Intra- and inter-modality registration of functional and anatomical clinical images

被引:2
|
作者
Eberl, S [1 ]
Braun, M [1 ]
机构
[1] Royal Prince Alfred Hosp, Dept PET & Nucl Med, Camperdown, NSW 2050, Australia
来源
关键词
D O I
10.1117/12.351630
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Image registration techniques spatially register clinical images of patients performed either at different times with the same modality (intra-modality) or with different modality (inter-modality), to facilitate assessment of change and to take full advantage of the frequently complementary information provided by the imaging modalities. Inter-subject registration permits comparison to normal data bases and averaging of data fi-om several subjects to improve statistical significance. Image registration is well established in the brain since the skull limits deformation of the brain between studies and the use of rigid body transformation can usually be justified for intra-subject registrations. A large range of image registration algorithms, ranging from completely manual to fully automatic, have been developed. These can be classified into external methods, which typically use fiducial markers, intrinsic techniques, which rely on the information contained in the patient image data and non-image based methods, which use information external to the data being registered. The technique of choice depends on the specific requirements of the application and it is unlikely that a single "best" technique can meet sometimes conflicting requirements (e.g. accuracy, speed, ease of use etc). While considerable progress has been made in image registration outside the brain, considerable challenges still remain. In this paper, we present the basic principles of image registration and practical issues arising from our experience with routine clinical use of image registration over several years.
引用
收藏
页码:102 / 114
页数:13
相关论文
共 50 条
  • [31] Treatment of Moving Tumors: An Inter-Modality Comparison under Realistic Clinical Conditions
    Court, L.
    Seco, J.
    Lu, X.
    Ebe, K.
    Mayo, C.
    Ionascu, D.
    Winey, B.
    Giakoumakis, N.
    Aristophanous, M.
    Berbeco, R.
    Rottmann, J.
    Bogdanov, M.
    Schofield, D.
    Lingos, T.
    MEDICAL PHYSICS, 2010, 37 (06) : 3435 - +
  • [32] FUSION-BASED MULTIMODAL MEDICAL IMAGE REGISTRATION COMBINING INTER-MODALITY METRIC AND DISENTANGLEMENT
    Ji, Yu
    Zhu, Zhenyu
    Wei, Ying
    2022 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (IEEE ISBI 2022), 2022,
  • [33] Intra and Inter-modality Incongruity Modeling and Adversarial Contrastive Learning for Multimodal Fake News Detection
    Wei, Siqi
    Wu, Bin
    PROCEEDINGS OF THE 4TH ANNUAL ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, ICMR 2024, 2024, : 666 - 674
  • [34] Improved inter-modality image registration using normalized mutual information with coarse-binned histograms
    Nam, Haewon
    Renaut, Rosemary A.
    Chen, Kewei
    Guo, Hongbin
    Farin, Gerald E.
    COMMUNICATIONS IN NUMERICAL METHODS IN ENGINEERING, 2009, 25 (06): : 583 - 595
  • [35] Inter-modality non-rigid breast image registration using finite-element method
    Krol, A
    Coman, IL
    Mandel, JA
    Baum, K
    Luo, M
    Feighn, DH
    Lipson, ED
    Beaumont, J
    2003 IEEE NUCLEAR SCIENCE SYMPOSIUM, CONFERENCE RECORD, VOLS 1-5, 2004, : 1958 - 1961
  • [36] Image to English translation and comprehension: INT2-VQA method based on inter-modality and intra-modality collaborations
    Sheng, Xianli
    PLOS ONE, 2023, 18 (08):
  • [37] Anatomical Connectivity Influences both Intra- and Inter-Brain Synchronizations
    Dumas, Guillaume
    Chavez, Mario
    Nadel, Jacqueline
    Martinerie, Jacques
    PLOS ONE, 2012, 7 (05):
  • [38] Semantic Features Aided Multi-Scale Reconstruction of Inter-Modality Magnetic Resonance Images
    Srinivasan, Preethi
    Kaur, Prabhjot
    Nigam, Aditya
    Bhavsar, Arnav
    2020 IEEE 33RD INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS(CBMS 2020), 2020, : 110 - 113
  • [39] Fast detection of the main anatomical structures in digital retinal images based on intra- and inter-structure relational knowledge
    Molina-Casado, Jose M.
    Carmona, Enrique J.
    Garcia-Feijoo, Julian
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2017, 149 : 55 - 68
  • [40] Improving compound-protein interaction prediction by focusing on intra-modality and inter-modality dynamics with a multimodal tensor fusion strategy
    Wang, Meng
    Wang, Jianmin
    Ji, Jianxin
    Ma, Chenjing
    Wang, Hesong
    He, Jia
    Song, Yongzhen
    Zhang, Xuan
    Cao, Yong
    Dai, Yanyan
    Hua, Menglei
    Qin, Ruihao
    Li, Kang
    Cao, Lei
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2024, 23 : 3714 - 3729