{"title":"Deep learning-based attenuation correction method in <sup>99m</sup>Tc-GSA SPECT/CT hepatic imaging: a phantom study.","authors":"Masahiro Miyai, Ryohei Fukui, Masahiro Nakashima, Sachiko Goto","doi":"10.1007/s12194-023-00762-x","DOIUrl":null,"url":null,"abstract":"<p><p>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 (SPECT<sub>NC</sub>) for AC in <sup>99m</sup>Tc-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 SPECT<sub>NC</sub> 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 (SPECT<sub>CTAC</sub>) and pseudo-CT attenuation correction (SPECT<sub>GAN</sub>) 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 SPECT<sub>CTAC</sub> and SPECT<sub>GAN</sub> were assessed using a structural similarity (SSIM) index. The pseudo-CT images produced a lower liver volume than the real CT images. SPECT<sub>CTAC</sub> exhibited a higher total count than SPECT<sub>NC</sub> and SPECT<sub>GAN</sub>, which were approximately 60% and 7% lower, respectively. The uniformities of SPECT<sub>CTAC</sub> and SPECT<sub>GAN</sub> were better than those of SPECT<sub>NC</sub>. The mean SSIM value for SPECT<sub>CTAC</sub> and SPECT<sub>GAN</sub> was 0.97. We proposed a deep learning-based AC approach to generate pseudo-CT images from SPECT<sub>NC</sub> images in <sup>99m</sup>Tc-GSA scintigraphy. SPECT<sub>GAN</sub> with AC using pseudo-CT images was similar to SPECT<sub>CTAC</sub>, demonstrating the possibility of SPECT/CT examination with reduced exposure to radiation.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiological Physics and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12194-023-00762-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/11/30 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
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.
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.