A machine learning-based pipeline for multi-organ/tissue patient-specific radiation dosimetry in CT.

Computed tomography Dosimetry Machine learning Monte Carlo Radiation

Journal

European radiology

ISSN: 1432-1084

Titre abrégé: Eur Radiol

Pays: Germany

ID NLM: 9114774

Informations de publication

Date de publication:
13 Aug 2024

Historique:

received: 15 01 2024

accepted: 18 07 2024

revised: 29 06 2024

medline: 13 8 2024

pubmed: 13 8 2024

entrez: 13 8 2024

Statut: aheadofprint

Résumé

To develop a machine learning-based pipeline for multi-organ/tissue personalized radiation dosimetry in CT. For the study, 95 chest CT scans and 85 abdominal CT scans were collected retrospectively. For each CT scan, a personalized Monte Carlo (MC) simulation was carried out. The produced 3D dose distributions and the respective CT examinations were utilized for the development of organ/tissue-specific dose prediction deep neural networks (DNNs). A pipeline that integrates a robust open-source organ segmentation tool with the dose prediction DNNs was developed for the automatic estimation of radiation doses for 30 organs/tissues including sub-volumes of the heart and lungs. The accuracy and time efficiency of the presented methodology was assessed. Statistical analysis (t-tests) was conducted to determine if the differences between the ground truth organ/tissue radiation dose estimates and the respective dose predictions were significant. The lowest median percentage differences between MC-derived organ/tissue doses and DNN dose predictions were observed for the lung vessels (4.3%), small bowel (4.7%), pulmonary artery (4.7%), and colon (5.2%), while the highest differences were observed for the right lung's upper lobe (13.3%), spleen (13.1%), pancreas (12.1%), and stomach (11.6%). Statistical analysis showed that the differences were not significant (p-value > 0.18). Furthermore, the mean inference time, regarding the validation cohort, of the developed methodology was 77.0 ± 11.0 s. The proposed workflow enables fast and accurate organ/tissue radiation dose estimations. The developed algorithms and dose prediction DNNs are publicly available ( https://github.com/eltzanis/multi-structure-CT-dosimetry ). The accuracy and time efficiency of the developed pipeline compose a useful tool for personalized dosimetry in CT. By adopting the proposed workflow, institutions can utilize an automated pipeline for patient-specific dosimetry in CT. Personalized dosimetry is ideal, but is time-consuming. The proposed pipeline composes a tool for facilitating patient-specific CT dosimetry in routine clinical practice. The developed workflow integrates a robust open-source segmentation tool with organ/tissue-specific dose prediction neural networks.

Identifiants

DOI: 10.1007/s00330-024-11002-0 PMID: 39136706

pubmed: 39136706

doi: 10.1007/s00330-024-11002-0

pii: 10.1007/s00330-024-11002-0

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Informations de copyright

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