Evaluating auto-contouring accuracy in reduced CT dose images for radiopharmaceutical therapies: Denoising and evaluation of 177Lu DOTATATE therapy dataset

IF 2 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of Applied Clinical Medical Physics Pub Date : 2025-03-02 DOI:10.1002/acm2.70066

Hung-Te Yang, Kuan-Yin Ko, Ching-Ching Yang

{"title":"Evaluating auto-contouring accuracy in reduced CT dose images for radiopharmaceutical therapies: Denoising and evaluation of 177Lu DOTATATE therapy dataset","authors":"Hung-Te Yang, Kuan-Yin Ko, Ching-Ching Yang","doi":"10.1002/acm2.70066","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Purpose</h3>\n \n <p>Reducing radiation dose attributed to computed tomography (CT) may compromise the accuracy of organ segmentation, an important step in <sup>177</sup>Lu DOTATATE therapy that affects both activity and mass estimates. This study aimed to facilitate CT dose reduction using deep learning methods for patients undergoing serial single photon emission computed tomography (SPECT)/CT imaging during <sup>177</sup>Lu DOTATATE therapy.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The <sup>177</sup>Lu DOTATATE patient dataset hosted in Deep Blue Data was used in this study. The noise insertion method incorporating the effect of bowtie filter, automatic exposure control, and electronic noise was applied to simulate images at four reduced dose levels. Organ segmentation was carried out using the TotalSegmentator model, while image denoising was performed with the DenseNet model. The impact of segmentation performance on the dosimetry accuracy of <sup>177</sup>Lu DOTATATE therapy was quantified by calculating the percent difference between a dose rate map segmented with a reference mask and the same dose rate map segmented with a test mask (PD<sub>dose</sub>) for spleen, right kidney, left kidney, and liver.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Before denoising, the mean ± standard deviation of PD<sub>dose</sub> for all critical organs were 2.31 ± 2.94%, 4.86 ± 9.42%, 8.39 ± 14.76%, 12.95 ± 19.99% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively. After denoising, the corresponding results were 1.69 ± 2.25%, 2.84 ± 4.46%, 3.72 ± 4.22%, 7.98 ± 15.05% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>As dose reduction increased, CT image segmentation gradually deteriorated, which in turn deteriorated the dosimetry accuracy of <sup>177</sup>Lu DOTATATE therapy. Improving CT image quality through denoising could enhance <sup>177</sup>Lu DOTATATE dosimetry, making it a valuable tool to support CT dose reduction for patients undergoing serial SPECT/CT imaging during treatment.</p>\n </section>\n </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 4","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70066","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Clinical Medical Physics","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/acm2.70066","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose

Reducing radiation dose attributed to computed tomography (CT) may compromise the accuracy of organ segmentation, an important step in ¹⁷⁷Lu DOTATATE therapy that affects both activity and mass estimates. This study aimed to facilitate CT dose reduction using deep learning methods for patients undergoing serial single photon emission computed tomography (SPECT)/CT imaging during ¹⁷⁷Lu DOTATATE therapy.

Methods

The ¹⁷⁷Lu DOTATATE patient dataset hosted in Deep Blue Data was used in this study. The noise insertion method incorporating the effect of bowtie filter, automatic exposure control, and electronic noise was applied to simulate images at four reduced dose levels. Organ segmentation was carried out using the TotalSegmentator model, while image denoising was performed with the DenseNet model. The impact of segmentation performance on the dosimetry accuracy of ¹⁷⁷Lu DOTATATE therapy was quantified by calculating the percent difference between a dose rate map segmented with a reference mask and the same dose rate map segmented with a test mask (PD_dose) for spleen, right kidney, left kidney, and liver.

Results

Before denoising, the mean ± standard deviation of PD_dose for all critical organs were 2.31 ± 2.94%, 4.86 ± 9.42%, 8.39 ± 14.76%, 12.95 ± 19.99% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively. After denoising, the corresponding results were 1.69 ± 2.25%, 2.84 ± 4.46%, 3.72 ± 4.22%, 7.98 ± 15.05% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively.

Conclusion

As dose reduction increased, CT image segmentation gradually deteriorated, which in turn deteriorated the dosimetry accuracy of ¹⁷⁷Lu DOTATATE therapy. Improving CT image quality through denoising could enhance ¹⁷⁷Lu DOTATATE dosimetry, making it a valuable tool to support CT dose reduction for patients undergoing serial SPECT/CT imaging during treatment.

Abstract Image

查看原文本刊更多论文

评估用于放射性药物治疗的降低 CT 剂量图像的自动轮廓准确性：对 177Lu DOTATATE 治疗数据集进行去噪和评估。

目的：减少计算机断层扫描（CT）的辐射剂量可能会损害器官分割的准确性，这是影响活动和质量估计的177Lu DOTATATE治疗的重要步骤。本研究旨在促进在177Lu DOTATATE治疗期间接受连续单光子发射计算机断层扫描(SPECT)/CT成像的患者使用深度学习方法减少CT剂量。方法：本研究使用深蓝数据托管的177Lu DOTATATE患者数据集。采用领结滤波、自动曝光控制和电子噪声相结合的噪声插入方法，模拟了四种降低剂量水平下的图像。使用TotalSegmentator模型进行器官分割，使用DenseNet模型进行图像去噪。通过计算脾脏、右肾、左肾和肝脏用参考掩膜分割的剂量率图与用测试掩膜（PDdose）分割的相同剂量率图之间的百分比差，量化分割性能对177Lu DOTATATE治疗剂量测定准确性的影响。结果：去噪前，各关键器官在降至正常剂量的20%、10%、5%、2.5%时的CT图像中PDdose的平均±标准差分别为2.31±2.94%、4.86±9.42%、8.39±14.76%、12.95±19.99%。去噪后，当剂量水平降至正常剂量的20%、10%、5%、2.5%时，相应的CT图像结果分别为1.69±2.25%、2.84±4.46%、3.72±4.22%、7.98±15.05%。结论：随着剂量减少的增加，CT图像分割逐渐恶化，从而降低了177Lu DOTATATE治疗的剂量学准确性。通过去噪提高CT图像质量可以增强177Lu DOTATATE剂量测定，使其成为支持治疗期间进行连续SPECT/CT成像的患者降低CT剂量的有价值的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Applied Clinical Medical Physics 医学-核医学

CiteScore

3.60

自引率

19.00%

发文量

331

审稿时长

3 months

期刊介绍： Journal of Applied Clinical Medical Physics is an international Open Access publication dedicated to clinical medical physics. JACMP welcomes original contributions dealing with all aspects of medical physics from scientists working in the clinical medical physics around the world. JACMP accepts only online submission. JACMP will publish: -Original Contributions: Peer-reviewed, investigations that represent new and significant contributions to the field. Recommended word count: up to 7500. -Review Articles: Reviews of major areas or sub-areas in the field of clinical medical physics. These articles may be of any length and are peer reviewed. -Technical Notes: These should be no longer than 3000 words, including key references. -Letters to the Editor: Comments on papers published in JACMP or on any other matters of interest to clinical medical physics. These should not be more than 1250 (including the literature) and their publication is only based on the decision of the editor, who occasionally asks experts on the merit of the contents. -Book Reviews: The editorial office solicits Book Reviews. -Announcements of Forthcoming Meetings: The Editor may provide notice of forthcoming meetings, course offerings, and other events relevant to clinical medical physics. -Parallel Opposed Editorial: We welcome topics relevant to clinical practice and medical physics profession. The contents can be controversial debate or opposed aspects of an issue. One author argues for the position and the other against. Each side of the debate contains an opening statement up to 800 words, followed by a rebuttal up to 500 words. Readers interested in participating in this series should contact the moderator with a proposed title and a short description of the topic