Neofytou Alexander Paul, Kowalik Grzegorz, Vidya Shankar Rohini, Kunze Karl, Moon Tracy, Mellor Nina, Neji Radhouene, Razavi Reza, Pushparajah Kuberan, Roujol Sébastien
School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK.
Magn Reson Med. 2025 Oct;94(4):1626-1634. doi: 10.1002/mrm.30574. Epub 2025 Jun 8.
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.
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 prospective slice tracking to keep the catheter in the central slice. If the catheter remains unidentified over several dynamics, the sequence reverts to Calibration mode. This artificial intelligence (AI)-based approach was evaluated prospectively in a three-dimensional-printed heart phantom and 3 patients undergoing MR-guided cardiac catheterization. This technique was also compared retrospectively in 2 patients with a previous non-AI automatic tracking method relying on operator-defined parameters.
In the phantom study, the tracking framework achieved 100% accuracy/sensitivity/specificity in both modes. Across all patients, the average accuracy/sensitivity/specificity were 100 ± 0/100 ± 0/100 ± 0% (Calibration) and 98.4 ± 0.8/94.1 ± 2.9/100.0 ± 0.0% (Runtime). The parametric, non-AI technique and the proposed parameter-free AI-based framework yielded identical accuracy (100%) in Calibration mode and similar accuracy range in Runtime mode (Patients 1 and 2: 100%-97%, and 100%-98%, respectively).
An AI-based prospective slice-tracking framework was developed for real-time, parameter-free, operator-independent, automatic tracking of gadolinium-filled balloon catheters. Its feasibility was successfully demonstrated in patients undergoing MRI-guided cardiac catheterization.
本概念验证研究引入了一种新颖的、基于深度学习的、无参数的自动切片跟踪技术，用于在磁共振引导的心脏导管插入术中进行连续导管跟踪和可视化。
所提出的序列包括校准模式和运行时模式。最初，校准模式使用固定的连续切片堆栈识别导管尖端的三维坐标。采用带有ResNet-34编码器的U-Net架构来识别导管尖端位置。一旦识别出来，序列便切换到运行时模式，动态获取以导管尖端为中心自动居中的三个连续切片。使用相同的网络从每个运行时堆栈中估计导管位置，并反馈给序列，实现前瞻性切片跟踪以将导管保持在中央切片中。如果在几次动态过程中导管仍未被识别，序列将恢复到校准模式。这种基于人工智能（AI）的方法在三维打印心脏模型和3例接受磁共振引导心脏导管插入术的患者中进行了前瞻性评估。该技术还在2例患者中与先前一种依赖操作员定义参数的非AI自动跟踪方法进行了回顾性比较。
在模型研究中，跟踪框架在两种模式下均实现了100%的准确率/灵敏度/特异性。在所有患者中，校准模式下的平均准确率/灵敏度/特异性为100±0/100±0/100±0%，运行时模式下为98.4±0.8/94.1±2.9/100.0±0.0%。参数化的非AI技术和所提出的无参数基于AI的框架在校准模式下产生相同的准确率（100%），在运行时模式下产生相似的准确率范围（患者1和患者2分别为100%-97%和100%-98%）。
开发了一种基于AI的前瞻性切片跟踪框架，用于实时、无参数、独立于操作员的钆填充球囊导管自动跟踪。其可行性在接受MRI引导心脏导管插入术的患者中得到了成功证明。
