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Rapid Biomechanics Simulation for Personalized Clinical Design

Periodic Reporting for period 2 - RAINBOW (Rapid Biomechanics Simulation for Personalized Clinical Design)

Reporting period: 2020-04-01 to 2022-03-31

Clinical experts make design decisions on treatments, interventions, or on devices. ICT empowers them with patient specific simulation models that enable better-informed design decisions. But patient-specific computational medicine is currently cumbersome, slow, and unintuitive; it relies on complex processing by technical experts, and it is hence far from reaching its full potential on clinical design, and scarcely used.

RAINBOW envisions next-generation biomechanics simulation and optimization tools for personalized clinical design that are rapidly setup for a particular patient, have a fast learning curve, are easy-to-use by clinical experts, and do not require intervention by a technical team. Research objectives entail automated processing of patient data; automated setup of representations and parameters, capability to manage variance across patients; robust and accurate simulation as a latent part of design tools; and fast optimization methods that allow intuitive exploration of the design space. Novel computational methods have been created to reach the objective of rapid biomechanics simulation. RAINBOW applies research solutions for diagnosis, prognosis, monitoring, surgical training, planning, guidance and design, and addresses health conditions such as osteoarthritis, scoliosis, hearing impairment, cardiovascular diseases, obesity etc.

RAINBOW has 7 academic participants: UCPH (medical imagine, machine learning), URJC (data-driven modeling), UL (computational mechanics), CARDIFF (model reduction), AAU, AU and Mines ParisTech, one hospital HH (bone modeling) and 7 industries: 3Shape (prosthesis), Kitware (imaging), Insimo (surgical simulation), GMV (eHealth), Synopsys (CAD/CAE), inuTech (numerics), and Anatascope (Patient specific modeling). This combined expertise ensures diverse impact and training of highly qualified individuals
Work performed is illustrated by the completion of deliverables:
“Survey on and tutorial on 2D interface tracking meshing”
“Medical image deformation methods based on homogenization”
“Reports on early scientific findings (All ESRs, all three scientific work packages)”
“Open source software for interface tracking meshing with examples and video tutorials”
“Open-source scripted prototype of metamodels for hyperelastic soft tissues with cutting”
“Integrated image to mesh software prototype”
“Intuitive interaction techniques for medical image deformation and manipulation”
“Demonstrations of early prototypes: Pre-scan Image registration for sur¬gery, RSA and FEA coupling, Jaw motion simulation, demonstrate interaction between gait analysis data of the spine and a musculoskeletal model”
“Interactive demonstrator for assessing uncertainty of geometry on model outputs”
“Sensitivity study of rheology parameters for predicting observed breast shapes”
“Software for optimization-based design of scoliosis braces”
“Open source scripted prototype software to discriminate between material parameters from output of experimental data”
“Demonstration of prototype for scoliosis brace simulation”
“Assessment clinical impact potential for hip growth and spinal muscle simulators”

Main results have been communicated in publications: 13 postprints,11 pre-prints and +100 presentations as listed here: and here
RAINBOW contributes to and goes beyond State of the Art in our continuous work on:
1) Solving challenges for segmentation-free computational meshing of medical images. To do this, we work on proposing new solutions using image registration for population studies and solutions for automated discretization using NURBS.
2) Expanding estimation-oriented modeling for hyperelasticity estimation to include other tissue properties and extending solutions for linear corotational elasticity homogenization on richer materials. Part of the solution is proposing a new approach to quantify uncertainty of deformable models using approximation theory in the context of finite element (FE) analysis.
3) Proposing means to reduce manual tuning of parameters and increase the efficiency of model refitting and registration: To optimize the refitting, we propose to eliminate segmentation, use inverse finite element models and interface tracking. To optimize the registration of pre-operative models to intra-operative images we rely on confidence intervals.
4) Innovating model-order reduction techniques by: applying them to segmentation-free data and using corotational homogenization which enables application in model and parameter estimation; introducing novel error control methods and domain decomposition to tackle complex cutting and contact problems; designing novel data-driven approaches to achieve compact models, both for musculoskeletal systems and for fluid dynamics.
5) Designing new parallel algorithms for constrained optimization problems, specifically for biomechanical finite element (FE) analysis, interface tracking, a hyperelastic FEM model in combination with non-smooth contact forces, multigrid contact mechanics on conforming mesh interfaces, and constrained dynamics for image registration.
6) Expanding recent results in interactive medical image deformation by proposing how to accurately handle nonlinear elasticity and how to naturally interact on touch screens in augmented ways accounting for the volumetric nature of medical images and offering to intuitively specify pre-cuts.
7) Introducing intuitive interfaces for computational design into the field of clinical design to design interfaces for clinicians by use of sensitivity analysis and mapping design-space constraints to intuitive handles

Results of the project:
- Completion of the scientific deliverables listed above

Impact achieved in the context of training of the 15 ESRs who have gained scientific, interdisciplinary and inter-sectorial insights and experience. By way of the dissemination of results, impact is also noticed in the knowledge exchange in the scientific community and in strategic discussions organized by REA.
On the longer run, potential impact is foreseen in the clinical setting, providing surgeons and medical experts with ICT solutions supporting the decisions on treatments and interventions by way of automated processing of patient data and management of variance across patients, as well as patient specific simulation models with which to interact easily and intuitively. This may play a beneficial role in diagnosis, prognosis, monitoring, surgical training, planning, guidance, design of prosthetics, implants, and medical devices.