Registration by tracking for sequential 2D MRI

• URL: http://arxiv.org/abs/2003.10819v1
• Date: Tue, 24 Mar 2020 13:12:42 GMT
• Title: Registration by tracking for sequential 2D MRI
• Authors: Niklas Gunnarsson, Jens Sj\"olund and Thomas B. Sch\"on
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our anatomy is in constant motion. With modern MR imaging it is possible to record this motion in real-time during an ongoing radiation therapy session. In this paper we present an image registration method that exploits the sequential nature of 2D MR images to estimate the corresponding displacement field. The method employs several discriminative correlation filters that independently track specific points. Together with a sparse-to-dense interpolation scheme we can then estimate of the displacement field. The discriminative correlation filters are trained online, and our method is modality agnostic. For the interpolation scheme we use a neural network with normalized convolutions that is trained using synthetic diffeomorphic displacement fields. The method is evaluated on a segmented cardiac dataset and when compared to two conventional methods we observe an improved performance. This improvement is especially pronounced when it comes to the detection of larger motions of small objects.

Related papers

• Highly efficient non-rigid registration in k-space with application to cardiac Magnetic Resonance Imaging [10.618048010632728]
  We propose a novel self-supervised deep learning-based framework, dubbed the Local-All Pass Attention Network (LAPANet) for non-rigid motion estimation. LAPANet was evaluated on cardiac motion estimation across various sampling trajectories and acceleration rates. The achieved high temporal resolution (less than 5 ms) for non-rigid motion opens new avenues for motion detection, tracking and correction in dynamic and real-time MRI applications.
  arXiv  Detail & Related papers  (2024-10-24T15:19:59Z)
• Intraoperative Registration by Cross-Modal Inverse Neural Rendering [61.687068931599846]
  We present a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration.
  arXiv  Detail & Related papers  (2024-09-18T13:40:59Z)
• Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring [71.60457491155451]
  Eliminating image blur produced by various kinds of motion has been a challenging problem. We propose a novel real-world deblurring filtering model called the Motion-adaptive Separable Collaborative Filter. Our method provides an effective solution for real-world motion blur removal and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-04-19T19:44:24Z)
• Neural Graphics Primitives-based Deformable Image Registration for On-the-fly Motion Extraction [9.599774878892665]
  Intra-fraction motion in radiotherapy is commonly modeled using deformable image registration (DIR) Existing methods often struggle to balance speed and accuracy, limiting their applicability in clinical scenarios. This study introduces a novel approach that harnesses Neural Graphics Primitives (NGP) to optimize the displacement vector field (DVF) We validate this approach on the 4D-CT lung dataset DIR-lab, achieving a target registration error (TRE) of 1.15pm1.15 mm within a remarkable time of 1.77 seconds.
  arXiv  Detail & Related papers  (2024-02-08T11:09:27Z)
• Deep Cardiac MRI Reconstruction with ADMM [7.694990352622926]
  We present a deep learning (DL)-based method for accelerated cine and multi-contrast reconstruction in the context of cardiac imaging. Our method optimize in both the image and k-space domains, allowing for high reconstruction fidelity.
  arXiv  Detail & Related papers  (2023-10-10T13:46:11Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Echocardiography Segmentation Using Neural ODE-based Diffeomorphic Registration Field [0.0]
  We present a novel method for diffevolution image registration using neural ordinary differential equations (Neural ODE) The proposed method, Echo-ODE, introduces several key improvements compared to the previous state-of-the-art. The results show that our method surpasses the previous state-of-the-art in multiple aspects.
  arXiv  Detail & Related papers  (2023-06-16T08:37:27Z)
• Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts [75.51482952586773]
  deep neural networks generally require plenty of labeled training data and are vulnerable to domain shifts between training and test data. We present a novel approach to geometric domain adaptation for image registration, adapting a model from a labeled source to an unlabeled target domain. Our method consistently improves on the baseline model by 50%/47% while even matching the accuracy of models trained on target data.
  arXiv  Detail & Related papers  (2022-07-01T12:16:42Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)
• DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images [10.434681088538866]
  We propose a novel deep learning-based fully unsupervised method for in vivo motion tracking on t-MRI images. Our method has been validated on a representative clinical t-MRI dataset.
  arXiv  Detail & Related papers  (2021-03-04T00:42:11Z)
• 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)

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.