Evaluation of magnetic resonance imaging and deep learning-based synthetic computed tomography for calcified intradural tumors - importance of domain-specific training and validation of synthetic imaging methods for clinical application.

ABSTRACT:

Purpose

For intradural spinal tumors, information on the degree of calcification is helpful to plan the surgery. Novel deep-learning algorithms allow to generate synthetic computed tomography (CT) images from magnetic resonance imaging (MRI).

Methods

We conducted a prospective observational cohort study, including n = 105 patients with spinal pathologies between 07/2022 - 09/2023, to validate the accuracy of BoneMRI (MRIGuidance BV©, Utrecht, the Netherlands). Patients underwent both conventional CT and MRI; synthetic CT images were generated from MRI source data with artificial intelligence (AI). For the scope of this post-hoc analysis, only patients with intradural tumors were selected.

Results

Five patients with intradural tumors of the spine were included (mean ag

SUBMITTER: Fischer G 

PROVIDER: S-EPMC12769552 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature