A model order reduction approach to create patient-specific mechanical models of human liver in computational medicine applications

Abstract

et al.. A model order reduction approach to create patient-specific mechanical models of human liver in computational medicine applications.. Computer Methods and Programs in Biomedicine, Elsevier, 2019, 170, pp.a b s t r a c tBackground and objective: This paper focuses on computer simulation aspects of Digital Twin models in the medical framework. In particular, it addresses the need of fast and accurate simulators for the mechanical response at tissue and organ scale and the capability of integrating patient-specific anatomy from medical images to pinpoint the individual variations from standard anatomical models. Methods:We propose an automated procedure to create mechanical models of the human liver with patient-specific geometry and real time capabilities. The method hinges on the use of Statistical Shape Analysis to extract the relevant anatomical features from a database of medical images and Model Order Reduction to compute an explicit parametric solution for the mechanical response as a function of such features. The Sparse Subspace Learning, coupled with a Finite Element solver, was chosen to create lowrank solutions using a non-intrusive sparse sampling of the feature space.Results: In the application presented in the paper, the statistical shape model was trained on a database of 385 three dimensional liver shapes, extracted from medical images, in order to create a parametrized representation of the liver anatomy. This parametrization and an additional parameter describing the breathing motion in linear elasticity were then used as input in the reduced order model. Results show a consistent agreement with the high fidelity Finite Element models built from liver images that were excluded from the training dataset. However, we evidence in the discussion the difficulty of having compact shape parametrizations arising from the extreme variability of the shapes found in the dataset and we propose potential strategies to tackle this issue. Conclusions:A method to represent patient-specific real-time liver deformations during breathing is proposed in linear elasticity. Since the proposed method does not require any adaptation to the direct Finite Element solver used in the training phase, the procedure can be easily extended to more complex nonlinear constitutive behaviors -such as hyperelasticity -and more general load cases. Therefore it can be integrated with little intrusiveness to generic simulation software including more sophisticated and realistic models.

Digital Twins for Multiple Sclerosis

“…The use of DTs in medical care is still in its infancy. So far, only in a few areas of medicine, DTs were applied, such as oncology (97-99), geriatrics (100, 101), cardiology (45,(102)(103)(104)(105)(106), epidemic outbreaks (107), genomic medicine (48, 108), internal medicine (109,110), orthopedics (111) and vascular medicine (112,113). For example, Corral-Acero et al present early steps of a DT in the field of cardiovascular medicine by describing the synergies between mechanistic and statistical models, the pillars of the DT (45).…”

“…The use of DTs in medical care is still in its infancy. So far, only in a few areas of medicine, DTs were applied, such as oncology ( 97 – 99 ), geriatrics ( 100 , 101 ), cardiology ( 45 , 102 – 106 ), epidemic outbreaks ( 107 ), genomic medicine ( 48 , 108 ), internal medicine ( 109 , 110 ), orthopedics ( 111 ) and vascular medicine ( 112 , 113 ). For example, Corral-Acero et al.…”

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

“…These methods allow to obtain real-time simulations by reducing the computational complexity without simplifying the physics of the model. Projection-based and collocation-based MOR methods are the most popular ones [13,14,15,16]. Projection-based MOR methods are divided in two categories: (1) a posteriori methods, such as the Proper Orthogonal Decomposition, which create a Reduced Order Model (ROM) from a large set of simulations called "Snapshots" [13] and require a computationally intensive offline phase; (2) a priori methods, such as the Proper Generalized Decomposition, which reduce the model during the problem solving process itself [14,17].…”

“…In [16] the ROM was created with a collocation-based MOR method called Space Subspace Learning [18]. The authors have developed a Digital Twin Model (DTM) of the liver by considering the large displacement approach in linear elasticity and quasi-static way.…”

Real-time simulations of human tongue movements with a reduced order model of a non-linear dynamic biomechanical model

“…This paper proposes to evaluate the Model Order Reduction (MOR) technique developed by ANSYSV R called Dynamic ROM Builder (DRB), which is accessible in the ANSYS Twin Builder product. Contrary to other recent MOR techniques that propose real-time computations for quasi-static analysis (Lauzeral et al 2019), the DRB is adapted to a transient analysis and should simulate, in real-time the non-linear dynamical behavior of the tongue model.…”