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

arXiv: https://arxiv.org/abs/2303.00212 · HTML: https://mlsanity.com/paper/2303.00212/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)
- [Objective task-based evaluation of artificial intelligence-based medical imaging methods: Framework, strategies and role of the physician](https://mlsanity.com/paper/2107.04540/) (2021)
- [A Physics-Guided Modular Deep-Learning Based Automated Framework for Tumor Segmentation in PET Images](https://mlsanity.com/paper/2002.07969/) (2020)
- [Deep neural networks-based denoising models for CT imaging and their efficacy](https://mlsanity.com/paper/2111.09539/) (2021)

## Built on by (most cited first, 4 of 4)

- [Perceive and Calibrate: Analyzing and Enhancing Robustness of Medical Multi-Modal Large Language Models](https://mlsanity.com/paper/2512.21964/) (2025)
- [DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT](https://mlsanity.com/paper/2306.04249/) (2023)
- [Artificial intelligence for simplified patient-centered dosimetry in radiopharmaceutical therapies](https://mlsanity.com/paper/2510.12714/) (2025)
- [Noise-Aware Visual Representation Learning for Medical Visual Question Answering](https://mlsanity.com/paper/2606.05535/) (2026)

## Abstract (arXiv)

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, evaluation on clinical tasks is crucial. Typically, these methods are designed to minimize some fidelity-based criterion between the predicted denoised image and some reference normal-dose image. However, while promising, studies have shown that these methods may have limited impact on the performance of clinical tasks in SPECT. To address this issue, we use concepts from the literature on model observers and our understanding of the human visual system to propose a DL-based denoising approach designed to preserve observer-related information for detection tasks. The proposed method was objectively evaluated on the task of detecting perfusion defect in myocardial perfusion SPECT images using a retrospective study with anonymized clinical data. Our results demonstrate that the proposed method yields improved performance on this detection task compared to using low-dose images. The results show that by preserving task-specific information, DL may provide a mechanism to improve observer performance in low-dose myocardial perfusion SPECT.