Abstract:In X-ray fluoroscopy, static overlays are used to visualize soft tissue. We propose a system for cardiac and respiratory motion compensation of these overlays. It consists of a 3-D motion model created from real-time MR imaging. Multiple sagittal slices are acquired and retrospectively stacked to consistent 3-D volumes. Slice stacking considers cardiac information derived from the ECG and respiratory information extracted from the images. Additionally, temporal smoothness of the stacking is enhanced. Motion is… Show more
“…For cardiac interventions including PCI, the organ motion is mainly affected by respiratory and cardiac motion. Many previous works often built a motion model parameterized by a cardiac signal derived from ECG and a respiratory signal obtained from diaphragm tracking ( [37,42,13]) or automatic PCAbased surrogate ( [15]). Some other works model only the respiratory motion in cardiac-gated images ( [35,21,30]).…”
Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using non-contrast-enhanced fluoroscopic images, since higher use of contrast agents increases the risk of kidney failure. When using fluoroscopic images, the interventional cardiologist needs to rely on a mental anatomical reconstruction. This paper reports on the development of a novel dynamic coronary roadmapping approach for improving visual feedback and reducing contrast use during PCI. The approach compensates cardiac and respiratory induced vessel motion by ECG alignment and catheter tip tracking in X-ray fluoroscopy, respectively. In particular, for accurate and robust tracking of the catheter tip, we proposed a new deep learning based Bayesian filtering method that integrates the detection outcome of a convolutional neural network and the motion estimation between frames using a particle filtering framework. The proposed roadmapping and tracking approaches were validated on clinical X-ray images, achieving accurate performance on both catheter tip tracking and dynamic coronary roadmapping experiments. In addition, our approach runs in real-time on a computer with a single GPU and has the potential to be integrated into the clinical workflow of PCI procedures, providing cardiologists with visual guidance during interventions without the need of extra use of contrast agent.
“…For cardiac interventions including PCI, the organ motion is mainly affected by respiratory and cardiac motion. Many previous works often built a motion model parameterized by a cardiac signal derived from ECG and a respiratory signal obtained from diaphragm tracking ( [37,42,13]) or automatic PCAbased surrogate ( [15]). Some other works model only the respiratory motion in cardiac-gated images ( [35,21,30]).…”
Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using non-contrast-enhanced fluoroscopic images, since higher use of contrast agents increases the risk of kidney failure. When using fluoroscopic images, the interventional cardiologist needs to rely on a mental anatomical reconstruction. This paper reports on the development of a novel dynamic coronary roadmapping approach for improving visual feedback and reducing contrast use during PCI. The approach compensates cardiac and respiratory induced vessel motion by ECG alignment and catheter tip tracking in X-ray fluoroscopy, respectively. In particular, for accurate and robust tracking of the catheter tip, we proposed a new deep learning based Bayesian filtering method that integrates the detection outcome of a convolutional neural network and the motion estimation between frames using a particle filtering framework. The proposed roadmapping and tracking approaches were validated on clinical X-ray images, achieving accurate performance on both catheter tip tracking and dynamic coronary roadmapping experiments. In addition, our approach runs in real-time on a computer with a single GPU and has the potential to be integrated into the clinical workflow of PCI procedures, providing cardiologists with visual guidance during interventions without the need of extra use of contrast agent.
“…With a tracking speed of 16 FPS, the framework can be used for real-time motion compensation, as the average maximum frame rate for modern intervention X-ray systems is 15 FPS. Using the 2D position of the proximal electrode of a CS catheter can recover up to 71% of respiratory motion, which is a similar result compared with [32]. This was compared against the static roadmap and the percentage of motion recovered is calculated as Our solution is not limited to the CS catheter and it could use any wire inserted into heart vessels or chambers as long as it remains stationary and only moves with respiratory and cardiac motions.…”
Objective: Catheters and wires are used extensively in cardiac catheterization procedures. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications such as motion compensation and co-registration between 2D and 3D imaging modalities. Detecting the complete length of a catheter or wire object as well as electrode positions on the catheter or wire is a challenging task. Method: In this paper, an automatic detection framework for catheters and wires is developed. It is based on path reconstruction from image tensors, which are eigen direction vectors generated from a multiscale vessel enhancement filter. A catheter or a wire object is detected as the smooth path along those eigen direction vectors. Furthermore, a real-time tracking method based on a template generated from the detection method was developed. Results: The proposed framework was tested on a total of 7,754 X-ray images. Detection errors for catheters and guidewires are 0.56 ± 0.28 mm and 0.68 ± 0.33 mm, respectively. The proposed framework was also tested and validated in two clinical applications. For motion compensation using catheter tracking, the 2D target registration errors (TRE) of 1.8 mm ± 0.9 mm was achieved. For coregistration between 2D X-ray images and 3D models from MRI images, a TRE of 2.3 ± 0.9 mm was achieved. Conclusion: A novel and fully automatic detection framework and its clinical applications are developed. Significance: The proposed framework can be applied to improve the accuracy of imageguidance systems for cardiac catheterization procedures.
“…Multiâinstitutional studies, ideally representing all of the available vendor specific overlay software and incorporating use of preâacquired clinically indicated MRI scans, would be particularly helpful in capturing enough patients for the assessment of congenital interventions . Registration issues may be partially ameliorated in the future by incorporation of cardiac and respiratory motion but XFM will always be limited by the fact that the overlay is based on imaging that does not change in realâtime with the patient's condition and stage of intervention. Ultimately, if soft tissue visualization is necessary to guide interventions, rather than use a 3D image acquired before the intervention and then overlaid onto live fluoroscopy, it would be ideal to perform cases under realâtime 3D soft tissue MRIâguidance.…”
Section: Discussionmentioning
confidence: 99%
“…Multi-institutional studies, ideally representing all of the available vendor specific overlay software and incorporating use of pre-acquired clinically indicated MRI scans, would be particularly helpful in capturing enough patients for the assessment of congenital interventions. 26 Registration issues may be partially ameliorated in the future by incorporation of cardiac and respiratory motion 21,22 but XFM will always be limited by the fact that the over-…”
Objectives
To determine whether Xâray fused with MRI (XFM) is beneficial for select transcatheter congenital heart disease interventions.
Background
Complex transcatheter interventions often require threeâdimensional (3D) soft tissue imaging guidance. Fusion imaging with live Xâray fluoroscopy can potentially improve and simplify procedures.
Methods
Patients referred for select congenital heart disease interventions were prospectively enrolled. Cardiac MRI data was overlaid on live fluoroscopy for procedural guidance. Likert scale operator assessments of value were recorded. Fluoroscopy time, radiation exposure, contrast dose, and procedure time were compared to matched cases from our institutional experience.
Results
Fortyâsix patients were enrolled. Preâcatheterization, same day cardiac MRI findings indicated intervention should be deferred in nine patients. XFMâguided cardiac catheterization was performed in 37 (median age 8.7 years [0.5â63âyears]; median weight 28âkg [5.6â110âkg]) with the following prespecified indications: pulmonary artery (PA) stenosis (n =â13), aortic coarctation (n =â12), conduit stenosis/insufficiency (n = 9), and ventricular septal defect (n = 3). Diagnostic catheterization showed intervention was not indicated in 12 additional cases. XFMâguided intervention was performed in the remaining 25. Fluoroscopy time was shorter for XFMâguided intervention cases compared to matched controls. There was no significant difference in radiation dose area product, contrast volume, or procedure time. Operator Likert scores indicated XFM provided useful soft tissue guidance in all cases and was never misleading.
Conclusions
XFM provides operators with meaningful threeâdimensional soft tissue data and reduces fluoroscopy time in select congenital heart disease interventions.
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