With the increased use of x-ray CT, the development of a market for CT screening exams, and the imaging of younger patients, there is a growing concern about the public health risk caused by the radiation dose delivered by x-ray CT. The reduction of this dose has therefore taken on increased importance, as evidenced by the recent NIH Summit on Managing Dose in CT with the mandate of achieving the routine sub-millisievert CT exam. Iterative reconstruction algorithms are a key part in accomplishing this goal, producing high-quality images from low-dose data by incorporating detailed models of the physics and statistics of the data acquisition process. Iterative algorithms based on these system models are beginning to enter the marketplace, but currently these algorithms suffer from three main limitations: (i) they are a very expensive add-on;(ii) they leave out detailed modeling of the physics, thus limiting the available dose reduction;and (iii) they are 10 - 100 times slower than standard reconstruction, preventing their use as a default for routine scans. The key to fully enabling iterative algorithms is acceleration of the backprojection and reprojection computational bottleneck, which is accomplished through the use of InstaRecon's fast hierarchical backprojection/reprojection operators. Accelerating the iterative algorithm enables it to run on a less expensive platform, delivering fast reconstruction rates, and opens the door to incorporation of other system modeling, allowing for further image quality improvement and dose reduction. Thus, low-dose imaging and iterative reconstruction can move from a high-end option to the default scanning mode for a wide range of CT scanner hardware. The overall goal of this SBIR project is to accelerate iterative reconstruction rates even further and incorporate additional system models to improve dose and artifact reduction capabilities. The system acceleration will be achieved through algorithmic modifications to the hierarchical operators and the iterative reconstruction loop itself. Additional system modeling wil be introduced at a reduced computational cost through incorporation into the hierarchical operators themselves, providing advanced, accelerated system models. The resulting system will be faster than existing iterative reconstruction platforms, run on less expensive hardware, with additional reduction in dose and artifact levels. Benefits of the new technology will include superior low-dose performance in dose-critical applications such as pediatric, screening for lung cancer or heart disease, and interventional imaging, and significant improvement in diagnostic quality of CT scans of large patients, or of patients with prosthetic implants or cardiac pacemakers. Moreover, this project will help make iterative algorithm-based low-dose imaging a common scanning modality, reducing the burden of CT x-ray exposure for the patient population at large.
This project promises dramatic acceleration of advanced image formation algorithms in CT, with improved dose reducing capabilities. The increased reconstruction rates make it possible for low-dose imaging to be brought into routine clinical use. The resulting product will improve the detection of lung cancer and heart disease, enable 3D CT image-guided surgery and accurate radiotherapy for cancer, improve the imaging of large patients and patients with prosthetic implants and cardiac pacemakers, and reduce healthcare costs.
