Anisotropic Fast-Marching on cartesian grids using Lattice Basis Reduction

We introduce a modification of the Fast Marching Algorithm, which solves the generalized eikonal equation associated to an arbitrary continuous riemannian metric, on a two or three dimensional domain. The algorithm has a logarithmic complexity in the maximum anisotropy ratio of the riemannian metric, which allows to handle extreme anisotropies for a reduced numerical cost. We prove the consistence of the algorithm, and illustrate its efficiency by numerical experiments. The algorithm relies on the computation at each grid point of a special system of coordinates: a reduced basis of the cartesian grid, with respect to the symmetric positive definite matrix encoding the desired anisotropy at this point.Comment: 28 pages, 12 figure

Volume overload impact on 3D right ventricular shape and strain: comparative analysis of tetralogy of Fallot and atrial septal defect patients

International audienc

Multiscale optical flow computation from the monogenic signal

National audienceWe have developed an algorithm for the estimation of cardiac motion from medical images. The algorithm exploits monogenic signal theory, recently introduced as an N-dimensional generalization of the analytic signal. The displacement is computed locally by assuming the conservation of the monogenic phase over time. A local affine displacement model replaces the standard translation model to account for more complex motions as contraction/expansion and shear. A coarse-to-fine B-spline scheme allows a robust and effective computation of the models parameters and a pyramidal refinement scheme helps handle large motions. Robustness against noise is increased by replacing the standard pointwise computation of the monogenic orientation with a more robust least-squares orientation estimate. This paper reviews the results obtained on simulated cardiac images from different modalities, namely 2D and 3D cardiac ultrasound and tagged magnetic resonance. We also show how the proposed algorithm represents a valuable alternative to state-of-the-art algorithms in the respective fields

Right ventricular function in acute pulmonary embolism: a three-dimensional strain study

International audienc

3D right ventricular strain: comparative analysis of Tetralogy of Fallot and atrial septal defect

International audienceBackground: Right ventricular (RV) function assessment is crucial in CHD patients, especially in atrial septaldefect (ASD) and Tetralogy of Fallot (TOF) patients. Indeed, prognosis is very different between TOF patientswith pulmonary regurgitation and ASD patients, and only little is known about 3D deformation in RV overload. Purpose: The aim of our study was to assess RV remodeling differences between ASD, TOF patients andcontrols. Methods: We performed a prospective case­control study. We included 10 patients with an ASD (mean age53.3±21y) and 10 with TOF (mean age 34.9±18y) who were older than 16 years old, and compared them toa control group free from any cardiovascular disease (N=44, mean age 42.5±15y). 3D transthoracic RVechocardiographic sequences were acquired. Myocardial tracking was performed by a semi­automaticcommercial software. Output RV meshes included spatial correspondences. They were post­processed to alignthe data temporally and extract local deformation. Global and local statistics provided deformation patterns foreach subgroup of subjects. Results: Overall, ASD and TOF patients had similar but reduced RV ejection fraction (respectively 44.3±10and 44.5±12 %) and dilated right ventricles (mean RV EDV 158.3±100 and 115.4±46mL) using 3D analysis.Similar RV global area strain (GAS), global longitudinal strain (GLS) and global circumferential strain (GCS)were observed between the two groups. Compared to controls, ASD patients had lower GAS (­22.2±8 vs­29.4±5%; p=0.01), lower GCS (­12.9±4 vs ­17.1±4%; p=0.009) but similar GLS (p=0.07). TOF patientshad also lower GAS (­25.1±6%; p=0.03) but lower GLS (­10.0±3%; p=0.01) and similar GCS (­15.5±4%;p=0.4) in comparison with the control group. However, ASD patients had significantly lower CS in theinfundibular, inlet and membranous septum as compared with TOF patients (respectively p=0.02, p=0.05 andp=0.03). Conclusion: Volume overload in ASD patients seems to impact circumferential strain and preserve longitudinalstrain, whereas TOF patients tend to have lower longitudinal strain with preserved circumferential strain,probably because of the combination of RV pressure and volume overload. A larger cohort of patients couldhelp understand the insights of RV remodeling in congenital heart disease using 3D speckle­tracking imagin

Improved myocardial scar visualization with fast free-breathing motion-compensated black-blood T₁-rho-prepared late gadolinium enhancement MRI.

Clinical guidelines recommend the use of bright-blood late gadolinium enhancement (BR-LGE) for the detection and quantification of regional myocardial fibrosis and scar. This technique, however, may suffer from poor contrast at the blood-scar interface, particularly in patients with subendocardial myocardial infarction. The purpose of this study was to assess the clinical performance of a two-dimensional black-blood LGE (BL-LGE) sequence, which combines free-breathing T <sub>1</sub> -rho-prepared single-shot acquisitions with an advanced non-rigid motion-compensated patch-based reconstruction. Extended phase graph simulations and phantom experiments were performed to investigate the performance of the motion-correction algorithm and to assess the black-blood properties of the proposed sequence. Fifty-one patients (37 men, 14 women; mean age, 55 ± 15 [SD] years; age range: 19-81 years) with known or suspected cardiac disease prospectively underwent free-breathing T <sub>1</sub> -rho-prepared BL-LGE imaging with inline non-rigid motion-compensated patch-based reconstruction at 1.5T. Conventional breath-held BR-LGE images were acquired for comparison purposes. Acquisition times were recorded. Two readers graded the image quality and relative contrasts were calculated. Presence, location, and extent of LGE were evaluated. BL-LGE images were acquired with full ventricular coverage in 115 ± 25 (SD) sec (range: 64-160 sec). Image quality was significantly higher on free-breathing BL-LGE imaging than on its breath-held BR-LGE counterpart (3.6 ± 0.7 [SD] [range: 2-4] vs. 3.9 ± 0.2 [SD] [range: 3-4]) (P <0.01) and was graded as diagnostic for 44/51 (86%) patients. The mean scar-to-myocardium and scar-to-blood relative contrasts were significantly higher on BL-LGE images (P < 0.01 for both). The extent of LGE was larger on BL-LGE (median, 5 segments [IQR: 2, 7 segments] vs. median, 4 segments [IQR: 1, 6 segments]) (P < 0.01), the method being particularly sensitive in segments with LGE involving the subendocardium or papillary muscles. In eight patients (16%), BL-LGE could ascertain or rule out a diagnosis otherwise inconclusive on BR-LGE. Free-breathing T <sub>1</sub> -rho-prepared BL-LGE imaging with inline motion compensated reconstruction offers a promising diagnostic technology for the non-invasive assessment of myocardial injuries

A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta

Background Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. Methods Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient’s anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. Results The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. Conclusions The suggested method has the potential to discover previously unknown 3D shape biomarkers from medical imaging data. Thus, it could contribute to improving diagnosis and risk stratification in complex cardiac disease

Deep Learning Formulation of ECGI for Data-driven Integration of Spatiotemporal Correlations and Imaging Information

International audienceThe challenge of non-invasive Electrocardiographic Imaging (ECGI) is to recreate the electrical activity of the heart using body surface potentials. Specifically, there are numerical difficulties due to the ill-posed nature of the problem. We propose a novel method based on Conditional Variational Autoencoders using Deep generative Neural Networks to overcome this challenge. By conditioning the electrical activity on heart shape and electrical potentials, our model is able to generate activation maps with good accuracy on simulated data (mean square error, MSE = 0.095). This method differs from other formulations because it naturally takes into account spatio-temporal correlations as well as the imaging substrate through convolutions and conditioning. We believe these features can help improving ECGI results

Meshless electrophysiological modeling of cardiac resynchronization therapy—benchmark analysis with finite-element methods in experimental data

Computational models of cardiac electrophysiology are promising tools for reducing the rates of non-response patients suitable for cardiac resynchronization therapy (CRT) by optimizing electrode placement. The majority of computational models in the literature are mesh-based, primarily using the finite element method (FEM). The generation of patient-specific cardiac meshes has traditionally been a tedious task requiring manual intervention and hindering the modeling of a large number of cases. Meshless models can be a valid alternative due to their mesh quality independence. The organization of challenges such as the CRT-EPiggy19, providing unique experimental data as open access, enables benchmarking analysis of different cardiac computational modeling solutions with quantitative metrics. We present a benchmark analysis of a meshless-based method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration. The simulation results obtained with the meshless model were equivalent to FEM, with the most relevant aspect for accurate CRT predictions being the parameter personalization strategy (e.g., regional conduction velocity distribution, including the Purkinje system and CRT lead distribution). © 2022 by the authors. Licensee MDPI, Basel, Switzerland