Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks.
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| Title: | Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks. |
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| Authors: | Salimi, Yazdan1 (AUTHOR), Akhavanallaf, Azadeh1 (AUTHOR), Mansouri, Zahra1 (AUTHOR), Shiri, Isaac1 (AUTHOR), Zaidi, Habib1,2,3,4 (AUTHOR) habib.zaidi@hcuge.ch |
| Source: | European Radiology. Dec2023, Vol. 33 Issue 12, p9411-9424. 14p. |
| Subjects: | Artificial neural networks, Computed tomography, Absorbed dose, Deep learning |
| Abstract: | Objective: We propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions. Methods: The voxel-wise dose maps corresponding to each source position/angle were calculated using Monte Carlo (MC) simulations considering patient- and scanner-specific characteristics (SP_MC). The dose distribution in a uniform cylinder was computed through MC calculations (SP_uniform). The density map and SP_uniform dose maps were fed into a residual deep neural network (DNN) to predict SP_MC through an image regression task. The whole-body dose maps reconstructed by the DNN and MC were compared in the 11 test cases scanned with two tube voltages through transfer learning with/without tube current modulation (TCM). The voxel-wise and organ-wise dose evaluations, such as mean error (ME, mGy), mean absolute error (MAE, mGy), relative error (RE, %), and relative absolute error (RAE, %), were performed. Results: The model performance for the 120 kVp and TCM test set in terms of ME, MAE, RE, and RAE voxel-wise parameters was − 0.0302 ± 0.0244 mGy, 0.0854 ± 0.0279 mGy, − 1.13 ± 1.41%, and 7.17 ± 0.44%, respectively. The organ-wise errors for 120 kVp and TCM scenario averaged over all segmented organs in terms of ME, MAE, RE, and RAE were − 0.144 ± 0.342 mGy, and 0.23 ± 0.28 mGy, − 1.11 ± 2.90%, 2.34 ± 2.03%, respectively. Conclusion: Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy suitable for organ-level absorbed dose estimation. Clinical relevance statement: We proposed a novel method for voxel dose map calculation using deep neural networks. This work is clinically relevant since accurate dose calculation for patients can be carried out within acceptable computational time compared to lengthy Monte Carlo calculations. Key Points: • We proposed a deep neural network approach as an alternative to Monte Carlo dose calculation. • Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy, suitable for organ-level dose estimation. • By generating a dose distribution from a single source position, our model can generate accurate and personalized dose maps for a wide range of acquisition parameters. [ABSTRACT FROM AUTHOR] |
| Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 173805911 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Salimi%2C+Yazdan%22">Salimi, Yazdan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Akhavanallaf%2C+Azadeh%22">Akhavanallaf, Azadeh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mansouri%2C+Zahra%22">Mansouri, Zahra</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shiri%2C+Isaac%22">Shiri, Isaac</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zaidi%2C+Habib%22">Zaidi, Habib</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<i> habib.zaidi@hcuge.ch</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Dec2023, Vol. 33 Issue 12, p9411-9424. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Absorbed+dose%22">Absorbed dose</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objective: We propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions. Methods: The voxel-wise dose maps corresponding to each source position/angle were calculated using Monte Carlo (MC) simulations considering patient- and scanner-specific characteristics (SP_MC). The dose distribution in a uniform cylinder was computed through MC calculations (SP_uniform). The density map and SP_uniform dose maps were fed into a residual deep neural network (DNN) to predict SP_MC through an image regression task. The whole-body dose maps reconstructed by the DNN and MC were compared in the 11 test cases scanned with two tube voltages through transfer learning with/without tube current modulation (TCM). The voxel-wise and organ-wise dose evaluations, such as mean error (ME, mGy), mean absolute error (MAE, mGy), relative error (RE, %), and relative absolute error (RAE, %), were performed. Results: The model performance for the 120 kVp and TCM test set in terms of ME, MAE, RE, and RAE voxel-wise parameters was − 0.0302 ± 0.0244 mGy, 0.0854 ± 0.0279 mGy, − 1.13 ± 1.41%, and 7.17 ± 0.44%, respectively. The organ-wise errors for 120 kVp and TCM scenario averaged over all segmented organs in terms of ME, MAE, RE, and RAE were − 0.144 ± 0.342 mGy, and 0.23 ± 0.28 mGy, − 1.11 ± 2.90%, 2.34 ± 2.03%, respectively. Conclusion: Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy suitable for organ-level absorbed dose estimation. Clinical relevance statement: We proposed a novel method for voxel dose map calculation using deep neural networks. This work is clinically relevant since accurate dose calculation for patients can be carried out within acceptable computational time compared to lengthy Monte Carlo calculations. Key Points: • We proposed a deep neural network approach as an alternative to Monte Carlo dose calculation. • Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy, suitable for organ-level dose estimation. • By generating a dose distribution from a single source position, our model can generate accurate and personalized dose maps for a wide range of acquisition parameters. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=173805911 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00330-023-09839-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 9411 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Computed tomography Type: general – SubjectFull: Absorbed dose Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Salimi, Yazdan – PersonEntity: Name: NameFull: Akhavanallaf, Azadeh – PersonEntity: Name: NameFull: Mansouri, Zahra – PersonEntity: Name: NameFull: Shiri, Isaac – PersonEntity: Name: NameFull: Zaidi, Habib IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 09387994 Numbering: – Type: volume Value: 33 – Type: issue Value: 12 Titles: – TitleFull: European Radiology Type: main |
| ResultId | 1 |
