2010
J.D. Tate, J.G. Stinstra, T.A. Pilcher, R.S. MacLeod.
“Implantable Cardioverter Defibrillator Predictive Simulation Validation,” In Computing in Cardiology, pp. 853-–856. September, 2010.
Despite the growing use of implantable cardioverter defibrillators (ICDs) in adults and children, there has been little progress in optimizing device and electrode placement. To facilitate effective placement of ICDs, especially in unique cases of children with congenital heart defects, we have developed a predictive model that evaluates the efficacy of a delivered shock. Most recently, we have also developed and carried out an experimental validation approach based on measurements from clinical cases. We have developed a method to obtain body surface potential maps of ICD discharges during implantation surgery and compared these measured potentials with simulated surface potentials to determine simulation accuracy.
Each study began with an full torso MRI or CT scan of the subject, from which we created patient specific geometric models. Using a customized limited leadset applied to the anterior surface of the torso away from the sterile field, we recorded body surface potentials during ICD testing. Subsequent X-ray images documented the actual location of ICD and electrodes for placement of the device in the geometric model. We then computed the defibrillation field, including body surface potentials, and compared them to the measured values.
Comparison of the simulated and measured potentials yielded very similar patterns and a typical correlation between 0.8 and 0.9 and a percentage error between 0.2 and 0.35. The high correlation of the potential maps suggest that the predictive simulation generates realistic potential values. Ongoing sensi- tivity studies will determine the robustness of the results and pave the way for use of this approach for predictive computational optimization studies before device implantation.
D.F. Wang, R.M. Kirby, C.R. Johnson.
“Resolution Strategies for the Finite-Element-Based Solution of the ECG Inverse Problem,” In IEEE Transactions on Biomedical Engineering, Vol. 57, No. 2, pp. 220--237. February, 2010.
D.F. Wang, R.M. Kirby, R.S. MacLeod, C.R. Johnson.
“A New Family of Variational-Form-Based Regularizers for Reconstructing Epicardial Potentials from Body-Surface Mapping,” In Computing in Cardiology, 2010, pp. 93--96. 2010.
C.H. Wolters, S. Lew, R.S. MacLeod, M.S. Hämäläinen.
“Combined EEG/MEG source analysis using calibrated finite element head models,” In Proc. of the 44th Annual Meeting, DGBMT, Note: to appear, http://conference.vde.com/bmt-2010, Rostock-Warnemünde, Germany, Oct.5-8, 2010 2010.
2009
T.J. Badger, R.S. Oakes, M. Daccarett, N.S. Burgon, N. Akoum, E.N. Fish, J.J. Blauer, S.N. Rao, Y. Adjei-Poku, E.G. Kholmovski, S. Vijayakumar, E.V. Di Bella, R.S. MacLeod, N.F. Marrouche.
“Temporal Left Atrial Lesion Formation After Ablation of Atrial Fibrillation,” In Heart Rhythm, Vol. 6, No. 2, pp. 161--168. February, 2009.
T.J. Badger, Y.A. Adjei-Poku, N.S. Burgon, S. Kalvaitis, A. Shaaban, D.N. Sommers, J.J.E. Blauer, E.N. Fish N. Akoum, T.S. Haslem, E.G. Kholmovski, R.S. MacLeod, D.G. Adler, N.F. Marrouche.
“Initial Experience of Assessing Esophageal Tissue Injury and Recovery Using Delayed-Enhancement MRI After Atrial Fibrillation Ablation,” In Circulation: Arrhythmia and Electrophysiology, Vol. 2, pp. 620--625. 2009.
M. Callahan, M.J. Cole, J.F. Shepherd, J.G. Stinstra, C.R. Johnson.
“A Meshing Pipeline for Biomedical Models,” In Engineering with Computers, Vol. 25, No. 1, SpringerLink, pp. 115-130. 2009.
DOI: 10.1007/s00366-008-0106-1
M. Datar, J. Cates, P.T. Fletcher, S. Gouttard, G. Gerig, R.T. Whitaker.
“Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging,” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science LNCS, Vol. 5762, pp. 167--174. 2009.
DOI: 10.1007/978-3-642-04271-3_21
PubMed ID: 20426109
P.T. Fletcher, J. Moeller, J.M. Phillips, S. Venkatasubramanian.
“Computing Hulls In Positive Definite Space,” In In Proceedings of the 19th Fall Workshop on Computational Geometry, November, 2009.
T. Fogal, J. Krüger.
“Size Matters - Revealing Small Scale Structures in Large Datasets,” In Proceedings of the World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, IFMBE Proceedings, Vol. 25/13, Springer Berlin Heidelberg, pp. 41--44. 2009.
C.D. Hansen, C.R. Johnson, V. Pascucci, C.T. Silva.
“Visualization for Data-Intensive Science,” In The Fourth Paradigm: Data-Intensive Science, Edited by S. Tansley and T. Hey and K. Tolle, Microsoft Research, pp. 153--164. 2009.
B.M. Isaacson, J.G. Stinstra, R.S. MacLeod, J.B. Webster, J.P. Beck, R.D. Bloebaum.
“Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees,” In JoVE, Vol. 29, 2009.
M. Kamali, L.J. Day, D.H. Brooks, X. Zhou, D.M. O'Malley.
“Automated identification of neurons in 3D confocal datasets from zebrafish brainstem,” In Journal of Microscopy, Vol. 233, No. 1, pp. 114--131. January, 2009.
DOI: 10.1111/j.1365-2818.2008.03102.x
PubMed ID: 19196418
PubMed Central ID: PMC2798854
Many kinds of neuroscience data are being acquired regarding the dynamic behaviour and phenotypic diversity of nerve cells. But as the size, complexity and numbers of 3D neuroanatomical datasets grow ever larger, the need for automated detection and analysis of individual neurons takes on greater importance. We describe here a method that detects and identifies neurons within confocal image stacks acquired from the zebrafish brainstem. The first step is to create a template that incorporates the location of all known neurons within a population - in this case the population of reticulospinal cells. Once created, the template is used in conjunction with a sequence of algorithms to determine the 3D location and identity of all fluorescent neurons in each confocal dataset. After an image registration step, neurons are segmented within the confocal image stack and subsequently localized to specific locations within the brainstem template - in many instances identifying neurons as specific, individual reticulospinal cells. This image-processing sequence is fully automated except for the initial selection of three registration points on a maximum projection image. In analysing confocal image stacks that ranged considerably in image quality, we found that this method correctly identified on average approximately 80% of the neurons (if we assume that manual detection by experts constitutes 'ground truth'). Because this identification can be generated approximately 100 times faster than manual identification, it offers a considerable time savings for the investigation of zebrafish reticulospinal neurons. In addition to its cell identification function, this protocol might also be integrated with stereological techniques to enhance quantification of neurons in larger databases. Our focus has been on zebrafish brainstem systems, but the methods described should be applicable to diverse neural architectures including retina, hippocampus and cerebral cortex.
J. Krüger, T. Fogal.
“Focus and Context - Visualization without the Complexity,” In Proceedings of the World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, IFMBE Proceedings, Vol. 25/13, Springer Berlin Heidelberg, pp. 44--48. 2009.
S. Lew, C.H. Wolters, T. Dierkes, C. Röer, R.S. MacLeod.
“Accuracy and run-time comparison for different potential approaches and iterative solvers in finite element method based EEG source analysis,” In Applied Numerical Mathematics, Vol. 59, pp. 1970--1988. 2009.
S. Lew, C.H. Wolters, A. Anwander, S. Makeig, R.S. MacLeod.
“Improved EEG Source Analysis Using Low-Resolution Conductivity Estimation in a Four-Compartment Finite Element Head Model,” In Human Brain Mapping, Vol. 30, pp. 2862--2878. 2009.
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.
“Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples,” In Philosophical Transactions of The Royal Society A, Mathematical, Physical & Engineering Sciences, Vol. 367, No. 1896, pp. 2293--2310. 2009.
H.G. Martinez, S.I. Prajapati, C.A. Estrada, F. Jimenez, M.P. Quinones, I. Wu, A. Bahadur, A. Sanderson, C.R. Johnson, M. Shim, C. Keller, S.S. Ahuja.
“Microscopic Computed Tomography Based Virtual Histology for Visualization and Morphometry of Atherosclerosis in Diabetic Apolipoprotein E Mutant Mice,” In Circulation: Journal of the American Heart Association, Vol. 120, No. 9, pp. 821--822. 2009.
H. Martinez, S. Prajapati, C. Estrada, F. Jimenez, I. Wu, A. Bahadur, A. Sanderson, C.R. Johnson, M. Shim, C. Keller, S. Ahuja.
“Microscopic Computed Tomography–Based Virtual Histology for Visualization and Morphometry of Atherosclerosis in Diabetic Apolipoprotein E Mutant Mice,” In Circulation, Vol. 120, No. 821--822, 2009.
M. Milanic, V. Jazbinsek, D.F. Wang, J. Sinstra, R.S. Macleod, D.H. Brooks, R. Hren.
“Evaluation of Approaches of Solving Electrocardiographic Imaging Problem,” In Proceeding of Computers in Cardiology 2010, Park City, Utah, September, 2009.
Page 13 of 24