Computational Tools for Next Generation Volumetric Cone Beam Computed Tomography

描述（由申请人提供）：锥形束CT（CBCT）成像正在成为图像引导介入和许多其他临床应用中不可或缺的工具。然而，目前CBCT成像中的数据处理方法在多个方面存在不足，这往往导致严重的伪影，并导致患者的高成像剂量。图像质量差给临床决策带来了很大的不确定性，严重阻碍了该技术的最大化利用。本项目旨在将CBCT发展成为一种临床上准确可靠的肿瘤靶区勾画、介入引导和放射治疗计划的技术。为了响应NIH PAR-09-218，我们组建了一个由放射肿瘤学，医学物理学，图像和信号处理，优化和应用数学领域的领导者组成的研究团队，并建立了统一这些研究人员的共同兴趣和专业知识的研究主题和项目。我们利用在CBCT成像和科学计算方面的丰富经验，开发下一代无伪影和超低剂量CBCT。将建立一些创新的策略来处理CBCT成像中的稀疏、噪声和缺失数据。具体目标是：（1）通过利用患者特定的先验数据和压缩感知实现超低剂量CBCT;（2）开发用于CBCT重建中的金属伪影去除的分而治之方法;（3）通过有效利用投影的相位间相关性获得无运动伪影的图像。该项目的成功完成将提供高质量的CBCT图像，成像剂量降低几个数量级。对于图像引导放射治疗，改进的图像质量将使准确的基于CBCT的剂量计算和重新规划成为可能，这将为下一代自适应治疗奠定基础，以最佳地补偿患者设置误差和分次间解剖结构变化。随着成像剂量的降低，所提出的技术将显著降低辐射诱导的继发性癌症的风险，并有助于在常规临床实践中安全有效地使用体积X射线成像技术。

DESCRIPTION (provided by applicant): Cone beam CT (CBCT) imaging is becoming an indispensable tool in image guided interventions and many other clinical applications. The data processing method in current CBCT imaging is, however, deficient in multiple aspects, which often leads to severe artifacts and results in high imaging dose to the patients. The poor image quality causes much uncertainty in clinical decision-making and seriously impedes the maximal utilization of the technology. This project is directed at developing CBCT into a clinically accurate and reliable technique for delineation of tumor target, interventional guidance and radiation therapy treatment planning. In response to NIH PAR-09-218, we have assembled a team of investigators comprised of leaders in the fields of radiation oncology, medical physics, image and signal processing, optimization and applied mathematics and established research themes and projects that unify the common interests and expertise of these investigators. We draw on our extensive experience in CBCT imaging and scientific computing to develop the next generation of artifact-free and ultra-low dose CBCT. A number of innovative strategies to dealing with sparse, noisy, and missing data in CBCT imaging will be established. Specific aims are: (1) To achieve ultra-low dose CBCT by utilization of patient-specific prior data and compressed sensing; (2) to develop a divide-and-conquer approach for metal artifact removal in CBCT reconstruction; and (3) to obtain motion artifact-free images by effective use of inter-phase correlation of the projections. Successful completion of this project will provide high quality CBCT images with orders of magnitude less imaging dose. For image guided radiation therapy, the improved image quality will make accurate CBCT-based dose calculation and replanning possible, which will lay the foundation for next generation of adaptive therapy to optimally compensate for the patient setup error and inter- fractional anatomy change. With the reduction in imaging dose, the proposed technique will significantly reduce the risk of radiation-induced secondary cancers and contribute to the safe and efficient use of volumetric X-ray imaging techniques in routine clinical practice.