Abstract
Background Quantification of edema and scar maps with cardiac MR images (cMRIs) enables effective Radiofrequency Ablation (RFA) of arrhythmias during the Electrophysiology (EP) procedure [1]. This demonstrates the paramount advantage over the EP catheterization under X-ray and ultrasound guidance. High-contrast and resolution cMRIs can be obtained preoperatively as a EP roadmap for surgical planning of RFA, whilst real-time MRI (rtMRI) can be used to guide catheterization and update the cMRI model [2] to provide intraoperative visualization of a 3D vascular map. A fast and efficient technique of non-rigid image co-registration is required. Although feature-based registration methods can be rapidly processed by computing sparse features, the outcome is sensitive to blurred images with artifacts that happens regularly in low-resolution rt-MRI, causing significant errors in feature detections. With the use of Field-programmable Gate Array (FPGA), we hypothesized that novel data structure and architecture of memory access can allow robust registration based on comparison of image intensity patterns, thus fulfilling the real-time requirements for clinical practice.
Highlights
Quantification of edema and scar maps with cardiac MR images enables effective Radiofrequency Ablation (RFA) of arrhythmias during the Electrophysiology (EP) procedure [1]
The 3D Demons was applied to the corresponding images in 3D
Acquiring image gradient is a common step in intensitybased registration methods [3] (e.g. Demons [4]), and the primary computation bottleneck
Summary
Quantification of edema and scar maps with cardiac MR images (cMRIs) enables effective Radiofrequency Ablation (RFA) of arrhythmias during the Electrophysiology (EP) procedure [1]. This demonstrates the paramount advantage over the EP catheterization under X-ray and ultrasound guidance. With the use of Field-programmable Gate Array (FPGA), we hypothesized that novel data structure and architecture of memory access can allow robust registration based on comparison of image intensity patterns, fulfilling the real-time requirements for clinical practice. Rapid computation of image registration is achieved by 1) the highly-customized PUs; 2) the parallelism of multiple PUs and pixel/voxel memories; and 3) bandwidth reduction through inter-PUs information exchange channels
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