Novel Algorithms for Reducing Radiation Dose of CT Perfusion

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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.