The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Implementation and evaluation of various demons deformable image registration algorithms on a GPU
Open accessAbstract
Online adaptive radiation therapy (ART) promises the ability to deliver an optimal treatment in response to daily patient anatomic variation. A major technical barrier for the clinical implementation of online ART is the requirement of rapid image segmentation. Deformable image registration (DIR) has been used as an automated segmentation method to transfer tumor/organ contours from the planning image to daily images. However, the current computational time of DIR is insufficient for online ART. In this work, this issue is addressed by using computer graphics processing units (GPUs). A gray-scale-based DIR algorithm called demons and five of its variants were implemented on GPUs using the compute unified device architecture (CUDA) programming environment. The spatial accuracy of these algorithms was evaluated over five sets of pulmonary 4D CT images with an average size of 256 x 256 x 100 and more than 1100 expert-determined landmark point pairs each. For all the testing scenarios presented in this paper, the GPU-based DIR computation required around 7 to 11 s to yield an average 3D error ranging from 1.5 to 1.8 mm. It is interesting to find out that the original passive force demons algorithms outperform subsequently proposed variants based on the combination of accuracy, efficiency and ease of implementation.
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“Bu uygulamalar özellikle çözünürlük arttığında ve bölütleme işlemi çoklu eşikleme şeklinde olduğunda yüksek işlem zamanı gerektirmektedir [69].”
From GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi (2020) · cites this paper as [69]
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GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi
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“Computational Sciences and Its Applications, 223233, (2008) [71] Gu X.”
Background
Metadata
- Authors
- Gu, X., Pan, H., Liang, Y., Castillo, R., Yang, D., Choi, D., Castillo, E., Majumdar, A., Guerrero, T., & Jiang, S. B.
- Affiliations
- University of California San Diego (US)
- The University of Texas MD Anderson Cancer Center (US)
- Washington University in St. Louis (US)
- San Diego Supercomputer Center (US)
- ORCID
- Venue
- Physics in Medicine and Biology
- Publisher
- IOP Publishing
- Published
- 2009-12-11
- Volume
- 55
- Issue
- 1
- Pages
- 207-219
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- PMC
- PMC7540904
- OpenAlex
- W2133277680
- Cited by
- 243
- References
- 33
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 2 · 2023: 5 · 2022: 7 · 2021: 8 · 2020: 12 · 2019: 13 · 2018: 13 · 2017: 12 · 2016: 17 · 2015: 29 · 2014: 22 · 2013: 35 · 2012: 20
- FWCI
- 19.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physics in Medicine and Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia
- National Institutes of Health
- National Cancer Institute (F32CA154045, R21CA141833, R21CA128230, T32CA119930)
- Topics
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Keywords
- deformable image registration, Demons algorithm, GPU acceleration, online adaptive radiotherapy, image segmentation, 4D-CT, lung CT, CUDA
- MeSH terms
- Algorithms, Computer Graphics, Humans, Image Processing, Computer-Assisted, Lung, Time Factors, Tomography, X-Ray Computed, Databases as Topic, Imaging, Three-Dimensional
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