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X射线散射。目前的放射治疗单位使用千伏（KV）X射线解剖成像（即。锥形束计算机断层扫描，CBCT）。 这些设备上的治疗和诊断成像应用目前处于初级水平。假设：用于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，并且还提高剂量重建（MV）的准确性，从而提高错误检测的灵敏度。开发一种混合散射估计算法，将确定性方法与新颖的快速Monte Carlo方法相结合。 2.使用MV和KV X射线束对该算法进行验证和优化。 3.联合收割机交错KV和MV成像，允许在治疗过程中进行诊断成像。*研究方法：基于我们之前的经验，将开发一种混合算法来估计KV和MV能量X射线束的患者X射线散射。 快速分析解决方案将估计单散射的X射线，而将每个体素视为散射源的新型Monte Carlo方法将估计多散射的X射线。 该方法将在MV治疗射束和KV（CBCT）诊断成像射束上进行验证，并在各种几何和拟人体模上的重建准确度和增强对比度（CBCT）以及患者重建剂量准确度（MV）方面进行定量评价。此外，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输送期间同时获得KV和MV图像数据，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顺序图像采集的定量比较来评估性能。***重要性：该研究计划将提供放射治疗单位的CBCT数据质量显著提高（即提高肿瘤可见性，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患者解剖结构的更准确表示。将为实时肿瘤跟踪和自适应放射治疗创造未来的研究机会。结论：该研究计划建立在我们开发散射估计算法及其应用的经验基础上，以提高放射治疗的准确性。最终，这些进展可能会在加拿大和世界各地使用，以改善所有接受放射治疗的患者的生活质量。