Advanced Computational Methods for Radiation Dose Optimization in CT Article Swipe
Shreekripa Rao
,
Krishna Sharan
,
Srinidhi Gururajarao Chandraguthi
,
Rechal Nisha Dsouza
,
Leena R. David
,
Sneha Ravichandran
,
Mubarak Taiwo Mustapha
,
Dilip Shettigar
,
Berna Uzun
,
Rajagopal Kadavigere
,
Suresh Sukumar
,
Dilber Uzun Ozsahin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14090921
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14090921
Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept “As Low As Reasonably Achievable”. Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. Methods: A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDIvol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Results: Significant variations were found in the DLP, CTDIvol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution’s DRL was significantly low. The optimized dose protocol showed a reduction in the CTDIvol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Conclusions: Optimized protocol DRLs were proposed for comparison purposes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics14090921
- https://www.mdpi.com/2075-4418/14/9/921/pdf?version=1714360835
- OA Status
- gold
- Cited By
- 5
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396576506
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396576506Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/diagnostics14090921Digital Object Identifier
- Title
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Advanced Computational Methods for Radiation Dose Optimization in CTWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-29Full publication date if available
- Authors
-
Shreekripa Rao, Krishna Sharan, Srinidhi Gururajarao Chandraguthi, Rechal Nisha Dsouza, Leena R. David, Sneha Ravichandran, Mubarak Taiwo Mustapha, Dilip Shettigar, Berna Uzun, Rajagopal Kadavigere, Suresh Sukumar, Dilber Uzun OzsahinList of authors in order
- Landing page
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https://doi.org/10.3390/diagnostics14090921Publisher landing page
- PDF URL
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https://www.mdpi.com/2075-4418/14/9/921/pdf?version=1714360835Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2075-4418/14/9/921/pdf?version=1714360835Direct OA link when available
- Concepts
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Medicine, Percentile, Nuclear medicine, Radiation therapy, Radiation treatment planning, Radiation dose, Ionizing radiation, Effective dose (radiation), Radiology, Computed tomography, Medical physics, Irradiation, Statistics, Nuclear physics, Physics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 4, 2024: 1Per-year citation counts (last 5 years)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.procedures | 85 |
| abstract_inverted_index.radiation, | 19 |
| abstract_inverted_index.strategies | 95 |
| abstract_inverted_index.tomography | 6, 55 |
| abstract_inverted_index.variations | 233 |
| abstract_inverted_index.volumetric | 132 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Significant | 232 |
| abstract_inverted_index.calculated. | 148 |
| abstract_inverted_index.dose-length | 138 |
| abstract_inverted_index.multiplying | 183 |
| abstract_inverted_index.treatments, | 4 |
| abstract_inverted_index.Conclusions: | 314 |
| abstract_inverted_index.high-quality | 102 |
| abstract_inverted_index.institution. | 127 |
| abstract_inverted_index.optimization | 68 |
| abstract_inverted_index.radiotherapy | 3, 264 |
| abstract_inverted_index.distribution. | 161 |
| abstract_inverted_index.significantly | 282 |
| abstract_inverted_index.Achievable”. | 47 |
| abstract_inverted_index.75th-percentile | 224 |
| abstract_inverted_index.cross-sectional | 111 |
| abstract_inverted_index.institution’s | 279 |
| abstract_inverted_index.radiotherapy-computed | 54 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5022732735, https://openalex.org/A5071225279 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 12 |
| corresponding_institution_ids | https://openalex.org/I29891158, https://openalex.org/I4210093039, https://openalex.org/I69050122 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.89667656 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |