Devis Peressutti

Estimating and resolving uncertainty in cardiac respiratory motion modelling

We present a novel method for cardiac respiratory motion estimation in image-guided interventions. The technique combines a preprocedure affine motion model and intra-procedure real-time images to estimate and correct for the respiratory motion of the heart. As well as making motion estimates, the model is able to quantify the uncertainty in these estimates. This uncertainty is resolved using a Bayesian approach based on a prior probability from the motion model and a likelihood term derived from the intraprocedure images. The proposed method is validated using MR-derived motion fields and simulated 3D real-time echocardiography data for 4 volunteers and compared to 3 other motion estimation techniques. Our Bayesian approach shows improvements in respiratory motion estimation for each volunteer of 7.0%, 4.6%, 7.7% and 5.3% respectively, compared to the use of a motion model only.

Cardiac and respiratory motion correction for simultaneous cardiac PET-MR

Cardiac PET is a versatile imaging technique providing important diagnostic information about ischemic heart diseases. Respiratory and cardiac motion of the heart can strongly impair image quality and therefore diagnostic accuracy of cardiac PET scans. The aim of this study was to investigate a new cardiac PET/MR approach providing respiratory and cardiac motion–compensated MR and PET images in less than 5 min. Methods: Free-breathing 3-dimensional MR data were acquired and retrospectively binned into multiple respiratory and cardiac motion states. Three-dimensional cardiac and respiratory motion fields were obtained with a nonrigid registration algorithm and used in motion-compensated MR and PET reconstructions to improve image quality. The improvement in image quality and diagnostic accuracy of the technique was assessed in simultaneous 18F-FDG PET/MR scans of a canine model of myocardial infarct and was demonstrated in a human subject. Results: MR motion fields were successfully used to compensate for in vivo cardiac motion, leading to improvements in full width at half maximum of the canine myocardium of 13% ± 5%, similar to cardiac gating but with a 90% ± 57% higher contrast-to-noise ratio between myocardium and blood. Motion correction led to an improvement in MR image quality in all subjects, with an increase in sharpness of the canine coronary arteries of 85% ± 72%. A functional assessment showed good agreement with standard MR cine scans with a difference in ejection fraction of −2% ± 3%. MR-based respiratory and cardiac motion information was used to improve the PET image quality of a human in vivo scan. Conclusion: The MR technique presented here provides both diagnostic and motion information that can be used to improve MR and PET image quality. Reliable respiratory and cardiac motion correction could make cardiac PET results more reproducible.

A novel Bayesian respiratory motion model to estimate and resolve uncertainty in image-guided cardiac interventions

In image-guided cardiac interventions, respiratory motion causes misalignments between the pre-procedure roadmap of the heart used for guidance and the intra-procedure position of the heart, reducing the accuracy of the guidance information and leading to potentially dangerous consequences. We propose a novel technique for motion-correcting the pre-procedural information that combines a probabilistic MRI-derived affine motion model with intra-procedure real-time 3D echocardiography (echo) images in a Bayesian framework. The probabilistic model incorporates a measure of confidence in its motion estimates which enables resolution of the potentially conflicting information supplied by the model and the echo data. Unlike models proposed so far, our method allows the final motion estimate to deviate from the model-produced estimate according to the information provided by the echo images, so adapting to the complex variability of respiratory motion. The proposed method is evaluated using gold-standard MRI-derived motion fields and simulated 3D echo data for nine volunteers and real 3D live echo images for four volunteers. The Bayesian method is compared to 5 other motion estimation techniques and results show mean/max improvements in estimation accuracy of 10.6%/18.9% for simulated echo images and 20.8%/41.5% for real 3D live echo data, over the best comparative estimation method.

A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: Application to cardiac resynchronisation therapy response prediction

&NA; We present a framework for combining a cardiac motion atlas with non‐motion data. The atlas represents cardiac cycle motion across a number of subjects in a common space based on rich motion descriptors capturing 3D displacement, velocity, strain and strain rate. The non‐motion data are derived from a variety of sources such as imaging, electrocardiogram (ECG) and clinical reports. Once in the atlas space, we apply a novel supervised learning approach based on random projections and ensemble learning to learn the relationship between the atlas data and some desired clinical output. We apply our framework to the problem of predicting response to Cardiac Resynchronisation Therapy (CRT). Using a cohort of 34 patients selected for CRT using conventional criteria, results show that the combination of motion and non‐motion data enables CRT response to be predicted with 91.2% accuracy (100% sensitivity and 62.5% specificity), which compares favourably with the current state‐of‐the‐art in CRT response prediction. HighlightsFramework to combine cardiac motion and deformation data with non‐motion data.Motion atlas use for comparison of LV displacement, velocity, strain and strain rate.Random projections and multiple kernel learning are used to extract relevant features.Application to response prediction in cardiac resynchronisation therapy.94% and 91% classification accuracy of super‐responders and non‐responders. Graphical abstract Figure. No caption available.

Prospective Identification of CRT Super Responders Using a Motion Atlas and Random Projection Ensemble Learning

Cardiac Resynchronisation Therapy (CRT) treats patients with heart failure and electrical dyssynchrony. However, many patients do not respond to therapy. We propose a novel framework for the prospective characterisation of CRT ‘super-responders’ based on motion analysis of the Left Ventricle (LV). A spatio-temporal motion atlas for the comparison of the LV motions of different subjects is built using cardiac MR imaging. Patients likely to present a super-response to the therapy are identified using a novel ensemble learning classification method based on random projections of the motion data. Preliminary results on a cohort of 23 patients show a sensitivity and specificity of 70% and 85%.

Towards Left Ventricular Scar Localisation Using Local Motion Descriptors

We propose a novel technique for the localisation of Left Ventricular LV scar based on local motion descriptors. Cardiac MR imaging is employed to construct a spatio-temporal motion atlas where the LV motion of different subjects can be directly compared. Local motion descriptors are derived from the motion atlas and dictionary learning is used for scar classification. Preliminary results on a cohort of 20 patients show a sensitivity and specificity of

Registration of Multiview Echocardiography Sequences Using a Subspace Error Metric

80\,\%

Learning Optimal Spatial Scales for Cardiac Strain Analysis Using a Motion Atlas

and