Dose distributions of proton therapy plans are robust against lowering the resolution of CTs combined with increasing noise

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2024-11-28 DOI:10.1002/mp.17530

Carla Frensch, Claus Maximilian Bäcker, Walter Jentzen, Ann-Kristin Lüvelsmeyer, Mohammadreza Teimoorisichani, Jörg Wulff, Beate Timmermann, Christian Bäumer

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引用次数: 0

Abstract

Background

Treatment planning in radiation therapy (RT) is performed on image sets acquired with commercial x-ray computed tomography (CT) scanners. Considering an increased frequency of verification scans for adaptive RT and the advent of alternatives to x-ray CTs, there is a need to review the requirements for image sets used in RT planning.

Purpose

This study aims to derive the required image quality (IQ) for the computation of the dose distribution in proton therapy (PT) regarding spatial resolution and the combination of spatial resolution and noise. The knowledge gained is used to explore the potential for dose reduction in tomography-guided PT.

Methods

Mathematical considerations indicate that the required spatial resolution for dose computation is on the scale of the set-up margins fed into the robust optimization. This hypothesis was tested by processing retrospectively 12 clinical PT cases, which reflect a variety of tumor localizations. Image sets were low-pass filtered and were made noisy in a generic manner. Dose distributions on the modified CT scans were computed with a Monte-Carlo dose engine. The similarity of these dose distributions with clinical ones was quantified with the gamma-index (1 mm/1%). The potential reduction of the x-ray exposure compared to the planning CT scan was estimated.

Results

Dose distributions within the irradiated volume were robust against low-pass filtering of the CTs with kernels up to a full-width-at-half-maximum of 4 mm, that is, the gamma pass rate (1 mm/1%) was $\geq$ 98%. The limit of the filter width was 6 mm for brain tumors and 8 mm for targets in the abdomen. These pass rates remained approximately unchanged if a limited amount of noise was added to the CT image sets. The estimated potential reductions of the x-ray exposure were at least a factor of 20.

Conclusions

The requirements on IQ in terms of spatial resolution in combination with noise for computing the dose in PT are clearly lower than the IQ of current clinical planning. The results apply, for example, to ultra-low dose x-ray CTs, proton CTs with coarse spatial detection, and attenuation images from the joint reconstruction of time-of-flight PET scans.

Abstract Image

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质子治疗计划的剂量分布不受 CT 分辨率降低和噪声增加的影响。

背景：放射治疗（RT）的治疗计划是通过商用 X 射线计算机断层扫描（CT）扫描仪获取的图像集执行的。目的：本研究旨在得出质子治疗（PT）中剂量分布计算所需的图像质量（IQ），涉及空间分辨率以及空间分辨率和噪声的组合。获得的知识将用于探索断层扫描引导质子治疗中减少剂量的潜力：方法：数学上的考虑表明，剂量计算所需的空间分辨率与输入稳健优化的设置余量相当。我们通过回顾性处理 12 个临床 PT 病例来验证这一假设，这些病例反映了不同的肿瘤定位。对图像集进行了低通滤波，并以通用的方式对其进行了噪声处理。使用蒙特卡洛剂量引擎计算了修改后 CT 扫描的剂量分布。这些剂量分布与临床剂量分布的相似性用伽马指数（1 毫米/1%）进行量化。与计划的 CT 扫描相比，对可能减少的 X 射线照射进行了估算：结果：辐照体积内的剂量分布对CT的低通滤波具有很强的抗干扰性，滤波核的最大全宽为4毫米，也就是说，伽马通过率（1毫米/1%）≥98%。脑肿瘤的滤波器宽度极限为 6 毫米，腹部目标的滤波器宽度极限为 8 毫米。如果在 CT 图像集中加入一定量的噪声，这些通过率大致保持不变。据估计，X 射线曝光量可能减少至少 20 倍：结论：计算 PT 剂量对空间分辨率和噪声的智商要求明显低于当前临床规划的智商要求。这些结果适用于超低剂量 X 射线 CT、粗空间检测质子 CT 和飞行时间 PET 扫描联合重建的衰减图像。

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

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.