Integration of cardiac and respiratory motion into MRI roadmaps fused with x‐ray. Issue 3 (8th February 2013)

• Record Type: Journal Article
• Authors: Faranesh, Anthony Z.; Kellman, Peter; Ratnayaka, Kanishka; Lederman, Robert J.
• Is Part Of: Medical physics. Volume 40:Issue 3(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-02-08
• Subjects: Magnetic resonance imaging -- Pneumodyamics, respiration -- Registration -- Edge enhancement -- Fluoroscopy; biomedical MRI -- blood vessels -- diagnostic radiography -- electrocardiography -- image enhancement -- image motion analysis -- image registration -- mean square error methods -- medical image processing -- pneumodynamics; x‐ray -- MRI -- fusion -- motion; Electrocardiography, i.e. ecg -- Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image -- Analysis of motion; Medical imaging -- Heart -- Medical X‐ray imaging -- Cardiac dynamics -- Magnetic resonance imaging -- Fluoroscopy -- X‐ray imaging -- Computed tomography -- Vascular system -- Medical magnetic resonance imaging; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4789919
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : Volumetric roadmaps overlaid on live x‐ray fluoroscopy may be used to enhance image guidance during interventional procedures. These roadmaps are often static and do not reflect cardiac or respiratory motion. In this work, the authors present a method for integrating cardiac and respiratory motion into magnetic resonance imaging (MRI)‐derived roadmaps to fuse with live x‐ray fluoroscopy images, and this method was tested in large animals. Methods: : Real‐time MR images were used to capture cardiac and respiratory motion. Nonrigid registration was used to calculate motion fields to deform a reference end‐expiration, end‐diastolic image to different cardiac and respiratory phases. These motion fields were fit to separate affine motion models for the aorta and proximal right coronary artery. Under x‐ray fluoroscopy, an image‐based navigator and ECG signal were used as inputs to deform the roadmap for live overlay. The in vivo accuracy of motion correction was measured in four swine as the ventilator tidal volume was varied. Results: : Motion correction reduced the root‐mean‐square error between the roadmaps and manually drawn centerlines, even under high tidal volume conditions. For the aorta, the error was reduced from 2.4 ± 1.5 mm to 2.2 ± 1.5 mm ( p < 0.05). For the proximal right coronary artery, the error was reduced from 8.8 ± 16.2 mm to 4.3 ± 5.2 mm ( p < 0.001). Using real‐time MRI and an affine motion model it is feasible to incorporate physiological cardiac and respiratory motion into MRI‐derived roadmaps to provide enhanced image guidance for interventional procedures. Conclusions: : A method has been presented for creating dynamic 3D roadmaps that incorporate cardiac and respiratory motion. These roadmaps can be overlaid on live X‐ray fluoroscopy to enhance image guidance for cardiac interventions.