Evaluation of models of sequestration flow in coronary arteries-Physiology versus anatomy?

BACKGROUND: Myocardial ischaemia results from insufficient coronary blood flow. Computed virtual fractional flow reserve (vFFR) allows quantification of proportional flow loss without the need for invasive pressure-wire testing. In the current study, we describe a novel, conductivity model of side branch flow, referred to as 'leak'. This leak model is a function of taper and local pressure, the latter of which may change radically when focal disease is present. This builds upon previous techniques, which either ignore side branch flow, or rely purely on anatomical factors. This study aimed to describe a new, conductivity model of side branch flow and compare this with established anatomical models. METHODS AND RESULTS: The novel technique was used to quantify vFFR, distal absolute flow (Qd) and microvascular resistance (CMVR) in 325 idealised 1D models of coronary arteries, modelled from invasive clinical data. Outputs were compared to an established anatomical model of flow. The conductivity model correlated and agreed with the reference model for vFFR (r = 0.895, p < 0.0001; ＋0.02, 95% CI 0.00 to ＋ 0.22), Qd (r = 0.959, p < 0.0001; -5.2 mL/min, 95% CI -52.2 to ＋13.0) and CMVR (r = 0.624, p < 0.0001; ＋50 Woods Units, 95% CI -325 to ＋2549). CONCLUSION: Agreement between the two techniques was closest for vFFR, with greater proportional differences seen for Qd and CMVR. The conductivity function assumes vessel taper was optimised for the healthy state and that CMVR was not affected by local disease. The latter may be addressed with further refinement of the technique or inferred from complementary image data. The conductivity technique may represent a refinement of current techniques for modelling coronary side-branch flow. Further work is needed to validate the technique against invasive clinical data.

A reduced complexity ECG imaging model for regularized inversion optimization.

The resolution of the inverse problem of electrocardiography represents a major interest in the diagnosis and catheter-based therapy of cardiac arrhythmia. In this context, the ability to simulate several cardiac electrical behaviors was crucial for evaluating and comparing the performance of inversion methods. For this application, existing models are either too complex or do not produce realistic cardiac patterns. In this work, a low-resolution heart-torso model generating realistic whole heart cardiac mappings and electrocardiograms in healthy and pathological cases is designed. This model was built upon a simplified heart-torso geometry and implements the monodomain formalism by using the finite element method. In addition, a model reduction step through a sensitivity analysis was proposed where parameters were identified using an evolutionary optimization approach. Finally, the study illustrates the usefulness of the proposed model by comparing the performance of different variants of Tikhonov-based inversion methods for the determination of the regularization parameter in healthy, ischemic and ventricular tachycardia scenarios. First, results of the sensitivity analysis show that among 58 parameters only 25 are influent. Note also that the level of influence of the parameters depends on the heart region. Besides, the synthesized electrocardiograms globally present the same characteristic shape compared to the reference once with a correlation value that reaches 88%. Regarding inverse problem, results highlight that only Robust Generalized Cross Validation and Discrepancy Principle provide best performance, with a quasi-perfect success rate for both, and a respective relative error, between the generated electrocardiograms to the reference one, of 0.75 and 0.62.

Effect of turbulence and viscosity models on wall shear stress derived biomarkers for aorta simulations.

Ascending aorta simulations provide insight into patient-specific hemodynamic conditions. Numerous studies have assessed fluid biomarkers which show a potential to aid clinicians in the diagnosis process. Unfortunately, there exists a large disparity in the computational methodology used to model turbulence and viscosity. Recognizing this disparity, some authors focused on analysing the influence of either the turbulence or viscosity models on the biomarkers in order to quantify the importance of these model choices. However, no analysis has yet been done on their combined effect. In order to fully understand and quantify the effect of the computational methodology, an assessment of the combined effect of turbulence and viscosity model choice was performed. Our results show that (1) non-Newtonian viscosity has greater impact (2.9-5.0%) on wall shear stress than Large Eddy Simulation turbulence modelling (0.1-1.4%), (2) the contribution of non-Newtonian viscosity is amplified when combined with a subgrid-scale turbulence model, (3) wall shear stress is underestimated when considering Newtonian viscosity by 2.9-5.0% and (4) cycle-to-cycle variability can impact the results as much as the numerical model if insufficient cycles are performed. These results demonstrate that, when assessing the effect of computational methodologies, the resultant combined effect of the different modelling assumptions differs from the aggregated effect of the isolated modifications. Accurate aortic flow modelling requires non-Newtonian viscosity and Large Eddy Simulation turbulence modelling.

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate.

OBJECTIVE: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict the ascending aortic aneurysm growth. MATERIAL AND METHODS: 70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified. RESULTS: the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth. CONCLUSION: global shape features might provide an important contribution for predicting the aneurysm growth.

Calibration of the Mechanical Boundary Conditions for a Patient-Specific Thoracic Aorta Model Including the Heart Motion Effect.

OBJECTIVE: We propose a procedure for calibrating 4 parameters governing the mechanical boundary conditions (BCs) of a thoracic aorta (TA) model derived from one patient with ascending aortic aneurysm. The BCs reproduce the visco-elastic structural support provided by the soft tissue and the spine and allow for the inclusion of the heart motion effect. METHODS: We first segment the TA from magnetic resonance imaging (MRI) angiography and derive the heart motion by tracking the aortic annulus from cine-MRI. A rigid-wall fluid-dynamic simulation is performed to derive the time-varying wall pressure field. We build the finite element model considering patient-specific material properties and imposing the derived pressure field and the motion at the annulus boundary. The calibration, which involves the zero-pressure state computation, is based on purely structural simulations. After obtaining the vessel boundaries from the cine-MRI sequences, an iterative procedure is performed to minimize the distance between them and the corresponding boundaries derived from the deformed structural model. A strongly-coupled fluid-structure interaction (FSI) analysis is finally performed with the tuned parameters and compared to the purely structural simulation. RESULTS AND CONCLUSION: The calibration with structural simulations allows to reduce maximum and mean distances between image-derived and simulation-derived boundaries from 8.64 mm to 6.37 mm and from 2.24 mm to 1.83 mm, respectively. The maximum root mean square error between the deformed structural and FSI surface meshes is 0.19 mm. This procedure may prove crucial for increasing the model fidelity in replicating the real aortic root kinematics.

Assessment of shape-based features ability to predict the ascending aortic aneurysm growth.

The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.

"Neuro-Fiber Mapping": An Original Concept of Spinal Cord Neural Network Spatial Targeting Using Live Electrostimulation Mapping to (Re-)Explore the Conus Medullaris Anatomy.

Spinal cord (SC) anatomy is often assimilated to a morphologically encapsulated neural entity, but its functional anatomy remains only partially understood. We hypothesized that it could be possible to re-explore SC neural networks by performing live electrostimulation mapping, based on "super-selective" spinal cord stimulation (SCS), originally designed as a therapeutical tool to address chronic refractory pain. As a starting point, we initiated a systematic SCS lead programming approach using live electrostimulation mapping on a chronic refractory perineal pain patient, previously implanted with multicolumn SCS at the level of the conus medullaris (T12-L1). It appeared possible to (re-)explore the classical anatomy of the conus medullaris using statistical correlations of paresthesia coverage mappings, resulting from 165 different electrical configurations tested. We highlighted that sacral dermatomes were not only located more medially but also deeper than lumbar dermatomes at the level of the conus medullaris, in contrast with classical anatomical descriptions of SC somatotopical organization. As we were finally able to find a morphofunctional description of "Philippe-Gombault's triangle" in 19th-century historical textbooks of neuroanatomy, remarkably matching these conclusions, the concept of "neuro-fiber mapping" was introduced.

Validation of a novel numerical model to predict regionalized blood flow in the coronary arteries.

Aims: Ischaemic heart disease results from insufficient coronary blood flow. Direct measurement of absolute flow (mL/min) is feasible, but has not entered routine clinical practice in most catheterization laboratories. Interventional cardiologists, therefore, rely on surrogate markers of flow. Recently, we described a computational fluid dynamics (CFD) method for predicting flow that differentiates inlet, side branch, and outlet flows during angiography. In the current study, we evaluate a new method that regionalizes flow along the length of the artery. Methods and results: Three-dimensional coronary anatomy was reconstructed from angiograms from 20 patients with chronic coronary syndrome. All flows were computed using CFD by applying the pressure gradient to the reconstructed geometry. Side branch flow was modelled as a porous wall boundary. Side branch flow magnitude was based on morphometric scaling laws with two models: a homogeneous model with flow loss along the entire arterial length; and a regionalized model with flow proportional to local taper. Flow results were validated against invasive measurements of flow by continuous infusion thermodilution (Coroventis™, Abbott). Both methods quantified flow relative to the invasive measures: homogeneous (r 0.47, P 0.006; zero bias; 95% CI -168 to +168 mL/min); regionalized method (r 0.43, P 0.013; zero bias; 95% CI -175 to +175 mL/min). Conclusion: During angiography and pressure wire assessment, coronary flow can now be regionalized and differentiated at the inlet, outlet, and side branches. The effect of epicardial disease on agreement suggests the model may be best targeted at cases with a stenosis close to side branches.

Segmentation of the aorta in systolic phase from 4D flow MRI: multi-atlas vs. deep learning.

OBJECTIVE: In the management of the aortic aneurysm, 4D flow magnetic resonance Imaging provides valuable information for the computation of new biomarkers using computational fluid dynamics (CFD). However, accurate segmentation of the aorta is required. Thus, our objective is to evaluate the performance of two automatic segmentation methods on the calculation of aortic wall pressure. METHODS: Automatic segmentation of the aorta was performed with methods based on deep learning and multi-atlas using the systolic phase in the 4D flow MRI magnitude image of 36 patients. Using mesh morphing, isotopological meshes were generated, and CFD was performed to calculate the aortic wall pressure. Node-to-node comparisons of the pressure results were made to identify the most robust automatic method respect to the pressures obtained with a manually segmented model. RESULTS: Deep learning approach presented the best segmentation performance with a mean Dice similarity coefficient and a mean Hausdorff distance (HD) equal to 0.92+/- 0.02 and 21.02+/- 24.20 mm, respectively. At the global level HD is affected by the performance in the abdominal aorta. Locally, this distance decreases to 9.41+/- 3.45 and 5.82+/- 6.23 for the ascending and descending thoracic aorta, respectively. Moreover, with respect to the pressures from the manual segmentations, the differences in the pressures computed from deep learning were lower than those computed from multi-atlas method. CONCLUSION: To reduce biases in the calculation of aortic wall pressure, accurate segmentation is needed, particularly in regions with high blood flow velocities. Thus, the deep learning segmen-tation method should be preferred.

Patient-specific finite element simulation of peripheral artery percutaneous transluminal angioplasty to evaluate the procedure outcome without stent implantation.

The purpose of this work is to present a patient-specific (PS) modeling approach for simulating percutaneous transluminal angioplasty (PTA) endovascular treatment and assessing the balloon sizing influence on short-term outcomes in peripheral arteries, i.e. without stent implantation. Two 3D PS stenosed femoral artery models, one with a dominant calcified atherosclerosis while the other with a lipidic plaque, were generated from pre-operative computed tomography angiography images. Elastoplastic constitutive laws were implemented within the plaque and artery models. Implicit finite element method (FEM) was used to simulate the balloon inflation and deflation for different sizings. Besides vessel strains, results were mainly evaluated in terms of the elastic recoil ratio (ERR) and lumen gain ratio (LGR) attained immediately after PTA. Higher LGR values were shown within the stenosed region of the lipidic patient. Simulated results also showed a direct and quantified correlation between balloon sizing and LGR and ERR for both patients after PTA, with a more significant influence on the lumen gain. The max principal strain values in the outer arterial wall increased at higher balloon sizes during inflation as well, with higher rates of increase when the plaque was calcified. Results show that our model could serve in finding a compromise for each stenosis type: maximizing the achieved lumen gain after PTA, but at the same time without damaging the arterial tissue. The proposed methodology can serve as a step toward a clinical decision support system to improve angioplasty balloon sizing selection prior to the surgery.

Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray's Law in Human Epicardial Coronary Arteries.

Background: Quantification of coronary blood flow is used to evaluate coronary artery disease, but our understanding of flow through branched systems is poor. Murray's law defines coronary morphometric scaling, the relationship between flow (Q) and vessel diameter (D) and is the basis for minimum lumen area targets when intervening on bifurcation lesions. Murray's original law (Q α DP) dictates that the exponent (P) is 3.0, whilst constant blood velocity throughout the system would suggest an exponent of 2.0. In human coronary arteries, the value of Murray's exponent remains unknown. Aim: To establish the exponent in Murray's power law relationship that best reproduces coronary blood flows (Q) and microvascular resistances (Rmicro) in a bifurcating coronary tree. Methods and Results: We screened 48 cases, and were able to evaluate inlet Q and Rmicro in 27 branched coronary arteries, taken from 20 patients, using a novel computational fluid dynamics (CFD) model which reconstructs 3D coronary anatomy from angiography and uses pressure-wire measurements to compute Q and Rmicro distribution in the main- and side-branches. Outputs were validated against invasive measurements using a Rayflow™ catheter. A Murray's power law exponent of 2.15 produced the strongest correlation and closest agreement with inlet Q (zero bias, r = 0.47, p = 0.006) and an exponent of 2.38 produced the strongest correlation and closest agreement with Rmicro (zero bias, r = 0.66, p = 0.0001). Conclusions: The optimal power law exponents for Q and Rmicro were not 3.0, as dictated by Murray's Law, but 2.15 and 2.38 respectively. These data will be useful in assessing patient-specific coronary physiology and tailoring revascularisation decisions.

Development of Digital Twins to Optimize Trauma Surgery and Postoperative Management. A Case Study Focusing on Tibial Plateau Fracture.

Background and context: Surgical procedures are evolving toward less invasive and more tailored approaches to consider the specific pathology, morphology, and life habits of a patient. However, these new surgical methods require thorough preoperative planning and an advanced understanding of biomechanical behaviors. In this sense, patient-specific modeling is developing in the form of digital twins to help personalized clinical decision-making. Purpose: This study presents a patient-specific finite element model approach, focusing on tibial plateau fractures, to enhance biomechanical knowledge to optimize surgical trauma procedures and improve decision-making in postoperative management. Study design: This is a level 5 study. Methods: We used a postoperative 3D X-ray image of a patient who suffered from depression and separation of the lateral tibial plateau. The surgeon stabilized the fracture with polymethyl methacrylate cement injection and bi-cortical screw osteosynthesis. A digital twin of the patient's fracture was created by segmentation. From the digital twin, four stabilization methods were modeled including two screw lengths, whether or not, to inject PMMA cement. The four stabilization methods were associated with three bone healing conditions resulting in twelve scenarios. Mechanical strength, stress distribution, interfragmentary strains, and fragment kinematics were assessed by applying the maximum load during gait. Repeated fracture risks were evaluated regarding to the volume of bone with stress above the local yield strength and regarding to the interfragmentary strains. Results: Stress distribution analysis highlighted the mechanical contribution of cement injection and the favorable mechanical response of uni-cortical screw compared to bi-cortical screw. Evaluation of repeated fracture risks for this clinical case showed fracture instability for two of the twelve simulated scenarios. Conclusion: This study presents a patient-specific finite element modeling workflow to assess the biomechanical behaviors associated with different stabilization methods of tibial plateau fractures. Strength and interfragmentary strains were evaluated to quantify the mechanical effects of surgical procedures. We evaluate repeated fracture risks and provide data for postoperative management.

Influence of balloon design, plaque material composition, and balloon sizing on acute post angioplasty outcomes: An implicit finite element analysis.

In this work we propose a generic modeling approach for simulating percutaneous transluminal angioplasty (PTA) endovascular treatment, and evaluating the influence of balloon design, plaque composition, and balloon sizing on acute post-procedural outcomes right after PTA, without stent implantation. Clinically-used PTA balloons were classified into two categories according to their compliance characteristics, and were modeled correspondingly. Self-defined elastoplastic constitutive laws were implemented within the plaque and artery models, after calibration based on experimental and clinical data. Finite element method (FEM) implicit solver was used to simulate balloon inflation and deflation. Besides balloon profile at max inflation, results are mainly assessed in terms of the elastic recoil ratio (ERR) and lumen gain ratio (LGR) obtained immediately after PTA. No variations in ERR nor LGR values were detected when the balloon design changed, despite the differences observed in their profile at max inflation. Moreover, LGR and ERR inversely varied with the augmentation of calcification level within the plaque (-11% vs. +4% respectively, from fully lipidic to fully calcified plaque). Furthermore, results showed a direct correlation between balloon sizing and LGR and ERR, with noticeably higher rates of change for LGR (+18% and +2% for LGR and ERR respectively for a calcified plaque and a balloon pressure increasing from 10 to 14 atm). However a larger LGR comes with a higher risk of arterial rupture. This proposed methodology opens the way for evaluation of angioplasty balloon selections towards clinical procedure optimization.

Patient-Specific Finite Element Models of Posterior Pedicle Screw Fixation: Effect of Screw's Size and Geometry.

Pedicle screw fixation is extensively performed to treat spine injuries or diseases and it is common for thoracolumbar fractures. Post-operative complications may arise from this surgery leading to back pain or revisions. Finite element (FE) models could be used to predict the outcomes of surgeries but should be verified when both simplified and realistic designs of screws are used. The aim of this study was to generate patient-specific Computed Tomography (CT)-based FE models of human vertebrae with two pedicle screws, verify the models, and use them to evaluate the effect of the screws' size and geometry on the mechanical properties of the screws-vertebra structure. FE models of the lumbar vertebra implanted with two pedicle screws were created from anonymized CT-scans of three patients. Compressive loads were applied to the head of the screws. The mesh size was optimized for realistic and simplified geometry of the screws with a mesh refinement study. Finally, the optimal mesh size was used to evaluate the sensitivity of the model to changes in screw's size (diameter and length) and geometry (realistic or simplified). For both simplified and realistic models, element sizes of 0.6 mm in the screw and 1.0 mm in the bone allowed to obtain relative differences of approximately 5% or lower. Changes in screw's length resulted in 4-10% differences in maximum deflection, 1-6% differences in peak stress in the screws, 10-22% differences in mean strain in the bone around the screw; changes in screw's diameter resulted in 28-36% differences in maximum deflection, 6-27% differences in peak stress in the screws, and 30-47% differences in mean strain in the bone around the screw. The maximum deflection predicted with realistic or simplified screws correlated very well (R 2 = 0.99). The peak stress in screws with realistic or simplified design correlated well (R 2 = 0.82) but simplified models underestimated the peak stress. In conclusion, the results showed that the diameter of the screw has a major role on the mechanics of the screw-vertebral structure for each patient. Simplified screws can be used to estimate the mechanical properties of the implanted vertebrae, but the systematic underestimation of the peak stress should be considered when interpreting the results from the FE analyses.

Machine-Learning based model order reduction of a biomechanical model of the human tongue.

BACKGROUND AND OBJECTIVES: This paper presents the results of a Machine-Learning based Model Order Reduction (MOR) method applied to a complex 3D Finite Element (FE) biomechanical model of the human tongue, in order to create a Digital Twin Model (DTM) that enables real-time simulations. The DTM is designed for future inclusion in a computer assisted protocol for tongue surgery planning. METHODS: The proposed method uses an "a posteriori" MOR that allows, from a limited number of simulations with the FE model, to predict in real time mechanical responses of the human tongue to muscle activations. RESULTS: The MOR method is evaluated for simulations associated with separate single tongue muscle activations. It is shown to be able to account with a sub-millimetric spatial accuracy for the non-linear dynamical behavior of the tongue model observed in these simulations. CONCLUSION: Further evaluations of the MOR method will include tongue movements induced by multiple muscle activations. At this stage our MOR method offers promising perspectives for the use of the tongue model in a clinical context to predict the impact of tongue surgery on tongue mobility. As a long term application, this DTM of the tongue could be used to predict the functional consequences of the surgery in terms of speech production and swallowing.

Numerical simulation of fenestrated graft deployment: Anticipation of stent graft and vascular structure adequacy.

Fenestrated endovascular aneurism repair (FEVAR) is a minimally invasive technique, and its success depends on the adequacy of the correspondence between the visceral arteries ostia and position of the fenestrations of the stent graft (SG) during its deployment in juxtarenal aneurisms. However, the fenestration position is generally determined from a preoperative computerised tomography (CT) scan, without considering the vascular deformation induced by the insertion of the endovascular tools. Catheterisation difficulties may occur during clinical procedures. Accordingly, the objective of this work is to present an initial proof of concept aimed at anticipating and optimising the position of the fenestrations, while considering the vascular deformation induced by the insertion of the endovascular tools. The proposed method relies on the finite element method to simulate the SG deployment in a vascular structure (VS), and considers the vascular deformation induced by the tools. After determining the optimal simulation parameters for a patient-specific case, the robustness of the method is demonstrated on six other representative anatomies. The simulated SG is also compared with post-deployment CT observations, and demonstrates good adequacy. The results show that the numerically corrected fenestration positions, as determined from the simulated results following the insertion of the endovascular tools, deviate from those of the standard plan (as determined from the preoperative CT scan). This indicates that the SG-VS adequacy could be improved via simulation-based planning, to anticipate potential catheterisation difficulties.

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers.

PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.

Patient-specific simulation of guidewire deformation during transcatheter aortic valve implantation.

Transcatheter aortic valve implantation is a recent mini-invasive procedure to implant an aortic valve prosthesis. Prosthesis positioning in transcatheter aortic valve implantation appears as an important aspect for the success of the intervention. Accordingly, we developed a patient-specific finite element framework to predict the insertion of the stiff guidewire, used to position the aortic valve. We simulated the guidewire insertion for 2 patients based on their pre-operative CT scans. The model was designed to primarily predict the position and the angle of the guidewires in the aortic valve, and the results were successfully compared with intraoperative images. The present paper describes extensively the numerical model, which was solved by using the ANSYS software with an implicit resolution scheme, as well as the stabilization techniques which were used to overcome numerical instabilities. We performed sensitivity analysis on the properties of the guidewire (curvature angle, curvature radius, and stiffness) and the conditions of insertion (insertion force and orientation). We also explored the influence of the model parameters. The accuracy of the model was quantitatively evaluated as the distance and the angle difference between the simulated guidewires and the intraoperative ones. A good agreement was obtained between the model predictions and intraoperative views available for 2 patient cases. In conclusion, we showed that the shape of the guidewire in the aortic valve was mainly determined by the geometry of the patient's aorta and by the conditions of insertion (insertion force and orientation).

Personalized modeling for real-time pressure ulcer prevention in sitting posture.

Ischial pressure ulcer is an important risk for every paraplegic person and a major public health issue. Pressure ulcers appear following excessive compression of buttock's soft tissues by bony structures, and particularly in ischial and sacral bones. Current prevention techniques are mainly based on daily skin inspection to spot red patches or injuries. Nevertheless, most pressure ulcers occur internally and are difficult to detect early. Estimating internal strains within soft tissues could help to evaluate the risk of pressure ulcer. A subject-specific biomechanical model could be used to assess internal strains from measured skin surface pressures. However, a realistic 3D non-linear Finite Element buttock model, with different layers of tissue materials for skin, fat and muscles, requires somewhere between minutes and hours to compute, therefore forbidding its use in a real-time daily prevention context. In this article, we propose to optimize these computations by using a reduced order modeling technique (ROM) based on proper orthogonal decompositions of the pressure and strain fields coupled with a machine learning method. ROM allows strains to be evaluated inside the model interactively (i.e. in less than a second) for any pressure field measured below the buttocks. In our case, with only 19 modes of variation of pressure patterns, an error divergence of one percent is observed compared to the full scale simulation for evaluating the strain field. This reduced model could therefore be the first step towards interactive pressure ulcer prevention in a daily set-up.

Finite element simulation of the insertion of guidewires during an EVAR procedure: example of a complex patient case, a first step toward patient-specific parameterized models.

Deformations of the vascular structure due to the insertion of tools during endovascular treatment of aneurysms of the abdominal aorta, unless properly anticipated during the preoperative planning phase, may be the source of intraoperative or postoperative complications. We propose here an explicit finite element simulation method which enables one to predict such deformations. This method is based on a mechanical model of the vascular structure which takes into account the nonlinear behavior of the arterial wall, the prestressing effect induced by the blood pressure and the mechanical support of the surrounding organs and structures. An analysis of the model sensitivity to the parameters used to represent this environment is done. This allows determining the parameters that have the largest influence on the quality of the prediction and also provides realistic values for each of them as no experimental data are available in the literature. Moreover, for the first time, the results are compared with 3D intraoperative data. This is done for a patient-specific case with a complex anatomy in order to assess the feasibility of the method. Finally, the predictive capability of the simulation is evaluated on a group of nine patients. The error between the final simulated and intraoperatively measured tool positions was 2.1 mm after the calibration phase on one patient. It results in a 4.6 ± 2.5 mm in average error for the blind evaluation on nine patients.