Development of attenuation correction methods using deep learning in brain‐perfusion single‐photon emission computed tomography

Abstract

Purpose Computed tomography (CT)‐based attenuation correction (CTAC) in single‐photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation correction (AC) without the need for additional CT images, a deep learning method was used to generate pseudo‐CT images has previously been reported, but it is limited because of cross‐modality transformation, resulting in misalignment and modality‐specific artifacts. This study aimed to develop a deep learning‐based approach using non‐attenuation‐corrected (NAC) images and CTAC‐based images for training to yield AC images in brain‐perfusion SPECT. This study also investigated whether the proposed approach is superior to conventional Chang’s AC (ChangAC). Methods In total, 236 patients who underwent brain‐perfusion SPECT were randomly divided into two groups: the training group (189 patients; 80%) and the test group (47 patients; 20%). Two models were constructed using Autoencoder (AutoencoderAC) and U‐Net (U‐NetAC), respectively. ChangAC, AutoencoderAC, and U‐NetAC approaches were compared with CTAC using qualitative analysis (visual evaluation) and quantitative analysis (normalized mean squared error [NMSE] and the percentage error in each brain region). Statistical analyses were performed using the Wilcoxon signed‐rank sum test and Bland‐Altman analysis. Results U‐NetAC had the highest visual evaluation score. The NMSE results for the U‐NetAC were the lowest, followed by AutoencoderAC and ChangAC (P < 0.001). Bland‐Altman analysis showed a fixed bias for ChangAC and AutoencoderAC and a proportional bias for ChangAC. ChangAC underestimated counts by 30–40% in all brain regions. AutoencoderAC and U‐NetAC produced mean errors of <1% and maximum errors of 3%, respectively. Conclusion New deep learning‐based AC methods for AutoencoderAC and U‐NetAC were developed. Their accuracy was higher than that obtained by ChangAC. U‐NetAC exhibited higher qualitative and quantitative accuracy than AutoencoderAC. We generated highly accurate AC images directly from NAC images without the need for intermediate pseudo‐CT images. To verify our models’ generalizability, external validation is required.

CT-free quantitative SPECT for automatic evaluation of %thyroid uptake based on deep-learning

“…Additionally, the time-consuming manual thyroid segmentation on CT canvas is challenging. In the literature, there are deep-learning-based CT-free AC studies for myocardial perfusion SPECT [ 2 , 5 ], brain perfusion SPECT [ 6 , 7 , 32 ] and dopamine-transporter brain SPECT [ 3 ]. Undoubtedly, AC using CT is essential for quantitative thyroid SPECT/CT, but thyroid-dedicated deep-learning study has not been investigated.…”

“…However, application of CT-based AC (CTAC) is yet to be a clinical routine in SPECT because of lack of proper clinical indication, concern about extra-radiation exposure, and necessity for hybrid SPECT/CT scanner [ 1 ]. Recent development of deep-learning may change the concept of CTAC because CT acquisition may be omitted through either μ-map generation from SPECT (indirect approach) [ 2 – 5 ] or creation of attenuation-corrected SPECT (direct approach) [ 6 , 7 ]. Deep-learning was also useful in organ segmentation [ 8 – 10 ].…”

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

“…Despite slight differences between the two scanners, e.g., brain orientation and field-of-view, spatial resolution, and CT slice thickness, their results showed similar trends for different AC methods. Murata et al ( 21 ) demonstrate that 2D autoencoder and U-Net-based direct DL-AC are better than NAC and Chang's AC for brain perfusion SPECT. Chen et al ( 23 ) suggest that CNN-estimated μ-map could be a promising substitute for CT-based μ-map for 123 I-FP-CIT scans.…”

“…For brain SPECT, Sakaguchi et al ( 20 ) developed a 2D convolutional neural networks (CNN)-based autoencoder for the direct generation of AC from NAC images for brain perfusion SPECT. Murata et al ( 21 ) compared Chang's AC with a 2D autoencoder and U-Net for DL-AC for brain perfusion SPECT. Chen et al have proposed CNN-based μ-map generation for brain perfusion SPECT ( 22 ) and 123 I-FP-CIT SPECT ( 23 ) using NAC SPECT input in simulations, demonstrating improved absolute quantification accuracy.…”