Sun Jingzhang, Yang Bang-Hung, Li Chien-Ying, Du Yu, Liu Yi-Hwa, Wu Tung-Hsin, Mok Greta S P
Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macao SAR, China.
Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Front Med (Lausanne). 2023 Feb 3;10:1083413. doi: 10.3389/fmed.2023.1083413. eCollection 2023.
Deep learning-based denoising is promising for myocardial perfusion (MP) SPECT. However, conventional convolutional neural network (CNN)-based methods use fixed-sized convolutional kernels to convolute one region within the receptive field at a time, which would be ineffective for learning the feature dependencies across large regions. The attention mechanism (Att) is able to learn the relationships between the local receptive field and other voxels in the image. In this study, we propose a 3D attention-guided generative adversarial network (AttGAN) for denoising fast MP-SPECT images.
Fifty patients who underwent 1184 MBq Tc-sestamibi stress SPECT/CT scan were retrospectively recruited. Sixty projections were acquired over 180° and the acquisition time was 10 s/view for the full time (FT) mode. Fast MP-SPECT projection images (1 s to 7 s) were generated from the FT list mode data. We further incorporated binary patient defect information (0 = without defect, 1 = with defect) into AttGAN (AttGAN-def). AttGAN, AttGAN-def, cGAN, and Unet were implemented using Tensorflow with the Adam optimizer running up to 400 epochs. FT and fast MP-SPECT projection pairs of 35 patients were used for training the networks for each acquisition time, while 5 and 10 patients were applied for validation and testing. Five-fold cross-validation was performed and data for all 50 patients were tested. Voxel-based error indices, joint histogram, linear regression, and perfusion defect size (PDS) were analyzed.
All quantitative indices of AttGAN-based networks are superior to cGAN and Unet on all acquisition time images. AttGAN-def further improves AttGAN performance. The mean absolute error of PDS by AttcGAN-def was 1.60 on acquisition time of 1 s/prj, as compared to 2.36, 2.76, and 3.02 by AttGAN, cGAN, and Unet.
Denoising based on AttGAN is superior to conventional CNN-based networks for MP-SPECT.
基于深度学习的去噪技术在心肌灌注（MP）单光子发射计算机断层显像（SPECT）中具有广阔前景。然而，传统的基于卷积神经网络（CNN）的方法使用固定大小的卷积核，一次仅对感受野内的一个区域进行卷积，这对于学习大区域间的特征依赖关系效率不高。注意力机制（Att）能够学习局部感受野与图像中其他体素之间的关系。在本研究中，我们提出一种用于快速MP-SPECT图像去噪的三维注意力引导生成对抗网络（AttGAN）。
回顾性招募了50例接受1184 MBq锝-司他比应激SPECT/CT扫描的患者。在180°范围内采集60个投影，全时（FT）模式下每个视图的采集时间为10秒。从FT列表模式数据生成快速MP-SPECT投影图像（1秒至7秒）。我们进一步将二元患者缺陷信息（0 =无缺陷，1 =有缺陷）纳入AttGAN（AttGAN-def）。使用Tensorflow和Adam优化器实现AttGAN、AttGAN-def、cGAN和Unet，运行至400个epoch。对于每个采集时间，使用35例患者的FT和快速MP-SPECT投影对来训练网络，而5例和10例患者分别用于验证和测试。进行五折交叉验证并对所有50例患者的数据进行测试。分析基于体素的误差指数、联合直方图、线性回归和灌注缺损大小（PDS）。
基于AttGAN的网络在所有采集时间图像上的所有定量指标均优于cGAN和Unet。AttGAN-def进一步提高了AttGAN的性能。在采集时间为1秒/投影时，AttcGAN-def的PDS平均绝对误差为1.60，而AttGAN、cGAN和Unet的该误差分别为2.36、2.76和3.02。
对于MP-SPECT，基于AttGAN的去噪优于传统的基于CNN的网络。
