Investigating the limited performance of a deep-learning-based SPECT
denoising approach: An observer-study-based characterization
- URL: http://arxiv.org/abs/2203.01918v1
- Date: Thu, 3 Mar 2022 18:51:59 GMT
- Title: Investigating the limited performance of a deep-learning-based SPECT
denoising approach: An observer-study-based characterization
- Authors: Zitong Yu and Md Ashequr Rahman and Abhinav K. Jha
- Abstract summary: We conducted a task-based characterization of a DL-based denoising approach for individual signal properties.
A CNN-based denoiser was trained to process the low-count images.
As in previous studies, we observed that the DL-based denoising method did not improve performance on signal-detection tasks.
- Score: 16.943040406235024
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multiple objective assessment of image-quality-based studies have reported
that several deep-learning-based denoising methods show limited performance on
signal-detection tasks. Our goal was to investigate the reasons for this
limited performance. To achieve this goal, we conducted a task-based
characterization of a DL-based denoising approach for individual signal
properties. We conducted this study in the context of evaluating a DL-based
approach for denoising SPECT images. The training data consisted of signals of
different sizes and shapes within a clustered-lumpy background, imaged with a
2D parallel-hole-collimator SPECT system. The projections were generated at
normal and 20% low count level, both of which were reconstructed using an OSEM
algorithm. A CNN-based denoiser was trained to process the low-count images.
The performance of this CNN was characterized for five different signal sizes
and four different SBR by designing each evaluation as an SKE/BKS
signal-detection task. Performance on this task was evaluated using an
anthropomorphic CHO. As in previous studies, we observed that the DL-based
denoising method did not improve performance on signal-detection tasks.
Evaluation using the idea of observer-study-based characterization demonstrated
that the DL-based denoising approach did not improve performance on the
signal-detection task for any of the signal types. Overall, these results
provide new insights on the performance of the DL-based denoising approach as a
function of signal size and contrast. More generally, the observer study-based
characterization provides a mechanism to evaluate the sensitivity of the method
to specific object properties and may be explored as analogous to
characterizations such as modulation transfer function for linear systems.
Finally, this work underscores the need for objective task-based evaluation of
DL-based denoising approaches.
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