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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：
10006737
负责人：
Jeffry R Alger
金额：
$ 82.07万
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 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10006737
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.

项目总结/文摘