Monte Carlo-based simulation of virtual 3 and 4-dimensional cone-beam computed tomography from computed tomography images: An end-to-end framework and a deep learning-based speedup strategy.

ABSTRACT:

Background and purpose

In radiotherapy, precise comparison of fan-beam computed tomography (CT) and cone-beam CT (CBCT) arises as a commonplace, yet intricate task. This paper proposes a publicly available end-to-end pipeline featuring an intrinsic deep-learning-based speedup technique for generating virtual 3D and 4D CBCT from CT images.

Materials and methods

Physical properties, derived from CT intensity information, are obtained through automated whole-body segmentation of organs and tissues. Subsequently, Monte Carlo (MC) simulations generate CBCT X-ray projections for a full circular arc around the patient employing acquisition settings matched with a clinical CBCT scanner (modeled according to Varian TrueBeam specifications). In addition to 3D CBCT reconstruction, a 4D

SUBMITTER: Madesta F 

PROVIDER: S-EPMC11458955 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature