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.

项目摘要/摘要 X射线计算机断层扫描（CT）已越来越多地用于医学诊断，目前已达到更多 在美国，每年超过1亿个CT扫描。越来越多的CT使用引发了人们对 辐射剂量对患者的影响。据估计，每2000 CT扫描将导致一个未来的癌症，即 每年有50,000例未来癌症的CT扫描。 CT脑灌注（CTP）是广泛的 使用的成像技术用于评估中风和脑血管疾病的血液动力学变化。 但是，CTP涉及患者的高辐射剂量，因为CTP扫描在40次的阶段重复 在相同的解剖位置，以捕获对比度的全部通过。几种技术 已应用于CTP扫描中的辐射剂量降低，包括减少管电流和管 电压以及降低降噪技术（例如迭代重建（IR））的使用。但是， 现有CTP扫描的最终辐射剂量仍然明显高于标准头部CT扫描。 由于复杂性和计算伯恩（Burnen），IR技术在CTP中的应用非常有限 用于处理损害临床工作流程的多个CTP图像。在第1阶段STTR项目中，我们 引入了一种基于K空间加权图像对比度（KWIC）的新型低剂量CTP成像方法 重建算法。我们在CTP幻影和临床CTP数据集中进行了彻底的评估， 并证明KWIC算法能够通过通过 75％不影响定量的图像质量和准确性（即1阶段STTR的里程碑）。 但是，原始的KWIC算法需要在预先指定的旋转角度进行快速切换的脉冲X射线 - A 硬件功能尚未由商业CT供应商实现。为了解决这个限制，我们 最近引入了称为K空间加权图像平均值（KWIA）的KWIC算法的变体 保存高空间和临时分辨率以及低剂量CTP数据的图像质量（〜75％剂量 还原）与标准CTP扫描的相当。最重要的是，KWIA不需要 修改现有CT硬件，并且在计算上简单快捷，因此具有低障碍 市场渗透。第二阶段STTR项目的目的是进一步优化和验证KWIA 在保留图像质量和 CTP幻影，临床CTP数据和动物研究的定量精度。我们将进一步发展 基于创新的深度学习（DL）算法，以解决KWIA的潜在运动和其他人工制品，以及 通过与CT供应商合作将开发算法的商业化。