# A detection-task-specific deep-learning method to improve the quality of sparse-view myocardial perfusion SPECT images

arXiv: https://arxiv.org/abs/2504.16171 · HTML: https://mlsanity.com/paper/2504.16171/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 22 Apr 2025.

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

- [Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks](https://mlsanity.com/paper/2104.14037/) (2021)
- [Impact of deep learning-based image super-resolution on binary signal detection](https://mlsanity.com/paper/2107.02338/) (2021)
- [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)
- [DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT](https://mlsanity.com/paper/2306.04249/) (2023)

## Abstract (arXiv)

Myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT) is a widely used and cost-effective diagnostic tool for coronary artery disease. However, the lengthy scanning time in this imaging procedure can cause patient discomfort, motion artifacts, and potentially inaccurate diagnoses due to misalignment between the SPECT scans and the CT-scans which are acquired for attenuation compensation. Reducing projection angles is a potential way to shorten scanning time, but this can adversely impact the quality of the reconstructed images. To address this issue, we propose a detection-task-specific deep-learning method for sparse-view MPI SPECT images. This method integrates an observer loss term that penalizes the loss of anthropomorphic channel features with the goal of improving performance in perfusion defect-detection task. We observed that, on the task of detecting myocardial perfusion defects, the proposed method yielded an area under the receiver operating characteristic (ROC) curve (AUC) significantly larger than the sparse-view protocol. Further, the proposed method was observed to be able to restore the structure of the left ventricle wall, demonstrating ability to overcome sparse-sampling artifacts. Our preliminary results motivate further evaluations of the method.