Deep learning-based multi-frequency denoising for myocardial perfusion SPECT.

IF 3 2区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

EJNMMI Physics Pub Date : 2024-10-02 DOI:10.1186/s40658-024-00680-w

Yu Du, Jingzhang Sun, Chien-Ying Li, Bang-Hung Yang, Tung-Hsin Wu, Greta S P Mok

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引用次数: 0

Abstract

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 ^99mTc-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.

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基于深度学习的心肌灌注 SPECT 多频去噪。

背景：基于深度学习（DL）的去噪方法已被证明能提高低剂量（LD）SPECT 的图像质量和定量准确性。然而，传统的基于深度学习的方法使用的是具有混合频率成分的 SPECT 图像。这项研究旨在开发一种集成的多频去噪网络，以进一步提高低剂量心肌灌注（MP）SPECT 去噪效果：方法：回顾性招募了 50 名接受常规 99mTc-sestamibi 压力 SPECT/CT 扫描的匿名患者。根据列表模式数据，将全剂量（FD）SPECT 的 10 秒采集时间缩短为每个投影 5 秒、2 秒和 1 秒，共 60 个投影，从而获得三个 LD 数据集。FD 和 LD 投影经傅立叶变换为幅值和相位图像，然后将其分为两个或三个频段。然后将每个频带反傅里叶变换回图像域。我们提出了一种三维综合注意力引导的多频段条件生成对抗网络（AttMFGAN），并与 AttGAN 和用于多频段去噪的单独 AttGAN（AttGAN-MF）进行了比较。待测试的低密度投影被分离成多频率分量，并输入到相应的网络以获得去噪分量，然后将这些分量相加以获得最终的去噪投影。在重建图像的心脏区域测量基于体素的误差指数。同时还分析了灌注缺损大小（PDS）：与传统的 AttGAN 相比，AttGAN-MF 和 AttMFGAN 在所有物理和临床指标上都表现出色。整合后的 AttMFGAN 优于 AttGAN-MF。与相应的三频段方法相比，使用两个频段进行多频段去噪的结果普遍更好：AttGAN-MF和AttMFGAN有望进一步改进LD MP SPECT去噪。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EJNMMI Physics Physics and Astronomy-Radiation

CiteScore

6.70

自引率

10.00%

发文量

审稿时长

13 weeks

期刊介绍： EJNMMI Physics is an international platform for scientists, users and adopters of nuclear medicine with a particular interest in physics matters. As a companion journal to the European Journal of Nuclear Medicine and Molecular Imaging, this journal has a multi-disciplinary approach and welcomes original materials and studies with a focus on applied physics and mathematics as well as imaging systems engineering and prototyping in nuclear medicine. This includes physics-driven approaches or algorithms supported by physics that foster early clinical adoption of nuclear medicine imaging and therapy.