Luyao Shi1, John A Onofrey2,3, Hui Liu2,4, Yi-Hwa Liu4,5, Chi Liu6,7. 1. Department of Biomedical Engineering, Yale University, New Haven, CT, USA. 2. Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA. 3. Department of Urology, Yale University, New Haven, CT, USA. 4. Department of Internal Medicine (Cardiology), Yale University, New Haven, CT, USA. 5. Department of Biomedical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan. 6. Department of Biomedical Engineering, Yale University, New Haven, CT, USA. chi.liu@yale.edu. 7. Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA. chi.liu@yale.edu.
Abstract
PURPOSE: Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission scanning and therefore scans on these systems are susceptible to attenuation artifacts. Moreover, the use of CT scans also increases radiation dose to patients and significant artifacts can occur due to the misregistration between the SPECT and CT scans as a result of patient motion. The purpose of this study is to develop an approach to estimate attenuation maps directly from SPECT emission data using deep learning methods. METHODS: Both photopeak window and scatter window SPECT images were used as inputs to better utilize the underlying attenuation information embedded in the emission data. The CT-based attenuation maps were used as labels with which cardiac SPECT/CT images of 65 patients were included for training and testing. We implemented and evaluated deep fully convolutional neural networks using both standard training and training using an adversarial strategy. RESULTS: The synthetic attenuation maps were qualitatively and quantitatively consistent with the CT-based attenuation map. The globally normalized mean absolute error (NMAE) between the synthetic and CT-based attenuation maps were 3.60% ± 0.85% among the 25 testing subjects. The SPECT reconstructed images corrected using the CT-based attenuation map and synthetic attenuation map are highly consistent. The NMAE between the reconstructed SPECT images that were corrected using the synthetic and CT-based attenuation maps was 0.26% ± 0.15%, whereas the localized absolute percentage error was 1.33% ± 3.80% in the left ventricle (LV) myocardium and 1.07% ± 2.58% in the LV blood pool. CONCLUSION: We developed a deep convolutional neural network to estimate attenuation maps for SPECT directly from the emission data. The proposed method is capable of generating highly reliable attenuation maps to facilitate attenuation correction for SPECT-only scanners for myocardial perfusion imaging.
PURPOSE: Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission scanning and therefore scans on these systems are susceptible to attenuation artifacts. Moreover, the use of CT scans also increases radiation dose to patients and significant artifacts can occur due to the misregistration between the SPECT and CT scans as a result of patient motion. The purpose of this study is to develop an approach to estimate attenuation maps directly from SPECT emission data using deep learning methods. METHODS: Both photopeak window and scatter window SPECT images were used as inputs to better utilize the underlying attenuation information embedded in the emission data. The CT-based attenuation maps were used as labels with which cardiac SPECT/CT images of 65 patients were included for training and testing. We implemented and evaluated deep fully convolutional neural networks using both standard training and training using an adversarial strategy. RESULTS: The synthetic attenuation maps were qualitatively and quantitatively consistent with the CT-based attenuation map. The globally normalized mean absolute error (NMAE) between the synthetic and CT-based attenuation maps were 3.60% ± 0.85% among the 25 testing subjects. The SPECT reconstructed images corrected using the CT-based attenuation map and synthetic attenuation map are highly consistent. The NMAE between the reconstructed SPECT images that were corrected using the synthetic and CT-based attenuation maps was 0.26% ± 0.15%, whereas the localized absolute percentage error was 1.33% ± 3.80% in the left ventricle (LV) myocardium and 1.07% ± 2.58% in the LV blood pool. CONCLUSION: We developed a deep convolutional neural network to estimate attenuation maps for SPECT directly from the emission data. The proposed method is capable of generating highly reliable attenuation maps to facilitate attenuation correction for SPECT-only scanners for myocardial perfusion imaging.
Entities:
Keywords:
Deep learning; Myocardial perfusion imaging; SPECT; Synthetic attenuation map
Authors: Mahsa Torkaman; Jaewon Yang; Luyao Shi; Rui Wang; Edward J Miller; Albert J Sinusas; Chi Liu; Grant T Gullberg; Youngho Seo Journal: Proc SPIE Int Soc Opt Eng Date: 2021-02-15
Authors: Jaewon Yang; Luyao Shi; Rui Wang; Edward J Miller; Albert J Sinusas; Chi Liu; Grant T Gullberg; Youngho Seo Journal: J Nucl Med Date: 2021-02-26 Impact factor: 11.082
Authors: Rui Wang; Hui Liu; Takuya Toyonaga; Luyao Shi; Jing Wu; John Aaron Onofrey; Yu-Jung Tsai; Mika Naganawa; Tianyu Ma; Yaqiang Liu; Ming-Kai Chen; Adam P Mecca; Ryan S O'Dell; Christopher H van Dyck; Richard E Carson; Chi Liu Journal: Med Phys Date: 2021-07-27 Impact factor: 4.506