| Title: |
Need for objective task‐based evaluation of deep learning‐based denoising methods: A study in the context of myocardial perfusion SPECT |
| Authors: |
Yu, Zitong; Rahman, Md Ashequr; Laforest, Richard; Schindler, Thomas H.; Gropler, Robert J.; Wahl, Richard L.; Siegel, Barry A.; Jha, Abhinav K. |
| Source: |
Medical Physics ; volume 50, issue 7, page 4122-4137 ; ISSN 0094-2405 2473-4209 |
| Publisher Information: |
Wiley |
| Publication Year: |
2023 |
| Collection: |
Wiley Online Library (Open Access Articles via Crossref) |
| Description: |
Background Artificial intelligence‐based methods have generated substantial interest in nuclear medicine. An area of significant interest has been the use of deep‐learning (DL)‐based approaches for denoising images acquired with lower doses, shorter acquisition times, or both. Objective evaluation of these approaches is essential for clinical application. Purpose DL‐based approaches for denoising nuclear‐medicine images have typically been evaluated using fidelity‐based figures of merit (FoMs) such as root mean squared error (RMSE) and structural similarity index measure (SSIM). However, these images are acquired for clinical tasks and thus should be evaluated based on their performance in these tasks. 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; and (3) demonstrate the utility of virtual imaging trials (VITs) to evaluate DL‐based methods. Methods A VIT to evaluate a DL‐based method for denoising myocardial perfusion SPECT (MPS) images was conducted. To conduct this evaluation study, we followed the recently published best practices for the evaluation of AI algorithms for nuclear medicine (the RELAINCE guidelines). An anthropomorphic patient population modeling clinically relevant variability was simulated. Projection data for this patient population at normal and low‐dose count levels (20%, 15%, 10%, 5%) were generated using well‐validated Monte Carlo‐based simulations. The images were reconstructed using a 3‐D ordered‐subsets expectation maximization‐based approach. Next, the low‐dose images were denoised using a commonly used convolutional neural network‐based approach. The impact of DL‐based denoising was evaluated using both fidelity‐based FoMs and area under the receiver operating characteristic curve (AUC), which quantified performance on the clinical task of detecting perfusion defects in MPS images as ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1002/mp.16407 |
| Availability: |
https://doi.org/10.1002/mp.16407; https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.16407 |
| Rights: |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
| Accession Number: |
edsbas.D913C5C7 |
| Database: |
BASE |