Coopération entre segmentation et mouvement pour l'estimation conjointe des déplacements pariétaux et des déformations myocardiaques

The work done in this thesis is related to the project 3DStrain the overall objective of which is to develop a generic framework for the parietal and regional tracking of the left ventricle and to adapt it the 3D + t cardiac imaging modalities used in clinical routine (3D ultrasound, SPECT, cine MRI). We worked on the parietal motion and myocardial deformation. We made the state-of-the-art on motion estimation approaches in general and on methods applied to imaging modalities in clinical practice to quantify myocardial deformation taking into account their specificities and limitations. We focused on tracking methods that optimize the similarity between the intensities between consecutive images of a sequence to estimate the spatial velocity field. They are based on the assumption of the invariance of image gray level (or optical flow) and regularization terms are used to solve the aperture problem. We proposed a regularization term well suited to physical and physiological properties of myocardial motion. The advantage of the proposed approach relies on its flexibility to estimate the dense field of myocardial motion on image sequences over the cardiac cycle. Motion is estimated while preserving myocardial wall discontinuities. However, the data similarity term used in our method is based only on the intensity of the image. It properly estimates the displacement field especially in the radial direction as the movement of circumferential twist is hardly visible on cine MRI in short axis view, the data we used for performing the experiments. To make the estimation more robust, we proposed a dynamic evolution model for the cardiac contraction and relaxation to introduce the temporal constraint ofthe dynamics of the heart. This model helps to estimate not only the dense field of myocardial displacement, but also other parameters of myocardial contractility (the contraction phase and asymmetry between systole and diastole) in variational data assimilation formalism. Automatic estimation of deformation and myocardial contractibility (the strain, phase and asymmetry) was validated against the cardiological and radiological expertise (Dr Elisabeth Coupez and Dr Lucie Cassagnes, CHU Clermont-Ferrand) through semi-quantitative scores of contraction called Wall Motion Score (WMS) and Wall Thickening Index (WTI). The proposed method provides promising results for both motion estimation results and the diagnosis indices for evaluation of myocardial dyskinesia. In order to gain in robustness and accuracy, it is necessary to perform the measurement of strain and indices of myocardial contraction precisely inside endocardial and epicardial walls. Therefore, we conducted a collaborative work with Kevin Bianchi, another PhD student on the project 3DStrain and we proposed a method of coupling of myocardial segmentation by deformable models and estimation of myocardial motion in a variational data assimilation framework.Pas de résumé disponibl

Probabilistic functional tractography of the human cortex revisited

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.Peer reviewe

cooperation between segmentation and movement for the joint estimation of the parietal displacements and myocardiac deformations

Pas de résumé disponibleThe work done in this thesis is related to the project 3DStrain the overall objective of which is to develop a generic framework for the parietal and regional tracking of the left ventricle and to adapt it the 3D + t cardiac imaging modalities used in clinical routine (3D ultrasound, SPECT, cine MRI). We worked on the parietal motion and myocardial deformation. We made the state-of-the-art on motion estimation approaches in general and on methods applied to imaging modalities in clinical practice to quantify myocardial deformation taking into account their specificities and limitations. We focused on tracking methods that optimize the similarity between the intensities between consecutive images of a sequence to estimate the spatial velocity field. They are based on the assumption of the invariance of image gray level (or optical flow) and regularization terms are used to solve the aperture problem. We proposed a regularization term well suited to physical and physiological properties of myocardial motion. The advantage of the proposed approach relies on its flexibility to estimate the dense field of myocardial motion on image sequences over the cardiac cycle. Motion is estimated while preserving myocardial wall discontinuities. However, the data similarity term used in our method is based only on the intensity of the image. It properly estimates the displacement field especially in the radial direction as the movement of circumferential twist is hardly visible on cine MRI in short axis view, the data we used for performing the experiments. To make the estimation more robust, we proposed a dynamic evolution model for the cardiac contraction and relaxation to introduce the temporal constraint ofthe dynamics of the heart. This model helps to estimate not only the dense field of myocardial displacement, but also other parameters of myocardial contractility (the contraction phase and asymmetry between systole and diastole) in variational data assimilation formalism. Automatic estimation of deformation and myocardial contractibility (the strain, phase and asymmetry) was validated against the cardiological and radiological expertise (Dr Elisabeth Coupez and Dr Lucie Cassagnes, CHU Clermont-Ferrand) through semi-quantitative scores of contraction called Wall Motion Score (WMS) and Wall Thickening Index (WTI). The proposed method provides promising results for both motion estimation results and the diagnosis indices for evaluation of myocardial dyskinesia. In order to gain in robustness and accuracy, it is necessary to perform the measurement of strain and indices of myocardial contraction precisely inside endocardial and epicardial walls. Therefore, we conducted a collaborative work with Kevin Bianchi, another PhD student on the project 3DStrain and we proposed a method of coupling of myocardial segmentation by deformable models and estimation of myocardial motion in a variational data assimilation framework

Estimation of Myocardial Strain and Contraction Phase From Cine MRI Using Variational Data Assimilation

International audienceThis paper presents a new method to estimate left ventricle deformations using variational data assimilation that combines image observations from cine MRI and a dynamic evolution model of the heart. The main contribution of the model is that it embeds parameters modeling the contraction / relaxation process. It estimates myocardial motion and contraction parameters simultaneously, providing accurate complementary information for diagnosis. The method was applied to synthetic datasets with known ground truth motion and to 47 patients MRI datasets acquired at three slice locations (base, mid-ventricle and apex). Radial and circumferential strain components were compared to those obtained with a reference tag tracking software, exhibiting good agreement with intraclass correlation coefficients (ICC) above 0.8. Results were also evaluated against wall motion score indices used to assess cardiac kinetics in clinical practice. The assimilation process overcame issues caused by temporal artifacts as a result of the dynamic model, compared to using the observation term alone. Moreover we found that the new dynamic model, consisting of a piecewise transport model acting independently on systole and diastole performed better than the standard continuous transport model, which oversmooths temporal variations. Estimated strain and contraction parameters significantly correlated to clinical scores, making them promising features for diagnosing not only hypokinesia but also dyskinesia

Joint Myocardial Motion and Contraction Phase Estimation from Cine MRI using Variational Data Assimilation.

International audienc

Validation of cadmium–zinc–telluride camera for measurement of left ventricular systolic performance

Erratum : 10.1007/s12350-017-0845-8"As shown in this erratum, the second author’s name needs to be changed from “Merlin Charles” to “Charles Merlin”. His affiliation needs to be changed to “Nuclear Medicine Department, Jean Perrin Cancer Center, Clermont-Ferrand, France”. The original article was corrected."International audienceBACKGROUND:There are paucity of data comparing measurements of left ventricular systolic performance using cadmium-zinc-telluride (CZT) semiconductor cameras with other imaging modalities. This study compared the new system with echocardiography (echo) and cardiac magnetic resonance (CMR) imaging.METHODS:60 Patients presenting with ST-elevated myocardial infarction (MI) were included. Each patient underwent echo, myocardial perfusion imaging using Spectrum Dynamics D-SPECT(r) (CZT-SPECT), and CMR 6 weeks after MI. The primary endpoint was the agreement between CZT-SPECT and CMR for left ventricular ejection fraction (LVEF) measurement.RESULTS:48 of the 60 patients underwent all 3 studies (echo, CMR, and CZT-SPECT) 40 days after admission. CZT-SPECT and CMR LVEF were well correlated (r = .79, P < .0001), as well as CZT-SPECT vs echo and CMR vs echo (r = .79 and .84, respectively, P < .0001). The segmental LV wall thickening and wall motion also showed good concordance between three techniques.CONCLUSIONS:CZT-SPECT is reliable for LVEF measurement

Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning

International audienceObjective : Intracranial electroencephalographic (iEEG) recordings contain “bad channels”, which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features.Methods : The features quantified signals’ variance, spatial–temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers.Results : We found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data.Conclusions : The proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data.Significance : This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals