ISSN 1004-4140
CN 11-3017/P
FENG Mei, AN Mei-jian. How to Determine Spatial Resolution for an Inverse Problem[J]. CT Theory and Applications, 2013, 22(4): 587-604.
Citation: FENG Mei, AN Mei-jian. How to Determine Spatial Resolution for an Inverse Problem[J]. CT Theory and Applications, 2013, 22(4): 587-604.

How to Determine Spatial Resolution for an Inverse Problem

More Information
  • Received Date: March 06, 2013
  • Available Online: December 28, 2022
  • The information of solution's spatial resolution is important for model appraisal in an inversion, however, the computation to determine a spatial resolution is nontrivial and often more difficult than to solve an inverse problem. Visual inspection of the restoration of a synthetic structure widely applied in tomographic studies can give indicative information on spatial resolution distribution, however, resolution matrix estimation can give quantitative information of spatial resolution length for a general inverse problem. Resolution matrices obtained by matrix operation may be divided into three classes: direct resolution matrix, regularized resolution matrix and hybrid resolution matrix. Each matrix can give part of the information on the inversion, and then the simultaneous implementation of all three resolution matrices in a single study can potentially provide a complete understanding on the resolution length information. An (2012) proposed a new class of resolution matrices generated by a simple one-parameter nonlinear inversion performed based on limited pairs of random synthetic models and their inverse solutions. The estimates were directly retrieved from synthetic models and their inverse solutions, and then it can include the information on the whole inversion procedure; The independence on the degree of inverse skill used and the absence of a requirement for matrix operations indicated that this approach is particularly suitable for very large linear/linearized inverse problems. Inversion examples even for 3D inversion problem demonstrated that reasonable resolution lengths can be determined from statistic spatial resolution calculation.
  • Related Articles

    [1]HE Weihong, FANG Tingsong, FU Xi, LAO Meiling, XIAO Xiuyun. Risk Factors of Vulnerable Coronary Plaque Formation in Type 2 Diabetes[J]. CT Theory and Applications, 2023, 32(4): 523-529. DOI: 10.15953/j.ctta.2023.036
    [2]LI Ling, ZHANG Mingxia, SUN Ying, DUAN Shuhong, GUO Jia, DU Changyue, LIU Mengke, ZHANG Yimeng, SUN Lei, HUO Meng, WANG Rengui. Imaging Study of COVID-19 Patients with Diabetes Mellitus by Computed Tomograpgh Quantitative Indicators Based on Deep Learning[J]. CT Theory and Applications, 2023, 32(3): 373-379. DOI: 10.15953/j.ctta.2023.020
    [3]ZHANG Fengling, ZHAO Li, LIU Jiabao. CT Features of Pulmonary Infection in Elderly Patients with Type 2 Diabetes Mellitus[J]. CT Theory and Applications, 2021, 30(5): 583-590. DOI: 10.15953/j.1004-4140.2021.30.05.06
    [4]LI Sichun, LI Zhihui, ZHANG Yaping. Predictive Value of CT Angiography Combined with Blood Lipid Level in Diabetes Mellitus with Coronary Artery Disease[J]. CT Theory and Applications, 2020, 29(6): 711-717. DOI: 10.15953/j.1004-4140.2020.29.06.09
    [5]WEN Qiping, WANG Yong, LI Youwei, YANG Jian, DONG Qingtao. Application of Multi-stage Double-flow Mass Injection Technique in Evaluating Right Ventricular Function in Coronary CTA[J]. CT Theory and Applications, 2020, 29(2): 229-240. DOI: 10.15953/j.1004-4140.2020.29.02.14
    [6]XIE Wan-meng, CHEN Jun, WANG Jun, XIE Xin-jia. A Primary Study of Magnetic Resonance Imaging-diffusion Weighted Imaging of Pancreas in Type 2 Diabetic Mellitus Patients[J]. CT Theory and Applications, 2016, 25(5): 563-569. DOI: 10.15953/j.1004-4140.2016.25.05.08
    [7]GAO Xiang, YUAN Yan-wen, JIN Er-hu, ZHANG Jie, HONG Xu, YANG Zheng-han, MA Da-qing. Study of Liver Fat Content in Patients with Newlydiagnosed Type 2 Diabetes Mellitus on Chemical Shift MRI[J]. CT Theory and Applications, 2016, 25(4): 385-392. DOI: 10.15953/j.1004-4140.2016.25.04.01
    [8]CHENG Ying, CAI Xin-qi, SUN Jun-qi, PANG Jun-gang, FANG Xian-lai, LU Yu-ling, ZHANG Hong-ping. Assessment of the Influence of Right Coronary Artery Severe Stenosis on Left Ventricular Function with 128 Slice Computed Tomography Coronary Angiography[J]. CT Theory and Applications, 2016, 25(1): 65-70. DOI: 10.15953/j.1004-4140.2016.25.01.08
    [9]YANG Chun-yu, SHEN Bi-xian, ZHAO Yue, HUANG Yin-ping, CHEN Sheng-ji, HUANG An-rong. Study on the Value of Dual Source CT Assessment of Correlation between Diabetes and Coronary Plaque[J]. CT Theory and Applications, 2014, 23(6): 913-921.
    [10]CHEN Yi-jia, CHEN Lun-gang, ZHA Yun-fei, PEI Zhi-jun, WU Lei. Feasibility of 64-MDCT in Evaluating Left Ventricular Diastolic Function[J]. CT Theory and Applications, 2014, 23(2): 299-308.

Catalog

    Article views (586) PDF downloads (17) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return