Sabrina Reiml, Daniel Toth, Maria Panayiotou, Bernhard Fahn, Rashed Karim, Jonathan Behar, Christopher Rinaldi, Reza Razavi, Kawal Rhode, Alexander Brost, Peter Mountney
KEYWORDS: Visualization, Heart, Lead, 3D modeling, Cardiology, Electrophysiology, Electrical breakdown, Image segmentation, Visual process modeling, 3D acquisition, 3D visualizations, Tissues, Magnetic resonance imaging
Heart failure is a serious disease affecting about 23 million people worldwide. Cardiac resynchronization therapy is used to treat patients suffering from symptomatic heart failure. However, 30% to 50% of patients have limited clinical benefit. One of the main causes is suboptimal placement of the left ventricular lead. Pacing in areas of myocardial scar correlates with poor clinical outcomes. Therefore precise knowledge of the individual patient’s scar characteristics is critical for delivering tailored treatments capable of improving response rates. Current research methods for scar assessment either map information to an alternative non-anatomical coordinate system or they use the image coordinate system but lose critical information about scar extent and scar distribution. This paper proposes two interactive methods for visualizing relevant scar information. A 2-D slice based approach with a scar mask overlaid on a 16 segment heart model and a 3-D layered mesh visualization which allows physicians to scroll through layers of scar from endocardium to epicardium. These complementary methods enable physicians to evaluate scar location and transmurality during planning and guidance. Six physicians evaluated the proposed system by identifying target regions for lead placement. With the proposed method more target regions could be identified.
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.
Benjamin Knowles, Dennis Caulfield, Matthew Ginks, Michael Cooklin, Julian Bostock, Aldo Rinaldi, Jaswinder Gill, Reza Razavi, Tobias Schaeffter, Kawal Rhode
The detection of radio-frequency ablation lesions has been shown to be feasible using delayed enhancement
magnetic resonance imaging (MRI). However, it is the determination of the lesion patterns that is of import
for correlation with clinical outcome and location of gaps. Visualisation of ablation patterns on two-dimensional
(2D) MR images is not intuitive. We present a technique for the three-dimensional (3D)
visualisation of ablation patterns by creating a surface from a segmentation of the cardiac chamber of
interest, fusing with the delayed enhancement MRI and integrating the MR signal along vectors normal to
the cardiac surface. Areas of delayed enhancement will have a larger integral value than healthy
myocardium. Maximum intensity projection (MIP) values were used to colour code the cardiac surface for
3D visualisation of the areas of delayed enhancement. The technique was applied to three patients with a
cardiac arrhythmia, with successful visualisation of the ablation pattern. Patterns of delayed enhancement
were correlated with ablation points derived from electro-anatomical mapping systems (EAMS) and were
found to have similar patterns. This visualisation technique allows for the intuitive visualisation of ablation
lesions and has many applications for use in electrophysiology.
Knee arthroscopy is a minimally invasive procedure that is routinely carried out for the diagnosis and treatment of
pathologies of the knee joint. A high level of expertise is required to carry out this procedure and therefore the clinical
training is extensive. There are several reasons for this that include the small field of view seen by the arthroscope and
the high degree of distortion in the video images. Several virtual arthroscopy simulators have been proposed to augment
the learning process. One of the limitations of these simulators is the generic models that are used. We propose to
develop a new virtual arthroscopy simulator that will allow the use of pathology-specific models with an increased level
of photo-realism. In order to generate these models we propose to use registered magnetic resonance images (MRI) and
arthroscopic video images collected from patients with a variety of knee pathologies. We present a method to perform
this registration based on the use of a combined X-ray and MR imaging system (XMR). In order to validate our
technique we carried out MR imaging and arthroscopy of a custom-made acrylic phantom in the XMR environment. The
registration between the two modalities was computed using a combination of XMR and camera calibration, and optical
tracking. Both two-dimensional (2D) and three-dimensional (3D) registration errors were computed and shown to be
approximately 0.8 and 3 mm, respectively. Further to this, we qualitatively tested our approach using a more realistic
plastic knee model that is used for the arthroscopy training.
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.
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).
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.
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.
XMR systems are a new type of interventional facility in which patients can be rapidly transferred between x-ray and MR systems on a floating table. We have previously developed a technique to register MR and x-ray images obtained from such systems. We are carrying out a program of XMR guided cardiac electrophysiology study (EPS) and radio frequency ablation (RFA). The aim of our work was to apply our registration technology to XMR guided EPS/RFA in order to integrate anatomical, electrophysiological and motion information. This would assist in guidance and allow us to validate and refine electromechanical models. Registration of the imaging modalities was achieved by a combination of system calibration and real-time optical tracking. Patients were initially imaged using MR imaging. An SSFP volume scan of the heart was acquired for anatomical information, followed by tagged scans for motion information. The patients were then transferred to the x-ray system. Tracked biplane x-ray images were acquired while electrical measurements were made from catheters placed in the heart. The relationship between the MR and x-ray images was determined. The MR volume scan of the heart was segmented and the tagged scans were analysed using a non-rigid registration algorithm to compute motion. The position of catheters was reconstructed within the MR cardiac anatomy. The anatomical, electrophysiological, and motion information were displayed in the same coordinate system. Simulations of electrical depolarisation and contraction were performed using electromechanical models of the myocardium. We present results for 2 initial cases. For patient 1, a contact mapping system was used for the EPS and for patient 2, a non-contact mapping system was used. Our XMR registration technique allows the integration of anatomical, electrophysiological, and motion information for patients undergoing EPS/RFA. This integrated approach has assisted in interventional guidance and has been used to validate electromechanical models of the myocardium.
Measuring changes in cardiac motion patterns can assist in diagnosing the onset of arrhythmia and ischaemia and in the follow-up of treatment. This work presents a methodology for measuring such motion changes from MR images. Non-rigid registration is used to track cardiac motion in a sequence of 3D tagged MR images. We use a cylindrical coordinate system to subdivide the myocardium
into smaller anatomically meaningful regions and to express motion derived measurements such as displacement and strain for each myocardial region during the cardiac cycle. In the first experiment we have evaluated the proposed methods using synthetic image sequences where the ground truth was available. These images were generated using a cardiac motion simulator for tagged MRI. Normal and abnormal motion fields were produced by modifying parameters in
a small region of the myocardium. In the second experiment we have acquired two separate tagged MR image sequences from five healthy volunteers. Both acquisitions have been carried out without moving the volunteer inside the scanner, thus avoiding potential misregistration errors due to subject motion between scans. In
addition, one of volunteers was subjected to stress during one of the
scans. In the final experiment we acquired tagged MR images from a patient with super-ventricular tachyarrhythmia, before and after radio frequency ablation. The image acquisition and catheter intervention were performed with a combined X-ray and MRI system. Detection results were correct on synthetic data and no region was incorrectly classified as having significant changes in the repetition studies. Significant changes in motion pattern were measured in the stress and ablation studies. Furthermore, results seem to corroborate that the ablation regularised cardiac contraction.
We have developed a waveform shape model-based algorithm for the extraction of blood flow from dynamic arterial x-ray angiographic images. We have carried out in-vitro validation of this technique. A pulsatile physiological blood flow circuit was constructed using an anthropomorphic cerebral vascular phantom to simulate the cerebral arterial circulation with whole blood as the fluid. Instantaneous recording of flow from an electromagnetic flow meter (EMF) provided the gold standard measurement. Biplane dynamic digital x-ray images of the vascular phantom with injection of contrast medium were acquired at 25 fps using a PC frame capture card with calibration using a Perspex cube. Principal component analysis was used to construct a shape model by collecting 434 flow waveforms from the EMF under varying flow conditions. Blood flow waveforms were calculated from the angiographic data by using our previous concentration-distance curve matching (ORG) algorithm and by using the new model-based (MB) algorithm. Both instantaneous and mean flow values calculated using the MB algorithm showed greater correlation, less bias, and lower variability than those calculated using the ORG algorithm when compared to the EMF values. We have successfully demonstrated that use of a priori waveform shape information can improve flow measurements from dynamic x-ray angiograms.
We have developed a weighted optical flow algorithm for the extraction of instantaneous blood velocity from dynamic digital x-ray images of blood vessels. We have carried out in- vitro validation of this technique. A pulsatile physiological blood flow circuit was constructed using sections of silicone tubing to simulate blood vessels with whole blood as the fluid. Instantaneous recording of flow from an electromagnetic flow meter (EMF) provided the gold standard measurement. Biplanar dynamic digital x-ray images of the blood vessel with injection of contrast medium were acquired at 25 fps using a PC frame capture card. Imaging of a Perspex calibration cube allowed 3D reconstruction of the vessel and determination of true dimensions. Blood flow waveforms were calculated off-line on a Sun workstation using the new algorithm. The correlation coefficient between instantaneous blood flow values obtained from the EMF and the x-ray method was r equals 0.871, n equals 1184, p less than 0.0001. The correlation coefficient for average blood flow was r equals 0.898, n equals 16, p less than 0.001. We have successfully demonstrated that our new algorithm can measure pulsatile blood flow in a vessel phantom. We aim to use this algorithm to measure blood flow clinically in patients undergoing vascular interventional procedures.
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