Novel Algorithms for Reducing Radiation Dose of CT Perfusion

减少 CT 灌注辐射剂量的新算法

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要 X射线计算机断层扫描（CT）在医学诊断中的应用越来越广泛，目前已达到更多美国每年进行超过 1 亿次 CT 扫描，CT 的使用不断增加，引发了人们对 CT 扫描的担忧。据估计，每 2000 次 CT 扫描就会导致一种未来癌症，即每年通过 1 亿次 CT 扫描发现 50,000 例未来癌症，CT 脑灌注 (CTP) 是一种广泛使用的技术。使用成像技术评估中风和脑血管疾病的血流动力学变化。然而，CTP 对患者来说涉及高辐射剂量，因为 CTP 扫描要重复 40 次左右在同一解剖位置，为了捕捉对比团的完整通道，需要采取多种技术。已应用于 CTP 扫描中减少辐射剂量，包括减少管电流和管电压，以及使用迭代重建 (IR) 等降噪技术。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。由于高复杂性和计算负担，IR技术在CTP中的应用非常有限为了处理损害临床工作流程的多个 CTP 图像，我们在第一阶段 STTR 项目中进行了处理。介绍了一种基于k空间加权图像对比度（KWIC）的新型低剂量CTP成像方法我们对 CTP 模型和临床 CTP 数据集进行了彻底的评估，并证明 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量化精度（即第一阶段 STTR 的里程碑）。然而，最初的 KWIC 算法需要以预先指定的旋转角度快速切换脉冲 X 射线 – 商业 CT 供应商尚未实现硬件功能，为了解决这一限制，我们。最近引入了 KWIC 算法的一种变体，称为 k 空间加权图像平均 (KWIA)，保留低剂量 CTP 数据的高空间和时间分辨率以及图像质量（~75% 剂量减少）与标准 CTP 扫描相当。最重要的是，KWIA 不需要。对现有 CT 硬件进行修改，计算简单、速度快，因此门槛较低第二阶段 STTR 项目的目的是进一步优化和验证 KWIA。算法可将 CTP 扫描的辐射剂量减少约 75%，同时保持图像质量和我们将进一步开发 CTP 模型、临床 CTP 数据和动物研究的量化准确性。基于创新深度学习 (DL) 的算法可解决 KWIA 中潜在的运动和其他伪影问题，以及通过与 CT 供应商合作，将开发的算法商业化。