Image registration represents one of the fundamental techniques in medical imaging and image-guided interventions. In this paper, we present a Convolutional Neural Network (CNN) framework for deformable transesophageal US/CT image registration, for the cardiac arrhythmias, and guidance therapy purposes. The framework consists of a CNN, a spatial transformer, and a resampler. The CNN expects concatenated pairs of moving and fixed images as its input, and estimates as output the parameters for the spatial transformer, which generates the displacement vector field that allows the resampler to wrap the moving image into the fixed image. In our method, we train the model to maximize standard image matching objective functions that are based on the image intensities. The network can be applied to perform non-rigid registration of a pair of CT/US images directly in one pass, avoiding so the time consuming computation of the classical iterative method.
Transesophageal high-intensity focused ultrasound (HIFU) energy can be used to treat cardiac arrhythmia efficiently non-invasively. Since the esophagus is located just behind the heart, it offers a perfect acoustic window. Hence, HIFU can be directed toward the heart to perform ablation. In a previous study a HIFU probe with one 2D US image perpendicular to the esophagus axis for guidance purpose has been proposed. A new dual-mode HIFU probe with two perpendicular 2D US imaging plane is now under development. In this paper we propose a therapy guidance system, based on an intensity-based registration of the two perpendicular 2D US to preoperative 3D CT. As a proof of concept we developed the following evaluation framework on a numerical phantom: 1) we define a ground truth (GT) initial pose inside a CT volume and simulated two perpendicular US images from the CT data; 2) we run the registration framework from 55 randomly defined pose initializations around the initial GT pose; and 3) we estimated the accuracy of the registration by (a) the transformation parameter estimation errors and (b) Target Registration Error (TRE).
Image-guided thermal ablations have become an important therapeutic option for patient with cardiac arrhythmia, it is minimally-invasive and provides better and faster patient recovery. However, to enhance the ablation guidance, the therapist needs to link by image registration the intraoperative images to the high-resolution anatomical preoperative imaging, in which the ablation path has been defined. In this work, we present a convolutional neural networks (CNNs) framework for transesophageal ultrasound/computed tomography image registration to solve the problem of high computation time of the classical iterative methods, which is not suitable for a real-time application. We propose the following process: we first pass the input moving and fixed image pairs through a siamese architecture consisting of convolutional layers, thus extracting features of moving and fixed maps analogous to dense local descriptors, then matching the feature maps, and finally pass this correspondence feature map into a registration network, which directly outputs the registration parameters set of the rigid registration. Accuracy of the registration is quantified based on the Target Registration Error (TRE) for specific anatomical landmarks. Results of the registration process show a median TRE of 2.2 mm for all the fiducial points, and the registration computation time was around 3 ms comparing to the classic iterative methods which takes around 70 seconds for one image pair. In our future work we are going to perform our approach on 2D/3D learning-based registration to refine the estimation of the transesophageal probe pose in the 3D preoperative volume.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.