Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools.

IF 1.5 4区 医学 Q3 SURGERY
Computer Assisted Surgery Pub Date : 2024-12-01 Epub Date: 2024-03-11 DOI:10.1080/24699322.2024.2327981
Matteo Rossi, Gabriele Belotti, Luca Mainardi, Guido Baroni, Pietro Cerveri
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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.

通过生成式人工智能工具精心设计的每日 CBCT,质子剂量测定覆盖计划 CT 的可行性。
放疗通常使用锥形束计算机断层扫描(CBCT)来定位病人和监控治疗。锥形束计算机断层扫描被认为对患者是安全的,因此适用于提供点剂量。然而,视场狭窄、光束硬化、散射辐射伪影和像素强度变化等局限性阻碍了在治疗过程中直接使用原始 CBCT 进行剂量重新计算。为解决这一问题,需要可靠的校正技术来去除伪影,并将像素强度重新映射为 Hounsfield 单位(HU)值。本研究提出了一种深度学习框架,用于校准用窄视场(FOV)系统获取的 CBCT 图像,并展示了其在质子治疗计划更新中的潜在用途。循环一致性生成对抗网络(cGAN)处理原始 CBCT 图像,以减少散射和重映射 HU。蒙特卡洛模拟用于生成 CBCT 扫描,从而可以只关注算法减少伪影和杯突效应的能力,而不考虑患者内部的纵向变异性,并在计划 CT(pCT)和校准 CBCT 剂量测定之间进行公平比较。为了展示该方法在真实世界数据中的可行性,我们还使用真实的 CBCT 进行了实验。测试在一个公开的数据集上进行,该数据集包含 40 名接受胰腺癌消融放射治疗的患者。与规划 CT 相比,模拟 CBCT 校准导致质子剂量测定的差异小于 2%。对危险器官的潜在毒性影响从大约 50%(未校准)下降到 2%(校准)。校准前后,3%/2 毫米的伽马通过率在复制规定剂量方面提高了约 37%(53.78% 对 90.26%)。真实数据也证实了这一点,相同标准下的表现略逊一筹(65.36% 对 87.20%)。这些结果可以证实,生成式人工智能使窄视场 CBCT 扫描的使用逐渐接近质子治疗计划更新的临床应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computer Assisted Surgery
Computer Assisted Surgery Medicine-Surgery
CiteScore
2.30
自引率
0.00%
发文量
13
审稿时长
10 weeks
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.
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