基于深度学习的心肌灌注单光子发射计算机断层扫描多频去噪
Deep learning-based multi-frequency denoising for myocardial perfusion SPECT.
作者信息
Du Yu, Sun Jingzhang, Li Chien-Ying, Yang Bang-Hung, Wu Tung-Hsin, Mok Greta S P
机构信息
Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.
Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau SAR, China.
出版信息
EJNMMI Phys. 2024 Oct 2;11(1):80. doi: 10.1186/s40658-024-00680-w.
BACKGROUND
Deep learning (DL)-based denoising has been proven to improve image quality and quantitation accuracy of low dose (LD) SPECT. However, conventional DL-based methods used SPECT images with mixed frequency components. This work aims to develop an integrated multi-frequency denoising network to further enhance LD myocardial perfusion (MP) SPECT denoising.
METHODS
Fifty anonymized patients who underwent routine Tc-sestamibi stress SPECT/CT scans were retrospectively recruited. Three LD datasets were obtained by reducing the 10 s acquisition time of full dose (FD) SPECT to be 5, 2 and 1 s per projection based on the list mode data for a total of 60 projections. FD and LD projections were Fourier transformed to magnitude and phase images, which were then separated into two or three frequency bands. Each frequency band was then inversed Fourier transformed back to the image domain. We proposed a 3D integrated attention-guided multi-frequency conditional generative adversarial network (AttMFGAN) and compared with AttGAN, and separate AttGAN for multi-frequency bands denoising (AttGAN-MF).The multi-frequency FD and LD projections of 35, 5 and 10 patients were paired for training, validation and testing. The LD projections to be tested were separated to multi-frequency components and input to corresponding networks to get the denoised components, which were summed to get the final denoised projections. Voxel-based error indices were measured on the cardiac region on the reconstructed images. The perfusion defect size (PDS) was also analyzed.
RESULTS
AttGAN-MF and AttMFGAN have superior performance on all physical and clinical indices as compared to conventional AttGAN. The integrated AttMFGAN is better than AttGAN-MF. Multi-frequency denoising with two frequency bands have generally better results than corresponding three-frequency bands methods.
CONCLUSIONS
AttGAN-MF and AttMFGAN are promising to further improve LD MP SPECT denoising.
背景
基于深度学习（DL）的去噪已被证明可提高低剂量（LD）SPECT的图像质量和定量准确性。然而，传统的基于DL的方法使用的是具有混合频率成分的SPECT图像。本研究旨在开发一种集成多频去噪网络，以进一步增强LD心肌灌注（MP）SPECT去噪效果。
方法
回顾性招募了50例接受常规锝- sestamibi负荷SPECT/CT扫描的匿名患者。基于列表模式数据，通过将全剂量（FD）SPECT的10秒采集时间减少到每个投影5、2和1秒，获得了三个LD数据集，总共60个投影。对FD和LD投影进行傅里叶变换，得到幅度和相位图像，然后将其分离为两个或三个频带。然后将每个频带进行傅里叶逆变换回到图像域。我们提出了一种3D集成注意力引导多频条件生成对抗网络（AttMFGAN），并与AttGAN以及用于多频带去噪的单独AttGAN（AttGAN-MF）进行比较。将35、5和10例患者的多频FD和LD投影进行配对，用于训练、验证和测试。将待测试的LD投影分离为多频成分，并输入到相应网络中以获得去噪成分，将这些成分相加得到最终的去噪投影。在重建图像的心脏区域测量基于体素的误差指数。还分析了灌注缺损大小（PDS）。
结果
与传统的AttGAN相比，AttGAN-MF和AttMFGAN在所有物理和临床指标上均具有优异的性能。集成的AttMFGAN优于AttGAN-MF。双频带的多频去噪通常比相应的三频带方法具有更好的结果。
结论
AttGAN-MF和AttMFGAN有望进一步改善LD MP SPECT去噪效果。
Zotero 插件