Descending aorta subject-specific one-dimensional model validated against in vivo data.

The aorta plays a major role in the cardiovascular system and its function and structure are primarily affected by aging, eating habits, life style and other cardiovascular risk factors, inducing increased stiffness which is associated with cardiovascular and cerebral morbi-mortality. Our objective was to develop and validate a robust subject-specific one-dimensional wave propagation numerical model of the descending aorta. This model with a cross-sectional area, velocity and pressure formulation is built using geometric and hemodynamic data measured on a specific person and is validated against in vivo data acquired on the same subject at three distinct anatomical locations along the thoracic aorta. We studied seven healthy volunteers, who underwent carotid applanation tonometry and aortic cardiovascular magnetic resonance (CMR). Responses of our model in terms of changes in central pressure waveform with arterial alterations were consistent with previously described physiological knowledge. Quantitative validation averaged over the three descending aortic locations and the seven subjects provided low rms errors (given in percentage of the maximal clinical value) between simulated and CMR data, i.e. area: 10±6%, velocity: 11±3%, flow rate: 9±3%. Finally, we also found low rms (5±2%) when comparing simulated pressure in the proximal aortic location against tonometric carotid pressure curves. In conclusion, this simple model performs similar to more complex models of the entire systemic arterial tree at a fraction of the cost, and could be of major usefulness in the non-invasive and local estimation of proximal biomechanical and hemodynamic indices.

Comparison of various methods for quantitative evaluation of myocardial infarct volume from magnetic resonance delayed enhancement data.

BACKGROUND: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) enables the estimation of myocardial infarct (MI) extent. Nevertheless, manual quantification is time consuming and subjective. We sought to assess MI volume with different quantitative methods in both acute (AMI) and chronic MI (CMI). METHODS: CMR was performed 50 ± 21 h after MI in 52 patients and was repeated 100 ± 21 days later in a subgroup of 34 patients. Then, necrosis volumes were quantified using: 1) manual delineation, 2) automated fuzzy c-means method, and 3) +2 to 6 SD thresholding approaches. Results were compared against peak values of serum Troponin I (TnI), creatine kinase (CK) and left ventricular (LV) functional parameters: LV ejection fraction (LVEF), indexed end-diastolic (EDVi), end-systolic volumes (ESVi) and the number of hypokinetic segments (NbHk). RESULTS: For CMI, quantitative evaluation of infarct size using manual, +2SD, +3 SD and fuzzy c-means provided equivalent results in terms of correlation coefficients for comparisons of MI volumes against LV function parameters (LVEF: r>0.79, p<0.0001; ESVi: r>0.82, p<0.0001, EDVi: r>0.67, p<0.0001, NbHk: r>0.54, p<0.0009). For AMI, +2SD and fuzzy c-means approaches provided higher correlations for comparisons of AMI volumes against biochemical markers (CK: r>0.79, p<0.0001,TnI: r>0.77, p<0.0001) and chronic LV function parameters (LVEF: r>0.82, p<0.0001, NbHk: r>0.59, p<0.0002). CONCLUSIONS: The fuzzy c-means and 2SD methods provided highest correlations with biochemical MI quantification as well as LV function parameters. The fuzzy c-means approach which does not require an arbitrary identification of the remote myocardium is fast and reproducible. It may be clinically useful in the evaluation of patients with MI.

An automatic respiratory gating method for the improvement of microcirculation evaluation: application to contrast-enhanced ultrasound studies of focal liver lesions.

Contrast-enhanced ultrasound (CEUS), with the recent development of both contrast-specific imaging modalities and microbubble-based contrast agents, allows noninvasive quantification of microcirculation in vivo. Nevertheless, functional parameters obtained by modeling contrast uptake kinetics could be impaired by respiratory motion. Accordingly, we developed an automatic respiratory gating method and tested it on 35 CEUS hepatic datasets with focal lesions. Each dataset included fundamental mode and cadence contrast pulse sequencing (CPS) mode sequences acquired simultaneously. The developed method consisted in (1) the estimation of the respiratory kinetics as a linear combination of the first components provided by a principal components analysis constrained by a prior knowledge on the respiratory rate in the frequency domain, (2) the automated generation of two respiratory-gated subsequences from the CPS mode sequence by detecting end-of-inspiration and end-of-expiration phases from the respiratory kinetics. The fundamental mode enabled a more reliable estimation of the respiratory kinetics than the CPS mode. The k-means algorithm was applied on both the original CPS mode sequences and the respiratory-gated subsequences resulting in clustering maps and associated mean kinetics. Our respiratory gating process allowed better superimposition of manually drawn lesion contours on k-means clustering maps as well as substantial improvement of the quality of contrast uptake kinetics. While the quality of maps and kinetics was satisfactory in only 11/35 datasets before gating, it was satisfactory in 34/35 datasets after gating. Moreover, noise amplitude estimated within the delineated lesions was reduced from 62 ± 21 to 40 ± 10 (p < 0.01) after gating. These findings were supported by the low residual horizontal (0.44 ± 0.29 mm) and vertical (0.15 ± 0.16 mm) shifts found during manual motion correction of each respiratory-gated subsequence. The developed technique could be used as a basis for accurate quantification of perfusion parameters for the evaluation and follow-up of patients under antiangiogenic therapies.

An automated four-point scale scoring of segmental wall motion in echocardiography using quantified parametric images.

The aim of this paper is to develop an automated method which operates on echocardiographic dynamic loops for classifying the left ventricular regional wall motion (RWM) in a four-point scale. A non-selected group of 37 patients (2 and 4 chamber views) was studied. Each view was segmented according to the standardized segmentation using three manually positioned anatomical landmarks (the apex and the angles of the mitral annulus). The segmented data were analyzed by two independent experienced echocardiographists and the consensual RWM scores were used as a reference for comparisons. A fast and automatic parametric imaging method was used to compute and display as static color-coded parametric images both temporal and motion information contained in left ventricular dynamic echocardiograms. The amplitude and time parametric images were provided to a cardiologist for visual analysis of RWM and used for RWM quantification. A cross-validation method was applied to the segmental quantitative indices for classifying RWM in a four-point scale. A total of 518 segments were analyzed. Comparison between visual interpretation of parametric images and the reference reading resulted in an absolute agreement (Aa) of 66% and a relative agreement (Ra) of 96% and kappa (κ) coefficient of 0.61. Comparison of the automated RWM scoring against the same reference provided Aa = 64%, Ra = 96% and κ = 0.64 on the validation subset. Finally, linear regression analysis between the global quantitative index and global reference scores as well as ejection fraction resulted in correlations of 0.85 and 0.79. A new automated four-point scale scoring of RWM was developed and tested in a non-selected database. Its comparison against a consensual visual reading of dynamic echocardiograms showed its ability to classify RWM abnormalities.

An automated quantification of the transmural myocardial infarct extent using cardiac DE-MR images.

Evaluating myocardial viability is an important prognostic factor in the follow-up of infarctions. Delayed Enhancement magnetic resonance (DE-MR) imaging allows precise delineation of the infarct transmural extent. Visual interpretation is the most commonly used method to assess the myocardial infarction (MI) transmural extent. This study proposes to automate the segmentation of the (DE) images prior to the estimation of the extent of infarcted tissue. Indeed the segmentation of the myocardium was performed using cine contraction images which present a high contrast between cavity and myocardium. After the segmentation, the segmental transmurality is estimated on a conventional five point scale. A head to head comparison was performed between visual and quantitative analysis of infarct transmurality on DE-MR imaging. Results on 921 sub-segments (9 patients) showed an absolute agreement of 80% and a relative agreement (with one point difference) of 97%.

Assessment of left ventricular contraction by parametric analysis of main motion (PAMM): theory and application for echocardiography.

The computerized study of the regional contraction of the left ventricle has undergone numerous developments, particularly in relation to echocardiography. A new method, parametric analysis of main motion (PAMM), is proposed in order to synthesize the information contained in a cine loop of images in parametric images. PAMM determines, for the intensity variation time curves (IVTC) observed in each pixel, two amplitude coefficients characterizing the continuous component and the alternating component; the variable component is generated from a mother curve by introducing a time shift coefficient and a scale coefficient. Two approaches, a PAMM data driven and a PAMM model driven (simpler and faster), are proposed. On the basis of the four coefficients, an amplitude image and an image of mean contraction time are synthesized and interpreted by a cardiologist. In all cases, both PAMM methods allow better IVTC adjustment than the other methods of parametric imaging used in echocardiography. A preliminary database comprising 70 segments is scored and compared with the visual analysis, taken from a consensus of two expert interpreters. The levels of absolute and relative concordance are 79% and 97%. PAMM model driven is a promising method for the rapid detection of abnormalities in left ventricle contraction.