Deep Learning–Based Estimation of Radiographic Position to Automatically Set Up the X-Ray Prime Factors.
Radiation dose and image quality in radiology are influenced by the X-ray prime factors: KVp, mAs, and source-detector distance. These parameters are set by the X-ray technician prior to the acquisition considering the radiographic position. A wrong setting of these parameters may result in exposure...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1661 - 1669 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280507&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280507 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280507 185280507 185280507 10.1007/s10278-024-01256-x 185280507 ppf: 1661 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning–Based Estimation of Radiographic Position to Automatically Set Up the X-Ray Prime Factors. aug: au: Del Cerro, C. F. Giménez, R. C. García-Blas, J. Sosenko, K. Ortega, J. M. Desco, M. Abella, M. affil: https://ror.org/03ths8210 Dept. Bioingeniería, Universidad Carlos III de Madrid, Leganés, Madrid, Spain sug: subj: Deep Learning Methods Automation Methods Radiographic Image Interpretation, Computer-Assisted Evaluation Body Positions Classification X-Rays Radiography Methods Radiation Dosage Human Funding Source Academic Medical Centers Spain Pilot Studies Radiologic Technologists Psychosocial Factors Blood Coagulation Factors Learning Methods Workflow Radiology Service Tomography, X-Ray Computed Models, Statistical Machine Learning Diagnostic Imaging Methods Color Descriptive Statistics Comparative Studies ab: Radiation dose and image quality in radiology are influenced by the X-ray prime factors: KVp, mAs, and source-detector distance. These parameters are set by the X-ray technician prior to the acquisition considering the radiographic position. A wrong setting of these parameters may result in exposure errors, forcing the test to be repeated with the increase of the radiation dose delivered to the patient. This work presents a novel approach based on deep learning that automatically estimates the radiographic position from a photograph captured prior to X-ray exposure, which can then be used to select the optimal prime factors. We created a database using 66 radiographic positions commonly used in clinical settings, prospectively obtained during 2022 from 75 volunteers in two different X-ray facilities. The architecture for radiographic position classification was a lightweight version of ConvNeXt trained with fine-tuning, discriminative learning rates, and a one-cycle policy scheduler. Our resulting model achieved an accuracy of 93.17% for radiographic position classification and increased to 95.58% when considering the correct selection of prime factors, since half of the errors involved positions with the same KVp and mAs values. Most errors occurred for radiographic positions with similar patient pose in the photograph. Results suggest the feasibility of the method to facilitate the acquisition workflow reducing the occurrence of exposure errors while preventing unnecessary radiation dose delivered to patients. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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