Deep learning-based attenuation map generation for myocardial perfusion SPECT

• Published in: European journal of nuclear medicine and molecular imaging Vol. 47; no. 10; pp. 2383 - 2395
• Main Authors: Shi, Luyao, Onofrey, John A., Liu, Hui, Liu, Yi-Hwa, Liu, Chi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020; Springer Nature B.V
• Subjects: Artificial neural networks; Attenuation; Cardiology; Computed tomography; Deep Learning; Emission analysis; Humans; Image Processing, Computer-Assisted; Image reconstruction; Imaging; Machine learning; Medical imaging; Medicine; Medicine & Public Health; Myocardium; Neural networks; Nuclear Medicine; Oncology; Original Article; Orthopedics; Perfusion; Photon emission; Photopeak; Radiation dosage; Radiology; Scanners; Scanning; Single photon emission computed tomography; Tomography; Tomography, Emission-Computed, Single-Photon; Tomography, X-Ray Computed; Training; Ventricle; Deep learning; Myocardial perfusion imaging; SPECT; Synthetic attenuation map
• DOI: 10.1007/s00259-020-04746-6
• ISSN: 1619-7070; 1619-7089

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.