Generative adversarial network-based attenuation correction for 99mTc-TRODAT-1 brain SPECT

Yu Du; Han Jiang; Ching-Ni Lin; Zhengyu Peng; Jingzhang Sun; Pai-Yi Chiu; Guang-Uei Hung; Greta S P Mok

doi:10.3389/fmed.2023.1171118

Generative adversarial network-based attenuation correction for ^99mTc-TRODAT-1 brain SPECT

Front Med (Lausanne). 2023 Aug 15:10:1171118. doi: 10.3389/fmed.2023.1171118. eCollection 2023.

Authors

Yu Du^{1

2}, Han Jiang¹, Ching-Ni Lin³, Zhengyu Peng¹, Jingzhang Sun¹, Pai-Yi Chiu⁴, Guang-Uei Hung⁵, Greta S P Mok^{1

2}

Affiliations

¹ Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.
² Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau SAR, China.
³ Department of Nuclear Medicine, Show Chwan Memorial Hospital, Lukong Town, Changhua County, Taiwan.
⁴ Department of Neurology, Show Chwan Memorial Hospital, Lukong Town, Changhua County, Taiwan.
⁵ Department of Nuclear Medicine, Chang Bing Show Chwan Memorial Hospital, Lukong Town, Changhua County, Taiwan.

Abstract

Background: Attenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT). Chang's method was developed for AC (Chang-AC) when CT-based AC was not available, assuming uniform attenuation coefficients inside the body contour. This study aims to evaluate Chang-AC and different deep learning (DL)-based AC approaches on ^99mTc-TRODAT-1 brain SPECT using clinical patient data on two different scanners.

Methods: Two hundred and sixty patients who underwent ^99mTc-TRODAT-1 SPECT/CT scans from two different scanners (scanner A and scanner B) were retrospectively recruited. The ordered-subset expectation-maximization (OS-EM) method reconstructed 120 projections with dual-energy scatter correction, with or without CT-AC. We implemented a 3D conditional generative adversarial network (cGAN) for the indirect deep learning-based attenuation correction (DL-AC_μ) and direct deep learning-based attenuation correction (DL-AC) methods, estimating attenuation maps (μ-maps) and attenuation-corrected SPECT images from non-attenuation-corrected (NAC) SPECT, respectively. We further applied cross-scanner training (cross-scanner indirect deep learning-based attenuation correction [cull-AC_μ] and cross-scanner direct deep learning-based attenuation correction [call-AC]) and merged the datasets from two scanners for ensemble training (ensemble indirect deep learning-based attenuation correction [eDL-AC_μ] and ensemble direct deep learning-based attenuation correction [eDL-AC]). The estimated μ-maps from (c/e)DL-AC_μ were then used in reconstruction for AC purposes. Chang's method was also implemented for comparison. Normalized mean square error (NMSE), structural similarity index (SSIM), specific uptake ratio (SUR), and asymmetry index (%ASI) of the striatum were calculated for different AC methods.

Results: The NMSE for Chang's method, DL-AC_μ, DL-AC, cDL-AC_μ, cDL-AC, eDL-AC_μ, and eDL-AC is 0.0406 ± 0.0445, 0.0059 ± 0.0035, 0.0099 ± 0.0066, 0.0253 ± 0.0102, 0.0369 ± 0.0124, 0.0098 ± 0.0035, and 0.0162 ± 0.0118 for scanner A and 0.0579 ± 0.0146, 0.0055 ± 0.0034, 0.0063 ± 0.0028, 0.0235 ± 0.0085, 0.0349 ± 0.0086, 0.0115 ± 0.0062, and 0.0117 ± 0.0038 for scanner B, respectively. The SUR and %ASI results for DL-AC_μ are closer to CT-AC, Followed by DL-AC, eDL-AC_μ, cDL-AC_μ, cDL-AC, eDL-AC, Chang's method, and NAC.

Conclusion: All DL-based AC methods are superior to Chang-AC. DL-AC_μ is superior to DL-AC. Scanner-specific training is superior to cross-scanner and ensemble training. DL-based AC methods are feasible and robust for ^99mTc-TRODAT-1 brain SPECT.

Keywords: 99mTc-TRODAT-1; attenuation correction; deep learning; dopamine transporter SPECT; generative adversarial network.

Grants and funding

This study received an External Industrial Research Collaboration Grant (EF062/FST/MSP/2023/ZJFCYLKJ) from Zhejiang Fuchuan Medical Technology Co., Ltd. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. This study was also supported by a research grant from Show Chwan Memorial Hospital (IRD-110015).