Center for Integrative Biomedical Computing

SCI Publications


P. Agrawal, R. T. Whitaker, S. Y. Elhabian. “Learning Deep Features for Shape Correspondence with Domain Invariance,” Subtitled “arXiv preprint arXiv:2102.10493,” 2021.


Correspondence-based shape models are key to various medical imaging applications that rely on a statistical analysis of anatomies. Such shape models are expected to represent consistent anatomical features across the population for population-specific shape statistics. Early approaches for correspondence placement rely on nearest neighbor search for simpler anatomies. Coordinate transformations for shape correspondence hold promise to address the increasing anatomical complexities. Nonetheless, due to the inherent shape-level geometric complexity and population-level shape variation, the coordinate-wise correspondence often does not translate to the anatomical correspondence. An alternative, group-wise approach for correspondence placement explicitly models the trade-off between geometric description and the population's statistical compactness. However, these models achieve limited success in resolving nonlinear shape correspondence. Recent works have addressed this limitation by adopting an application-specific notion of correspondence through lifting positional data to a higher dimensional feature space. However, they heavily rely on manual expertise to create domain-specific features and consistent landmarks. This paper proposes an automated feature learning approach, using deep convolutional neural networks to extract correspondence-friendly features from shape ensembles. Further, an unsupervised domain adaptation scheme is introduced to augment the pretrained geometric features with new anatomies. Results on anatomical datasets of human scapula, femur, and pelvis bones demonstrate that …

J. A. Bergquist, W. W. Good, B. Zenger, J. D. Tate, L. C. Rupp, R. S. MacLeod. “The Electrocardiographic Forward Problem: A Benchmark Study,” In Computers in Biology and Medicine, Vol. 134, Pergamon, pp. 104476. 2021.


Electrocardiographic forward problems are crucial components for noninvasive electrocardiographic imaging (ECGI) that compute torso potentials from cardiac source measurements. Forward problems have few sources of error as they are physically well posed and supported by mature numerical and computational techniques. However, the residual errors reported from experimental validation studies between forward computed and measured torso signals remain surprisingly high.

To test the hypothesis that incomplete cardiac source sampling, especially above the atrioventricular (AV) plane is a major contributor to forward solution errors.

We used a modified Langendorff preparation suspended in a human-shaped electrolytic torso-tank and a novel pericardiac-cage recording array to thoroughly sample the cardiac potentials. With this carefully controlled experimental preparation, we minimized possible sources of error, including geometric error and torso inhomogeneities. We progressively removed recorded signals from above the atrioventricular plane to determine how the forward-computed torso-tank potentials were affected by incomplete source sampling.

We studied 240 beats total recorded from three different activation sequence types (sinus, and posterior and anterior left-ventricular free-wall pacing) in each of two experiments. With complete sampling by the cage electrodes, all correlation metrics between computed and measured torso-tank potentials were above 0.93 (maximum 0.99). The mean root-mean-squared error across all beat types was also low, less than or equal to 0.10 mV. A precipitous drop in forward solution accuracy was observed when we included only cage measurements below the AV plane.

First, our forward computed potentials using complete cardiac source measurements set a benchmark for similar studies. Second, this study validates the importance of complete cardiac source sampling above the AV plane to produce accurate forward computed torso potentials. Testing ECGI systems and techniques with these more complete and highly accurate datasets will improve inverse techniques and noninvasive detection of cardiac electrical abnormalities.

S. R. Black, A. Janson, M. Mahan, J. Anderson, C. R. Butson. “Identification of Deep Brain Stimulation Targets for Neuropathic Pain After Spinal Cord Injury Using Localized Increases in White Matter Fiber Cross‐Section,” In Neuromodulation: Technology at the Neural Interface, John Wiley & Sons, Inc., 2021.


The spinal cord injury (SCI) patient population is overwhelmingly affected by neuropathic pain (NP), a secondary condition for which therapeutic options are limited and have a low degree of efficacy. The objective of this study was to identify novel deep brain stimulation (DBS) targets that may theoretically benefit those with NP in the SCI patient population. We hypothesize that localized changes in white matter identified in SCI subjects with NP compared to those without NP could be used to develop an evidence‐based approach to DBS target identification.

Materials and Methods
To classify localized neurostructural changes associated with NP in the SCI population, we compared white matter fiber density (FD) and cross‐section (FC) between SCI subjects with NP (N = 17) and SCI subjects without NP (N = 15) using diffusion‐weighted magnetic resonance imaging (MRI). We then identified theoretical target locations for DBS using fiber bundles connected to significantly altered regions of white matter. Finally, we used computational models of DBS to determine if our theoretical target locations could be used to feasibly activate our fiber bundles of interest.
We identified significant increases in FC in the splenium of the corpus callosum in pain subjects when compared to controls. We then isolated five fiber bundles that were directly connected to the affected region of white matter. Our models were able to predict that our fiber bundles of interest can be feasibly activated with DBS at reasonable stimulation amplitudes and with clinically relevant implantation approaches.
Altogether, we identified neuroarchitectural changes associated with NP in the SCI cohort and implemented a novel, evidence‐driven target selection approach for DBS to guide future research in neuromodulation treatment of NP after SCI.

W. W. Good, B. Zenger, J. A. Bergquist, L. C. Rupp, K. K. Gillette, M. A.F. Gsell, G. Plank, R. S. MacLeod. “Quantifying the spatiotemporal influence of acute myocardial ischemia on volumetric conduction velocity,” In Journal of Electrocardiology, Vol. 66, Churchill Livingstone, pp. 86-94. 2021.


Acute myocardial ischemia occurs when coronary perfusion to the heart is inadequate, which can perturb the highly organized electrical activation of the heart and can result in adverse cardiac events including sudden cardiac death. Ischemia is known to influence the ST and repolarization phases of the ECG, but it also has a marked effect on propagation (QRS); however, studies investigating propagation during ischemia have been limited.

We estimated conduction velocity (CV) and ischemic stress prior to and throughout 20 episodes of experimentally induced ischemia in order to quantify the progression and correlation of volumetric conduction changes during ischemia. To estimate volumetric CV, we 1) reconstructed the activation wavefront; 2) calculated the elementwise gradient to approximate propagation direction; and 3) estimated conduction speed (CS) with an inverse-gradient technique.
We found that acute ischemia induces significant conduction slowing, reducing the global median speed by 20 cm/s. We observed a biphasic response in CS (acceleration then deceleration) early in some ischemic episodes. Furthermore, we noted a high temporal correlation between ST-segment changes and CS slowing; however, when comparing these changes over space, we found only moderate correlation (corr. = 0.60).
This study is the first to report volumetric CS changes (acceleration and slowing) during episodes of acute ischemia in the whole heart. We showed that while CS changes progress in a similar time course to ischemic stress (measured by ST-segment shifts), the spatial overlap is complex and variable, showing extreme conduction slowing both in and around regions experiencing severe ischemia.

W. W. Good, K. Gillette, B. Zenger, J. Bergquist, L. C. Rupp, J. D. Tate, D. Anderson, M. Gsell, G. Plank, R. S. Macleod. “Estimation and validation of cardiac conduction velocity and wavefront reconstruction using epicardial and volumetric data,” In IEEE Transactions on Biomedical Engineering, IEEE, 2021.
DOI: 10.1109/TBME.2021.3069792


Objective: In this study, we have used whole heart simulations parameterized with large animal experiments to validate three techniques (two from the literature and one novel) for estimating epicardial and volumetric conduction velocity (CV). Methods: We used an eikonal-based simulation model to generate ground truth activation sequences with prescribed CVs. Using the sampling density achieved experimentally we examined the accuracy with which we could reconstruct the wavefront, and then examined the robustness of three CV estimation techniques to reconstruction related error. We examined a triangulation-based, inverse-gradient-based, and streamline-based techniques for estimating CV cross the surface and within the volume of the heart. Results: The reconstructed activation times agreed closely with simulated values, with 50-70% of the volumetric nodes and 97-99% of the epicardial nodes were within 1 ms of the ground truth. We found close agreement between the CVs calculated using reconstructed versus ground truth activation times, with differences in the median estimated CV on the order of 3-5% volumetrically and 1-2% superficially, regardless of what technique was used. Conclusion: Our results indicate that the wavefront reconstruction and CV estimation techniques are accurate, allowing us to examine changes in propagation induced by experimental interventions such as acute ischemia, ectopic pacing, or drugs. Significance: We implemented, validated, and compared the performance of a number of CV estimation techniques. The CV estimation techniques implemented in this study produce accurate, high-resolution CV fields that can be used to study propagation in the heart experimentally and clinically.

R. Kamali, J. Kump, E. Ghafoori, M. Lange, N. Hu, T. J. Bunch, D. J. Dosdall, R. S. Macleod, R. Ranjan. “Area Available for Atrial Fibrillation to Propagate Is an Important Determinant of Recurrence After Ablation,” In JACC: Clinical Electrophysiology, Elsevier, 2021.


This study sought to evaluate atrial fibrillation (AF) ablation outcomes based on scar patterns and contiguous area available for AF wavefronts to propagate.

J. Salinet, R. Molero, F. S. Schlindwein, J. Karel, M. Rodrigo, J. L. Rojo-Álvarez, O. Berenfeld, A. M. Climent, B. Zenger, F. Vanheusden, J. G. S. Paredes, R. MacLeod, F. Atienza, M. S. Guillem, M. Cluitmans, P. Bonizzi. “Electrocardiographic imaging for atrial fibrillation: a perspective from computer models and animal experiments to clinical value,” In Frontiers in Physiology, Vol. 12, Frontiers Media, April, 2021.
DOI: 10.3389/fphys.2021.653013


Salinet et al. Electrocardiographic Imaging for Atrial Fibrillation treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.

V. Vedam-Mai, K. Deisseroth, J. Giordano, G. Lazaro-Munoz, W. Chiong, N. Suthana, J. Langevin, J. Gill, W. Goodman, N. R. Provenza, C. H. Halpern, R. S. Shivacharan, T. N. Cunningham, S. A. Sheth, N. Pouratian, K. W. Scangos, H. S. Mayberg, A. Horn, K. A. Johnson, C. R. Butson, R. Gilron, C. de Hemptinne, R. Wilt, M. Yaroshinsky, S. Little, P. Starr, G. Worrell, P. Shirvalkar, E. Chang, J. Volkmann, M. Muthuraman, S. Groppa, A. A. Kühn, L. Li, M. Johnson, K. J. Otto, R. Raike, S. Goetz, C. Wu, P. Silburn, B. Cheeran, Y. J. Pathak, M. Malekmohammadi, A. Gunduz, J. K. Wong, S. Cernera, A. W. Shukla, A. Ramirez-Zamora, W. Deeb, A. Patterson, K. D. Foote, M. S. Okun. “Proceedings of the Eighth Annual Deep Brain Stimulation Think Tank: Advances in Optogenetics, Ethical Issues Affecting DBS Research, Neuromodulatory Approaches for Depression, Adaptive Neurostimulation, and Emerging DBS Technologies,” In Frontiers in Human Neuroscience, Vol. 15, pp. 169. 2021.
ISSN: 1662-5161
DOI: 10.3389/fnhum.2021.644593


We estimate that 208,000 deep brain stimulation (DBS) devices have been implanted to address neurological and neuropsychiatric disorders worldwide. DBS Think Tank presenters pooled data and determined that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. The DBS Think Tank was founded in 2012 providing a space where clinicians, engineers, researchers from industry and academia discuss current and emerging DBS technologies and logistical and ethical issues facing the field. The emphasis is on cutting edge research and collaboration aimed to advance the DBS field. The Eighth Annual DBS Think Tank was held virtually on September 1 and 2, 2020 (Zoom Video Communications) due to restrictions related to the COVID-19 pandemic. The meeting focused on advances in: (1) optogenetics as a tool for comprehending neurobiology of diseases and on optogenetically-inspired DBS, (2) cutting edge of emerging DBS technologies, (3) ethical issues affecting DBS research and access to care, (4) neuromodulatory approaches for depression, (5) advancing novel hardware, software and imaging methodologies, (6) use of neurophysiological signals in adaptive neurostimulation, and (7) use of more advanced technologies to improve DBS clinical outcomes. There were 178 attendees who participated in a DBS Think Tank survey, which revealed the expansion of DBS into several indications such as obesity, post-traumatic stress disorder, addiction and Alzheimer’s disease. This proceedings summarizes the advances discussed at the Eighth Annual DBS Think Tank.

Y. Wan, H.A. Holman, C. Hansen. “Interactive Analysis for Large Volume Data from Fluorescence Microscopy at Cellular Precision,” In Computers & Graphics, Vol. 98, Pergamon, pp. 138-149. 2021.


The main objective for understanding fluorescence microscopy data is to investigate and evaluate the fluorescent signal intensity distributions as well as their spatial relationships across multiple channels. The quantitative analysis of 3D fluorescence microscopy data needs interactive tools for researchers to select and focus on relevant biological structures. We developed an interactive tool based on volume visualization techniques and GPU computing for streamlining rapid data analysis. Our main contribution is the implementation of common data quantification functions on streamed volumes, providing interactive analyses on large data without lengthy preprocessing. Data segmentation and quantification are coupled with brushing and executed at an interactive speed. A large volume is partitioned into data bricks, and only user-selected structures are analyzed to constrain the computational load. We designed a framework to assemble a sequence of GPU programs to handle brick borders and stitch analysis results. Our tool was developed in collaboration with domain experts and has been used to identify cell types. We demonstrate a workflow to analyze cells in vestibular epithelia of transgenic mice.

L. Zhou, C. R. Johnson, D. Weiskopf. “Data-Driven Space-Filling Curves,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 27, No. 2, IEEE, pp. 1591-1600. 2021.
DOI: 10.1109/TVCG.2020.3030473


We propose a data-driven space-filling curve method for 2D and 3D visualization. Our flexible curve traverses the data elements in the spatial domain in a way that the resulting linearization better preserves features in space compared to existing methods. We achieve such data coherency by calculating a Hamiltonian path that approximately minimizes an objective function that describes the similarity of data values and location coherency in a neighborhood. Our extended variant even supports multiscale data via quadtrees and octrees. Our method is useful in many areas of visualization, including multivariate or comparative visualization,ensemble visualization of 2D and 3D data on regular grids, or multiscale visual analysis of particle simulations. The effectiveness of our method is evaluated with numerical comparisons to existing techniques and through examples of ensemble and multivariate datasets.


A. P. Janson, D. N. Anderson, C. R. Butson. “Activation robustness with directional leads and multi-lead configurations in deep brain stimulation,” In Journal of Neural Engineering, Vol. 17, No. 2, IOP Publishing, pp. 026012. March, 2020.
DOI: 10.1088/1741-2552/ab7b1d


Objective: Clinical outcomes from deep brain stimulation (DBS) can be highly variable, and two critical factors underlying this variability are the location and type of stimulation. In this study we quantified how robustly DBS activates a target region when taking into account a range of different lead designs and realistic variations in placement. The objective of the study is to assess the likelihood of achieving target activation.

Approach: We performed finite element computational modeling and established a metric of performance robustness to evaluate the ability of directional and multi-lead configurations to activate target fiber pathways while taking into account location variability. A more robust lead configuration produces less variability in activation across all stimulation locations around the target.

Main results: Directional leads demonstrated higher overall performance robustness compared to axisymmetric leads, primarily 1-2 mm outside of the target. Multi-lead configurations demonstrated higher levels of robustness compared to any single lead due to distribution of electrodes in a broader region around the target.

Significance: Robustness measures can be used to evaluate the performance of existing DBS lead designs and aid in the development of novel lead designs to better accommodate known variability in lead location and orientation. This type of analysis may also be useful to understand how DBS clinical outcome variability is influenced by lead location among groups of patients.

K. A. Johnson, G. Duffley, D. Nesterovich Anderson, J. L. Ostrem, M. Welter, J. C. Baldermann, J. Kuhn, D. Huys, V. Visser-Vandewalle, T. Foltynie, L. Zrinzo, M. Hariz, A. F. G. Leentjens, A. Y. Mogilner, M. H. Pourfar, L. Almeida, A. Gunduz, K. D. Foote, M. S. Okun, C. R. Butson. “Structural connectivity predicts clinical outcomes of deep brain stimulation for Tourette syndrome,” In Brain, July, 2020.
ISSN: 0006-8950
DOI: 10.1093/brain/awaa188


Deep brain stimulation may be an effective therapy for select cases of severe, treatment-refractory Tourette syndrome; however, patient responses are variable, and there are no reliable methods to predict clinical outcomes. The objectives of this retrospective study were to identify the stimulation-dependent structural networks associated with improvements in tics and comorbid obsessive-compulsive behaviour, compare the networks across surgical targets, and determine if connectivity could be used to predict clinical outcomes. Volumes of tissue activated for a large multisite cohort of patients (n = 66) implanted bilaterally in globus pallidus internus (n = 34) or centromedial thalamus (n = 32) were used to generate probabilistic tractography to form a normative structural connectome. The tractography maps were used to identify networks that were correlated with improvement in tics or comorbid obsessive-compulsive behaviour and to predict clinical outcomes across the cohort. The correlated networks were then used to generate ‘reverse’ tractography to parcellate the total volume of stimulation across all patients to identify local regions to target or avoid. The results showed that for globus pallidus internus, connectivity to limbic networks, associative networks, caudate, thalamus, and cerebellum was positively correlated with improvement in tics; the model predicted clinical improvement scores (P = 0.003) and was robust to cross-validation. Regions near the anteromedial pallidum exhibited higher connectivity to the positively correlated networks than posteroventral pallidum, and volume of tissue activated overlap with this map was significantly correlated with tic improvement (P < 0.017). For centromedial thalamus, connectivity to sensorimotor networks, parietal-temporal-occipital networks, putamen, and cerebellum was positively correlated with tic improvement; the model predicted clinical improvement scores (P = 0.012) and was robust to cross-validation. Regions in the anterior/lateral centromedial thalamus exhibited higher connectivity to the positively correlated networks, but volume of tissue activated overlap with this map did not predict improvement (P > 0.23). For obsessive-compulsive behaviour, both targets showed that connectivity to the prefrontal cortex, orbitofrontal cortex, and cingulate cortex was positively correlated with improvement; however, only the centromedial thalamus maps predicted clinical outcomes across the cohort (P = 0.034), but the model was not robust to cross-validation. Collectively, the results demonstrate that the structural connectivity of the site of stimulation are likely important for mediating symptom improvement, and the networks involved in tic improvement may differ across surgical targets. These networks provide important insight on potential mechanisms and could be used to guide lead placement and stimulation parameter selection, as well as refine targets for neuromodulation therapies for Tourette syndrome.

F. Wang, N. Marshak, W. Usher, C. Burstedde, A. Knoll, T. Heister, C. R. Johnson. “CPU Ray Tracing of Tree-Based Adaptive Mesh Refinement Data,” In Eurographics Conference on Visualization (EuroVis) 2020, Vol. 39, No. 3, 2020.


Adaptive mesh refinement (AMR) techniques allow for representing a simulation’s computation domain in an adaptive fashion. Although these techniques have found widespread adoption in high-performance computing simulations, visualizing their data output interactively and without cracks or artifacts remains challenging. In this paper, we present an efficient solution for direct volume rendering and hybrid implicit isosurface ray tracing of tree-based AMR (TB-AMR) data. We propose a novel reconstruction strategy, Generalized Trilinear Interpolation (GTI), to interpolate across AMR level boundaries without cracks or discontinuities in the surface normal. We employ a general sparse octree structure supporting a wide range of AMR data, and use it to accelerate volume rendering, hybrid implicit isosurface rendering and value queries. We demonstrate that our approach achieves artifact-free isosurface and volume rendering and provides higher quality output images compared to existing methods at interactive rendering rates.

L. Zhou, M. Rivinius, C. R. Johnson,, D. Weiskopf. “Photographic High-Dynamic-Range Scalar Visualization,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 26, No. 6, IEEE, pp. 2156-2167. 2020.


We propose a photographic method to show scalar values of high dynamic range (HDR) by color mapping for 2D visualization. We combine (1) tone-mapping operators that transform the data to the display range of the monitor while preserving perceptually important features based on a systematic evaluation and (2) simulated glares that highlight high-value regions. Simulated glares are effective for highlighting small areas (of a few pixels) that may not be visible with conventional visualizations; through a controlled perception study, we confirm that glare is preattentive. The usefulness of our overall photographic HDR visualization is validated through the feedback of expert users.


T. Athawale, C. R. Johnson. “Probabilistic Asymptotic Decider for Topological Ambiguity Resolution in Level-Set Extraction for Uncertain 2D Data,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 25, No. 1, IEEE, pp. 1163-1172. Jan, 2019.
DOI: 10.1109/TVCG.2018.2864505


We present a framework for the analysis of uncertainty in isocontour extraction. The marching squares (MS) algorithm for isocontour reconstruction generates a linear topology that is consistent with hyperbolic curves of a piecewise bilinear interpolation. The saddle points of the bilinear interpolant cause topological ambiguity in isocontour extraction. The midpoint decider and the asymptotic decider are well-known mathematical techniques for resolving topological ambiguities. The latter technique investigates the data values at the cell saddle points for ambiguity resolution. The uncertainty in data, however, leads to uncertainty in underlying bilinear interpolation functions for the MS algorithm, and hence, their saddle points. In our work, we study the behavior of the asymptotic decider when data at grid vertices is uncertain. First, we derive closed-form distributions characterizing variations in the saddle point values for uncertain bilinear interpolants. The derivation assumes uniform and nonparametric noise models, and it exploits the concept of ratio distribution for analytic formulations. Next, the probabilistic asymptotic decider is devised for ambiguity resolution in uncertain data using distributions of the saddle point values derived in the first step. Finally, the confidence in probabilistic topological decisions is visualized using a colormapping technique. We demonstrate the higher accuracy and stability of the probabilistic asymptotic decider in uncertain data with regard to existing decision frameworks, such as deciders in the mean field and the probabilistic midpoint decider, through the isocontour visualization of synthetic and real datasets.

T. M. Athawale, K. A. Johnson, C. R. Butson, C. R. Johnson. “A statistical framework for quantification and visualisation of positional uncertainty in deep brain stimulation electrodes,” In Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol. 7, No. 4, Taylor & Francis, pp. 438-449. 2019.
DOI: 10.1080/21681163.2018.1523750


Deep brain stimulation (DBS) is an established therapy for treating patients with movement disorders such as Parkinson’s disease. Patient-specific computational modelling and visualisation have been shown to play a key role in surgical and therapeutic decisions for DBS. The computational models use brain imaging, such as magnetic resonance (MR) and computed tomography (CT), to determine the DBS electrode positions within the patient’s head. The finite resolution of brain imaging, however, introduces uncertainty in electrode positions. The DBS stimulation settings for optimal patient response are sensitive to the relative positioning of DBS electrodes to a specific neural substrate (white/grey matter). In our contribution, we study positional uncertainty in the DBS electrodes for imaging with finite resolution. In a three-step approach, we first derive a closed-form mathematical model characterising the geometry of the DBS electrodes. Second, we devise a statistical framework for quantifying the uncertainty in the positional attributes of the DBS electrodes, namely the direction of longitudinal axis and the contact-centre positions at subvoxel levels. The statistical framework leverages the analytical model derived in step one and a Bayesian probabilistic model for uncertainty quantification. Finally, the uncertainty in contact-centre positions is interactively visualised through volume rendering and isosurfacing techniques. We demonstrate the efficacy of our contribution through experiments on synthetic and real datasets. We show that the spatial variations in true electrode positions are significant for finite resolution imaging, and interactive visualisation can be instrumental in exploring probabilistic positional variations in the DBS lead.

P. R. Atkins, Y. Shin, P. Agrawal, S. Y. Elhabian, R. T. Whitaker, J. A. Weiss, S. K. Aoki, C. L. Peters, A. E. Anderson. “Which Two-dimensional Radiographic Measurements of Cam Femoroacetabular Impingement Best Describe the Three-dimensional Shape of the Proximal Femur?,” In Clinical Orthopaedics and Related Research, Vol. 477, No. 1, 2019.



Many two-dimensional (2-D) radiographic views are used to help diagnose cam femoroacetabular impingement (FAI), but there is little consensus as to which view or combination of views is most effective at visualizing the magnitude and extent of the cam lesion (ie, severity). Previous studies have used a single image from a sequence of CT or MR images to serve as a reference standard with which to evaluate the ability of 2-D radiographic views and associated measurements to describe the severity of the cam lesion. However, single images from CT or MRI data may fail to capture the apex of the cam lesion. Thus, it may be more appropriate to use measurements of three-dimensional (3-D) surface reconstructions from CT or MRI data to serve as an anatomic reference standard when evaluating radiographic views and associated measurements used in the diagnosis of cam FAI.


The purpose of this study was to use digitally reconstructed radiographs and 3-D statistical shape modeling to (1) determine the correlation between 2-D radiographic measurements of cam FAI and 3-D metrics of proximal femoral shape; and 2) identify the combination of radiographic measurements from plain film projections that were most effective at predicting the 3-D shape of the proximal femur.


This study leveraged previously acquired CT images of the femur from a convenience sample of 37 patients (34 males; mean age, 27 years, range, 16-47 years; mean body mass index [BMI], 24.6 kg/m, range, 19.0-30.2 kg/m) diagnosed with cam FAI imaged between February 2005 and January 2016. Patients were diagnosed with cam FAI based on a culmination of clinical examinations, history of hip pain, and imaging findings. The control group consisted of 59 morphologically normal control participants (36 males; mean age, 29 years, range, 15-55 years; mean BMI, 24.4 kg/m, range, 16.3-38.6 kg/m) imaged between April 2008 and September 2014. Of these controls, 30 were cadaveric femurs and 29 were living participants. All controls were screened for evidence of femoral deformities using radiographs. In addition, living control participants had no history of hip pain or previous surgery to the hip or lower limbs. CT images were acquired for each participant and the surface of the proximal femur was segmented and reconstructed. Surfaces were input to our statistical shape modeling pipeline, which objectively calculated 3-D shape scores that described the overall shape of the entire proximal femur and of the region of the femur where the cam lesion is typically located. Digital reconstructions for eight plain film views (AP, Meyer lateral, 45° Dunn, modified 45° Dunn, frog-leg lateral, Espié frog-leg, 90° Dunn, and cross-table lateral) were generated from CT data. For each view, measurements of the α angle and head-neck offset were obtained by two researchers (intraobserver correlation coefficients of 0.80-0.94 for the α angle and 0.42-0.80 for the head-neck offset measurements). The relationships between radiographic measurements from each view and the 3-D shape scores (for the entire proximal femur and for the region specific to the cam lesion) were assessed with linear correlation. Additionally, partial least squares regression was used to determine which combination of views and measurements was the most effective at predicting 3-D shape scores.


Three-dimensional shape scores were most strongly correlated with α angle on the cross-table view when considering the entire proximal femur (r = -0.568; p < 0.001) and on the Meyer lateral view when considering the region of the cam lesion (r = -0.669; p < 0.001). Partial least squares regression demonstrated that measurements from the Meyer lateral and 90° Dunn radiographs produced the optimized regression model for predicting shape scores for the proximal femur (R = 0.405, root mean squared error of prediction [RMSEP] = 1.549) and the region of the cam lesion (R = 0.525, RMSEP = 1.150). Interestingly, views with larger differences in the α angle and head-neck offset between control and cam FAI groups did not have the strongest correlations with 3-D shape.


Considered together, radiographic measurements from the Meyer lateral and 90° Dunn views provided the most effective predictions of 3-D shape of the proximal femur and the region of the cam lesion as determined using shape modeling metrics.


Our results suggest that clinicians should consider using the Meyer lateral and 90° Dunn views to evaluate patients in whom cam FAI is suspected. However, the α angle and head-neck offset measurements from these and other plain film views could describe no more than half of the overall variation in the shape of the proximal femur and cam lesion. Thus, caution should be exercised when evaluating femoral head anatomy using the α angle and head-neck offset measurements from plain film radiographs. Given these findings, we believe there is merit in pursuing research that aims to develop the framework necessary to integrate statistical shape modeling into clinical evaluation, because this could aid in the diagnosis of cam FAI.

R. Bhalodia, S. Y. Elhabian, L. Kavan, R. T. Whitaker. “A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration,” In Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, In Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019, Springer International Publishing, pp. 391--400. 2019.


Spatial transformations are enablers in a variety of medical image analysis applications that entail aligning images to a common coordinate systems. Population analysis of such transformations is expected to capture the underlying image and shape variations, and hence these transformations are required to produce anatomically feasible correspondences. This is usually enforced through some smoothness-based generic metric or regularization of the deformation field. Alternatively, population-based regularization has been shown to produce anatomically accurate correspondences in cases where anatomically unaware (i.e., data independent) regularization fail. Recently, deep networks have been used to generate spatial transformations in an unsupervised manner, and, once trained, these networks are computationally faster and as accurate as conventional, optimization-based registration methods. However, the deformation fields produced by these networks require smoothness penalties, just as the conventional registration methods, and ignores population-level statistics of the transformations. Here, we propose a novel neural network architecture that simultaneously learns and uses the population-level statistics of the spatial transformations to regularize the neural networks for unsupervised image registration. This regularization is in the form of a bottleneck autoencoder, which learns and adapts to the population of transformations required to align input images by encoding the transformations to a low dimensional manifold. The proposed architecture produces deformation fields that describe the population-level features and associated correspondences in an anatomically relevant manner and are statistically compact relative to the state-of-the-art approaches while maintaining computational efficiency. We demonstrate the efficacy of the proposed architecture on synthetic data sets, as well as 2D and 3D medical data.

G. Duffley, D. N. Anderson, J. Vorwerk, A. C. Dorval, C. R. Butson. “Evaluation of methodologies for computing the deep brain stimulation volume of tissue activated,” In Journal of Neural Engineering, Aug, 2019.
DOI: 10.1088/1741-2552/ab3c95


Computational models are a popular tool for predicting the effects of deep brain stimulation (DBS) on neural tissue. One commonly used model, the volume of tissue activated (VTA), is computed using multiple methodologies. We quantified differences in the VTAs generated by five methodologies: the traditional axon model method, the electric field norm, and three activating function based approaches - the activating function at each grid point in the tangential direction (AF-Tan) or in the maximally activating direction (AF-3D), and the maximum activating function along the entire length of a tangential fiber (AF-Max).

Approach: We computed the VTA using each method across multiple stimulation settings. The resulting volumes were compared for similarity, and the methodologies were analyzed for their differences in behavior.

Main Results: Activation threshold values for both the electric field norm and the activating function vary with regards to electrode configuration, pulse width, and frequency. All methods produced highly similar volumes for monopolar stimulation. For bipolar electrode configurations, only the maximum activating function along the tangential axon method, AF-Max, produced similar volumes to those produced by the axon model method. Further analysis revealed that both of these methods are biased by their exclusive use of tangential fiber orientations. In contrast, the activating function in the maximally activating direction method, AF-3D, produces a VTA that is free of axon orientation and projection bias.

Significance: Simulating tangentially oriented axons, the standard approach of computing the VTA, is too computationally expensive for widespread implementation and yields results biased by the assumption of tangential fiber orientation. In this work, we show that a computationally efficient method based on the activating function, AF-Max, reliably reproduces the VTAs generated by direct axon modeling. Further, we propose another method, AF-3D as a potentially superior model for representing generic neural tissue activation.

M. Han, I. Wald, W. Usher, Q. Wu, F. Wang, V. Pascicci, C. D. Hansen, C. R. Johnson. “Ray Tracing Generalized Tube Primitives: Method and Applications,” In Computer Graphics Forum, Vol. 38, No. 3, John Wiley & Sons Ltd., 2019.


We present a general high-performance technique for ray tracing generalized tube primitives. Our technique efficiently supports tube primitives with fixed and varying radii, general acyclic graph structures with bifurcations, and correct transparency with interior surface removal. Such tube primitives are widely used in scientific visualization to represent diffusion tensor imaging tractographies, neuron morphologies, and scalar or vector fields of 3D flow. We implement our approach within the OSPRay ray tracing framework, and evaluate it on a range of interactive visualization use cases of fixed- and varying-radius streamlines, pathlines, complex neuron morphologies, and brain tractographies. Our proposed approach provides interactive, high-quality rendering, with low memory overhead.