WarpEM: Dynamic Time Warping for Accurate Catheter Registration in EM-guided Procedures

• URL: http://arxiv.org/abs/2308.03652v1
• Date: Mon, 7 Aug 2023 15:07:21 GMT
• Title: WarpEM: Dynamic Time Warping for Accurate Catheter Registration in EM-guided Procedures
• Authors: Ardit Ramadani, Peter Ewert, Heribert Schunkert, Nassir Navab
• Abstract summary: This paper introduces a novel automated catheter registration method for EM-guided MIEP. The method utilizes 3D signal temporal analysis, such as Dynamic Time Warping (DTW) algorithms, to improve registration accuracy and reliability. The results indicate that the DTW method yields accurate and reliable registration outcomes, with a mean error of $2.22$mm.
• Score: 42.72633281833923
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate catheter tracking is crucial during minimally invasive endovascular procedures (MIEP), and electromagnetic (EM) tracking is a widely used technology that serves this purpose. However, registration between preoperative images and the EM tracking system is often challenging. Existing registration methods typically require manual interactions, which can be time-consuming, increase the risk of errors and change the procedural workflow. Although several registration methods are available for catheter tracking, such as marker-based and path-based approaches, their limitations can impact the accuracy of the resulting tracking solution, consequently, the outcome of the medical procedure. This paper introduces a novel automated catheter registration method for EM-guided MIEP. The method utilizes 3D signal temporal analysis, such as Dynamic Time Warping (DTW) algorithms, to improve registration accuracy and reliability compared to existing methods. DTW can accurately warp and match EM-tracked paths to the vessel's centerline, making it particularly suitable for registration. The introduced registration method is evaluated for accuracy in a vascular phantom using a marker-based registration as the ground truth. The results indicate that the DTW method yields accurate and reliable registration outcomes, with a mean error of $2.22$mm. The introduced registration method presents several advantages over state-of-the-art methods, such as high registration accuracy, no initialization required, and increased automation.

Related papers

• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge [1.7625447004432986]
  A robust, efficient, and accurate registration method is highly desired by the scientific community and hospitals specializing in digital pathology. We propose a two-step hybrid method consisting of (i) deep learning- and feature-based initial alignment algorithm, and (ii) intensity-based nonrigid registration. The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain.
  arXiv  Detail & Related papers  (2024-04-19T16:19:30Z)
• Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery [52.63271687382495]
  We present an approach that automatically solves the registration problem for lumbar spinal fusion surgery in a radiation-free manner. A deep neural network was trained to segment the lumbar spine and simultaneously predict its orientation, yielding an initial pose for preoperative models. An intuitive surgical guidance is provided thanks to the integration into an augmented reality based navigation system.
  arXiv  Detail & Related papers  (2023-08-05T16:26:41Z)
• GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration [62.41725951450803]
  Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the field. We construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyper parameter-free balance on multiple losses. Our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference.
  arXiv  Detail & Related papers  (2023-06-26T13:32:09Z)
• A Transformer-based Network for Deformable Medical Image Registration [5.388525884890891]
  Deformable medical image registration plays an important role in clinical diagnosis and treatment. Deep learning (DL) based image registration methods have been widely investigated and showed excellent performance in computational speed. This paper has proposed the transformer based image registration method.
  arXiv  Detail & Related papers  (2022-02-24T13:45:45Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint [20.905285486843006]
  We propose a novel unsupervised learning-based framework to achieve accurate and efficient multi-contrast MR image registrations. Specifically, an end-to-end coarse-to-fine network architecture consisting of affine and deformable transformations is designed. Our method is about 10 times faster than the most competitive method of SyN (Affine) when testing on a CPU.
  arXiv  Detail & Related papers  (2020-08-05T01:16:45Z)
• A Novel Approach for Correcting Multiple Discrete Rigid In-Plane Motions Artefacts in MRI Scans [63.28835187934139]
  We propose a novel method for removing motion artefacts using a deep neural network with two input branches. The proposed method can be applied to artefacts generated by multiple movements of the patient.
  arXiv  Detail & Related papers  (2020-06-24T15:25:11Z)
• Registration by tracking for sequential 2D MRI [0.0]
  We present an image registration method that exploits the sequential nature of 2D MR images to estimate the displacement field. The method is evaluated on a segmented cardiac dataset and when compared to two conventional methods we observe an improved performance.
  arXiv  Detail & Related papers  (2020-03-24T13:12:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.