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

arXiv: https://arxiv.org/abs/2306.04249 · HTML: https://mlsanity.com/paper/2306.04249/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SUNet: Swin Transformer UNet for Image Denoising](https://mlsanity.com/paper/2202.14009/) (2022)
- [Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks](https://mlsanity.com/paper/2104.14037/) (2021)
- [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)
- [A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT](https://mlsanity.com/paper/2303.00212/) (2023)
- [Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT](https://mlsanity.com/paper/2303.02110/) (2023)

## Built on by (most cited first, 1 of 1)

- [A detection-task-specific deep-learning method to improve the quality of sparse-view myocardial perfusion SPECT images](https://mlsanity.com/paper/2504.16171/) (2025)

## Abstract (arXiv)

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. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a Detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (N = 338). The evaluation was performed at low-dose levels of 6.25%, 12.5% and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic DL-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.