Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Biomedical Computing

Biomedical computing combines the diagnostic and investigative aspects of biology and medical science with the power and problem-solving capabilities of modern computing. Computers are used to accelerate research learning, simulate patient behavior and visualize complex biological models.


chris

Chris Johnson

Inverse Problems
Computational Electrophysiology
rob

Rob MacLeod

ECG Imaging
Cardiac Disease
Computational Electrophysiology
jeff

Jeff Weiss

Computational Biomechanics
orly

Orly Alter

Computational Biology
butson

Chris Butson

Neuromodulation
Deep Brain Simulation
Transcranial Magnetic
Stimulation (TMS)

bidone

Tamara Bidone

Computational Models
Simulations of Biological Systems
Multi-Physics Models of Cancer Cells


Associated Centers:


Publications in Biomedical Computing:


A New Family of Variational-Form-Based Regularizers for Reconstructing Epicardial Potentials from Body-Surface Mapping
D.F. Wang, R.M. Kirby, R.S. MacLeod, C.R. Johnson. In Computing in Cardiology, 2010, pp. 93--96. 2010.



Effects of idealized joint geometry on finite element predictions of cartilage contact stresses in the hip
A.E. Anderson, B.J. Ellis, S.A. Maas, J.A. Weiss. In Journal of Biomechanics, Vol. 43, No. 7, pp. 1351--1357. May, 2010.

Computational models may have the ability to quantify the relationship between hip morphology, cartilage mechanics and osteoarthritis. Most models have assumed the hip joint to be a perfect ball and socket joint and have neglected deformation at the bone-cartilage interface. The objective of this study was to analyze finite element (FE) models of hip cartilage mechanics with varying degrees of simplified geometry and a model with a rigid bone material assumption to elucidate the effects on predictions of cartilage stress. A previously validated subject-specific FE model of a cadaveric hip joint was used as the basis for the models. Geometry for the bone-cartilage interface was either: (1) subject-specific (i.e. irregular), (2) spherical, or (3) a rotational conchoid. Cartilage was assigned either a varying (irregular) or constant thickness (smoothed). Loading conditions simulated walking, stair-climbing and descending stairs. FE predictions of contact stress for the simplified models were compared with predictions from the subject-specific model. Both spheres and conchoids provided a good approximation of native hip joint geometry (average fitting error ∼0.5 mm). However, models with spherical/conchoid bone geometry and smoothed articulating cartilage surfaces grossly underestimated peak and average contact pressures (50% and 25% lower, respectively) and overestimated contact area when compared to the subject-specific FE model. Models incorporating subject-specific bone geometry with smoothed articulating cartilage also underestimated pressures and predicted evenly distributed patterns of contact. The model with rigid bones predicted much higher pressures than the subject-specific model with deformable bones. The results demonstrate that simplifications to the geometry of the bone-cartilage interface, cartilage surface and bone material properties can have a dramatic effect on the predicted magnitude and distribution of cartilage contact pressures in the hip joint.

Keywords: mrl



Resolution Strategies for the Finite-Element-Based Solution of the ECG Inverse Problem
D.F. Wang, R.M. Kirby, C.R. Johnson. In IEEE Transactions on Biomedical Engineering, Vol. 57, No. 2, pp. 220--237. February, 2010.



Using the stochastic collocation method for the uncertainty quantification of drug concentration due to depot shape variability
J.S. Preston, T. Tasdizen, C.M. Terry, A.K. Cheung, R.M. Kirby. In IEEE Transactions on Biomedical Engineering, Vol. 56, No. 3, Note: Epub 2008 Dec 2, pp. 609--620. 2009.
PubMed ID: 19272865



Global Effects of DNA Replication and DNA Replication Origin Activity on Eukaryotic Gene Expression,
L. Omberg, J.R. Meyerson, K. Kobayashi, L.S. Drury, J.F.X. Diffley, O. Alter. In Nature Molecular Systems Biology, Vol. 5, No. 312, pp. (published online). October, 2009.
DOI: 10.1038/msb.2009.70



Incorporating patient breathing variability into a stochastic model of dose deposition for stereotactic body radiation therapy
S.E. Geneser, R.M. Kirby, Brian Wang, B. Salter, S. Joshi. In Information Processing in Medical Imaging, Lecture Notes in Computer Science LNCS, Vol. 5636, pp. 688--700. 2009.
PubMed ID: 19694304



Finite Element Discretization Strategies for the Inverse Electrocardiographic (ECG) Problem
D.F. Wang, R.M. Kirby, C.R. Johnson. In Proceedings of the 11th World Congress on Medical Physics and Biomedical Engineering, Munich, Germany, Vol. 25/2, pp. 729-732. September, 2009.



Finite Element Refinements for Inverse Electrocardiography: Hybrid-Shaped Elements, High-Order Element Truncation and Variational Gradient Operator
D.F. Wang, R.M. Kirby, C.R. Johnson. In Proceeding of Computers in Cardiology 2009, Park City, September, 2009.



Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples
R.S. MacLeod, J.G. Stinstra, S. Lew, R.T. Whitaker, D.J. Swenson, M.J. Cole, J. Krüger, D.H. Brooks, C.R. Johnson. In Philosophical Transactions of The Royal Society A, Mathematical, Physical & Engineering Sciences, Vol. 367, No. 1896, pp. 2293--2310. 2009.



Application of Stochastic Finite Element Methods to Study the Sensitivity of ECG Forward Modeling to Organ Conductivity
S.E. Geneser, R.M. Kirby, R.S. MacLeod. In IEEE Transations on Biomedical Engineering, Vol. 55, No. 1, pp. 31--40. January, 2008.



Visual Analysis of Bioelectric Fields
X. Tricoche, R.S. MacLeod, C.R. Johnson. In Visualization in Medicine and Life Sciences, Mathematics and Visualization, Springer-Verlag, pp. 205--220. 2008.



CRA-NIH Computing Research Challenges in Biomedicine Workshop Recommendations
D. Reed, C.R. Johnson. Note: Computing Research Association (CRA), 2007.



A Tensor Higher-Order Singular Value Decomposition for Integrative Analysis of DNA Microarray Data From Different Studies,
L. Omberg, G.H. Golub, O. Alter. In Proceedings of the National Academy of Sciences, Vol. 104, No. 47, Proceedings of the National Academy of Sciences, pp. 18371–-18376. November, 2007.
DOI: 10.1073/pnas.0709146104



Genomic Signal Processing: From Matrix Algebra to Genetic Networks
O. Alter. In Microarray Data Analysis: Methods in Molecular Biology, Vol. 377, Edited by M.J. Korenberg, Humana Press, Totowa, pp. 17--59. 2007.
DOI: 10.1007/978-1-59745-390-5_2



BioMesh3D: A Meshing Pipeline for Biomedical Models
SCI Institute Technical Report, M. Callahan, M.J. Cole, J.F. Shepherd, J.G. Stinstra, C.R. Johnson. No. UUSCI-2007-009, University of Utah, 2007.



Hexahedral Mesh Generation for Biomedical Models in SCIRun
SCI Institute Technical Report, J.F. Shepherd, C.R. Johnson. No. UUSCI-2007-008, University of Utah, 2007.



A Meshing Pipeline for Biomedical Computing
M. Callahan, M.J. Cole, J.F. Shepherd, J.G. Stinstra, C.R. Johnson. In Engineering with Computers, Special Issue on Computational Bioengineering, pp. (in press). 2007.



Discovery of Principles of Nature from Mathematical Modeling of DNA Microarray Data
O. Alter. In Proceedings of the National Academy of Sciences, Vol. 103, No. 44, Proceedings of the National Academy of Sciences, pp. 16063--16064. October, 2006.
DOI: 10.1073/pnas.0607650103



Singular Value Decomposition of Genome-Scale mRNA Lengths Distribution Reveals Asymmetry in RNA Gel Electrophoresis Band Broadening,
O. Alter, G. H. Golub. In Proceedings of the National Academy of Sciences, Vol. 103, No. 32, Proceedings of the National Academy of Sciences, pp. 11828--11833. July, 2006.
DOI: 10.1073/pnas.0604756103



Biomedical Computing and Visualization
C.R. Johnson, D.M. Weinstein. In Proceedings of the Twenty-Ninth Australasian Computer Science Conference (ACSC2006): Conferences in Research and Practice in Information Technology (CRPIT), Hobart, Australia, Vol. 48, Edited by Vladimir Estivill-Castro and Gill Dobbie, pp. 3-10. 2006.