Medical Imaging Informatics: Towards a Personalized Computational Patient

International audienceMedical Imaging Informatics has become a fast evolving discipline at the crossing of Informatics, Computational Sciences, and Medicine that is profoundly changing medical practices, for the patients' benefit. Keywords Medical Imaging Informatics, personalized computational patient, computational anatomy, computational medicine Yearb Med Inform 2016;xxx http://dx.doi.org/10.15265/IY-2016-002 Published online xxx In 2002, my preface to the IMIA Yearbook was entitled " From Digital Anatomy to Virtual Scalpels and Image-Guided Therapy ". It was announcing a revolution in medicine brought by the extensive use of medical image computing to better assist the diagnosis and therapy of the patient. Today, the promised revolution is here: Medical images are omnipresent at the hospital, and Medical Imaging Informatics is required more than ever to exploit their flood of information. All around the world, medical image computing is used to extract the clinically relevant information from medical images, and to present this information in a way that is clinically useful to the physician. This is mainly done through the construction of a computational and personalized model of the patient. Building a computational model of the human body requires dedicated algorithms that take into account a thorough knowledge of the human anatomy and physiology. Huge progress has been made during the last decades to describe and simulate the structure and functions of organs thanks to advanced mathematical, biological, physical, and chemical models of the living tissues at various scales from the nanoscopic (molecular) to microscopic (cellular), mesoscopic (tissue), and macroscopic (organic) scales. Computational models of the human body rely on a set of parameters that allow, for instance, to specify the structure and function of organs. Generic models are based on average parameters estimated over a population. Confronted to in vivo anatomical and functional images and signals of a singular patient, those parameters are adjusted by efficient personalization algorithms in order to reproduce more precisely the observed structures and functions leading to the personalized computational model of this particular patient. The personalized computational model of the patient is then used to provide quantitative and objective measurements on the patient's condition to better assess the diagnosis. It is also used to predict a pathological evolution resulting in a better assessment of the prognosis. Finally, the computational model of the patient is extensively used to plan and simulate the effect of a therapy, in order to optimize its actual delivery. These three steps-computer aided diagnosis, prognosis, and therapy-announce the fast development of the computational medicine at the service of the physician. The tremendous progress of Medical Imaging Informatics also accompanies the evolution of normative and reactive medicine towards a more personalized, precise, preventive, and predictive medicine. This progress relies on numerous algorithmic advances in medical image analysis and inverse problem solving. It also relies on continuous advances in the modeling of human anatomy and physiology. It benefits from the improvement of medical image acquisition techniques, and from the introduction of new imaging modalities at various scales. It is supported by the regula

Left Ventricular Trabeculations Decrease the Wall Shear Stress and Increase the Intra-Ventricular Pressure Drop in CFD Simulations

The aim of the present study is to characterize the hemodynamics of left ventricular (LV) geometries to examine the impact of trabeculae and papillary muscles (PMs) on blood flow using high performance computing (HPC). Five pairs of detailed and smoothed LV endocardium models were reconstructed from high-resolution magnetic resonance images (MRI) of ex-vivo human hearts. The detailed model of one LV pair is characterized only by the PMs and few big trabeculae, to represent state of art level of endocardial detail. The other four detailed models obtained include instead endocardial structures measuring ≥1 mm2 in cross-sectional area. The geometrical characterizations were done using computational fluid dynamics (CFD) simulations with rigid walls and both constant and transient flow inputs on the detailed and smoothed models for comparison. These simulations do not represent a clinical or physiological scenario, but a characterization of the interaction of endocardial structures with blood flow. Steady flow simulations were employed to quantify the pressure drop between the inlet and the outlet of the LVs and the wall shear stress (WSS). Coherent structures were analyzed using the Q-criterion for both constant and transient flow inputs. Our results show that trabeculae and PMs increase the intra-ventricular pressure drop, reduce the WSS and disrupt the dominant single vortex, usually present in the smoothed-endocardium models, generating secondary small vortices. Given that obtaining high resolution anatomical detail is challenging in-vivo, we propose that the effect of trabeculations can be incorporated into smoothed ventricular geometries by adding a porous layer along the LV endocardial wall. Results show that a porous layer of a thickness of 1.2·10−2 m with a porosity of 20 kg/m2 on the smoothed-endocardium ventricle models approximates the pressure drops, vorticities and WSS observed in the detailed models.This paper has been partially funded by CompBioMed project, under H2020-EU.1.4.1.3 European Union’s Horizon 2020 research and innovation programme, grant agreement n◦ 675451. FS is supported by a grant from Severo Ochoa (n◦ SEV-2015-0493-16-4), Spain. CB is supported by a grant from the Fundació LaMarató de TV3 (n◦ 20154031), Spain. TI and PI are supported by the Institute of Engineering in Medicine, USA, and the Lillehei Heart Institute, USA.Peer ReviewedPostprint (published version

Microstructural analysis of skeletal muscle force generation during aging.

Human aging results in a progressive decline in the active force generation capability of skeletal muscle. While many factors related to the changes of morphological and structural properties in muscle fibers and the extracellular matrix (ECM) have been considered as possible reasons for causing age-related force reduction, it is still not fully understood why the decrease in force generation under eccentric contraction (lengthening) is much less than that under concentric contraction (shortening). Biomechanically, it was observed that connective tissues (endomysium) stiffen as ages, and the volume ratio of connective tissues exhibits an age-related increase. However, limited skeletal muscle models take into account the microstructural characteristics as well as the volume fraction of tissue material. This study aims to provide a numerical investigation in which the muscle fibers and the ECM are explicitly represented to allow quantitative assessment of the age-related force reduction mechanism. To this end, a fiber-level honeycomb-like microstructure is constructed and modeled by a pixel-based Reproducing Kernel Particle Method (RKPM), which allows modeling of smooth transition in biomaterial properties across material interfaces. The numerical investigation reveals that the increased stiffness of the passive materials of muscle tissue reduces the force generation capability under concentric contraction while maintains the force generation capability under eccentric contraction. The proposed RKPM microscopic model provides effective means for the cellular-scale numerical investigation of skeletal muscle physiology. NOVELTY STATEMENT: A cellular-scale honeycomb-like microstructural muscle model constructed from a histological cross-sectional image of muscle is employed to study the causal relations between age-associated microstructural changes and age-related force loss using Reproducing Kernel Particle Method (RKPM). The employed RKPM offers an effective means for modeling biological materials based on pixel points in the medical images and allow modeling of smooth transition in the material properties across interfaces. The proposed microstructure-informed muscle model enables quantitative evaluation on how cellular-scale compositions contribute to muscle functionality and explain differences in age-related force changes during concentric, isometric and eccentric contractions