Deep learning-based prospective slice tracking for continuous catheter visualization during MRI-guided cardiac catheterization.

ABSTRACT:

Purpose

This proof-of-concept study introduces a novel, deep learning-based, parameter-free, automatic slice-tracking technique for continuous catheter tracking and visualization during MR-guided cardiac catheterization.

Methods

The proposed sequence includes Calibration and Runtime modes. Initially, Calibration mode identifies the catheter tip's three-dimensional coordinates using a fixed stack of contiguous slices. A U-Net architecture with a ResNet-34 encoder is used to identify the catheter tip location. Once identified, the sequence then switches to Runtime mode, dynamically acquiring three contiguous slices automatically centered on the catheter tip. The catheter location is estimated from each Runtime stack using the same network and fed back to the sequence, enabling

SUBMITTER: Neofytou AP 

PROVIDER: S-EPMC12309870 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature