A cone-beam optical CT based on a convergent light source - Characterization and optimization

Author(s)

Silveira, M. A. ; Pavoni, J. F. ; Baffa, O.

• Document type: Journal article
• Source: PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS; v. 123, p. 10-pg., 2024-06-19.

Abstract

Purpose: Employing a Fresnel lens and a point-like light source to create a convergent light beam for the camera effectively minimizes stray light and enhances image quality in optical computed tomography (OCT), benefiting 3D dosimetry applications. This study outlines the development of an economical cone-beam optical computed scanner for 3D dosimetry. Methods: Optical performance was assessed by calculating modulation transfer function (MTF) with pattern charts. Stray light was evaluated by imaging a cylinder flask and a square grid with 5 mm diameter holes to determine the stray-to-primary ratio. Reconstruction quality was determined using SIRT-TV and compared with spectrophotometry attenuation coefficients, with the best regularization parameter (lambda = 0.01) chosen based on contrast-to-noise ratio (CNR). Dosimetry performance was assessed by determining percentage dose depth (PDD) for a 6MV beam with a 5 x 5 cm(2) field using FXO-f gel dosimeter, compared with ionization chamber data. Results: MTF evaluation yielded >= 50 % agreement with pattern charts. Stray-to-primary ratio was less than 0.1 or 10 % of the total signal. Reconstruction showed low noise and artifacts, with optimal CNR at lambda = 0.01. Attenuation coefficients from optical CT aligned with spectrometer measurements within 1.2 %. PDD calculated with FXO-f gel dosimeter closely matched ionization chamber data (<1.2 % difference), achieving a dose resolution of 0.1 Gy.<br /> Conclusion: The built and optimization the de optical-CT based on a convergent beam is read to perform the 3D quality assurance in clinical applications. (AU)

• FAPESP's process: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat
• Grantee: Oswaldo Baffa Filho
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 21/02254-6 - Applications of machine learning tools in radiotherapy
• Grantee: Juliana Fernandes Pavoni
• Support Opportunities: Regular Research Grants