Quantifying day-to-day variations in 4DCBCT-based PCA motion models Article Swipe
Salam Dhou
,
John H. Lewis
,
Weixing Cai
,
Dan Ionascu
,
Christopher Williams
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/2057-1976/ab817e
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/2057-1976/ab817e
The aim of this paper is to quantify the day-to-day variations of motion models derived from pre-treatment 4-dimensional cone beam CT (4DCBCT) fractions for lung cancer stereotactic body radiotherapy (SBRT) patients. Motion models are built by (1) applying deformable image registration (DIR) on each 4DCBCT image with respect to a reference image from that day, resulting in a set of displacement vector fields (DVFs), and (2) applying principal component analysis (PCA) on the DVFs to obtain principal components representing a motion model. Variations were quantified by comparing the PCA eigenvectors of the motion model built from the first day of treatment to the corresponding eigenvectors of the other motion models built from each successive day of treatment. Three metrics were used to quantify the variations: root mean squared (RMS) difference in the vectors, directional similarity, and an introduced metric called the Euclidean Model Norm (EMN). EMN quantifies the degree to which a motion model derived from the first fraction can represent the motion models of subsequent fractions. Twenty-one 4DCBCT scans from five SBRT patient treatments were used in this retrospective study. Experimental results demonstrated that the first two eigenvectors of motion models across all fractions have smaller RMS (0.00017), larger directional similarity (0.528), and larger EMN (0.678) than the last three eigenvectors (RMS: 0.00025, directional similarity: 0.041, and EMN: 0.212). The study concluded that, while the motion model eigenvectors varied from fraction to fraction, the first few eigenvectors were shown to be more stable across treatment fractions than others. This supports the notion that a pre-treatment motion model built from the first few PCA eigenvectors may remain valid throughout a treatment course. Future work is necessary to quantify how day-to-day variations in these models will affect motion reconstruction accuracy for specific clinical tasks.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/2057-1976/ab817e
- OA Status
- green
- Cited By
- 7
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3012424826
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3012424826Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/2057-1976/ab817eDigital Object Identifier
- Title
-
Quantifying day-to-day variations in 4DCBCT-based PCA motion modelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-03-19Full publication date if available
- Authors
-
Salam Dhou, John H. Lewis, Weixing Cai, Dan Ionascu, Christopher WilliamsList of authors in order
- Landing page
-
https://doi.org/10.1088/2057-1976/ab817ePublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/11293621Direct OA link when available
- Concepts
-
Principal component analysis, Eigenvalues and eigenvectors, Image registration, Mathematics, Metric (unit), Artificial intelligence, Similarity (geometry), Computer science, Nuclear medicine, Medicine, Physics, Image (mathematics), Engineering, Quantum mechanics, Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 2, 2022: 2, 2021: 1Per-year citation counts (last 5 years)
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44Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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