Center for Integrative Biomedical Computing

SCI Publications


SCI Institute. Note: SCIRun: A Scientific Computing Problem Solving Environment, Scientific Computing and Imaging Institute (SCI), Download from:, 2015.

CIBC. Note: Seg3D: Volumetric Image Segmentation and Visualization. Scientific Computing and Imaging Institute (SCI), Download from:, 2015.


J.J.E. Blauer, D. Swenson, K. Higuchi, G. Plank, R. Ranjan, N. Marrouche,, R.S. MacLeod. “Sensitivity and Specificity of Substrate Mapping: An In Silico Framework for the Evaluation of Electroanatomical Substrate Mapping Strategies,” In Journal of Cardiovascular Electrophysiology, In Journal of Cardiovascular Electrophysiology, Vol. 25, No. 7, Note: Featured on journal cover., pp. 774--780. May, 2014.


Background - Voltage mapping is an important tool for characterizing proarrhythmic electrophysiological substrate, yet it is subject to geometric factors that influence bipolar amplitudes and thus compromise performance. The aim of this study was to characterize the impact of catheter orientation on the ability of bipolar amplitudes to accurately discriminate between healthy and diseased tissues.

Methods and Results - We constructed a three-dimensional, in-silico, bidomain model of cardiac tissue containing transmural lesions of varying diameter. A planar excitation wave was stimulated and electrograms were sampled with a realistic catheter model at multiple positions and orientations. We carried out validation studies in animal experiments of acute ablation lesions mapped with a clinical mapping system. Bipolar electrograms sampled at higher inclination angles of the catheter with respect to the tissue demonstrated improvements in both sensitivity and specificity of lesion detection. Removing low voltage electrograms with concurrent activation of both electrodes, suggesting false attenuation of the bipolar electrogram due to alignment with the excitation wavefront, had little effect on the accuracy of voltage mapping.

Conclusions - Our results demonstrate possible mechanisms for the impact of catheter orientation on voltage mapping accuracy. Moreover, results from our simulations suggest that mapping accuracy may be improved by selectively controlling the inclination of the catheter to record at higher angles with respect to the tissue.

Keywords: arrhythmia, computer-based model, electroanatomical mapping, voltage mapping, bipolar electrogram

J. Bronson, J.A. Levine, R.T. Whitaker. “Lattice cleaving: a multimaterial tetrahedral meshing algorithm with guarantees,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), pp. 223--237. 2014.
DOI: 10.1109/TVCG.2013.115
PubMed ID: 24356365


We introduce a new algorithm for generating tetrahedral meshes that conform to physical boundaries in volumetric domains consisting of multiple materials. The proposed method allows for an arbitrary number of materials, produces high-quality tetrahedral meshes with upper and lower bounds on dihedral angles, and guarantees geometric fidelity. Moreover, the method is combinatoric so its implementation enables rapid mesh construction. These meshes are structured in a way that also allows grading, to reduce element counts in regions of homogeneity. Additionally, we provide proofs showing that both element quality and geometric fidelity are bounded using this approach.

S. Eichelbaum, M. Dannhauer, M. Hlawitschka , D.H. Brooks, T.R. Knosche, G. Scheuermanna. “Visualizing Simulated Electrical Fields from Electroencephalography and Transcranial Electric Brain Stimulation: A Comparative Evaluation,” In Neuroimage, 2014.
DOI: 10.1016/j.neuroimage.2014.04.085


Electrical activity of neuronal populations is a crucial aspect of brain activity. This activity is not measured directly but recorded as electrical potential changes using head surface electrodes (electroencephalogram - EEG). Head surface electrodes can also be deployed to inject electrical currents in order to modulate brain activity (transcranial electric stimulation techniques) for therapeutic and neuroscientific purposes. In electroencephalography and noninvasive electric brain stimulation, electrical fields mediate between electrical signal sources and regions of interest (ROI). These fields can be very complicated in structure, and are influenced in a complex way by the conductivity profile of the human head. Visualization techniques play a central role to grasp the nature of those fields because such techniques allow for an effective conveyance of complex data and enable quick qualitative and quantitative assessments. The examination of volume conduction effects of particular head model parameterizations (e.g., skull thickness and layering), of brain anomalies (e.g., holes in the skull, tumors), location and extent of active brain areas (e.g., high concentrations of current densities) and around current injecting electrodes can be investigated using visualization. Here, we evaluate a number of widely used visualization techniques, based on either the potential distribution or on the current-flow. In particular, we focus on the extractability of quantitative and qualitative information from the obtained images, their effective integration of anatomical context information, and their interaction. We present illustrative examples from clinically and neuroscientifically relevant cases and discuss the pros and cons of the various visualization techniques.

Keywords: Visualization, Bioelectric Field, EEG, tDCS, Human Brain

B. Erem P.M. van Dam, D.H. Brooks. “Identifying model inaccuracies and solution uncertainties in noninvasive activation-based imaging of cardiac excitation using convex relaxation,” In IEEE Trans Med Imaging, Vol. 33, No. 4, pp. 902--912. 2014.
DOI: 10.1109/TMI.2014.2297952
PubMed ID: 24710159
PubMed Central ID: PMC3982205


Noninvasive imaging of cardiac electrical function has begun to move towards clinical adoption. Here, we consider one common formulation of the problem, in which the goal is to estimate the spatial distribution of electrical activation times during a cardiac cycle. We address the challenge of understanding the robustness and uncertainty of solutions to this formulation. This formulation poses a nonconvex, nonlinear least squares optimization problem. We show that it can be relaxed to be convex, at the cost of some degree of physiological realism of the solution set, and that this relaxation can be used as a framework to study model inaccuracy and solution uncertainty. We present two examples, one using data from a healthy human subject and the other synthesized with the ECGSIM software package. In the first case, we consider uncertainty in the initial guess and regularization parameter. In the second case, we mimic the presence of an ischemic zone in the heart in a way which violates a model assumption. We show that the convex relaxation allows understanding of spatial distribution of parameter sensitivity in the first case, and identification of model violation in the second.

B. Erem, J. Coll-Font, R.M. Orellana, P. Stovicek, D.H. Brooks. “Using transmural regularization and dynamic modeling for noninvasive cardiac potential imaging of endocardial pacing with imprecise thoracic geometry,” In IEEE Trans Med Imaging, Vol. 33, No. 3, pp. 726--738. 2014.
DOI: 10.1109/TMI.2013.2295220
PubMed ID: 24595345
PubMed Central ID: PMC3950945


Cardiac electrical imaging from body surface potential measurements is increasingly being seen as a technology with the potential for use in the clinic, for example for pre-procedure planning or during-treatment guidance for ventricular arrhythmia ablation procedures. However several important impediments to widespread adoption of this technology remain to be effectively overcome. Here we address two of these impediments: the difficulty of reconstructing electric potentials on the inner (endocardial) as well as outer (epicardial) surfaces of the ventricles, and the need for full anatomical imaging of the subject's thorax to build an accurate subject-specific geometry. We introduce two new features in our reconstruction algorithm: a nonlinear low-order dynamic parameterization derived from the measured body surface signals, and a technique to jointly regularize both surfaces. With these methodological innovations in combination, it is possible to reconstruct endocardial activation from clinically acquired measurements with an imprecise thorax geometry. In particular we test the method using body surface potentials acquired from three subjects during clinical procedures where the subjects' hearts were paced on their endocardia using a catheter device. Our geometric models were constructed using a set of CT scans limited in axial extent to the immediate region near the heart. The catheter system provides a reference location to which we compare our results. We compare our estimates of pacing site localization, in terms of both accuracy and stability, to those reported in a recent clinical publication , where a full set of CT scans were available and only epicardial potentials were reconstructed.

T. Fogal, F. Proch, A. Schiewe, O. Hasemann, A. Kempf, J. Krüger. “Freeprocessing: Transparent in situ visualization via data interception,” In Proceedings of the 14th Eurographics Conference on Parallel Graphics and Visualization, EGPGV, Eurographics Association, 2014.


In situ visualization has become a popular method for avoiding the slowest component of many visualization pipelines: reading data from disk. Most previous in situ work has focused on achieving visualization scalability on par with simulation codes, or on the data movement concerns that become prevalent at extreme scales. In this work, we consider in situ analysis with respect to ease of use and programmability. We describe an abstraction that opens up new applications for in situ visualization, and demonstrate that this abstraction and an expanded set of use cases can be realized without a performance cost.

Y. Gur, C.R. Johnson. “Generalized HARDI Invariants by Method of Tensor Contraction,” In Proceedings of the 2014 IEEE International Symposium on Biomedical Imaging (ISBI), pp. 718--721. April, 2014.


We propose a 3D object recognition technique to construct rotation invariant feature vectors for high angular resolution diffusion imaging (HARDI). This method uses the spherical harmonics (SH) expansion and is based on generating rank-1 contravariant tensors using the SH coefficients, and contracting them with covariant tensors to obtain invariants. The proposed technique enables the systematic construction of invariants for SH expansions of any order using simple mathematical operations. In addition, it allows construction of a large set of invariants, even for low order expansions, thus providing rich feature vectors for image analysis tasks such as classification and segmentation. In this paper, we use this technique to construct feature vectors for eighth-order fiber orientation distributions (FODs) reconstructed using constrained spherical deconvolution (CSD). Using simulated and in vivo brain data, we show that these invariants are robust to noise, enable voxel-wise classification, and capture meaningful information on the underlying white matter structure.

Keywords: Diffusion MRI, HARDI, invariants

C.D. Hansen, M. Chen, C.R. Johnson, A.E. Kaufman, H. Hagen (Eds.). “Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization,” Mathematics and Visualization, Springer, 2014.
ISBN: 978-1-4471-6496-8

S. Kurugol, K. Kose, B. Park, J.G. Dy, D.H. Brooks, M. Rajadhyaksha. “Automated Delineation of Dermal-Epidermal Junction In Reflectance Confocal Microscopy Image Stacks Of Human Skin,” In Journal of Investigative Dermatology, September, 2014.
DOI: 10.1038/jid.2014.379
PubMed ID: 25184959


Reflectance confocal microscopy (RCM) images skin non-invasively, with optical sectioning and nuclear-level resolution comparable to that of pathology. Based on assessment of the dermal-epidermal junction (DEJ) and morphologic features in its vicinity, skin cancer can be diagnosed in vivo with high sensitivity and specificity. However, the current visual, qualitative approach for reading images leads to subjective variability in diagnosis. We hypothesize that machine learning-based algorithms may enable a more quantitative, objective approach. Testing and validation was performed with two algorithms that can automatically delineate the DEJ in RCM stacks of normal human skin. The test set was composed of 15 fair and 15 dark skin stacks (30 subjects) with expert labellings. In dark skin, in which the contrast is high due to melanin, the algorithm produced an average error of 7.9±6.4 μm. In fair skin, the algorithm delineated the DEJ as a transition zone, with average error of 8.3±5.8 μm for the epidermis-to-transition zone boundary and 7.6±5.6 μm for the transition zone-to-dermis. Our results suggest that automated algorithms may quantitatively guide the delineation of the DEJ, to assist in objective reading of RCM images. Further development of such algorithms may guide assessment of abnormal morphological features at the DEJ.

C. McGann, N. Akoum, A. Patel, E. Kholmovski, P. Revelo, K. Damal, B. Wilson, J. Cates, A. Harrison, R. Ranjan, N.S. Burgon, T. Greene, D. Kim, E.V. Dibella, D. Parker, R.S. MacLeod, N.F. Marrouche. “Atrial fibrillation ablation outcome is predicted by left atrial remodeling on MRI,” In Circ Arrhythm Electrophysiol, Vol. 7, No. 1, pp. 23--30. 2014.
DOI: 10.1161/CIRCEP.113.000689
PubMed ID: 24363354


Although catheter ablation therapy for atrial fibrillation (AF) is becoming more common, results vary widely, and patient selection criteria remain poorly defined. We hypothesized that late gadolinium enhancement MRI (LGE-MRI) can identify left atrial (LA) wall structural remodeling (SRM) and stratify patients who are likely or not to benefit from ablation therapy.

LGE-MRI was performed on 426 consecutive patients with AF without contraindications to MRI before undergoing their first ablation procedure and on 21 non-AF control subjects. Patients were categorized by SRM stage (I-IV) based on the percentage of LA wall enhancement for correlation with procedure outcomes. Histological validation of SRM was performed comparing LGE-MRI with surgical biopsy. A total of 386 patients (91%) with adequate LGE-MRI scans were included in the study. After ablation, 123 patients (31.9%) experienced recurrent atrial arrhythmias during the 1-year follow-up. Recurrent arrhythmias (failed ablations) occurred at higher SRM stages with 28 of 133 (21.0%) in stage I, 40 of 140 (29.3%) in stage II, 24 of 71 (33.8%) in stage III, and 30 of 42 (71.4%) in stage IV. In multivariate analysis, ablation outcome was best predicted by advanced SRM stage (hazard ratio, 4.89; P

M. Milanič, V. Jazbinšek, R.S. MacLeod, D.H. Brooks, R. Hren. “Assessment of regularization techniques for electrocardiographic imaging,” In Journal of electrocardiology, Vol. 47, No. 1, pp. 20--28. 2014.
DOI: 10.1016/j.jelectrocard.2013.10.004


A widely used approach to solving the inverse problem in electrocardiography involves computing potentials on the epicardium from measured electrocardiograms (ECGs) on the torso surface. The main challenge of solving this electrocardiographic imaging (ECGI) problem lies in its intrinsic ill-posedness. While many regularization techniques have been developed to control wild oscillations of the solution, the choice of proper regularization methods for obtaining clinically acceptable solutions is still a subject of ongoing research. However there has been little rigorous comparison across methods proposed by different groups. This study systematically compared various regularization techniques for solving the ECGI problem under a unified simulation framework, consisting of both 1) progressively more complex idealized source models (from single dipole to triplet of dipoles), and 2) an electrolytic human torso tank containing a live canine heart, with the cardiac source being modeled by potentials measured on a cylindrical cage placed around the heart. We tested 13 different regularization techniques to solve the inverse problem of recovering epicardial potentials, and found that non-quadratic methods (total variation algorithms) and first-order and second-order Tikhonov regularizations outperformed other methodologies and resulted in similar average reconstruction errors.

B.R. Parmar, T.R. Jarrett, N.S. Burgon, E.G. Kholmovski, N.W. Akoum, N. Hu, R.S. Macleod, N.F. Marrouche, R. Ranjan. “Comparison of Left Atrial Area Marked Ablated in Electroanatomical Maps with Scar in MRI,” In Journal of Cardiovascular Electrophysiology, 2014.
DOI: 10.1111/jce.12357



Three-dimensional electroanatomic mapping (EAM) is routinely used to mark ablated areas during radiofrequency ablation. We hypothesized that, in atrial fibrillation (AF) ablation, EAM overestimates scar formation in the left atrium (LA) when compared to the scar seen on late-gadolinium enhancement magnetic resonance imaging (LGE-MRI).

Methods and Results

Of the 235 patients who underwent initial ablation for AF at our institution between August 2011 and December 2012, we retrospectively identified 70 patients who had preprocedural magnetic resonance angiography merged with LA anatomy in EAM software and had a 3-month postablation LGE-MRI for assessment of scar. Ablated area was marked intraprocedurally using EAM software and quantified retrospectively. Scarred area was quantified in 3-month postablation LGE-MRI. The mean ablated area in EAM was 30.5 ± 7.5% of the LA endocardial surface and the mean scarred area in LGE-MRI was 13.9 ± 5.9% (P < 0.001). This significant difference in the ablated area marked in the EAM and scar area in the LGE-MRI was present for each of the 3 independent operators. Complete pulmonary vein (PV) encirclement representing electrical isolation was observed in 87.8% of the PVs in EAM as compared to only 37.4% in LGE-MRI (P < 0.001).


In AF ablation, EAM significantly overestimates the resultant scar as assessed with a follow-up LGE-MRI.

Keywords: atrial fibrillation, magnetic resonance imaging, radiofrequency ablation

R. Sicat, J. Krüger, T. Möller, M. Hadwiger. “Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. PP, No. 99, pp. 1--1. 2014.
ISBN: 1077-2626
DOI: 10.1109/TVCG.2014.2346324


This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.

J. Sourati, D. Erdogmus, J.G. Dy, D.H. Brooks. “Accelerated learning-based interactive image segmentation using pairwise constraints,” In IEEE Transactions on Medical Image Processing, Vol. 23, No. 7, pp. 3057-3070. July, 2014.
DOI: 10.1109/TIP.2014.2325783
PubMed ID: 24860031
PubMed Central ID: PMC4096329


Algorithms for fully automatic segmentation of images are often not sufficiently generic with suitable accuracy, and fully manual segmentation is not practical in many settings. There is a need for semiautomatic algorithms, which are capable of interacting with the user and taking into account the collected feedback. Typically, such methods have simply incorporated user feedback directly. Here, we employ active learning of optimal queries to guide user interaction. Our work in this paper is based on constrained spectral clustering that iteratively incorporates user feedback by propagating it through the calculated affinities. The original framework does not scale well to large data sets, and hence is not straightforward to apply to interactive image segmentation. In order to address this issue, we adopt advanced numerical methods for eigen-decomposition implemented over a subsampling scheme. Our key innovation, however, is an active learning strategy that chooses pairwise queries to present to the user in order to increase the rate of learning from the feedback. Performance evaluation is carried out on the Berkeley segmentation and Graz-02 image data sets, confirming that convergence to high accuracy levels is realizable in relatively few iterations.


B. Burton, B. Erem, K. Potter, P. Rosen, C.R. Johnson, D. Brooks, R.S. Macleod. “Uncertainty Visualization in Forward and Inverse Cardiac Models,” In Computing in Cardiology CinC, pp. 57--60. 2013.
ISSN: 2325-8861


Quantification and visualization of uncertainty in cardiac forward and inverse problems with complex geometries is subject to various challenges. Specific to visualization is the observation that occlusion and clutter obscure important regions of interest, making visual assessment difficult. In order to overcome these limitations in uncertainty visualization, we have developed and implemented a collection of novel approaches. To highlight the utility of these techniques, we evaluated the uncertainty associated with two examples of modeling myocardial activity. In one case we studied cardiac potentials during the repolarization phase as a function of variability in tissue conductivities of the ischemic heart (forward case). In a second case, we evaluated uncertainty in reconstructed activation times on the epicardium resulting from variation in the control parameter of Tikhonov regularization (inverse case). To overcome difficulties associated with uncertainty visualization, we implemented linked-view windows and interactive animation to the two respective cases. Through dimensionality reduction and superimposed mean and standard deviation measures over time, we were able to display key features in large ensembles of data and highlight regions of interest where larger uncertainties exist.

C. Butson, G. Tamm, S. Jain, T. Fogal, J. Krüger. “Evaluation of Interactive Visualization on Mobile Computing Platforms for Selection of Deep Brain Stimulation Parameters,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 1, pp. 108--117. January, 2013.
DOI: 10.1109/TVCG.2012.92
PubMed ID: 22450824


In recent years there has been significant growth in the use of patient-specific models to predict the effects of neuromodulation therapies such as deep brain stimulation (DBS). However, translating these models from a research environment to the everyday clinical workflow has been a challenge, primarily due to the complexity of the models and the expertise required in specialized visualization software. In this paper, we deploy the interactive visualization system ImageVis3D Mobile , which has been designed for mobile computing devices such as the iPhone or iPad, in an evaluation environment to visualize models of Parkinson’s disease patients who received DBS therapy. Selection of DBS settings is a significant clinical challenge that requires repeated revisions to achieve optimal therapeutic response, and is often performed without any visual representation of the stimulation system in the patient. We used ImageVis3D Mobile to provide models to movement disorders clinicians and asked them to use the software to determine: 1) which of the four DBS electrode contacts they would select for therapy; and 2) what stimulation settings they would choose. We compared the stimulation protocol chosen from the software versus the stimulation protocol that was chosen via clinical practice (independently of the study). Lastly, we compared the amount of time required to reach these settings using the software versus the time required through standard practice. We found that the stimulation settings chosen using ImageVis3D Mobile were similar to those used in standard of care, but were selected in drastically less time. We show how our visualization system, available directly at the point of care on a device familiar to the clinician, can be used to guide clinical decision making for selection of DBS settings. In our view, the positive impact of the system could also translate to areas other than DBS.

Keywords: Biomedical and Medical Visualization, Mobile and Ubiquitous Visualization, Computational Model, Clinical Decision Making, Parkinson’s Disease, SciDAC, ImageVis3D

J. Coll-Font, B. Erem, A. Karma, D.H. Brooks. “An inverse spectral method to localize discordant alternans regions on the heart from body surface measurements,” In Functional Imaging and Modeling of the Heart, pp. 241--248. 2013.
DOI: 10.1007/978-3-642-38899-6_29


Spatially discordant T-wave alternans (TWA) has been shown to be linked to the genesis of ventricular fibrillation. Identification of discordant TWA through spatial characterization of TWA patterns in the heart has the potential to improve sudden cardiac death risk stratification. In this paper we present a method to solve a new variant of the inverse problem in electrocardiography that is tailored to estimate the TWA regions on the heart from non-invasive measurements on the body surface. We evaluate our method using both body surface potentials synthesized from heart surface potentials generated with ECGSIM and from potentials measured on a canine heart, and we show that this method detects the main regions in the heart undergoing TWA.

A. Daducci, E.J. Canales-Rodriguez, M. Descoteaux, E. Garyfallidis, Y. Gur, Y.-C Lin, M. Mani, S. Merlet, M. Paquette, A. Ramirez-Manzanares, M. Reisert, P.R. Rodrigues, F. Sepehrband, E. Caruyer, J. Choupan, R. Deriche, M. Jacob, G. Menegaz, V. Prckovska, M. Rivera, Y. Wiaux, J.-P. Thiran. “Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion MRI,” In IEEE Transactions on Medical Imaging, Vol. 33, No. 2, pp. 384--399. 2013.
ISSN: 0278-0062
DOI: 10.1109/TMI.2013.2285500


Validation is arguably the bottleneck in the diffusion MRI community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the "HARDI reconstruction challenge" organized in the context of the "ISBI 2012" conference. Evaluated methods encompass a mixture of classical techniques well-known in the literature such as Diffusion Tensor, Q-Ball and Diffusion Spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach.