Segmentation-free PVC for Cardiac SPECT using a Densely-connected
Multi-dimensional Dynamic Network
- URL: http://arxiv.org/abs/2206.12344v1
- Date: Fri, 24 Jun 2022 15:31:14 GMT
- Title: Segmentation-free PVC for Cardiac SPECT using a Densely-connected
Multi-dimensional Dynamic Network
- Authors: Huidong Xie, Zhao Liu, Luyao Shi, Kathleen Greco, Xiongchao Chen, Bo
Zhou, Attila Feher, John C. Stendahl, Nabil Boutagy, Tassos C. Kyriakides, Ge
Wang, Albert J. Sinusas, Chi Liu
- Abstract summary: Partial volume correction (PVC) methods incorporating high-resolution anatomical information from CT or MRI have been demonstrated to be effective.
In this work, we develop a deep-learning-based method for fast cardiac SPECT PVC without anatomical information and associated organ segmentation.
- Score: 11.546783296332961
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In nuclear imaging, limited resolution causes partial volume effects (PVEs)
that affect image sharpness and quantitative accuracy. Partial volume
correction (PVC) methods incorporating high-resolution anatomical information
from CT or MRI have been demonstrated to be effective. However, such
anatomical-guided methods typically require tedious image registration and
segmentation steps. Accurately segmented organ templates are also hard to
obtain, particularly in cardiac SPECT imaging, due to the lack of hybrid
SPECT/CT scanners with high-end CT and associated motion artifacts. Slight
mis-registration/mis-segmentation would result in severe degradation in image
quality after PVC. In this work, we develop a deep-learning-based method for
fast cardiac SPECT PVC without anatomical information and associated organ
segmentation. The proposed network involves a densely-connected
multi-dimensional dynamic mechanism, allowing the convolutional kernels to be
adapted based on the input images, even after the network is fully trained.
Intramyocardial blood volume (IMBV) is introduced as an additional
clinical-relevant loss function for network optimization. The proposed network
demonstrated promising performance on 28 canine studies acquired on a GE
Discovery NM/CT 570c dedicated cardiac SPECT scanner with a 64-slice CT using
Technetium-99m-labeled red blood cells. This work showed that the proposed
network with densely-connected dynamic mechanism produced superior results
compared with the same network without such mechanism. Results also showed that
the proposed network without anatomical information could produce images with
statistically comparable IMBV measurements to the images generated by
anatomical-guided PVC methods, which could be helpful in clinical translation.
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