Advances and challenges in deformable image registration: From image fusion to complex motion modelling
Open accessAbstract
Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or disease patterns. While the original focus of image registration has predominantly been on correcting for rigid-body motion of brain image volumes acquired at different scanning sessions, often with different modalities, the advent of dedicated longitudinal and cross-sectional brain studies soon necessitated the development of more sophisticated methods that are able to detect and measure local structural or functional changes, or group differences. Moving outside of the brain, cine imaging and dynamic imaging required the development of deformable image registration to directly measure or compensate for local tissue motion. Since then, deformable image registration has become a general enabling technology. In this work we will present our own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field.
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Traditional registration algorithms' computational performance is hampered by high dimension and non-convex properties, and their capability to capture complex deformations is limited.
“Their computational performance, however, is hampered by the high dimension and non-convex properties, and their capability to capture complex deformations is limited (18).”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [18]
Section 2.2 says traditional B-spline registration has difficulty with motion discontinuities and describes newer methods that avoid local minima and reduce computational complexity. The paper also says continuous optimization can be prone to local minima. These passages support limitations in modelling complex motion and aspects of computational difficulty, but they do not attribute poor computational performance specifically to high dimensionality or non-convexity. (LLM, conf=0.85)
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Cites
Deformable medical image registration: a survey
Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2265603 · Cited by 1,587 · see citing works
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Research works printed but not linked: 5
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- Papież, B.W., Franklin, J., Heinrich, M.P., Gleeson, F.V., Schnabel, J.A., 2015. Liver motion estimation via locally adaptive over-segmentation regulariza- 310 tion, in: Proc. Medical Image Computing and Computer-Assisted Interven- tion, Springer LNCS 9351. pp. 427-434.
- Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, 320 D.J., 1999. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging 18, 712-721.
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A review of deep learning-based deformable medical image registration
Zou, J., Gao, B., Song, Y., & Qin, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1047215 · Cited by 91 · see citing works
“Their computational performance, however, is hampered by the high dimension and non-convex properties, and their capability to capture complex deformations is limited (18).”
Background
- Authors
- Schnabel, J. A., Heinrich, M. P., Papież, B. W., & Brady, S. J. M.
- Affiliations
- King's College London (GB)
- University of Oxford (GB)
- Corresponding author
- Schnabel, Julia A.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 33
- Pages
- 145-148
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2473954490
- Cited by
- 60
- References
- 1
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 11 · 2021: 4 · 2020: 7 · 2019: 7 · 2018: 12 · 2017: 7
- Type
- journal article
- Language
- en
- Open access
- green
- kclpure.kcl.ac.uk
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by-nc-nd)
- Funders
- Cancer Research UK
- Engineering and Physical Sciences Research Council (EP/P001009/1, EP/H050892/1, EP/N026993/1, EP/M000133/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- deformable image registration, multimodal image fusion, tissue motion, longitudinal neuroimaging, tumor imaging, cine imaging, dynamic imaging
- MeSH terms
- Algorithms, Brain, Cross-Sectional Studies, Humans, Image Processing, Computer-Assisted, Motion, Neoplasms
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