Deep learning-based attenuation correction method in 99mTc-GSA SPECT/CT hepatic imaging: a phantom study.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-11-30 DOI:10.1007/s12194-023-00762-x
Masahiro Miyai, Ryohei Fukui, Masahiro Nakashima, Sachiko Goto
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引用次数: 0

Abstract

This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC single-photon emission computed tomography images (SPECTNC) for AC in 99mTc-galactosyl human albumin diethylenetriamine pentaacetic acid (GSA) scintigraphy and to reduce patient dosage. A cycle-consistent generative network (CycleGAN) model was used to generate pseudo-CT images. The training datasets comprised approximately 850 liver phantom images obtained from SPECTNC and real CT images. The training datasets were then input to CycleGAN, and pseudo-CT images were output. SPECT images with real-time CT attenuation correction (SPECTCTAC) and pseudo-CT attenuation correction (SPECTGAN) were acquired. The difference in liver volume between real CT and pseudo-CT images was evaluated. Total counts and uniformity were then used to evaluate the effects of AC. Additionally, the similarity coefficients of SPECTCTAC and SPECTGAN were assessed using a structural similarity (SSIM) index. The pseudo-CT images produced a lower liver volume than the real CT images. SPECTCTAC exhibited a higher total count than SPECTNC and SPECTGAN, which were approximately 60% and 7% lower, respectively. The uniformities of SPECTCTAC and SPECTGAN were better than those of SPECTNC. The mean SSIM value for SPECTCTAC and SPECTGAN was 0.97. We proposed a deep learning-based AC approach to generate pseudo-CT images from SPECTNC images in 99mTc-GSA scintigraphy. SPECTGAN with AC using pseudo-CT images was similar to SPECTCTAC, demonstrating the possibility of SPECT/CT examination with reduced exposure to radiation.

基于深度学习的99mTc-GSA SPECT/CT肝脏成像衰减校正方法:一项幻象研究。
本研究旨在评估一种基于深度学习的衰减校正(AC)方法,从非AC单光子发射计算机断层扫描图像(SPECTNC)生成伪计算机断层扫描(CT)图像,用于99mtc -半乳糖人白蛋白二乙烯三胺五乙酸(GSA)闪烁成像,并减少患者剂量。采用循环一致生成网络(CycleGAN)模型生成伪ct图像。训练数据集包括从SPECTNC和真实CT图像中获得的大约850个肝脏幻象图像。然后将训练数据集输入CycleGAN,输出伪ct图像。获得实时CT衰减校正(specctac)和伪CT衰减校正(SPECTGAN)的SPECT图像。评估真实CT与伪CT图像的肝脏体积差异。然后使用总数和均匀性来评估AC的效果。此外,使用结构相似性(SSIM)指数评估specctac和SPECTGAN的相似系数。伪CT图像显示肝脏体积小于真实CT图像。specctac的总计数高于SPECTNC和SPECTGAN,后者分别低约60%和7%。specctac和SPECTGAN的均匀性优于SPECTNC。specctac和SPECTGAN的平均SSIM值为0.97。我们提出了一种基于深度学习的AC方法,从99mTc-GSA科学成像中的SPECTNC图像生成伪ct图像。使用伪CT图像的SPECTGAN与使用AC的specctac相似,证明了在减少辐射暴露的情况下进行SPECT/CT检查的可能性。
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来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
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