Matteo Rossi, Gabriele Belotti, Luca Mainardi, Guido Baroni, Pietro Cerveri
{"title":"Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools.","authors":"Matteo Rossi, Gabriele Belotti, Luca Mainardi, Guido Baroni, Pietro Cerveri","doi":"10.1080/24699322.2024.2327981","DOIUrl":null,"url":null,"abstract":"<p><p>Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into Hounsfield Units (HU) values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow field of view (FOV) systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent generative adversarial networks (cGAN) processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm's ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT (pCT) and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2 mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"29 1","pages":"2327981"},"PeriodicalIF":1.5000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Assisted Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/24699322.2024.2327981","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/3/11 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"SURGERY","Score":null,"Total":0}
引用次数: 0
Abstract
Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into Hounsfield Units (HU) values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow field of view (FOV) systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent generative adversarial networks (cGAN) processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm's ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT (pCT) and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2 mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.
期刊介绍:
omputer Assisted Surgery aims to improve patient care by advancing the utilization of computers during treatment; to evaluate the benefits and risks associated with the integration of advanced digital technologies into surgical practice; to disseminate clinical and basic research relevant to stereotactic surgery, minimal access surgery, endoscopy, and surgical robotics; to encourage interdisciplinary collaboration between engineers and physicians in developing new concepts and applications; to educate clinicians about the principles and techniques of computer assisted surgery and therapeutics; and to serve the international scientific community as a medium for the transfer of new information relating to theory, research, and practice in biomedical imaging and the surgical specialties.
The scope of Computer Assisted Surgery encompasses all fields within surgery, as well as biomedical imaging and instrumentation, and digital technology employed as an adjunct to imaging in diagnosis, therapeutics, and surgery. Topics featured include frameless as well as conventional stereotactic procedures, surgery guided by intraoperative ultrasound or magnetic resonance imaging, image guided focused irradiation, robotic surgery, and any therapeutic interventions performed with the use of digital imaging technology.