Seeing Organ Function (SOF)

Anders Ynnerman at the visualization table

Seeing organ function (SOF) is a visionary project with ambitious goals of understanding how human organs behave. The project is important for the development of image-based health care and takes on very important technical challenges. We work interdisciplinary, linking medical research and clinical use with novel techniques. SOF brings together the expertise found at CMIV, ranging from medicine, over medical visualization to image analysis and biomedical engineering. This interdisciplinary project thereby inspire novel work on discovering new findings about the function of human organs.

Explore the Organs

The main cause of death in our part of the world is cardiovascular disease and the fastest growing cause of death is degenerative brain diseases. The overall goal of this projects is to develop methods for the creation of image-driven patient specific organ models. The models will allow for exploratory simulation of function that enable groundbreaking medical research on organ function. The long-term goal is clinical use of patient specific functional organ models in the diagnostic workflow.

This challenging goal calls for concerted efforts on development of novel technical approaches in all the stages of the image-based creation of the organ model. Also important is the execution of the simulation incorporating the boundary conditions derived from the patient-specific imaging data. The work is conducted in an interdisciplinary medical-technical cycle intimately linking medical research, clinical use, and technical development and is summarized in the following key areas.

Data Acquisition

Myelin is crucial for efficient signal transmission over long ranges in the nervous system. Degradation of myelin impairs the signal transmission and eventually leads to brain atrophy and brain dysfunction. We have developed a new model for estimating the brain volume, degree of myelination and degree of oedema, which is now available for MR-scanners worldwide. We have also developed a novel method for multi-fiber reconstruction based on a mixture of non-central Wishart distributions, which better captures the true fibre orientation distribution and outperforms the previously proposed probabilistic models.

Patient-Specific Modeling and Simulation

A novel numerical framework was developed for patient-specific modelling and simulation of blood flow and muscle function based on imaging data. With segmentation of the whole heart as boundary conditions, simulation of cardiac hemodynamics was obtained with outstanding geometrical detail. In addition, simulations of deep brain stimulation (DBS) have been used for investigation of optimal positioning in DBS for Tourette syndrome obsessive compulsive disorder.

Exploring and Explaining Multimodal Data in Clinical Populations

Multimodal data are acquired to understand brain-body interactions involved in for example the sleep and pain disorders narcolepsy and irritable bowel syndrome (IBS). We have developed a novel framework for mechanistic modelling of time dependent neuroimaging data. The framework has the ability to explain the measured hemodynamic responses to neural activity e.g. fMRI data, in terms of activity in for example excitatory and inhibitory neurons. The model explains and predict brain function and can thus have the potential to become biomarkers of disease. To further aid scientific reasoning and hypothesis formulation in these brain co-cohort studies, we have developed a data analytics and exploration environment allowing neuroscientists to visually explore all data in a single application. This visual environment fills an important gap when it comes to analyzing patient group data having both a spatial and an abstract nature.

Key Publications
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Daniel Jönsson, Albin Bergström, Camilla Forsell, Rozalyn Simon, Maria Engström, Anders Ynnerman, Ingrid Hotz (2019)

Eurographics Workshop on Visual Computing for Biology and Medicine Continue to DOI

Jonas Lantz, Vikas Gupta, Lilian Henriksson, Matts Karlsson, Anders Persson, Carljohan Carlhäll, Tino Ebbers (1947)

Annals of Biomedical Engineering , s.413-424 Continue to DOI

Sebastian Sten, Karin Lundengård, Suzanne Tyson Witt, Gunnar Cedersund, Fredrik Elinder, Maria Engström (2017)

NeuroImage , Vol.158 , s.219-231 Continue to DOI

Contacts
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CMIV
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