The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Four-dimensional deformable image registration using trajectory modeling
Open accessAbstract
A four-dimensional deformable image registration (4D DIR) algorithm, referred to as 4D local trajectory modeling (4DLTM), is presented and applied to thoracic 4D computed tomography (4DCT) image sets. The theoretical framework on which this algorithm is built exploits the incremental continuity present in 4DCT component images to calculate a dense set of parameterized voxel trajectories through space as functions of time. The spatial accuracy of the 4DLTM algorithm is compared with an alternative registration approach in which component phase to phase (CPP) DIR is utilized to determine the full displacement between maximum inhale and exhale images. A publically available DIR reference database (http://www.dir-lab.com) is utilized for the spatial accuracy assessment. The database consists of ten 4DCT image sets and corresponding manually identified landmark points between the maximum phases. A subset of points are propagated through the expiratory 4DCT component images. Cubic polynomials were found to provide sufficient flexibility and spatial accuracy for describing the point trajectories through the expiratory phases. The resulting average spatial error between the maximum phases was 1.25 mm for the 4DLTM and 1.44 mm for the CPP. The 4DLTM method captures the long-range motion between 4DCT extremes with high spatial accuracy.
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A deep learning framework for unsupervised affine and deformable image registration
de Vos, B. D., Berendsen, F. F., Viergever, M. A., Sokooti, H., Staring, M., & Išgum, I. (2019) · Medical Image Analysis · doi:10.1016/j.media.2018.11.010 · Cited by 818
MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration
Heinrich, M. P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F. V., Brady, S. M., & Schnabel, J. A. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.05.008 · Cited by 707
MRF-Based Deformable Registration and Ventilation Estimation of Lung CT
Heinrich, H. P., Jenkinson, M., Brady, M., & Schnabel, J. A. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2246577 · Cited by 311
A review of deep learning-based three-dimensional medical image registration methods
Xiao, H., Teng, X., Liu, C., Li, T., Ren, G., Yang, R., Shen, D., & Cai, J. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-175 · Cited by 77
Metadata
- Authors
- Castillo, E., Castillo, R., Martinez, J., Shenoy, M., & Guerrero, T.
- Venue
- Physics in Medicine and Biology
- Publisher
- IOP Publishing
- Published
- 2009-12-11
- Volume
- 55
- Issue
- 1
- Pages
- 305-327
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- OpenAlex
- W2075559423
- Cited by
- 261
- References
- 42
- FWCI
- 10.86
- Type
- journal article
- Language
- en
- Open access
- bronze
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