A Constant-Force End-Effector With Online Force Adjustment for Robotic Ultrasonography.

In this letter, we propose a novel constant-force end-effector (CFEE) to address current limitations in robotic ultrasonography. The CFEE uses a parallel, motor-spring-based solution to precisely generate constant operating forces over a wide range and enable the ultrasound (US) probe to adapt to the abdominal contours autonomously. A displacement measurement unit was developed to realize the acquisition of probe position and precise control of the operating force. Moreover, the operating force can be adjusted online to maintain safety and continuity of operation. Simulations and experiments were carried out to evaluate the performance. Results show that the proposed CFEE can provide constant forces of 4-12 N with displacements of 0-8 mm. The maximum relative error of force generation is 8.28%, and the accuracy and precision for displacement measurement are 0.29 mm and ±0.16 mm, respectively. Various operating forces can be adjusted online during the same operation. Ultrasound images acquired by the proposed CFEE are of equally good quality compared to a manual sonographer scan. The proposed CFEE would have potential further medical applications.

Multimodality imaging for patient evaluation and guidance of catheter ablation for atrial fibrillation - current status and future perspective.

Left atrial catheter ablation is an established non-pharmacological therapy for the treatment of atrial fibrillation. The importance of a noninvasive multimodality imaging approach is emphasized by the current guidelines for the various phases of the ablation work-up e.g. patient identification, therapy guidance and procedural evaluation. Advances in the capabilities of imaging modalities and the increasing cost of healthcare warrant a review of the multimodality approach. This review discusses the application of cardiac imaging for pulmonary vein and left atrial ablation divided into stages: pre-procedural stage (assessment of left atrial dimensions, left atrial appendage thrombus and pulmonary vein anatomy), peri-procedural stage (integration of anatomical and electrical information) and post-procedural stage (evaluation of efficacy by assessment of tissue properties). Each section is dedicated to one of the subtopics of a stage, allowing a thorough comparison to be made between the strengths and weaknesses of the different imaging modalities and the identification of one that exhibits the potential for a single technique approach.

A statistical model of catheter motion from interventional x-ray images: application to image-based gating.

The motion and deformation of catheters that lie inside cardiac structures can provide valuable information about the motion of the heart. In this paper we describe the formation of a novel statistical model of the motion of a coronary sinus (CS) catheter based on principal component analysis of tracked electrode locations from standard mono-plane x-ray fluoroscopy images. We demonstrate the application of our model for the purposes of retrospective cardiac and respiratory gating of x-ray fluoroscopy images in normal dose x-ray fluoroscopy images, and demonstrate how a modification of the technique allows application to very low dose scenarios. We validated our method on ten mono-plane imaging sequences comprising a total of 610 frames from ten different patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. For normal dose images we established systole, end-inspiration and end-expiration gating with success rates of 100%, 92.1% and 86.9%, respectively. For very low dose applications, the method was tested on the same ten mono-plane x-ray fluoroscopy sequences without noise and with added noise at signal to noise ratio (SNR) values of √50, √10, √8, √6, √5, √2 and √1 to simulate the image quality of increasingly lower dose x-ray images. The method was able to detect the CS catheter even in the lowest SNR images with median errors not exceeding 2.6 mm per electrode. Furthermore, gating success rates of 100%, 71.4% and 85.7% were achieved at the low SNR value of √2, representing a dose reduction of more than 25 times. Thus, the technique has the potential to extract useful information whilst substantially reducing the radiation exposure.

Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes.

Patient-specific cardiac modeling can help in understanding pathophysiology and therapy planning. However it requires to combine functional and anatomical data in order to build accurate models and to personalize the model geometry, kinematics, electrophysiology and mechanics. Personalizing the electromechanical coupling from medical images is a challenging task. We use the Bestel-Clément-Sorine (BCS) electromechanical model of the heart, which provides reasonable accuracy with a reasonable number of parameters (14 for each ventricle) compared to the available clinical data at the organ level. We propose a personalization strategy from cine MRI data in two steps. We first estimate global parameters with an automatic calibration algorithm based on the Unscented Transform which allows to initialize the parameters while matching the volume and pressure curves. In a second step we locally personalize the contractilities of all AHA (American Heart Association) zones of the left ventricle using the reduced order unscented Kalman filtering on Regional Volumes. This personalization strategy was validated synthetically and tested successfully on eight healthy and three pathological cases.

Benchmarking framework for myocardial tracking and deformation algorithms: an open access database.

In this paper we present a benchmarking framework for the validation of cardiac motion analysis algorithms. The reported methods are the response to an open challenge that was issued to the medical imaging community through a MICCAI workshop. The database included magnetic resonance (MR) and 3D ultrasound (3DUS) datasets from a dynamic phantom and 15 healthy volunteers. Participants processed 3D tagged MR datasets (3DTAG), cine steady state free precession MR datasets (SSFP) and 3DUS datasets, amounting to 1158 image volumes. Ground-truth for motion tracking was based on 12 landmarks (4 walls at 3 ventricular levels). They were manually tracked by two observers in the 3DTAG data over the whole cardiac cycle, using an in-house application with 4D visualization capabilities. The median of the inter-observer variability was computed for the phantom dataset (0.77 mm) and for the volunteer datasets (0.84 mm). The ground-truth was registered to 3DUS coordinates using a point based similarity transform. Four institutions responded to the challenge by providing motion estimates for the data: Fraunhofer MEVIS (MEVIS), Bremen, Germany; Imperial College London - University College London (IUCL), UK; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Inria-Asclepios project (INRIA), France. Details on the implementation and evaluation of the four methodologies are presented in this manuscript. The manually tracked landmarks were used to evaluate tracking accuracy of all methodologies. For 3DTAG, median values were computed over all time frames for the phantom dataset (MEVIS=1.20mm, IUCL=0.73 mm, UPF=1.10mm, INRIA=1.09 mm) and for the volunteer datasets (MEVIS=1.33 mm, IUCL=1.52 mm, UPF=1.09 mm, INRIA=1.32 mm). For 3DUS, median values were computed at end diastole and end systole for the phantom dataset (MEVIS=4.40 mm, UPF=3.48 mm, INRIA=4.78 mm) and for the volunteer datasets (MEVIS=3.51 mm, UPF=3.71 mm, INRIA=4.07 mm). For SSFP, median values were computed at end diastole and end systole for the phantom dataset(UPF=6.18 mm, INRIA=3.93 mm) and for the volunteer datasets (UPF=3.09 mm, INRIA=4.78 mm). Finally, strain curves were generated and qualitatively compared. Good agreement was found between the different modalities and methodologies, except for radial strain that showed a high variability in cases of lower image quality.

Preliminary specificity study of the Bestel-Clément-Sorine electromechanical model of the heart using parameter calibration from medical images.

Patient-specific cardiac modelling can help in understanding pathophysiology and predict therapy effects. This requires the personalization of the geometry, kinematics, electrophysiology and mechanics. We use the Bestel-Clément-Sorine (BCS) electromechanical model of the heart, which provides reasonable accuracy with a reduced parameter number compared to the available clinical data at the organ level. We propose a preliminary specificity study to determine the relevant global parameters able to differentiate the pathological cases from the healthy controls. To this end, a calibration algorithm on global measurements is developed. This calibration method was tested successfully on 6 volunteers and 2 heart failure cases and enabled to tune up to 7 out of the 14 necessary parameters of the BCS model, from the volume and pressure curves. This specificity study confirmed domain-knowledge that the relaxation rate is impaired in post-myocardial infarction heart failure and the myocardial stiffness is increased in dilated cardiomyopathy heart failures.

Understanding the mechanisms amenable to CRT response: from pre-operative multimodal image data to patient-specific computational models.

This manuscript describes our recent developments towards better understanding of the mechanisms amenable to cardiac resynchronization therapy response. We report the results from a full multimodal dataset corresponding to eight patients from the euHeart project. The datasets include echocardiography, MRI and electrophysiological studies. We investigate two aspects. The first one focuses on pre-operative multimodal image data. From 2D echocardiography and 3D tagged MRI images, we compute atlas based dyssynchrony indices. We complement these indices with presence and extent of scar tissue and correlate them with CRT response. The second one focuses on computational models. We use pre-operative imaging to generate a patient-specific computational model. We show results of a fully automatic personalized electromechanical simulation. By case-per-case discussion of the results, we highlight the potential and key issues of this multimodal pipeline for the understanding of the mechanisms of CRT response and a better patient selection.

Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation.

Cardiac resynchronisation therapy (CRT) is an effective treatment for patients with congestive heart failure and a wide QRS complex. However, up to 30% of patients are non-responders to therapy in terms of exercise capacity or left ventricular reverse remodelling. A number of controversies still remain surrounding patient selection, targeted lead implantation and optimisation of this important treatment. The development of biophysical models to predict the response to CRT represents a potential strategy to address these issues. In this article, we present how the personalisation of an electromechanical model of the myocardium can predict the acute haemodynamic changes associated with CRT. In order to introduce such an approach as a clinical application, we needed to design models that can be individualised from images and electrophysiological mapping of the left ventricle. In this paper the personalisation of the anatomy, the electrophysiology, the kinematics and the mechanics are described. The acute effects of pacing on pressure development were predicted with the in silico model for several pacing conditions on two patients, achieving good agreement with invasive haemodynamic measurements: the mean error on dP/dt(max) is 47.5±35mmHgs(-1), less than 5% error. These promising results demonstrate the potential of physiological models personalised from images and electrophysiology signals to improve patient selection and plan CRT.

Evaluation of a real-time hybrid three-dimensional echo and X-ray imaging system for guidance of cardiac catheterisation procedures.

Minimally invasive cardiac surgery is made possible by image guidance technology. X-ray fluoroscopy provides high contrast images of catheters and devices, whereas 3D ultrasound is better for visualising cardiac anatomy. We present a system in which the two modalities are combined, with a trans-esophageal echo volume registered to and overlaid on an X-ray projection image in real-time. We evaluate the accuracy of the system in terms of both temporal synchronisation errors and overlay registration errors. The temporal synchronisation error was found to be 10% of the typical cardiac cycle length. In 11 clinical data sets, we found an average alignment error of 2.9 mm. We conclude that the accuracy result is very encouraging and sufficient for guiding many types of cardiac interventions. The combined information is clinically useful for placing the echo image in a familiar coordinate system and for more easily identifying catheters in the echo volume.

Whole heart segmentation of cardiac MRI using multiple path propagation strategy.

Automatic segmentation of cardiac MRI is an important but challenging task in clinical study of cardiac morphology. Recently, fusing segmentations from multiple classifiers has been shown to achieve more accurate results than a single classifier. In this work, we propose a new strategy, MUltiple Path Propagation and Segmentation (MUPPS), in contrast with the currently widely used multi-atlas propagation and segmentation (MAPS) scheme. We showed that MUPPS outperformed the standard MAPS in the experiment using twenty-one in vivo cardiac MR images. Furthermore, we studied and compared different path selection strategies for the MUPPS, to pursue an efficient implementation of the segmentation framework. We showed that the path ranking scheme using the image similarity after an affine registration converged faster and only needed eleven classifiers from the atlas repository. The fusion of eleven propagation results using the proposed path ranking scheme achieved a mean Dice score of 0.911 in the whole heart segmentation and the highest gain of accuracy was obtained from myocardium segmentation.

Coupled personalisation of electrophysiology models for simulation of induced ischemic ventricular tachycardia.

Despite recent efforts in cardiac electrophysiology modelling, there is still a strong need to make macroscopic models usable in planning and assistance of the clinical procedures. This requires model personalisation i.e. estimation of patient-specific model parameters and computations compatible with clinical constraints. Fast macroscopic models allow a quick estimation of the tissue conductivity, but are often unreliable in prediction of arrhythmias. On the other side, complex biophysical models are quite expensive for the tissue conductivity estimation, but are well suited for arrhythmia predictions. Here we present a coupled personalisation framework, which combines the benefits of the two models. A fast Eikonal (EK) model is used to estimate the conductivity parameters, which are then used to set the parameters of a biophysical model, the Mitchell-Schaeffer (MS) model. Additional parameters related to Action Potential Duration (APD) and APD restitution curves for the tissue are estimated for the MS model. This framework is applied to a clinical dataset provided with an hybrid X-Ray/MR imaging on an ischemic patient. This personalised MS Model is then used for in silico simulation of clinical Ventricular Tachycardia (VT) stimulation protocol to predict the induction of VT. This proof of concept opens up possibilities of using VT induction modelling directly in the intervention room, in order to plan the radio-frequency ablation lines.

Registering preprocedure volumetric images with intraprocedure 3-D ultrasound using an ultrasound imaging model.

For many image-guided interventions there exists a need to compute the registration between preprocedure image(s) and the physical space of the intervention. Real-time intraprocedure imaging such as ultrasound (US) can be used to image the region of interest directly and provide valuable anatomical information for computing this registration. Unfortunately, real-time US images often have poor signal-to-noise ratio and suffer from imaging artefacts. Therefore, registration using US images can be challenging and significant preprocessing is often required to make the registrations robust. In this paper we present a novel technique for computing the image-to-physical registration for minimally invasive cardiac interventions using 3-D US. Our technique uses knowledge of the physics of the US imaging process to reduce the amount of preprocessing required on the 3-D US images. To account for the fact that clinical US images normally undergo significant image processing before being exported from the US machine our optimization scheme allows the parameters of the US imaging model to vary. We validated our technique by computing rigid registrations for 12 cardiac US/magnetic resonance imaging (MRI) datasets acquired from six volunteers and two patients. The technique had mean registration errors of 2.1-4.4 mm, and 75% capture ranges of 5-30 mm. We also demonstrate how the same approach can be used for respiratory motion correction: on 15 datasets acquired from five volunteers the registration errors due to respiratory motion were reduced by 45%-92%.

Respiratory motion correction for image-guided cardiac interventions using 3-D echocardiography.

In this paper, we investigate the use of 3-D echocardiography (echo) data for respiratory motion correction of roadmaps in image-guided cardiac interventions. This is made possible by tracking and calibrating the echo probe and registering it to the roadmap coordinate system. We compare two techniques. The first uses only echo-echo registration to predict a motion-correction transformation in roadmap coordinates. The second combines echo-echo registration with a model of the respiratory motion of the heart. Using experiments with cardiac MRI and 3-D echo data acquired from eight volunteers, we demonstrate that the second technique is more robust than the first, resulting in motion-correction transformations that were accurate to within 5mm in 60% of cases, compared to 42% for the echo-only technique, based on subjective visual assessments. Objective validation showed that the model-based technique had an accuracy of 3.3 + or - 1.1mm, compared to 4.1 + or - 2.2mm for the echo only technique. The greater errors of the echo-only technique were mostly found away from the area of echo coverage. The model-based technique was more robust away from this area, and also has significant benefits in terms of computational cost.

A subject-specific technique for respiratory motion correction in image-guided cardiac catheterisation procedures.

We describe a system for respiratory motion correction of MRI-derived roadmaps for use in X-ray guided cardiac catheterisation procedures. The technique uses a subject-specific affine motion model that is quickly constructed from a short pre-procedure MRI scan. We test a dynamic MRI sequence that acquires a small number of high resolution slices, rather than a single low resolution volume. Additionally, we use prior knowledge of the nature of cardiac respiratory motion by constraining the model to use only the dominant modes of motion. During the procedure the motion of the diaphragm is tracked in X-ray fluoroscopy images, allowing the roadmap to be updated using the motion model. X-ray image acquisition is cardiac gated. Validation is performed on four volunteer datasets and three patient datasets. The accuracy of the model in 3D was within 5mm in 97.6% of volunteer validations. For the patients, 2D accuracy was improved from 5 to 13mm before applying the model to 2-4mm afterwards. For the dynamic MRI sequence comparison, the highest errors were found when using the low resolution volume sequence with an unconstrained model.

Anisotropic wave propagation and apparent conductivity estimation in a fast electrophysiological model: application to XMR interventional imaging.

Cardiac arrhythmias are increasingly being treated using ablation procedures. Development of fast electrophysiological models and estimation of parameters related to conduction pathologies can aid in the investigation of better treatment strategies during Radio-frequency ablations. We present a fast electrophysiological model incorporating anisotropy of the cardiac tissue. A global-local estimation procedure is also outlined to estimate a hidden parameter (apparent electrical conductivity) present in the model. The proposed model is tested on synthetic and real data derived using XMR imaging. We demonstrate a qualitative match between the estimated conductivity parameter and possible pathology locations. This approach opens up possibilities to directly integrate modelling in the intervention room.

Using combined x-ray and MR imaging for prostate I-125 post-implant dosimetry: phantom validation and preliminary patient work.

Post-implantation dosimetry is an important element of permanent prostate brachytherapy. This process relies on accurate localization of implanted seeds relative to the surrounding organs. Localization is commonly achieved using CT images, which provide suboptimal prostate delineation. On MR images, conversely, prostate visualization is excellent but seed localization is imprecise due to distortion and susceptibility artefacts. This paper presents a method based on fused MR and x-ray images acquired consecutively in a combined x-ray and MRI interventional suite. The method does not rely on any explicit registration step but on a combination of system calibration and tracking. A purpose-built phantom was imaged using MRI and x-rays, and the images were successfully registered. The same protocol was applied to three patients where combining soft tissue information from MRI with stereoscopic seed identification from x-ray imaging facilitated post-implant dosimetry. This technique has the potential to improve on dosimetry using either CT or MR alone.

Simulation of cardiac pathologies using an electromechanical biventricular model and XMR interventional imaging.

Simulating cardiac electromechanical activity is of great interest for a better understanding of pathologies and for therapy planning. Design and validation of such models is difficult due to the lack of clinical data. XMR systems are a new type of interventional facility in which patients can be rapidly transferred between X-ray and MR systems. Our goal is to design and validate an electromechanical model of the myocardium using XMR imaging. The proposed model is computationally fast and uses clinically observable parameters. We present the integration of anatomy, electrophysiology, and motion from patient data. Pathologies are introduced in the model and simulations are compared to measured data. Initial qualitative comparison on the two clinical cases presented is encouraging. Once fully validated, these models will make it possible to simulate different interventional strategies.

Tissue deformation and shape models in image-guided interventions: a discussion paper.

This paper promotes the concept of active models in image-guided interventions. We outline the limitations of the rigid body assumption in image-guided interventions and describe how intraoperative imaging provides a rich source of information on spatial location of anatomical structures and therapy devices, allowing a preoperative plan to be updated during an intervention. Soft tissue deformation and variation from an atlas to a particular individual can both be determined using non-rigid registration. Established methods using free-form deformations have a very large number of degrees of freedom. Three examples of deformable models--motion models, biomechanical models and statistical shape models--are used to illustrate how prior information can be used to restrict the number of degrees of freedom of the registration algorithm and thus provide active models for image-guided interventions. We provide preliminary results from applications for each type of model.

A fast-marching approach to cardiac electrophysiology simulation for XMR interventional imaging.

Cardiac ablation procedures are becoming more routine to treat arrhythmias. The development of electrophysiological models will allow investigation of treatment strategies. However, current models are computationally expensive and often too complex to be adjusted with current clinical data. In this paper, we have proposed a fast algorithm to solve Eikonal-based models on triangular meshes. These models can be used to extract hidden parameters of the cardiac function from clinical data in a very short time, thus could be used during interventions. We propose a first approach to estimate these parameters, and have tested it on synthetic and real data derived using XMR imaging. We demonstrated a qualitative matching between the estimated parameter and XMR data. This novel approach opens up possibilities to directly integrate modelling in the interventional room.

Localization of abnormal conduction pathways for tachyarrhythmia treatment using tagged MRI.

Tachyarrhythmias are pathological fast heart rhythms often caused by abnormally conducting myocardial areas (foci). Treatment by radio-frequency (RF) ablation uses electrode-catheters to monitor and destroy foci. The procedure is normally guided with x-rays (2D), and thus prone to errors in location and excessive radiation exposure. Our main goal is to provide pre- and intra-operative 3D MR guidance in XMR systems by locating the abnormal conduction pathways. We address the inverse electro-mechanical relation by using motion in order to infer electrical propagation. For this purpose we define a probabilistic measure of the onset of regional myocardial activation, derived from 3D motion fields obtained by tracking tagged MR sequences with non-rigid registration. Activation isochrones are then derived to determine activation onset. We also compare regional motion between two different image acquisitions, thus assisting in diagnosing arrhythmia, in follow up of treatment, and in determining whether the ablation was successful. Difference maps of isochrones and other motion descriptors are computed to determine abnormal patterns. Validation was carried out using an electromechanical model of the heart, synthetic data, a cardiac MRI atlas of motion and geometry, MRI data from 6 healthy volunteers (one of them subjected to stress), and an MRI study on a patient with tachyarrhythmia, before and after RF ablation. A pre-operative MRI study on a second patient with tachyarrhythmia was used to test the methodology in a clinical scenario, predicting the abnormally conducting region.