Modeling the transport of photons from source to detector as a means of improving radiation therapy by enhanced utilization of kV and MV x-ray imaging

对光子从源到探测器的传输进行建模，作为通过增强 kV 和 MV X 射线成像的利用来改进放射治疗的一种手段

• 批准号: RGPIN-2015-05623
• 负责人: Mccurdy, Boyd
• 金额: $ 1.75万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679853
• 关键词: Modeling; transport; photons; source; detector

Introduction: This research program seeks to develop new algorithms to remove contaminating x-ray scatter from diagnostic and therapeutic medical images obtained in modern radiation therapy. Current radiation treatment units use kilovoltage (KV) x-rays for anatomical imaging (ie. cone-beam computed tomography, CBCT) just prior to therapy delivery using a megavoltage (MV) x-ray beam. Therapy and diagnostic imaging applications on these units are currently at a rudimentary level.***Hypothesis: A hybrid scatter estimation algorithm for both MV and KV energy x-rays will improve the accuracy of image reconstruction and density conversion (CBCT) and also improve the accuracy of dose reconstruction (MV) providing increased sensitivity for error detection. ***Objectives: 1. Develop a hybrid scatter estimation algorithm combining a deterministic approach with a novel, fast Monte Carlo method. 2. Validate and optimize this algorithm using both MV and KV x-ray beams. 3. Combine interleaved KV and MV imaging allowing diagnostic imaging during therapy delivery.***Methods: Building on our previous experience, a hybrid algorithm will be developed to estimate x-ray scatter from patients for both KV and MV energy x-ray beams. A fast analytical solution will estimate singly-scattered x-rays, while a novel Monte Carlo approach treating each voxel as a scatter source will estimate multiply-scattered x-rays. The method will be validated on both MV therapeutic beams and KV (CBCT) diagnostic imaging beams and quantitatively evaluated in terms of reconstruction accuracy and improved contrast (CBCT), and patient reconstructed dose accuracy (MV) on a variety of geometric and anthropomorphic phantoms. Furthermore, an interleaved acquisition approach will be developed to concurrently obtain KV and MV image data during radiation treatment delivery through the use of XML control software, minimizing the impact of patient motion. Performance will be assessed through quantitative comparison of image quality results to standard sequential image acquisition.***Significance: This research program will provide significantly improved quality CBCT data on radiation treatment units (i.e. improved tumour visibility, accurate density conversions). For MV imaging, more accurate patient dose reconstructions will be provided, improving sensitivity in detecting delivery errors. Interleaved image acquisition will provide a more accurate representation of patient anatomy during treatment. Future research opportunities for real-time tumour tracking and adaptive radiation therapy will be created.***Conclusion: This research program builds on our experience developing scatter estimation algorithms and their applications to improve the accuracy of radiation treatments. Eventually these advances may be used throughout Canada and the world to improve quality of life for all patients treated with radiation.**

简介：该研究项目旨在开发新的算法，以消除现代放射治疗中获得的诊断和治疗医学图像中的污染性 X 射线散射。目前的放射治疗装置在使用兆伏 (MV) X 射线束进行治疗之前，使用千伏 (KV) X 射线进行解剖成像（即锥形束计算机断层扫描，CBCT）。 这些设备的治疗和诊断成像应用目前还处于初级水平。***假设：MV 和 KV 能量 X 射线的混合散射估计算法将提高图像重建和密度转换 (CBCT) 的准确性，并提高剂量重建 (MV) 的准确性，从而提高错误检测的灵敏度。 ***目标： 1. 开发一种混合散射估计算法，将确定性方法与新颖的快速蒙特卡罗方法相结合。 2. 使用 MV 和 KV X 射线束验证并优化该算法。 3. 结合交错的 KV 和 MV 成像，允许在治疗期间进行诊断成像。***方法：根据我们之前的经验，将开发一种混合算法来估计患者对 KV 和 MV 能量 X 射线束的 X 射线散射。 快速分析解决方案将估计单散射 X 射线，而将每个体素视为散射源的新颖蒙特卡罗方法将估计多重散射 X 射线。 该方法将在 MV 治疗光束和 KV (CBCT) 诊断成像光束上进行验证，并在各种几何和拟人体模上的重建精度和改进对比度 (CBCT) 以及患者重建剂量精度 (MV) 方面进行定量评估。此外，还将开发一种交错采集方法，通过使用 XML 控制软件在放射治疗期间同时获取 KV 和 MV 图像数据，从而最大限度地减少患者运动的影响。 将通过图像质量结果与标准连续图像采集的定量比较来评估性能。***意义：该研究计划将显着提高放射治疗设备的 CBCT 数据质量（即提高肿瘤可见度、准确的密度转换）。对于 MV 成像，将提供更准确的患者剂量重建，提高检测输送错误的灵敏度。 交错图像采集将在治疗期间提供更准确的患者解剖结构表示。未来将创造实时肿瘤跟踪和自适应放射治疗的研究机会。***结论：该研究项目建立在我们开发散射估计算法及其应用的经验之上，以提高放射治疗的准确性。最终，这些进步可能会在加拿大和世界各地得到应用，以改善所有接受放射治疗的患者的生活质量。 **