Luo Jianwen
Tsinghua University
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Chinese Science Bulletin | 2017
Shu YueXia; Zhao Li-li; Jiang Jiehui; Yan Zhuang-zhi; Luo Jianwen; Liu Xin
Recently, X-ray luminescence optical tomography (XLOT) has beennproposed as a promising optical molecular imaging modality. XLOT,nor equivalently, X-ray luminescence computed tomography (XLCT), isna tomography imaging technique based on the X-ray excited nanophosphorsn(NPs). Briefly, when NPs in imaged object are irradiated with X-ray,nNPs will emit visible or near infrared (NIR) luminescence. By mathematicallynmodeling the light transportation and solving an inverse problem,nthe three dimensional (3D) distribution of NPs in imaged object cannbe recovered. Compared with the conventional optical molecular imagingnmodalities, XLOT has ability to eliminate the autofluorescence andnprovide the increased penetration depth in tissues. As a result, thenimaging technique is well suited for biomedical researches. Especially,nXLOT provides the potential for clinical applications due to the increasednpenetration depth. Based on the excitation patterns of X-ray beams, the current XLOTnimaging systems can be classed into three types, i.e., the pencil-beamnsystem, the fan-beam system, and the cone-beam system. Among thesensystems, the pencil-beam XLOT has the highest spatial resolution andnthe lowest time resolution. In contrast, the cone-beam XLOT system,nespecially the single-view cone-beam XLOT system, has the shortestndata acquisition time. But, the imaging quality is relatively lowerncompared to the pencil-beam system. Hence, there is a tradeoff betweennthe spatial resolution and time resolution for XLOT imaging technique. After acquring the measurement data by the above XLOT system, then3D distribution of NPs in imaged object can be recovered by the reconstructionnmethods. For XLOT reconstruction, there are two types of methods,ni.e., the filtered back projection (FBP) method and the reconstructionnmethod based on photon migration model. For the pencil-beam XLOT system,nthe reconstruction is generally performed by FBP method, which isnsimilar to XCT reconstruction method. For the cone-beam XLOT system,nthe NPs in imaged object are generally resolved by using the methodnbased on photon migration model. It is worth noting that the reconstructionnbased on migration mode is a high ill-posed problem due to the highnscattering of light in biological tissues. To alleviate the ill-posedness,nthe compressive sensing technique and a priori informationnmethod (e.g., excitation a priori information) cannbe used. In addition, the imaging probes play an important role in XLOTnimaging. Currently, the rare-earth nanophosphors have be widely usednin XLOT due to their optical properties. Of course, the rare-earthnnanophosphors are not sole NPs for XLOT imaging. Other NPs, e.g.,nquantum dots and gold nanoclusters, have also been used as the imagingnprobes. Further, with the advances in NPs, more applications wouldnbe expected in bio-medical researches. To sum up, for XLOT excellent performances, during the last fewnyears, the continuous research efforts have been made to develop newnimaging systems, build robust reconstruction methods, design efficientnNPs, and expand the applications of XLOT. This paper reviews the developmentsnof XLOT, including the basic imaging principles, system compositions,nand advantages and disadvantages of different XLOT imaging systems.nSubsequently, the corresponding reconstruction methods and the imagingnprobes are classified and summarized. Finally, we discuss the applicationsnof XLOT in the preclinical diagnosis, and the future research directionnof XLOT.
Archive | 2015
Luo Jianwen; Liu Jing; He Qiong
Ultrasonics | 2017
He Qiong; Tong Ling; Huang Lingyun; Liu Jing; Chen Yinran; Luo Jianwen
Archive | 2017
Cao Yanping; Li Guoyang; Luo Jianwen; He Qiong
Archive | 2017
Luo Jianwen; He Qiong; Cao Yanping; Li Guoyang
Archive | 2017
Luo Jianwen; He Qiong; Cao Yanping; Li Guoyang
Archive | 2017
Luo Jianwen; Liu Jing; He Qiong
Zhongguo Yixue Wulixue Zazhi | 2016
Huang Chengwu; Luo Jianwen
Zhongguo Yixue Wulixue Zazhi | 2016
Huang Chengwu; Luo Jianwen
Zhongguo Shengwu Yixue Gongcheng Xuebao | 2016
Chen Yinran; He Qiong; Luo Jianwen