Center for Integrative Biomedical Computing

SCI Publications


S.S. Kuppahally, N. Akoum, T.J. Badger, N.S. Burgon, T. Haslam, E. Kholmovski, R.S. Macleod, C. McGann, N.F. Marrouche. “Echocardiographic left atrial reverse remodeling after catheter ablation of atrial fibrillation is predicted by preablation delayed enhancement of left atrium by magnetic resonance imaging,” In American Heart Journal, Vol. 160, No. 5, pp. 877--884. 2010.
DOI: 10.1016/j.ahj.2010.07.003
PubMed ID: 21095275
PubMed Central ID: PMC2995281


Atrial fibrosis is a hallmark of atrial structural remodeling (SRM) and leads to structural and functional impairment of left atrial (LA) and persistence of atrial fibrillation (AF). This study was conducted to assess LA reverse remodeling after catheter ablation of AF in mild and moderate-severe LA SRM.

Catheter ablation was performed in 68 patients (age 62 ± 14 years, 68% males) with paroxysmal (n = 26) and persistent (n = 42) AF. The patients were divided into group 1 with mild LA SRM (10%, n = 37) by delayed enhancement magnetic resonance imaging (DEMRI). Two-dimensional echocardiography, LA strain, and strain rate during left ventricular systole by velocity vector imaging were performed pre and at 6 ± 3 months postablation. The long-term outcome was monitored for 12 months.

Patients in group 1 were younger (57 ± 15 vs 66 ± 13 years, P = .009) with a male predominance (80% vs 57%, P < .05) as compared to group 2. Postablation, group 1 had significant increase in average LA strain (??: 14% vs 4%, P < .05) and strain rate (??: 0.5 vs 0.1 cm/s, P < .05) as compared to group 2. There was a trend toward more patients with persistent AF in group 2 (68% vs 55%, P = .2), but it was not statistically significant. Group 2 had more AF recurrences (41% vs 16%, P = .02) at 12 months after ablation.

Mild preablation LA SRM by DEMRI predicts favorable LA structural and functional reverse remodeling and long-term success after catheter ablation of AF, irrespective of the paroxysmal or persistent nature of AF.

S. Kurugol, N. Ozay, J.G. Dy, G.C. Sharp, D.H. Brooks. “Locally Deformable Shape Model to Improve 3D Level Set based Esophagus Segmentation,” In Proceedings of the IAPR International Conference on Pattern Recognition, pp. 3955--3958. August, 2010.
PubMed ID: 21731883
PubMed Central ID: PMC3127393


In this paper we propose a supervised 3D segmentation algorithm to locate the esophagus in thoracic CT scans using a variational framework. To address challenges due to low contrast, several priors are learned from a training set of segmented images. Our algorithm first estimates the centerline based on a spatial model learned at a few manually marked anatomical reference points. Then an implicit shape model is learned by subtracting the centerline and applying PCA to these shapes. To allow local variations in the shapes, we propose to use nonlinear smooth local deformations. Finally, the esophageal wall is located within a 3D level set framework by optimizing a cost function including terms for appearance, the shape model, smoothness constraints and an air/contrast model.

J.A. Levine, D.J. Swenson, Z. Fu, R.S. MacLeod, R.T. Whitaker. “A Comparison of Delaunay Based Meshing Algorithms for Electrophysiological Cardiac Simulations,” In Virtual Physiological Human, pp. 181--183. 2010.

C. Mahnkopf, T.J. Badger, N.S. Burgon, M. Daccarett, T.S. Haslam, C.T. Badger, C.J. McGann, N. Akoum, E. Kholmovski, R.S. Macleod, N.F. Marrouche. “Evaluation of the left atrial substrate in patients with lone atrial fibrillation using delayed-enhanced MRI: implications for disease progression and response to catheter ablation,” In Heart Rhythm, Vol. 7, No. 10, pp. 1475--1481. 2010.
PubMed ID: 20601148

T.A. Pilcher, J.D. Tate, J.G. Stinstra, E.V. Saarel, M.D. Puchalski, and R.S. MacLeod. “Partially extracted defibrillator coils and pacing leads alter defibrillation thresholds,” In Proceedings of the 15th International Academy of Cardiology World Congress of Cardiology, 2010.

K. Potter, J.M. Kniss, R. Riesenfeld, C.R. Johnson. “Visualizing Summary Statistics and Uncertainty,” In Computer Graphics Forum, Vol. 29, No. 3, Wiley-Blackwell, pp. 823--831. Aug, 2010.

N.M. Segerson, M. Daccarett, T.J. Badger, A. Shabaan, N. Akoum, E.N. Fish, S. Rao, N.S. Burgon, Y. Adjei-Poku, E. Kholmovski, S. Vijayakumar, E.V. DiBella, R.S. MacLeod, N.F. Marrouche. “Magnetic resonance imaging-confirmed ablative debulking of the left atrial posterior wall and septum for treatment of persistent atrial fibrillation: rationale and initial experience,” In Journal of Cardiovascular Electrophysiology, Vol. 21, No. 2, pp. 126--132. 2010.
PubMed ID: 19804549

J.G. Stinstra, R.S. MacLeod, C.S. Henriquez. “Incorporating Histology into a 3D Microscopic Computer Model of Myocardium to Study Propagation at a Cellular Level,” In Annals of Biomedical Engineering (ABME), Vol. 38, No. 4, pp. 3399--1414. 2010.
DOI: 10.1007/s10439-009-9883-y

D. Swenson, J.A. Levine, Z. Fu, J.D. Tate, R.S. MacLeod. “The Effect of Non-Conformal Finite Element Boundaries on Electrical Monodomain and Bidomain Simulations,” In Computing in Cardiology, Vol. 37, IEEE, pp. 97--100. 2010.
ISSN: 0276-6547

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,, Rostock-Warnemünde, Germany, Oct.5-8, 2010 2010.


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.