We present a novel method to register three-dimensional echocardiography (echo) images with magnetic resonance
images (MRI) based on anatomical features, which could be used in the registration pipeline for overlaying MRI-derived
roadmaps onto two-dimensional live X-ray images in electrophysiology (EP) procedures. The features used in image
registration are the surface of the left ventricle and a manually defined centerline of the descending aorta. The MR-derived
surface is generated using a fully automated algorithm, and the echo-derived surface is produced using a semi-automatic
process. We test our method on six volunteers and three patients. We validated registration accuracy using two
methods. The first calculated a root mean square distance error using anatomical landmarks. The second method used
catheters as landmarks in one clinical EP procedure. Results show a mean error of 4.24 mm, which is acceptable for our
clinical application, and no failed registrations were observed. In addition, our algorithm works on clinical data, is fast
and only requires a small amount of manual input, and so it is applicable to use during EP procedures.
This paper presents the evaluation of the use of multimodality skin markers for the registration of cardiac magnetic
resonance (MR) image data to x-ray fluoroscopy data for the guidance of cardiac electrophysiology procedures. The
approach was validated using a phantom study and 3 patients undergoing pulmonary vein (PV) isolation for the treatment
of paroxysmal atrial fibrillation. In the patient study, skin markers were affixed to the patients' chest and used to register
pre-procedure cardiac MR image data to intra-procedure fluoroscopy data. Registration errors were assessed using
contrast angiograms of the left atrium that were available in 2 out of 3 cases. A clinical expert generated "gold standard"
registrations by adjusting the registration manually. Target registration errors (TREs) were computed using points on the
PV ostia. Ablation locations were computed using biplane x-ray imaging. Registration errors were further assessed by
computing the distances of the ablation points to the registered left atrial surface for all 3 patients. The TREs were 6.0 &
3.1mm for patients 1 & 2. The mean ablation point errors were 6.2, 3.8, & 3.0mm for patients 1, 2, & 3. These results are
encouraging in the context of a 5mm clinical accuracy requirement for this type of procedure. We conclude that
multimodality skin markers have the potential to provide anatomical image integration for x-ray guided cardiac
electrophysiology procedures, especially if coupled with an accurate respiratory motion compensation strategy.
We present a novel method to calibrate a 3D ultrasound probe which has a 2D transducer array. By optically tracking a calibrated 3D probe we are able to produce extended field of view 3D ultrasound images. Tracking also enables us to register our ultrasound images to other tracked and calibrated surgical instruments or to other tracked and calibrated imaging devices. Our method applies rigid intensity-based image registration to three or more ultrasound images. These images can either be of a simple phantom, or could potentially be images of the patient. In this latter case we would have an automated calibration system which required no phantom, no image segmentation and was optimized to the patient's ultrasound characteristics i.e. speed of sound. We have carried out experiments using a simple calibration phantom and with ultrasound images of a volunteer's liver. Results are compared to an independent gold-standard. These showed our method to be accurate to 1.43mm using the phantom images and 1.56mm using the liver data, which is slightly better than the traditional point-based calibration method (1.7mm in our experiments).
A hybrid X-ray and magnetic resonance imaging system (XMR) has been proposed as an interventional guidance for
cardiovascular catheterisation procedure. However, very few hospitals can benefit from the XMR system because of its
limited availability. In this paper we describe a new guidance strategy for cardiovascular catheterisation procedure. In
our technique, intra-operative patient position is estimated by using a chest surface reconstructed from a
photogrammetry system. The chest surface is then registered with the same surface derived from pre-procedure magnetic
resonance (MR) images. The catheterisation procedure can therefore be guided by a roadmap derived from the MR
images. Patients were required to hold the breath at end expiration during MRI acquisition. The surface matching
accuracy is improved by using a robust trimmed iterative closest point (ICP) matching algorithm, which is especially
designed for incomplete surface matching. Compared to the XMR system, the proposed guidance strategy is low cost
and easy to set up. Experimental data were acquired from 6 volunteers and 1 patient. The patient data were collected
during an electrophysiology procedure. In 6 out of 7 subjects, the experimental results show our method is accurate in
term of reciprocal residual error (range from 1.66m to 3.75mm) and constant (closed-loop TREs range from 1.49mm to
3.55mm). For one subject, trimmed ICP failed to find the optimal transform matrix (residual = 4.89, TRE = 9.32) due to
the poor quality of the photogrammetry-reconstructed surface. More studies are being carried on in clinical trials.
This paper presents a technique for compensating for respiratory motion and deformation in an augmented
reality system for cardiac catheterisation procedures. The technique uses a subject-specific affine model of
cardiac motion which is quickly constructed from a pre-procedure magnetic resonance imaging (MRI) scan.
Respiratory phase information is acquired during the procedure by tracking the motion of the diaphragm in
real-time X-ray images. This information is used as input to the model which uses it to predict the position
of structures of interest during respiration. 3-D validation is performed on 4 volunteers and 4 patients using a
leave-one-out test on manually identified anatomical landmarks in the MRI scan, and 2-D validation is performed
by using the model to predict the respiratory motion of structures of the heart which contain catheters that are
visible in X-ray images. The technique is shown to reduce 3-D registration errors due to respiratory motion from
up to 15mm down to less than 5mm, which is within clinical requirements for many procedures. 2-D validation
showed that accuracy improved from 14mm to 2mm. In addition, we use the model to analyse the effects of
different types of breathing on the motion and deformation of the heart, specifically increasing the breathing rate
and depth of breathing. Our findings suggest that the accuracy of the model is reduced if the subject breathes
in a different way during model construction and application. However, models formed during deep breathing
may be accurate enough to be applied to other types of breathing.
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