Novel Algorithms for Reducing Radiation Dose of CT Perfusion

减少 CT 灌注辐射剂量的新算法

• 批准号: 10220967
• 负责人: Jeffry R Alger
• 金额: $ 82.16万
• 依托单位: HURA IMAGING, INC
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10220967
• 关键词: Address; Adoption; Affect; Algorithms; American Heart Association; Anatomy; Angiography; Animals; Bolus Infusion; Brain; Brain Neoplasms; Cerebrovascular Disorders; Clinical; Collaborations; Data; Data Set; Decision Making; Development; Diagnosis; Dose; Evaluation; Future; Goals; Guidelines; Head; Heart; Image; Imaging Techniques; Imaging technology; Impairment; Infarction; Location; Low Dose Radiation; Malignant Neoplasms; Medical; Methods; Modification; Monitor; Morphologic artifacts; Motion; Noise; Organ; Patients; Pattern; Penetration; Perfusion; Phase; Physiologic pulse; Public Health; Radiation Dose Unit; Reperfusion Therapy; Roentgen Rays; Rotation; Scanning; Signal Transduction; Small Business Technology Transfer Research; Specific qualifier value; Speed; Stroke; Techniques; Technology; Time; Traumatic Brain Injury; Tube; Variant; Vendor; X-Ray Computed Tomography; acute stroke; base; brain tissue; contrast imaging; deep learning; denoising; hemodynamics; imaging modality; innovation; low dose computed tomography; novel; perfusion imaging; preservation; radiation effect; reconstruction; temporal measurement; voltage

Project Summary/Abstract X-ray computed tomography (CT) has been increasingly used in medical diagnosis, currently reaching more than 100 million CT scans every year in the US. The increasing use of CT has sparked concern over the effects of radiation dose on patients. It is estimated that every 2000 CT scans will cause one future cancer, i.e., 50,000 cases of future cancers from 100 million CT scans every year. CT brain perfusion (CTP) is a widely used imaging technique for the evaluation of hemodynamic changes in stroke and cerebrovascular disorders. However, CTP involves high radiation dose for patients as the CTP scan is repeated on the order of 40 times at the same anatomical location, in order to capture the full passage of the contrast bolus. Several techniques have been applied for radiation dose reduction in CTP scans, including reduction of tube current and tube voltage, as well as the use of noise reduction techniques such as iterative reconstruction (IR). However, the resultant radiation dose of existing CTP scans is still significantly higher than that of a standard head CT scan. The application of IR techniques in CTP is very limited due to the high complexity and computational burden for processing multiple CTP images that impairs clinical workflow. During the Phase 1 STTR project, we introduced a novel low dose CTP imaging method based on the k-space weighted image contrast (KWIC) reconstruction algorithm. We performed thorough evaluation in both a CTP phantom and clinical CTP datasets, and demonstrated that the KWIC algorithm is able to reduce the radiation dose of existing CTP techniques by 75% without affecting the image quality and accuracy of quantification (i.e., Milestone of Phase 1 STTR). However, the original KWIC algorithm requires rapid-switching pulsed X-ray at pre-specified rotation angles – a hardware capability yet to be implemented by commercial CT vendors. In order to address this limitation, we recently introduced a variant of the KWIC algorithm termed k-space weighted image average (KWIA) that preserves high spatial and temporal resolutions as well as image quality of low dose CTP data (~75% dose reduction) to be comparable to those of standard CTP scans. Most importantly, KWIA does not require modification of existing CT hardware and is computationally simple and fast, therefore has a low barrier for market penetration. The purpose of the Phase 2 STTR project is to further optimize and validate the KWIA algorithm for reducing radiation dose of CTP scans by ~75% while preserving the image quality and quantification accuracy in CTP phantom, clinical CTP data and animal studies. We will further develop innovative deep-learning (DL) based algorithms to address potential motion and other artifacts in KWIA, and commercialize the developed algorithms by collaborating with CT vendors.

项目总结/摘要 X射线计算机断层扫描（CT）已越来越多地用于医疗诊断，目前已达到更多 在美国每年有超过一亿次CT扫描CT的使用越来越多，引发了人们对 辐射剂量对患者的影响。据估计，每2000次CT扫描将导致一种未来的癌症，即， 每年1亿次CT扫描中有5万个未来癌症病例。CT脑灌注成像（CTP）是一种广泛应用的 采用影像学技术评价脑卒中和脑血管疾病的血流动力学变化。 然而，CTP涉及患者的高辐射剂量，因为CTP扫描重复约40次 在相同的解剖位置，以便捕获造影剂团的完全通过。几种技术 已应用于CTP扫描中的辐射剂量减少，包括减少管电流和管 电压，以及使用降噪技术，如迭代重建（IR）。但 现有CTP扫描的最终辐射剂量仍然显著高于标准头部CT扫描的辐射剂量。 红外技术在CTP中的应用由于其复杂度高、计算量大而受到很大限制 用于处理损害临床工作流程的多个CTP图像。在第一阶段STTR项目中，我们 介绍了一种基于k空间加权图像对比度（KWIC）的低剂量CTP成像新方法 重建算法我们在CTP体模和临床CTP数据集中进行了全面的评估， 并证明了KWIC算法能够通过以下方式降低现有CTP技术的辐射剂量： 75%而不影响图像质量和定量准确性（即，第1阶段STTR的里程碑）。 然而，原始的KWIC算法需要以预先指定的旋转角度α快速切换脉冲X射线 商业CT供应商尚未实现硬件能力。为了解决这一限制，我们 最近引入了KWIC算法的变体，称为k空间加权图像平均（KWIA）， 保留低剂量CTP数据（~75%剂量）的高空间和时间分辨率以及图像质量 减少）以与标准CTP扫描的那些相当。最重要的是，KWIA不需要 修改现有的CT硬件，并且计算简单快速，因此具有低的障碍， 市场渗透。第二阶段STTR项目的目的是进一步优化和验证KWIA 一种算法，可将CTP扫描的辐射剂量降低约75%，同时保持图像质量， CTP体模、临床CTP数据和动物研究中的定量准确度。我们将进一步发展 基于深度学习（DL）的创新算法，以解决KWIA中的潜在运动和其他伪影，以及 通过与CT供应商合作，将开发的算法商业化。