How does CBCT reconstruction algorithm impact on deformably mapped targets and accumulated dose distributions? Article Swipe
Weihua Mao
,
Chang Liu
,
Stephen Gardner
,
Mohamed A. Elshaikh
,
I. Aref
,
Joon K. Lee
,
Deepak Pradhan
,
Farzan Siddiqui
,
Karen Snyder
,
Akila Kumarasiri
,
Bo Zhao
,
Joshua Kim
,
Haisen Li
,
Ning Wen
,
Benjamin Movsas
,
Indrin J. Chetty
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1002/acm2.13328
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1002/acm2.13328
Purpose We performed quantitative analysis of differences in deformable image registration (DIR) and deformable dose accumulation (DDA) computed on CBCT datasets reconstructed using the standard (Feldkamp‐Davis‐Kress: FDK_CBCT) and a novel iterative (iterative_CBCT) CBCT reconstruction algorithms. Methods Both FDK_CBCT and iterative_CBCT images were reconstructed for 323 fractions of treatment for 10 prostate cancer patients. Planning CT images were deformably registered to each CBCT image data set. After daily dose distributions were computed, they were mapped to planning CT to obtain deformed doses. Dosimetric and image registration results based CBCT images reconstructed by two algorithms were compared at three levels: (A) voxel doses over entire dose calculation volume, (B) clinical constraint results on targets and sensitive structures, and (C) contours propagated to CBCT images using DIR results based on three algorithms (SmartAdapt, Velocity, and Elastix) were compared with manually delineated contours as ground truth. Results (A) Average daily dose differences and average normalized DDA differences between FDK_CBCT and iterative_CBCT were ≤1 cGy. Maximum daily point dose differences increased from 0.22 ± 0.06 Gy (before the deformable dose mapping operation) to 1.33 ± 0.38 Gy after the deformable dose mapping. Maximum differences of normalized DDA per fraction were up to 0.80 Gy (0.42 ± 0.19 Gy). (B) Differences in target minimum doses were up to 8.31 Gy (−0.62 ± 4.60 Gy) and differences in critical structure doses were 0.70 ± 1.49 Gy. (C) For mapped prostate contours based on iterative_CBCT (relative to standard FDK_CBCT), dice similarity coefficient increased by 0.10 ± 0.09 ( p < 0.0001), mass center distances decreased by 2.5 ± 3.0 mm ( p < 0.00005), and Hausdorff distances decreased by 3.3 ± 4.4 mm ( p < 0.00015). Conclusions The new iterative CBCT reconstruction algorithm leads to different mapped volumes of interest, deformed and cumulative doses than results based on conventional FDK_CBCT.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/acm2.13328
- OA Status
- gold
- Cited By
- 3
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3192934433
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3192934433Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/acm2.13328Digital Object Identifier
- Title
-
How does CBCT reconstruction algorithm impact on deformably mapped targets and accumulated dose distributions?Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-10Full publication date if available
- Authors
-
Weihua Mao, Chang Liu, Stephen Gardner, Mohamed A. Elshaikh, I. Aref, Joon K. Lee, Deepak Pradhan, Farzan Siddiqui, Karen Snyder, Akila Kumarasiri, Bo Zhao, Joshua Kim, Haisen Li, Ning Wen, Benjamin Movsas, Indrin J. ChettyList of authors in order
- Landing page
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https://doi.org/10.1002/acm2.13328Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1002/acm2.13328Direct OA link when available
- Concepts
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Voxel, Nuclear medicine, Iterative reconstruction, Image registration, Iterative closest point, Cone beam computed tomography, Medicine, Algorithm, Computer science, Mathematics, Computed tomography, Artificial intelligence, Radiology, Image (mathematics), Point cloudTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 1, 2024: 1, 2022: 1Per-year citation counts (last 5 years)
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24Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.volumes | 291 |
| abstract_inverted_index.(−0.62 | 215 |
| abstract_inverted_index.0.0001), | 253 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Elastix) | 133 |
| abstract_inverted_index.FDK_CBCT | 38, 155 |
| abstract_inverted_index.Planning | 54 |
| abstract_inverted_index.analysis | 5 |
| abstract_inverted_index.clinical | 108 |
| abstract_inverted_index.compared | 95, 135 |
| abstract_inverted_index.computed | 18 |
| abstract_inverted_index.contours | 118, 139, 234 |
| abstract_inverted_index.critical | 222 |
| abstract_inverted_index.datasets | 21 |
| abstract_inverted_index.deformed | 80, 294 |
| abstract_inverted_index.fraction | 194 |
| abstract_inverted_index.manually | 137 |
| abstract_inverted_index.mapping. | 187 |
| abstract_inverted_index.planning | 76 |
| abstract_inverted_index.prostate | 51, 233 |
| abstract_inverted_index.standard | 25, 240 |
| abstract_inverted_index.(relative | 238 |
| abstract_inverted_index.0.00005), | 266 |
| abstract_inverted_index.0.00015). | 279 |
| abstract_inverted_index.FDK_CBCT) | 27 |
| abstract_inverted_index.FDK_CBCT. | 303 |
| abstract_inverted_index.Hausdorff | 268 |
| abstract_inverted_index.Velocity, | 131 |
| abstract_inverted_index.algorithm | 286 |
| abstract_inverted_index.computed, | 71 |
| abstract_inverted_index.decreased | 257, 270 |
| abstract_inverted_index.different | 289 |
| abstract_inverted_index.distances | 256, 269 |
| abstract_inverted_index.fractions | 46 |
| abstract_inverted_index.increased | 166, 245 |
| abstract_inverted_index.interest, | 293 |
| abstract_inverted_index.iterative | 31, 283 |
| abstract_inverted_index.patients. | 53 |
| abstract_inverted_index.performed | 3 |
| abstract_inverted_index.sensitive | 114 |
| abstract_inverted_index.structure | 223 |
| abstract_inverted_index.treatment | 48 |
| abstract_inverted_index.Dosimetric | 82 |
| abstract_inverted_index.FDK_CBCT), | 241 |
| abstract_inverted_index.algorithms | 93, 129 |
| abstract_inverted_index.constraint | 109 |
| abstract_inverted_index.cumulative | 296 |
| abstract_inverted_index.deformable | 9, 14, 174, 185 |
| abstract_inverted_index.deformably | 58 |
| abstract_inverted_index.delineated | 138 |
| abstract_inverted_index.normalized | 151, 191 |
| abstract_inverted_index.operation) | 177 |
| abstract_inverted_index.propagated | 119 |
| abstract_inverted_index.registered | 59 |
| abstract_inverted_index.similarity | 243 |
| abstract_inverted_index.Conclusions | 280 |
| abstract_inverted_index.Differences | 205 |
| abstract_inverted_index.algorithms. | 35 |
| abstract_inverted_index.calculation | 105 |
| abstract_inverted_index.coefficient | 244 |
| abstract_inverted_index.differences | 7, 148, 153, 165, 189, 220 |
| abstract_inverted_index.structures, | 115 |
| abstract_inverted_index.(SmartAdapt, | 130 |
| abstract_inverted_index.accumulation | 16 |
| abstract_inverted_index.conventional | 302 |
| abstract_inverted_index.quantitative | 4 |
| abstract_inverted_index.registration | 11, 85 |
| abstract_inverted_index.distributions | 69 |
| abstract_inverted_index.reconstructed | 22, 43, 90 |
| abstract_inverted_index.iterative_CBCT | 40, 157, 237 |
| abstract_inverted_index.reconstruction | 34, 285 |
| abstract_inverted_index.(iterative_CBCT) | 32 |
| abstract_inverted_index.(Feldkamp‐Davis‐Kress: | 26 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5064839560, https://openalex.org/A5080219046, https://openalex.org/A5075401316, https://openalex.org/A5100606309, https://openalex.org/A5042690121, https://openalex.org/A5103102832, https://openalex.org/A5070906860, https://openalex.org/A5006860803, https://openalex.org/A5010536792, https://openalex.org/A5013510512, https://openalex.org/A5011424635, https://openalex.org/A5004625690, https://openalex.org/A5032295905, https://openalex.org/A5102886776, https://openalex.org/A5044269137, https://openalex.org/A5046590952 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 16 |
| corresponding_institution_ids | https://openalex.org/I154057602 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.68278252 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |