Novel Reconstruction Paradigm for Multiphasic CT Imaging of Kidney Cancer

多期CT扫描采集以短时间间隔分隔的多个CT图像，以便捕获 不同的对比度增强模式。这些不同的模式为以下方面提供了补充信息 诊断和疾病分期。多期CT扫描在腹部和骨盆检查中很常见。在……里面 特别是，不确定的肾脏病变或血尿需要四次CT扫描才能区分所有肾脏病变 子类型。多期CT的好处是毋庸置疑的。然而，一次四期CT扫描平均有四次 是单次CT扫描的辐射剂量的倍数。此外，这些检查通常定期进行，以 监测疾病进展，这在与辐射剂量相关的风险方面引起了严重关切 传给病人的。多期扫描中的CT图像在对比度方面彼此不同 增强模式，以及由于患者呼吸而导致的器官位置的轻微变化。不过， 这些CT图像之间存在高度的空间和强度相关性。当前临床 重建方法独立地重建多期CT检查的每个阶段，丢弃 相关性。所有CT相的相关图像内容目前是一个未开发的信息来源， 将显著提高图像质量和剂量利用率。在此应用程序中，我们的目标是开发 一种新的图像重建范例，在多阶段扫描中联合重建所有图像。在这 在这种模式下，每个单独阶段的图像将受益于在多阶段采集中获得的所有数据。 然后，改进的图像质量可以用来减少对患者的辐射剂量，或者更有信心地 也许更频繁地关注皮损对治疗的反应，既有标准的护理，也有 调查性的。提出的算法建立在最新的基于迭代重建模型的基础上 方法；它利用单个图像内的自相似性以及 多相扫描。客观的、基于任务的图像质量评估(IQ)是这项工作成功的关键 研究提案。我们将在算法开发期间使用这种类型的评估来监控进度 和指导，并仔细评估建议和竞争对手之间的图像质量收益 方法：经过18个月的开发。智商评估包括两个部分：噪音 减少，这与剂量减少和解剖准确性直接相关。我们将设计病变检测 和解剖错误检测任务，并依赖于与接收器操作相关联的特定于任务的度量 独具特色的方法，既使用模型，也使用人类观察者。该提案的预期结果是 一种适用于具有改善图像的初始临床相关证据的真实患者数据的软件原型 质量，可以用来换取更低的辐射剂量。有针对性地减少剂量是一个接近于 阶段数。这项提议将使多阶段数据处理和分析模式发生转变 肾癌患者的CT扫描。

A multiphase CT scan acquires multiple CT images separated by short time intervals in order to capture different contrast enhancement patterns. These different patterns provide complementary information for diagnosis and disease staging. Multiphase CT scans are common in abdominal and pelvis exams. In particular, indeterminate renal lesions or hematuria require four CT acquisitions to distinguish all renal lesion subtypes. The benefit of multiphase CT is unequivocal. However, a four-phase CT scan has on average four times the radiation dose of a single CT scan. Moreover, these exams are often performed periodically to monitor disease progression, which raises serious concerns in terms of risks associated with radiation dose imparted to the patient. The CT images in a multiphase scan differ from each other in terms of contrast enhancement patterns, and also slightly in terms of organ position due to patient breathing. Nevertheless, there is a high level of spatial and intensity correlation between these CT images. Current clinical reconstruction methods reconstruct each phase of a multiphase CT exam independently, discarding the correlation. The correlated image content of all CT phases is currently an untapped source of information that will enable dramatic improvements in image quality and dose utilization. In this application, we aim to develop a novel image reconstruction paradigm that jointly reconstructs all images in a multiphase scan. In this paradigm, the image at each individual phase will benefit from all acquired data in a multiphase acquisition. The improved image quality can then be used to reduce radiation dose to the patient, or to more confidently and perhaps more frequently follow the response of lesions to therapies, both standard of care and investigational. The proposed algorithm builds on state-of-the-art model based iterative reconstruction methods; it utilizes self-similarity both within a single image and among the multiple correlated images in a multiphase scan. Objective, task-based assessment of image quality (IQ) is critical to the success of this research proposal. We will use this type of assessment during algorithm development for progress monitoring and guidance, and also to carefully evaluate image quality gains between the proposed and competing methods as achieved after 18 months of development. The IQ evaluation includes two components: noise reduction which is directly linked to dose reduction, and anatomical accuracy. We will design lesion detection and anatomical error detection tasks, and rely on task-specific metrics associated with receiver operating characteristic methodology, using both model and human observers. The expected outcome of the proposal is a software prototype that is applicable to real patient data with initial clinically-relevant proof of improved image quality, which could be traded for lower radiation dose. The targeted reduction in dose is a factor close to the number of phases. The proposal will enable a paradigm shift in data processing and analysis for multiphase CT scans of kidney cancer patients.