Search Results for author: Barry A. Siegel

Found 6 papers, 0 papers with code

DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT

no code implementations7 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.

Denoising

Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT

no code implementations3 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.

Denoising SSIM

A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT

no code implementations1 Mar 2023 Md Ashequr Rahman, Zitong Yu, Barry A. Siegel, Abhinav K. Jha

However, while promising, studies have shown that these methods may have limited impact on the performance of clinical tasks in SPECT.

Denoising

Observer study-based evaluation of a stochastic and physics-based method to generate oncological PET images

no code implementations5 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

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

no code implementations5 Oct 2016 Marie-Charlotte Desseroit, Florent Tixier, Wolfgang Weber, Barry A. Siegel, Catherine Cheze Le Rest, Dimitris Visvikis, Mathieu Hatt

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.

Computed Tomography (CT) Quantization

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