Local Appearance Knowledge and Shape Variation Models for Muscle Segmentation

In this report, we present a novel prior knowledge representation of shape variation using diffusion wavelets and applied for medical image segmentation. One of the major advantage of our approach is that it can reflect arbitrary and continuous interdependencies in the training data. In contrast to state-of-the-art methods our framework during the learning stage optimizes the coefficients as well as the number and the position of landmarks using geometric (reconstructed surface) constraints. Saliency is encoded in the model and segmentation is expressed through the extraction of the corresponding features in a new data-set. The resulting paradigm supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. In another hand, our report deals with a different model search methodology where we apply an approach related to active feature models; the location of landmarks is updated iteratively, using local features, and the canonical correlation analysis. We report promising results on two challenging medical data sets, that illustrate the potential of our method

Manifold-driven Grouping of Skeletal Muscle Fibers

In this report, we present a manifold clustering method for the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. To this end, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. The obtained metrics between fiber tracts encompasses both diffusion and localization information. As far as clustering is concerned, we use two methods. The first approach is based on diffusion maps and k-means clustering in the spectral embedding space. The second approach uses a linear programming formulation of prototype-based clustering. This formulation allows for classification over manifolds without the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. The experimental validation of the proposed framework is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects

Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model

International audienceThe objective of this paper is to propose and assess an estimation procedure - based on data assimilation principles - well-suited to obtain some regional values of key biophysical parameters in a beating heart model, using actual Cine-MR images. The motivation is twofold: (1) to provide an automatic tool for personalizing the characteristics of a cardiac model in order to achieve predictivity in patient-specific modeling, and (2) to obtain some useful information for diagnosis purposes in the estimated quantities themselves. In order to assess the global methodology we specifically devised an animal experiment in which a controlled infarct was produced and data acquired before and after infarction, with an estimation of regional tissue contractility - a key parameter directly affected by the pathology - performed for every measured stage. After performing a preliminary assessment of our proposed methodology using synthetic data, we then demonstrate a full-scale application by first estimating contractility values associated with 6 regions based on the AHA subdivision, before running a more detailed estimation using the actual AHA segments. The estimation results are assessed by comparison with the medical knowledge of the specific infarct, and with late enhancement MR images. We discuss their accuracy at the various subdivision levels, in the light of the inherent modeling limitations and of the intrinsic information contents featured in the data

120 Superiority of CT scan over transthoracic echocardiography in predicting aortic regurgitation after TAVI

BackgroundParavalvular aortic regurgitation (AR) occurs in up to 86% of patients undergoing Transcatheter Aortic Valve Implantation (TAVI). Its prevalence remains unchanged after one year follow-up but its determinants are unclear. We sought to evaluate the impact of annulus measurement by transthoracic echocardiography (TTE) and by CT scan on the occurrence of AR.MethodsThe study included 43 symptomatic patients (83±8 years, 72% in NYHA≥III) with severe aortic stenosis [0.76±0.19cm2, mean gradient 42±14mmHg] who underwent TAVI using CoreValve® LLC Percutaneous Aortic Valve Implantation System, Medtronic, Minneapolis USA. Left ventricular outflow tract (LVOT) area was computed from LVOT diameter (21±2mm) by TTE using a spherical model and from CT using an ellipsoidal model according to the larger (25±3mm) and the smaller outflow tract diameters (22±3mm). These data were compared to the prosthesis area and the occurrence of AR after TAVI.ResultsIn patients with AR greater or equal to 2/4 (32%), LVOT area measured by CT was significantly greater as compared to patients with no or mild AR (478±65mm 2 vs. 411±85mm2, p=0.009). Furthermore, the difference between actual prosthesis area and LVOT area measured by CT scan was significantly smaller (113±55 vs. 171±67, p=0.009) in patients with significant AR (≥2/4) after TAVI. In contrast, LVOT area from TTE did not correlate with AR severity.ConclusionCT scan is more accurate than TTE for calculating LVOT area for prosthesis sizing before TAVI in order to avoid post-implantation AR

Lower extremity muscle pathology in myotonic dystrophy type 1 assessed by quantitative MRI

Objective: To determine the value of quantitative MRI in providing imaging biomarkers for disease in 20 different upper and lower leg muscles of patients with myotonic dystrophy type 1 (DM1). Methods: We acquired images covering these muscles in 33 genetically and clinically well-characterized patients with DM1 and 10 unaffected controls. MRIs were recorded with a Dixon method to determine muscle fat fraction, muscle volume, and contractile muscle volume, and a multi-echo spin-echo sequence was used to determine T2 water relaxation time (T2water), reflecting putative edema. Results: Muscles in patients with DM1 had higher fat fractions than muscles of controls (15.6 ± 11.1% vs 3.7 ± 1.5%). In addition, patients had smaller muscle volumes (902 ± 232 vs 1,097 ± 251 cm3), smaller contractile muscle volumes (779 ± 247 vs 1,054 ± 246 cm3), and increased T2water (33.4 ± 1.0 vs 31.9 ± 0.6 milliseconds), indicating atrophy and edema, respectively. Lower leg muscles were affected most frequently, especially the gastrocnemius medialis and soleus. Distribution of fat content per muscle indicated gradual fat infiltration in DM1. Between-patient variation in fat fraction was explained by age (≈45%), and another ≈14% was explained by estimated progenitor CTG repeat length (r2 = 0.485) and somatic instability (r2 = 0.590). Fat fraction correlated with the 6-minute walk test (r = −0.553) and muscular impairment rating scale (r = 0.537) and revealed subclinical muscle involvement. Conclusion: This cross-sectional quantitative MRI study of 20 different lower extremity muscles in patients with DM1 revealed abnormal values for muscle fat fraction, volume, and T2water, which therefore may serve as objective biomarkers to assess disease state of skeletal muscles in these patients

Manifold-driven Grouping of Skeletal Muscle Fibers

In this report, we present a manifold clustering method for the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. To this end, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. The obtained metrics between fiber tracts encompasses both diffusion and localization information. As far as clustering is concerned, we use two methods. The first approach is based on diffusion maps and k-means clustering in the spectral embedding space. The second approach uses a linear programming formulation of prototype-based clustering. This formulation allows for classification over manifolds without the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. The experimental validation of the proposed framework is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects

Consensus-based technical recommendations for clinical translation of renal BOLD MRI.

Harmonization of acquisition and analysis protocols is an important step in the validation of BOLD MRI as a renal biomarker. This harmonization initiative provides technical recommendations based on a consensus report with the aim to move towards standardized protocols that facilitate clinical translation and comparison of data across sites. We used a recently published systematic review paper, which included a detailed summary of renal BOLD MRI technical parameters and areas of investigation in its supplementary material, as the starting point in developing the survey questionnaires for seeking consensus. Survey data were collected via the Delphi consensus process from 24 researchers on renal BOLD MRI exam preparation, data acquisition, data analysis, and interpretation. Consensus was defined as ≥ 75% unanimity in response. Among 31 survey questions, 14 achieved consensus resolution, 12 showed clear respondent preference (65-74% agreement), and 5 showed equal (50/50%) split in opinion among respondents. Recommendations for subject preparation, data acquisition, processing and reporting are given based on the survey results and review of the literature. These technical recommendations are aimed towards increased inter-site harmonization, a first step towards standardization of renal BOLD MRI protocols across sites. We expect this to be an iterative process updated dynamically based on progress in the field

Local Appearance Knowledge and Shape Variation Models for Muscle Segmentation

In this report, we present a novel prior knowledge representation of shape variation using diffusion wavelets and applied for medical image segmentation. One of the major advantage of our approach is that it can reflect arbitrary and continuous interdependencies in the training data. In contrast to state-of-the-art methods our framework during the learning stage optimizes the coefficients as well as the number and the position of landmarks using geometric (reconstructed surface) constraints. Saliency is encoded in the model and segmentation is expressed through the extraction of the corresponding features in a new data-set. The resulting paradigm supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. In another hand, our report deals with a different model search methodology where we apply an approach related to active feature models; the location of landmarks is updated iteratively, using local features, and the canonical correlation analysis. We report promising results on two challenging medical data sets, that illustrate the potential of our method

Patient-specific biomechanical modeling of cardiac amyloidosis – A case study

International audienceWe present a patient-specific biomechanical modeling framework and an initial case study for investigating cardiac amyloidosis (CA). Our patient-specific heartbeat simulations are in good agreement with the data, and our model calibration indicates that the major effect of CA in the biophysical behavior lies in a dramatic increase of the passive stiffness. We also conducted a preliminary trial for predicting the effects of pharmacological treatments – which is an important clinical challenge – based on the model combined with a simple venous return representation. This requires further investigation and validation, albeit provides some valuable preliminary insight