Remote intensive management to improve antiplatelet adherence in acute myocardial infarction: a secondary analysis of the randomized controlled IMMACULATE trial.

This study aim to investigate if remote intensive coaching for the first 6 months post-AMI will improve adherence to the twice-a-day antiplatelet medication, ticagrelor. Between July 8, 2015, to March 29, 2019, AMI patients were randomly assigned to remote intensive management (RIM) or standard care (SC). RIM participants underwent 6 months of weekly then two-weekly consultations to review medication side effects and medication adherence coaching by a centralized nurse practitioner team, whereas SC participants received usual cardiologist face-to-face consultations. Adherence to ticagrelor were determined using pill counting and serial platelet reactivity measurements for 12 months. A total of 149 (49.5%) of participants were randomized to RIM and 152 (50.5%) to SC. Adherence to ticagrelor was similar between RIM and SC group at 1 month (94.4 ± 0.7% vs. 93.6±14.7%, p = 0.537), 6 months (91.0±14.6% vs. 90.6±14.8%, p = 0.832) and 12 months (87.4±17.0% vs. 89.8±12.5%, p = 0.688). There was also no significant difference in platelet reactivity between the RIM and SC groups at 1 month (251AU*min [212-328] vs. 267AU*min [208-351], p = 0.399), 6 months (239AU*min [165-308] vs. 235AU*min [171-346], p = 0.610) and 12 months (249AU*min [177-432] vs. 259AU*min [182-360], p = 0.678). Sensitivity analysis did not demonstrate any association of ticagrelor adherence with bleeding events and major adverse cardiovascular events. RIM, comprising 6 months of intensive coaching by nurse practitioners, did not improve adherence to the twice-a-day medication ticagrelor compared with SC among patients with AMI. A gradual decline in ticagrelor adherence over 12 months was observed despite 6 months of intensive coaching.

Enhanced Thrombin Generation Is Associated with Worse Left Ventricular Scarring after ST-Segment Elevation Myocardial Infarction: A Cohort Study.

Acute myocardial infarction (AMI) is associated with heightened thrombin generation. There are limited data relating to thrombin generation and left ventricular (LV) scarring and LV dilatation in post-MI LV remodeling. We studied 113 patients with ST-segment elevation myocardial infarction (STEMI) who had undergone primary percutaneous coronary intervention (PPCI) (n = 76) or pharmaco-invasive management (thrombolysis followed by early PCI, n = 37). Endogenous thrombin potential (ETP) was measured at baseline, 1 month and 6 months. Cardiovascular magnetic resonance imaging was performed at baseline and 6 months post-MI. Outcomes studied were an increase in scar change, which was defined as an increase in left ventricular infarct size of any magnitude detected by late gadolinium enhancement, adverse LV remodeling, defined as dilatation (increase) of left ventricular end-diastolic volume (LVEDV) by more than 20% and an increase in left ventricular ejection fraction (LVEF). The mean age was 55.19 ± 8.25 years and 91.2% were men. The baseline ETP was similar in the PPCI and pharmaco-invasive groups (1400.3 nM.min vs. 1334.1 nM.min, p = 0.473). Each 10-unit increase in baseline ETP was associated with a larger scar size (adjusted OR 1.020, 95% CI 1.002-1.037, p = 0.027). Baseline ETP was not associated with adverse LV remodeling or an increase in LVEF. There was no difference in scar size or adverse LV remodeling among patients undergoing PPCI vs. pharmaco-invasive management or patients receiving ticagrelor vs. clopidogrel. Enhanced thrombin generation after STEMI is associated with a subsequent increase in myocardial scarring but not LV dilatation or an increase in LVEF at 6 months post-MI.

Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology.

BACKGROUND: Clinical natural language processing (cNLP) systems are of crucial importance due to their increasing capability in extracting clinically important information from free text contained in electronic health records (EHRs). The conversion of a nonstructured representation of a patient's clinical history into a structured format enables medical doctors to generate clinical knowledge at a level that was not possible before. Finally, the interpretation of the insights gained provided by cNLP systems has a great potential in driving decisions about clinical practice. However, carrying out robust evaluations of those cNLP systems is a complex task that is hindered by a lack of standard guidance on how to systematically approach them. OBJECTIVE: Our objective was to offer natural language processing (NLP) experts a methodology for the evaluation of cNLP systems to assist them in carrying out this task. By following the proposed phases, the robustness and representativeness of the performance metrics of their own cNLP systems can be assured. METHODS: The proposed evaluation methodology comprised five phases: (1) the definition of the target population, (2) the statistical document collection, (3) the design of the annotation guidelines and annotation project, (4) the external annotations, and (5) the cNLP system performance evaluation. We presented the application of all phases to evaluate the performance of a cNLP system called "EHRead Technology" (developed by Savana, an international medical company), applied in a study on patients with asthma. As part of the evaluation methodology, we introduced the Sample Size Calculator for Evaluations (SLiCE), a software tool that calculates the number of documents needed to achieve a statistically useful and resourceful gold standard. RESULTS: The application of the proposed evaluation methodology on a real use-case study of patients with asthma revealed the benefit of the different phases for cNLP system evaluations. By using SLiCE to adjust the number of documents needed, a meaningful and resourceful gold standard was created. In the presented use-case, using as little as 519 EHRs, it was possible to evaluate the performance of the cNLP system and obtain performance metrics for the primary variable within the expected CIs. CONCLUSIONS: We showed that our evaluation methodology can offer guidance to NLP experts on how to approach the evaluation of their cNLP systems. By following the five phases, NLP experts can assure the robustness of their evaluation and avoid unnecessary investment of human and financial resources. Besides the theoretical guidance, we offer SLiCE as an easy-to-use, open-source Python library.

A deep learning pipeline for automatic analysis of multi-scan cardiovascular magnetic resonance.

BACKGROUND: Cardiovascular magnetic resonance (CMR) sequences are commonly used to obtain a complete description of the function and structure of the heart, provided that accurate measurements are extracted from images. New methods of extraction of information are being developed, among them, deep neural networks are powerful tools that showed the ability to perform fast and accurate segmentation. Iq1n order to reduce the time spent by reading physicians to process data and minimize intra- and inter-observer variability, we propose a fully automatic multi-scan CMR image analysis pipeline. METHODS: Sequence specific U-Net 2D models were trained to perform the segmentation of the left ventricle (LV), right ventricle (RV) and aorta in cine short-axis, late gadolinium enhancement (LGE), native T1 map, post-contrast T1, native T2 map and aortic flow sequences depending on the need. The models were trained and tested on a set of data manually segmented by experts using semi-automatic and manual tools. A set of parameters were computed from the resulting segmentations such as the left ventricular and right ventricular ejection fraction (EF), LGE scar percentage, the mean T1, T1 post, T2 values within the myocardium, and aortic flow. The Dice similarity coefficient, Hausdorff distance, mean surface distance, and Pearson correlation coefficient R were used to assess and compare the results of the U-Net based pipeline with intra-observer variability. Additionally, the pipeline was validated on two clinical studies. RESULTS: The sequence specific U-Net 2D models trained achieved fast (≤ 0.2 s/image on GPU) and precise segmentation over all the targeted region of interest with high Dice scores (= 0.91 for LV, = 0.92 for RV, = 0.93 for Aorta in average) comparable to intra-observer Dice scores (= 0.86 for LV, = 0.87 for RV, = 0.95 for aorta flow in average). The automatically and manually computed parameters were highly correlated (R = 0.91 in average) showing results superior to the intra-observer variability (R = 0.85 in average) for every sequence presented here. CONCLUSION: The proposed pipeline allows for fast and robust analysis of large CMR studies while guaranteeing reproducibility, hence potentially improving patient's diagnosis as well as clinical studies outcome.

Remote Postdischarge Treatment of Patients With Acute Myocardial Infarction by Allied Health Care Practitioners vs Standard Care: The IMMACULATE Randomized Clinical Trial.

Importance: There are few data on remote postdischarge treatment of patients with acute myocardial infarction. Objective: To compare the safety and efficacy of allied health care practitioner-led remote intensive management (RIM) with cardiologist-led standard care (SC). Design, Setting, and Participants: This intention-to-treat feasibility trial randomized patients with acute myocardial infarction undergoing early revascularization and with N-terminal-pro-B-type natriuretic peptide concentration more than 300 pg/mL to RIM or SC across 3 hospitals in Singapore from July 8, 2015, to March 29, 2019. RIM participants underwent 6 months of remote consultations that included ß-blocker and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (ACE-I/ARB) dose adjustment by a centralized nurse practitioner team while SC participants were treated face-to-face by their cardiologists. Main Outcomes and Measures: The primary safety end point was a composite of hypotension, bradycardia, hyperkalemia, or acute kidney injury requiring hospitalization. To assess the efficacy of RIM in dose adjustment of ß-blockers and ACE-I/ARBs compared with SC, dose intensity scores were derived by converting comparable doses of different ß-blockers and ACE-I/ARBs to a scale from 0 to 5. The primary efficacy end point was the 6-month indexed left ventricular end-systolic volume (LVESV) adjusted for baseline LVESV. Results: Of 301 participants, 149 (49.5%) were randomized to RIM and 152 (50.5%) to SC. RIM and SC participants had similar mean (SD) age (55.3 [8.5] vs 54.7 [9.1] years), median (interquartile range) N-terminal-pro-B-type natriuretic peptide concentration (807 [524-1360] vs 819 [485-1320] pg/mL), mean (SD) baseline left ventricular ejection fraction (57.4% [11.1%] vs 58.1% [10.3%]), and mean (SD) indexed LVESV (32.4 [14.1] vs 30.6 [11.7] mL/m2); 15 patients [5.9%] had a left ventricular ejection fraction <40%. The primary safety end point occurred in 0 RIM vs 2 SC participants (1.4%) (P = .50). The mean ß-blocker and ACE-I/ARB dose intensity score at 6 months was 3.03 vs 2.91 (adjusted mean difference, 0.12 [95% CI, -0.02 to 0.26; P = .10]) and 2.96 vs 2.77 (adjusted mean difference, 0.19 [95% CI, -0.02 to 0.40; P = .07]), respectively. The 6-month indexed LVESV was 28.9 vs 29.7 mL/m2 (adjusted mean difference, -0.80 mL/m2 [95% CI, -3.20 to 1.60; P = .51]). Conclusions and Relevance: Among low-risk patients with revascularization after myocardial infarction, RIM by allied health care professionals was feasible and safe. There were no differences in achieved medication doses or indices of left ventricular remodeling. Further studies of RIM in higher-risk cohorts are warranted. Trial Registration: ClinicalTrials.gov Identifier: NCT02468349.

Improved tagged cardiac MRI myocardium strain analysis by leveraging cine segmentation.

BACKGROUND AND OBJECTIVES: Tagged MR images provide an effective way for regional analysis of the myocardium strain. A reliable myocardium strain analysis requires both correct segmentation and accurate motion tracking of the myocardium during the cardiac cycle. While many algorithms have been proposed for accurate tracking of the myocardium in tagged MR images, little focus has been placed on ensuring correct segmentation of the tagged myocardium during the cardiac cycle. Myocardial strain analysis is usually done by segmenting the myocardium in end-diastole, generating a mesh from the segmentation, propagating the mesh through the cardiac cycle using the output deformation field from motion tracking, and measuring strain on the deforming mesh. Due to the imposed tag strips on the anatomy, identification of the myocardium boundaries is challenging in tagged MR images. As a result, there is no guarantee that the propagated mesh is annotating the myocardium accurately through the cardiac cycle. Moreover, clinical studies indicate that incorrect myocardium annotation can result in overestimation of myocardial strains. METHODS: We introduce a method to improve reliability of strain analysis by proposing a mesh which correctly segments the myocardium in tagged MRI by leveraging the available cine MRI segmentation. In particular, we generate a series of mesh proposals using the cine MRI segmentation and find the propagated mesh proposal which gives the most accurate full-cycle myocardium segmentation. RESULTS: The mesh selection algorithm was tested on 22 2D MRI scans of diseased and healthy hearts. The proposed algorithm provided more accurate whole-cycle myocardium segmentation compared to the propagated end-diastolic mesh. Regional myocardium strain was measured for 10 3D MRI scans of healthy volunteers using the proposed mesh and the end-diastolic mesh. The measured strain using the proposed mesh was more similar to the expected myocardium strain for a healthy heart than the measured strain using the end-diastolic mesh. CONCLUSION: The proposed approach provides accurate whole-cycle tagged myocardium segmentation and more reliable myocardium strain analysis.

Cardiac motion and spillover correction for quantitative PET imaging using dynamic MRI.

PURPOSE: Cardiac positron emission tomography/magnetic resonance imaging (PET/MRI) acquisition presents novel clinical applications thanks to the combination of viability and metabolic imaging (PET) and functional and structural imaging (MRI). However, the resolution of PET, as well as cardiac and respiratory motion in nongated cardiac imaging acquisition protocols, leads to a reduction in image quality and severe quantitative bias. Respiratory or cardiac motion is customarily addressed with gated reconstruction which results in higher noise. METHODS: Inspired by a method that has been used in brain PET, a practical correction approach, designed to overcome these existing limitations for quantitative PET imaging, was developed and applied in the context of cardiac PET/MRI. The correction approach for PET data consists of computing the mean density map of each underlying moving region, as obtained with MRI, and translating them to the PET space taking into account the PET spatial and temporal resolution. Using these tissue density maps, the method then constructs a system of linear equations that models the activity recovery and cross-contamination coefficients, which can be solved for the true activity values. Physical and numerical cardiac phantoms were employed in order to quantify the proposed correction. The full correction pipeline was then used to assess differences in metabolic function between scar and healthy myocardium in eight patients with recent acute myocardial infarction using [11 C]-acetate. Data from ten additional patients, injected with [18 F]-FDG, were used to compare the method to the standard electrocardiography (ECG)-gated approach. RESULTS: The proposed method resulted in better recovery (from 32% to 95% on the simulated phantom model) and less residual activity than the standard approach. Higher signal-to-noise and contrast-to-noise ratios than ECG-gating were also witnessed (Signal-to-noise ratio (SNR) increased from 2.92 to 5.24, contrast-to-noise ratio (CNR) increased from 62.9 to 145.9 when compared to a four-gate reconstruction). Finally, the relevance of this correction using [11 C]-acetate PET patient data, for which erroneous physiological conclusions could have been made based on the uncorrected data, was established as the correction led to the expected clinical results. CONCLUSIONS: An efficient and simple method to correct for the quantitative biases in PET measurements caused by cardiac motion has been developed. Validation experiments using phantom and patient data showed improved accuracy and reliability with this approach when compared to simpler strategies such as gated acquisition or optimal regions of interest (ROI).

18F-FDG PET-MRI with T1 MOLLI mapping to detect systemic sclerosis bowel inflammation and fibrosis.

BACKGROUND: Systemic sclerosis-associated gastrointestinal tract involvement (SSc-GIT) is an independent predictor of 2-year mortality in early SSc. Availability of non-invasive investigations will facilitate early diagnosis and monitoring. HYPOTHESIS: We investigate the role of 18F-FDG-PET-MRI in SSc-GIT, hypothesizing that i) higher bowel FDG-PET uptake, a surrogate biomarker for inflammation, distinguishes healthy bowel from inflamed SSc-GIT; ii) MRI T1-MOLLI mapping, a surrogate biomarker for cardiac fibrosis, distinguishes healthy bowel from fibrotic SSc-GIT. METHODS: In this prospective study, 16 SSc patients and 15 healthy controls were recruited. All SSc patients and 5 controls underwent PET-MRI (with T1-MOLLI mapping) on a Siemens 3T mMR; 10 controls underwent MRI without PET. Manual segmentation of the large and small bowels was performed jointly by two trained analysts in order to report T1 and PET values. Control dataset was used to assess normal healthy range. Mean T1 values, mean Tissue-to-Background (TBR) PET values, as well as amount of supposedly abnormal bowel (measured using the healthy ranges) was compared using Student's t-test and Cohen's d effect size. RESULTS: Mean T1 values in large (1113 ±â€¯182 ms vs 856 ±â€¯176 ms; p-value < 0.001) and small bowel (1331 ±â€¯239 ms vs 1169 ±â€¯118 ms; p = 0.02) were higher in SSc patients than controls. 87.5% of the SSc patients' bowel had at least a grade 3 segmental FDG-PET uptake, while no controls showed more than a grade 2 segmental uptake. Patients had higher large bowel mean PET TBR (1.12 ±â€¯0.22) than controls (0.82 ±â€¯0.20, p = 0.02). Using PET and T1 thresholds defined using the control PET-MR data, the percentage of supposedly healthy (non-fibrotic and non-inflamed) tissue was significantly lower in SSc patients (81.1 ±â€¯13.1%) than controls (95.7 ±â€¯3.1%, p = 0.03) for the large bowel. CONCLUSION: Our novel study of FDG-PET-MRI in SSc-GIT demonstrated promising results in non-invasively evaluating concurrently bowel inflammation and fibrosis.

Hybrid PET/CT and PET/MRI imaging of vulnerable coronary plaque and myocardial scar tissue in acute myocardial infarction.

BACKGROUND: Following an acute coronary syndrome, combined CT and PET with 18F-NaF can identify coronary atherosclerotic plaques that have ruptured or eroded. However, the processes behind 18F-NaF uptake in vulnerable plaques remain unclear. METHODS AND RESULTS: Ten patients with STEMI were scanned after 18F-NaF injection, for 75 minutes in a Siemens PET/MR scanner using delayed enhancement (LGE). They were then scanned in a Siemens PET/CT scanner for 10 minutes. Tissue-to-background ratio (TBR) was compared between the culprit lesion in the IRA and remote non-culprit lesions in an effort to independently validate prior studies. Additionally, we performed a proof-of-principle study comparing TBR in scar tissue and remote myocardium using LGE images and PET/MR or PET/CT data. From the 33 coronary lesions detected on PET/CT, TBRs for culprit lesions were higher than for non-culprit lesions (TBR = 2.11 ± 0.45 vs 1.46 ± 0.48; P < 0.001). Interestingly, the TBR measured on the PET/CT was higher for infarcted myocardium than for remote myocardium (TBR = 0.81 ± 0.10 vs 0.71 ± 0.05; P = 0.003). These results were confirmed using the PET/MR data (TBR = 0.81 ± 0.10 for scar, TBR = 0.71 ± 0.06 for healthy myocardium, P = 0.03). CONCLUSIONS: We confirmed the potential of 18F-NaF PET/CT imaging to detect vulnerable coronary lesions. Moreover, we demonstrated proof-of-principle that 18F-NaF concurrently detects myocardial scar tissue.

Automatic basal slice detection for cardiac analysis.

Identification of the basal slice in cardiac imaging is a key step to measuring the ejection fraction of the left ventricle. Despite all the effort placed on automatic cardiac segmentation, basal slice identification is routinely performed manually. Manual identification, however, suffers from high interobserver variability. As a result, an automatic algorithm for basal slice identification is required. Guidelines published in 2013 identify the basal slice based on the percentage of myocardium surrounding the blood cavity in the short-axis view. Existing methods, however, assume that the basal slice is the first short-axis view slice below the mitral valve and are consequently at times identifying the incorrect short-axis slice. Correct identification of the basal slice under the Society for Cardiovascular Magnetic Resonance guidelines is challenging due to the poor image quality and blood movement during image acquisition. This paper proposes an automatic tool that utilizes the two-chamber view to determine the basal slice while following the guidelines. To this end, an active shape model is trained to segment the two-chamber view and create temporal binary profiles from which the basal slice is identified. From the 51 tested cases, our method obtains 92% and 84% accurate basal slice detection for the end-systole and the end-diastole, respectively.

Influence of the short-axis cine acquisition protocol on the cardiac function evaluation: A reproducibility study.

PURPOSE: To define the optimal cardiac short-axis cine acquisition protocol for the assessment of the left and rightventricular functions. MATERIALS AND METHODS: 20 volunteers were recruited and breath-hold CINE images were acquired on a Siemens Prisma 3T MRI. Four short-axis acquisition planes were defined from the 4-chamber view. AV Junctions: short-axis slices parallel to the plane that cuts through the external right and left atrioventricular junctions. Left AV Junctions: short-axis slices parallel to the plane that cuts through both left atrioventricular junctions. Septum: short-axis slices perpendicular to the septum with one cutting through the septum junction. LongAxis: short-axis slices perpendicular to the long axis with one cutting through the septum junction. Intra and inter reproducibility was assessed using Bland-Altman coefficient of variation (CV) and Lin's concordance correlation coefficient (CCC). The influence of the protocol on the ejection fraction (EF) and stroke volume (SV) was quantified statistically using pair-wise CV and Pearson's correlation coefficient R (2). RESULTS: All protocols led to high reproducibility for the LV EF (mean intra CV = 3.83%, mean inter CV = 4.81%, lowest CV = 4.20% (AV junctions) and highest CV = 5.24% (Left AV Junctions)). Reproducibility of the RV measurements was lower (mean intra CV = 7.84%, mean inter CV = 9.17%). Septum protocol led to significantly lower variability compared to the other 3 protocols for RV EF (CV = 7.62% (Septum), CV = 8.42% (Long Axis), CV = 9.54% (Left AV Junctions) and CV = 11.08% (AV Junctions) with Lin's CCC varying from 0.4 (AV Junctions) to 0.69 (Septum) for inter-observer reproducibility). No differences in group average for clinical parameters was found for both LV and RV clinical measurements. However, patient-specific RV EF evaluation is dependent on the chosen protocol (CV = 9.95%, R (2) = 0.52). CONCLUSION: Based on the results of the study cine mode short-axis acquisitions should be planned perpendicular to the septum in order to guarantee optimal RV and LV measurements.

Generation of synthetic but visually realistic time series of cardiac images combining a biophysical model and clinical images.

We propose a new approach for the generation of synthetic but visually realistic time series of cardiac images based on an electromechanical model of the heart and real clinical 4-D image sequences. This is achieved by combining three steps. The first step is the simulation of a cardiac motion using an electromechanical model of the heart and the segmentation of the end diastolic image of a cardiac sequence. We use biophysical parameters related to the desired condition of the simulated subject. The second step extracts the cardiac motion from the real sequence using nonrigid image registration. Finally, a synthetic time series of cardiac images corresponding to the simulated motion is generated in the third step by combining the motion estimated by image registration and the simulated one. With this approach, image processing algorithms can be evaluated as we know the ground-truth motion underlying the image sequence. Moreover, databases of visually realistic images of controls and patients can be generated for which the underlying cardiac motion and some biophysical parameters are known. Such databases can open new avenues for machine learning approaches.

Fast parameter calibration of a cardiac electromechanical model from medical images based on the unscented transform.

Patient-specific cardiac modelling can help in understanding pathophysiology and predict therapy planning. However, it requires to personalize the model geometry, kinematics, electrophysiology and mechanics. Calibration aims at providing proper initial values of parameters before performing the personalization stage which involves solving an inverse problem. We propose a fast automatic calibration method of the mechanical parameters of a complete electromechanical model of the heart based on a sensitivity analysis and the Unscented Transform algorithm. A new implementation of the complete Bestel-Clement-Sorine (BCS) cardiac model is also proposed, in a modular and efficient framework. A complete sensitivity analysis is performed that reveals which observations on the volume evolution are significant to characterize the global behaviour of the myocardium. We show that the calibration method gives satisfying results by optimizing up to 5 parameters of the BCS model in only one iteration. This method was evaluated synthetically as well as on 7 volunteers with a mean relative error from the real data of 10 %. This calibration is designed to replace manual parameter estimation as well as initialization steps that precede automatic personalization algorithms based on images.

Towards an interactive electromechanical model of the heart.

In this work, we develop an interactive framework for rehearsal of and training in cardiac catheter ablation, and for planning cardiac resynchronization therapy. To this end, an interactive and real-time electrophysiology model of the heart is developed to fit patient-specific data. The proposed interactive framework relies on two main contributions. First, an efficient implementation of cardiac electrophysiology is proposed, using the latest graphics processing unit computing techniques. Second, a mechanical simulation is then coupled to the electrophysiological signals to produce realistic motion of the heart. We demonstrate that pathological mechanical and electrophysiological behaviour can be simulated.

Cardiac mechanical parameter calibration based on the unscented transform.

Patient-specific cardiac modelling can help in understanding pathophysiology and predict therapy planning. However it requires to personalize the model geometry, kinematics, electrophysiology and mechanics. Calibration aims at providing global values (space invariant) of parameters before performing the personalization stage which involves solving an inverse problem to find regional values. We propose an automatic calibration method of the mechanical parameters of the Bestel-Clément-Sorine (BCS) electromechanical model of the heart based on the Unscented Transform algorithm. A sensitivity analysis is performed that reveals which observations on the volume and pressure evolution are significant to characterize the global behaviour of the myocardium. We show that the calibration method gives satisfying results by optimizing up to 7 parameters of the BCS model in only one iteration. This method was evaluated on 7 volunteers and 2 heart failure patients, with a mean relative error from the real data of 11%. This calibration enabled furthermore a preliminary study of the specific parameters to the studied pathologies.

Multiplicative Jacobian Energy Decomposition method for fast porous visco-hyperelastic soft tissue model.

Simulating soft tissues in real time is a significant challenge since a compromise between biomechanical accuracy and computational efficiency must be found. In this paper, we propose a new discretization method, the Multiplicative Jacobian Energy Decomposition (MJED) which is an alternative to the classical Galerkin FEM (Finite Element Method) formulation. This method for discretizing non-linear hyperelastic materials on linear tetrahedral meshes leads to faster stiffness matrix assembly for a large variety of isotropic and anisotropic materials. We show that our new approach, implemented within an implicit time integration scheme, can lead to fast and realistic liver deformations including hyperelasticity, porosity and viscosity.

Fast porous visco-hyperelastic soft tissue model for surgery simulation: application to liver surgery.

Understanding and modeling liver biomechanics represents a significant challenge due to its complex nature. In this paper, we tackle this issue in the context of real-time surgery simulation where a compromise between biomechanical accuracy and computational efficiency must be found. We describe a realistic liver model including hyperelasticity, porosity and viscosity that is implemented within an implicit time integration scheme. To optimize its computation, we introduce the Multiplicative Jacobian Energy Decomposition (MJED) method for discretizing hyperelastic materials on linear tetrahedral meshes which leads to faster matrix assembly than the standard Finite Element Method. Visco-hyperelasticity is modeled by Prony series while the mechanical effect of liver perfusion is represented with a linear Darcy law. Dynamic mechanical analysis has been performed on 60 porcine liver samples in order to identify some viscoelastic parameters. Finally, we show that liver deformation can be simulated in real-time on a coarse mesh and study the relative effects of the hyperelastic, viscous and porous components on the liver biomechanics.