape and heterogeneity features in PET and low-dose CT varied greatly amongst metrics.
