Probing Dose Limits in Cardiac SPECT with Reconstruction and Personalized Imaging

描述(申请人提供)：患者在医学成像过程中的辐射暴露已成为一个主要问题，其中计算机断层扫描(CT)和心脏单光子发射计算机断层扫描(SPECT)心肌灌注成像(MPI)是最大的贡献者。在这个项目中，我们的目标将是基于廉价的软件技术，大幅降低心脏SPECT MPI的辐射剂量，这些技术可以很容易地转化为临床实践。我们的初步结果表明，辐射剂量至少有可能减少八分之一，这可能导致仅在美国每年就估计减少6500例癌症死亡。通俗地说，心脏SPECT MPI通常用于评估心脏病，使医生能够评估到达心壁的血流、心脏的运动，以及心壁的组织是否活着。这种形式的成像包括给患者注射放射性药物(示踪剂)，使患者暴露在辐射中；因此，我希望将成像过程中使用的示踪剂剂量降至最低。图像质量取决于使用的示踪剂的数量，而这些水平是在最近的技术改进之前选择的，这些技术改进可以以较低的剂量产生高质量的图像；因此，有明显的证据表明剂量可以降低，而且实际上目前有相当大的兴趣这样做。我们正在提出一个转化研究项目，以彻底和定量地研究在不牺牲图像质量的情况下减少给药剂量的程度。这项工作尤其将建立在我们开发的新的四维(4D)图像重建技术和呼吸运动补偿方法的基础上。此外，我们还提出了一种新的剂量分配方法，我们称之为“个性化成像”，在这种方法中，计算机算法将被训练来预测，对于给定的患者，获得当前图像质量水平所需的最小剂量，因此，所用的剂量不超过所需的剂量。在4D重建中，计算机算法跟踪心脏的跳动，并使用这些信息通过以保留图像细节的方式平滑图像噪声来改善图像质量。在呼吸运动补偿中，患者的呼吸和身体运动由外部传感器跟踪，这些信息被用来减少图像中运动模糊的出现，从而提高诊断的准确性。建议的“个性化成像”方法建立在我们团队在机器学习方面的丰富经验基础上。我们预计，这些不同策略的结合将大大减少辐射剂量，而且由于改进是基于软件的，因此这些增强的成本非常低。该项目将产生用于临床的图像重建算法和参数的建议，以及相应的示踪剂剂量建议。“个性化成像”方法将作为一个用户友好的计算机程序来实现，该程序可以为每个给定的患者定制剂量。

DESCRIPTION (provided by applicant): Radiation exposure of patients during medical imaging has become a major concern, with computed tomography (CT) and cardiac single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) being the biggest contributors. In this project our aim will be to dramatically reduce radiation dose in cardiac SPECT MPI based on inexpensive software techniques that can be translated readily to clinical practice. Our initial results suggest that at least an eightfold reduction in radiation doe might be possible, which could lead to an estimated reduction of 6500 cancer deaths per year in the U.S. alone. In lay terms, cardiac SPECT MPI is used routinely to evaluate heart disease, allowing the physician to assess blood flow reaching the heart wall, the motion of the heart, and whether the heart wall's tissue is viable. This form of imaging involves administration of a radioactive pharmaceutical (tracer) to the patient, which exposes the patient to radiation; thus, i would be desirable to minimize tracer dose used during imaging. Image quality is determined by the amount of tracer used, and these levels were chosen prior to recent technological improvements that can produce high-quality images at lower dose; therefore, there is clear evidence that doses can be lowered, and indeed there is considerable current interest in doing so. We are proposing a translational research project to thoroughly and quantitatively study the extent to which administered dose might be reduced without sacrificing image quality. This work will build, in particular, on new four-dimensional (4D) image reconstruction techniques and respiratory motion compensation approaches we have developed. Furthermore, we propose a new dosing approach we call "personalized imaging", in which a computer algorithm will be trained to predict, for a given patient, the minimum dose required to obtain the current level of image quality, so that the administered dose is no more than necessary. In 4D reconstruction, a computer algorithm tracks the heart's beating motion, and uses this information to improve image quality by smoothing image noise in a way that preserves image details. In respiratory motion compensation, the patient's breathing and body motions are tracked by external sensors, and this information is used to reduce the appearance of motion blur in the images, and thereby improve diagnostic accuracy. The proposed "personalized imaging" approach builds on our group's extensive experience in machine learning. We expect that the combination of these various strategies will greatly reduce radiation dose, and because the improvements are based on software, the cost of these enhancements is very low. The project will result in recommendations for image reconstruction algorithms and parameters to be used in the clinic, along with corresponding tracer dose recommendations. The "personalized imaging" method will be implemented as a user-friendly computer program that customizes the dose for each given patient.