Deep learning-based attenuation map generation for myocardial perfusion SPECT

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.

Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer

“…As a matter of fact, Shan et al [43] and Wu et al [44] had proposed cascaded network structures with basic network of UNet [15] or sequential CNN layers, and demonstrated their efficiency in low-dose CT. As cascade network is also potentially efficient in low-dose CT, our CasRedSCAN could be adapted to limited-view low-dose CT that may further reduce the radiation dose and acquisition time. Lastly, we believe our CasRedSCAN could be adapted to other tomography imaging modalities with similar applications, such as SPECT, PET, and Cryo-ET [45]- [47].…”

The promise of artificial intelligence and deep learning in PET and SPECT imaging

“…Dong et al [85] applied a similar approach in whole-body PET imaging using Cycle-GAN [85] reporting a mean PET quantification bias of 0.12% ± 2.98%. Shi et al [86] proposed a novel approach to generate sCT images in 99m Tc-tetrofosmin myocardial perfusion SPECT imaging taking advantage of two images produced using different energy windows providing different representations of scattered and primary photon distributions. A multi-channel conditional GAN model was trained using SPECT images reconstructed using different energy windows as input to predict the corresponding sCT image.…”

“…To the best of our knowledge, only two studies, one focusing on brain imaging [98] and the other on wholebody imaging [93], which used a large number of training sets. Most deep learning-based ASC studies were performed in PET imaging with a limited number of works reported for SPECT imaging [86,99].…”

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy

“…Standalone SPECT cameras face the challenges of quantification and susceptibility to attenuation artifacts. In this regard, Shi et al proposed a novel deep learning-based framework for estimation of the attenuation maps from SPECT data (Shi et al 2020). This framework relies on both photopeak and scatter windows of SPECT images (based on the recorded energy of the photons) to extract the latent attenuation information from SPECT emission data.…”