Computed tomography (CT) has become a mainstay in diagnostic imaging. The high clinical impact of CT has led to rapidly increased utilization and, at the same time, public concerns about the potential risk to patients. Although many technologies have been developed to reduce radiation dose from CT, significant additional improvements are needed to minimize radiation dose without compromising diagnostic performance. In this U01 project, the interdisciplinary team will develop dose-saving technologies for three key CT imaging chain components and demonstrate their clinical benefits by systematic integration and evaluation. Specifically, the three components comprise (1) a fully dynamic x-ray source that adaptively modulates the radiation incident on a patient; (2) an energy-discriminating photon-counting x-ray detector with small detector pitch, high count-rate, and five energy windows; and (3) an advanced low-dose spectral reconstruction methods in a compressive sensing and statistical reconstruction framework. The proposed source, detector and reconstruction technologies are highly synergistic. These technologies will be integrated into a CT test-bed system to provide high dose efficiency permitting sub-mSv CT scans for common diagnostic procedures, fine spatial resolution nearly doubling what is provided by the state-of-the-art commercial CT scanners, and equally important, spectral imaging capabilities to facilitate or enable material characterization and other applications. The performance of the proposed CT test-bed will be thoroughly investigated in numerical simulation, and validated with physical and ex vivo experiments. The characterized performance will set the stage for the development of future clinical CT scanners. Because the novel technologies developed in this project are compatible with 3rd generation fan-beam/multi-slice CT system geometries, it would be relatively easy for manufacturers to translate them into products for broad clinical use. If the aims of the U01 project are realized, the benefits to the radiological community will be dramatic: general-purpose CT imaging at radiation dose less than 1 mSv, the largest improvement in CT spatial resolution in over 30 years, and powerful spectral CT imaging capabilities with numerous opportunities.

Public Health Relevance

Radiation dose from CT is a growing public health concern. We will develop novel x-ray source, detector, and reconstruction technologies that not only reduce the radiation dose but also improve image quality. These technologies will be integrated into a test-bed system and fully characterized to set the stage for future clinical translation.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01EB017140-02
Application #
8928614
Study Section
Special Emphasis Panel (ZEB1-OSR-D (M1))
Program Officer
Sastre, Antonio
Project Start
2014-09-17
Project End
2018-05-31
Budget Start
2015-06-01
Budget End
2016-05-31
Support Year
2
Fiscal Year
2015
Total Cost
$1,343,711
Indirect Cost
$104,447
Name
Stanford University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Persson, Mats; Rajbhandary, Paurakh L; Pelc, Norbert J (2018) A framework for performance characterization of energy-resolving photon-counting detectors. Med Phys 45:4897-4915
Shan, Hongming; Zhang, Yi; Yang, Qingsong et al. (2018) 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network. IEEE Trans Med Imaging 37:1522-1534
Zhang, Yanbo; Yu, Hengyong (2018) Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography. IEEE Trans Med Imaging 37:1370-1381
Yuan, Yuan; Zhang, Yanbo; Yu, Hengyong (2018) Adaptive Nonlocal Means Method for Denoising Basis Material Images From Dual-Energy Computed Tomography. J Comput Assist Tomogr 42:972-981
Chen, Hu; Zhang, Yi; Chen, Yunjin et al. (2018) LEARN: Learned Experts' Assessment-Based Reconstruction Network for Sparse-Data CT. IEEE Trans Med Imaging 37:1333-1347
Yang, Qingsong; Yan, Pingkun; Zhang, Yanbo et al. (2018) Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss. IEEE Trans Med Imaging 37:1348-1357
Kong, Huihua; Liu, Rui; Pan, Jinxiao et al. (2018) Evaluation of an Analytic Reconstruction Method as a Platform for Spectral Cone-beam CT. IEEE Access 6:21314-21323
Salehjahromi, Morteza; Zhang, Yanbo; Yu, Hengyong (2018) Iterative spectral CT reconstruction based on low rank and average-image-incorporated BM3D. Phys Med Biol 63:155021
Yuan, Yuan; Zhang, Yanbo; Yu, Hengyong (2018) Optimization of Energy Combination for Gold-based Contrast Agents below K-edges in Dual-energy Micro-CT. IEEE Trans Radiat Plasma Med Sci 2:187-193
Wang, Qian; Zhu, Yining; Yu, Hengyong (2017) Locally linear constraint based optimization model for material decomposition. Phys Med Biol 62:8314-8340

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