Machine-learning based quantification of lung shunt fraction from 99mTc-MAA SPECT/CT for selective internal radiation therapy of liver tumors using TriDFusion (3DF).

IF 3 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING EJNMMI Physics Pub Date : 2025-03-11 DOI:10.1186/s40658-025-00732-9
Daniel Lafontaine, Finn Augensen, Adam Kesner, Raoul Vincent, Assen Kirov, Simone Krebs, Heiko Schöder, John L Humm
{"title":"Machine-learning based quantification of lung shunt fraction from 99mTc-MAA SPECT/CT for selective internal radiation therapy of liver tumors using TriDFusion (3DF).","authors":"Daniel Lafontaine, Finn Augensen, Adam Kesner, Raoul Vincent, Assen Kirov, Simone Krebs, Heiko Schöder, John L Humm","doi":"10.1186/s40658-025-00732-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Prior to selective internal radiotherapy of liver tumors, a determination of the lung shunt fraction (LSF) is performed using 99mTc- macroaggregated albumin (99mTc-MAA) injected into the hepatic artery. Most commonly planar but sometimes SPECT/CT images are acquired upon which regions of interests are drawn manually to define the liver and the lung. The LSF is then calculated by taking the count ratios between these two organs. An accurate estimation of the LSF is necessary to avoid an excessive pulmonary irradiation dose.</p><p><strong>Methods: </strong>In this study, we propose a computational, semi-automatic approach for LSF calculation from SPECT/CT scans, based on machine learning 3D segmentation, implemented within TriDFusion (3DF). We retrospectively compared this approach with the LSF calculated using the standard planar approach on 150 patients. Using CT images (from the SPECT/CT) as a blueprint, the TotalSegmentor machine learning algorithm automatically computes masks for the liver and lungs. Then, the SPECT attenuation-corrected images are fused with the CT and, based on the CT segmentation mask, TriDFusion (3DF) generates volume-of- interest (VOI) regions on the SPECT images. The liver and lung VOIs are further augmented to compensate for breathing motion. Finally, the LSF is calculated using the number of counts in the respective VOIs. Measurements using an anthropomorphic 3D-printed phantom with variable 99mTc activity concentrations for the liver and lungs were performed to validate the accuracy of the algorithm.</p><p><strong>Results: </strong>On average, LSF determined from 2D planar images were between 21 and 70% higher than those determined from SPECT/CT data. Semi-automated determination of the LSF using TriDFusion (3DF) analysis of SPECT-CT acquisitions was within 4-12% of the phantom-determined ratio measurements (ground truth).</p><p><strong>Conclusions: </strong>The utilization of TriDFusion (3DF) AI 3D Lung Shunt is a precise method for quantifying lung shunt fraction (LSF) and is more accurate than planar 2D image-based estimates. By incorporating machine learning segmentation and compensating for breathing motion, the approach underscores the potential of artificial intelligence (AI)-driven techniques to revolutionize pulmonary imaging, providing clinicians with efficient and reliable tools for treatment planning and patient management.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"22"},"PeriodicalIF":3.0000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893963/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EJNMMI Physics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s40658-025-00732-9","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Prior to selective internal radiotherapy of liver tumors, a determination of the lung shunt fraction (LSF) is performed using 99mTc- macroaggregated albumin (99mTc-MAA) injected into the hepatic artery. Most commonly planar but sometimes SPECT/CT images are acquired upon which regions of interests are drawn manually to define the liver and the lung. The LSF is then calculated by taking the count ratios between these two organs. An accurate estimation of the LSF is necessary to avoid an excessive pulmonary irradiation dose.

Methods: In this study, we propose a computational, semi-automatic approach for LSF calculation from SPECT/CT scans, based on machine learning 3D segmentation, implemented within TriDFusion (3DF). We retrospectively compared this approach with the LSF calculated using the standard planar approach on 150 patients. Using CT images (from the SPECT/CT) as a blueprint, the TotalSegmentor machine learning algorithm automatically computes masks for the liver and lungs. Then, the SPECT attenuation-corrected images are fused with the CT and, based on the CT segmentation mask, TriDFusion (3DF) generates volume-of- interest (VOI) regions on the SPECT images. The liver and lung VOIs are further augmented to compensate for breathing motion. Finally, the LSF is calculated using the number of counts in the respective VOIs. Measurements using an anthropomorphic 3D-printed phantom with variable 99mTc activity concentrations for the liver and lungs were performed to validate the accuracy of the algorithm.

Results: On average, LSF determined from 2D planar images were between 21 and 70% higher than those determined from SPECT/CT data. Semi-automated determination of the LSF using TriDFusion (3DF) analysis of SPECT-CT acquisitions was within 4-12% of the phantom-determined ratio measurements (ground truth).

Conclusions: The utilization of TriDFusion (3DF) AI 3D Lung Shunt is a precise method for quantifying lung shunt fraction (LSF) and is more accurate than planar 2D image-based estimates. By incorporating machine learning segmentation and compensating for breathing motion, the approach underscores the potential of artificial intelligence (AI)-driven techniques to revolutionize pulmonary imaging, providing clinicians with efficient and reliable tools for treatment planning and patient management.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
EJNMMI Physics
EJNMMI Physics Physics and Astronomy-Radiation
CiteScore
6.70
自引率
10.00%
发文量
78
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.
期刊最新文献
A novel method for harmonization of PET image spatial resolution without phantoms. Machine-learning based quantification of lung shunt fraction from 99mTc-MAA SPECT/CT for selective internal radiation therapy of liver tumors using TriDFusion (3DF). Automated segmentation of the sacro-iliac joints, posterior spinal joints and discovertebral units on low-dose computed tomography for Na[18F]F PET lesion detection in spondyloarthritis patients. Myocardial perfusion imaging SPECT left ventricle segmentation with graphs. The first human study of add-on PET: A PET-integrated RF coil for 3 T MRI.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1