基于深度学习的双视角心肌灌注SPECT成像重建方法研究。

IF 2 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
American journal of nuclear medicine and molecular imaging Pub Date : 2025-02-25 eCollection Date: 2025-01-01 DOI:10.62347/MLFB9278
Hui Liu, Yajing Zhang, Zhenlei Lyu, Li Cheng, Lilei Gao, Jing Wu, Yaqiang Liu
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引用次数: 0

摘要

单光子发射计算机断层扫描(SPECT)在临床上广泛应用于心肌灌注成像(MPI)。然而,传统的双头SPECT扫描仪需要较长的扫描时间和龙门架旋转,这限制了SPECT MPI的应用。在这项工作中,我们提出了一种基于深度学习的方法来重建双视图投影,旨在减少采集时间并实现基于传统双头SPECT扫描仪的MPI非旋转成像。采用U-Net进行双视图投影重建。最初,2D U-Nets用于评估双视图投影作为输入的各种数据组织方案,包括铺设投影、交错投影和堆叠投影,以及有无衰减图。随后,我们开发了三维U-Nets,使用最优数据组织方案作为输入,进一步提高重建性能。该数据集由GE NM/CT 640扫描仪上获得的99mTc-tetrofosmin示踪剂共116次SPECT/CT扫描组成。使用定量指标和绝对百分比误差评估重建性能,同时使用全视图投影的重建图像作为参考图像。与参考图像相比,二维U-Nets提供了合理的横向视图图像,但无论数据组织方案如何,都表现出轻微的轴向不连续。合并衰减图减少了这种轴向不连续。在定量上,使用叠加投影和衰减图训练的二维U-Net获得了最好的性能,归一化平均绝对误差为0.6%±0.3%,结构相似指数(SSIM)为0.93±0.04。3D U-Net进一步改善了性能,轴向不连续更小,SSIM更高,为0.94±0.03。左室腔和心肌的局部绝对误差百分比分别为1.8±16.8%和-2.0±6.3%。我们开发了一种基于深度学习的图像重建方法,用于传统SPECT扫描仪的双视图投影。经过衰减图叠加投影训练的三维U-Net在非旋转成像中是有效的,可以用于动态心肌灌注成像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of a deep learning-based reconstruction approach utilizing dual-view projection for myocardial perfusion SPECT imaging.

Single-photon emission computed tomography (SPECT) is widely used in myocardial perfusion imaging (MPI) in clinic. However, conventional dual-head SPECT scanners require lengthy scanning times and gantry rotation, which limits the application of SPECT MPI. In this work, we proposed a deep learning-based approach to reconstruct dual-view projections, aiming to reduce acquisition time and enable non-rotational imaging for MPI based on conventional dual-head SPECT scanners. U-Net was adopted for the dual-view projection reconstruction. Initially, 2D U-Nets were used to evaluate various data organization schemes for dual-view projection as input, including paved projection, interleaved projection, and stacked projection, with and without an attenuation map. Subsequently, we developed 3D U-Nets using the optimal data organization scheme as input to further enhance reconstruction performance. The dataset consisted of a total of 116 SPECT/CT scans with 99mTc-tetrofosmin tracer acquired on a GE NM/CT 640 scanner. Reconstruction performance was assessed using quantitative metrices and absolute percentage errors, while the reconstruction images from the full-view projection were used as reference images. The 2D U-Nets provided reasonable transverse view images but exhibited slight axial discontinuity compared to the reference images, regardless of the data organization schemes. Incorporating the attenuation map reduced this axial discontinuity. Quantitatively, the 2D U-Net trained using both stacked projection and attenuation map achieved the best performance, with a normalized mean absolute error of 0.6%±0.3% and a structural similarity index measure (SSIM) of 0.93±0.04. The 3D U-Net further improved the performance with less axial discontinuity and a higher SSIM of 0.94±0.03. The localized absolute percentage errors were 1.8±16.8% and -2.0±6.3% in the left ventricular (LV) cavity and myocardium, respectively. We developed a deep learning-based image reconstruction approach for dual-view projection from a conventional SPECT scanner. The 3D U-Net, trained with the stacked projection with an attenuation map is effective for non-rotational imaging and could benefit dynamic myocardium perfusion imaging.

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来源期刊
American journal of nuclear medicine and molecular imaging
American journal of nuclear medicine and molecular imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
自引率
4.00%
发文量
4
期刊介绍: The scope of AJNMMI encompasses all areas of molecular imaging, including but not limited to: positron emission tomography (PET), single-photon emission computed tomography (SPECT), molecular magnetic resonance imaging, magnetic resonance spectroscopy, optical bioluminescence, optical fluorescence, targeted ultrasound, photoacoustic imaging, etc. AJNMMI welcomes original and review articles on both clinical investigation and preclinical research. Occasionally, special topic issues, short communications, editorials, and invited perspectives will also be published. Manuscripts, including figures and tables, must be original and not under consideration by another journal.
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