Investigation of Sparse Data Mouse Imaging Using Micro-CT with a Carbon-Nanotube-Based X-ray Source

作     者：SIDKY Emil Y ZHOU Otto 

作者机构：Department of RadiologyUniversity of Chicago5841 S.Maryland AvenueChicagoIL 60637USA 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2010年第15卷第1期

页      面：74-78页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：Supported in part by National Institutes of Health (Nos.EB000225,CA120540, EB004204,CA119343,S10 RR021039,and P30CA14599) supported in part by the DoD Predoctoral Training Grant (No.BC083239) supported in part by the NIH Career Development Award SPORE (CA125183-03) 

主　　题：computed tomography image reconstruction compressed sensing TV minimization 

摘      要：There has been a renewed interest in algorithm development for image reconstruction from highly incomplete data in computed tomography （CT）. Such algorithms may lead to reduced imaging dose and time, and to the design of innovative configurations tailored to specific imaging tasks. In recent years, a carbon-nanotube （CNT）-based field-emission x-ray source has been developed, which offers easy electronic control of radiation and thus can be an ideal candidate for gated imaging. We have recently proposed algorithms for image reconstruction from fan- and cone-beam data collected at highly sparse angular views through minimization of the total-variation （TV） of the image subject to the condition that the estimated data are consistent with the measured data, In this work, we investigate and demonstrate the application of the TV-minimization algorithm to reconstructing images from mouse data acquired with a CNT-based CT scanner at a number of views much lower than what is used in conventional CT imaging. The results demon- strate that the TV-minimization algorithm can yield images with quality comparable to those obtained from a large number of views by use of the conventional algorithms. The significance of the work may lie in that the substantial reduction of projection views promised by the TV-minimization algorithm can be exploited for reducing imaging dose and time or for improving temporal resolution in tasks such as dynamic imaging.