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.

SCI Publications

2013


N.P. Singh, J. Hinkle, S. Joshi, P.T. Fletcher. “A Vector Momenta Formulation of Diffeomorphisms for Improved Geodesic Regression and Atlas Construction,” In Proceedings of the 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), Note: Received Best Student Paper Award, pp. 1219--1222. 2013.
DOI: 10.1109/ISBI.2013.6556700

ABSTRACT

This paper presents a novel approach for diffeomorphic image regression and atlas estimation that results in improved convergence and numerical stability. We use a vector momenta representation of a diffeomorphism's initial conditions instead of the standard scalar momentum that is typically used. The corresponding variational problem results in a closed form update for template estimation in both the geodesic regression and atlas estimation problems. While we show that the theoretical optimal solution is equivalent to the scalar momenta case, the simplification of the optimization problem leads to more stable and efficient estimation in practice. We demonstrate the effectiveness of our method for atlas estimation and geodesic regression using synthetically generated shapes and 3D MRI brain scans.

Keywords: LDDMM, Geodesic regression, Atlas, Vector Momentum



N.P. Singh, J. Hinkle, S. Joshi, P.T. Fletcher. “A Hierarchical Geodesic Model for Diffeomorphic Longitudinal Shape Analysis,” In Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI), Lecture Notes in Computer Science (LNCS), pp. (accepted). 2013.

ABSTRACT

Hierarchical linear models (HLMs) are a standard approach for analyzing data where individuals are measured repeatedly over time. However, such models are only applicable to longitudinal studies of Euclidean data. In this paper, we propose a novel hierarchical geodesic model (HGM), which generalizes HLMs to the manifold setting. Our proposed model explains the longitudinal trends in shapes represented as elements of the group of diffeomorphisms. The individual level geodesics represent the trajectory of shape changes within individuals. The group level geodesic represents the average trajectory of shape changes for the population. We derive the solution of HGMs on diffeomorphisms to estimate individual level geodesics, the group geodesic, and the residual geodesics. We demonstrate the effectiveness of HGMs for longitudinal analysis of synthetically generated shapes and 3D MRI brain scans.



M. Szegedi, J. Hinkle, P. Rassiah, V. Sarkar, B. Wang, S. Joshi, B. Salter. “Four‐dimensional tissue deformation reconstruction (4D TDR) validation using a real tissue phantom,” In Journal of Applied Clinical Medical Physics, Vol. 14, No. 1, pp. 115-132. 2013.
DOI: 10.1120/jacmp.v14i1.4012

ABSTRACT

Calculation of four‐dimensional (4D) dose distributions requires the remapping of dose calculated on each available binned phase of the 4D CT onto a reference phase for summation. Deformable image registration (DIR) is usually used for this task, but unfortunately almost always considers only endpoints rather than the whole motion path. A new algorithm, 4D tissue deformation reconstruction (4D TDR), that uses either CT projection data or all available 4D CT images to reconstruct 4D motion data, was developed. The purpose of this work is to verify the accuracy of the fit of this new algorithm using a realistic tissue phantom. A previously described fresh tissue phantom with implanted electromagnetic tracking (EMT) fiducials was used for this experiment. The phantom was animated using a sinusoidal and a real patient‐breathing signal. Four‐dimensional computer tomography (4D CT) and EMT tracking were performed. Deformation reconstruction was conducted using the 4D TDR and a modified 4D TDR which takes real tissue hysteresis (4D TDRHysteresis) into account. Deformation estimation results were compared to the EMT and 4D CT coordinate measurements. To eliminate the possibility of the high contrast markers driving the 4D TDR, a comparison was made using the original 4D CT data and data in which the fiducials were electronically masked. For the sinusoidal animation, the average deviation of the 4D TDR compared to the manually determined coordinates from 4D CT data was 1.9 mm, albeit with as large as 4.5 mm deviation. The 4D TDR calculation traces matched 95% of the EMT trace within 2.8 mm. The motion hysteresis generated by real tissue is not properly projected other than at endpoints of motion. Sinusoidal animation resulted in 95% of EMT measured locations to be within less than 1.2 mm of the measured 4D CT motion path, enabling accurate motion characterization of the tissue hysteresis. The 4D TDRHysteresis calculation traces accounted well for the hysteresis and matched 95% of the EMT trace within 1.6 mm. An irregular (in amplitude and frequency) recorded patient trace applied to the same tissue resulted in 95% of the EMT trace points within less than 4.5 mm when compared to both the 4D CT and 4D TDRHysteresis motion paths. The average deviation of 4D TDRHysteresis compared to 4D CT datasets was 0.9 mm under regular sinusoidal and 1.0 mm under irregular patient trace animation. The EMT trace data fit to the 4D TDRHysteresis was within 1.6 mm for sinusoidal and 4.5 mm for patient trace animation. While various algorithms have been validated for end‐to‐end accuracy, one can only be fully confident in the performance of a predictive algorithm if one looks at data along the full motion path. The 4D TDR, calculating the whole motion path rather than only phase‐ or endpoints, allows us to fully characterize the accuracy of a predictive algorithm, minimizing assumptions. This algorithm went one step further by allowing for the inclusion of tissue hysteresis effects, a real‐world effect that is neglected when endpoint‐only validation is performed. Our results show that the 4D TDRHysteresis correctly models the deformation at the endpoints and any intermediate points along the motion path.

PACS numbers: 87.55.km, 87.55.Qr, 87.57.nf, 87.85.Tu


2012


S. Durrleman, M.W. Prastawa, S. Joshi, G. Gerig, A. Trouve. “Topology Preserving Atlas Construction from Shape Data without Correspondence using Sparse Parameters,” In Proceedings of MICCAI 2012, Lecture Notes in Computer Science (LNCS), pp. 223--230. October, 2012.

ABSTRACT

Statistical analysis of shapes, performed by constructing an atlas composed of an average model of shapes within a population and associated deformation maps, is a fundamental aspect of medical imaging studies. Usual methods for constructing a shape atlas require point correspondences across subjects, which are difficult in practice. By contrast, methods based on currents do not require correspondence. However, existing atlas construction methods using currents suffer from two limitations. First, the template current is not in the form of a topologically correct mesh, which makes direct analysis on shapes difficult. Second, the deformations are parametrized by vectors at the same location as the normals of the template current which often provides a parametrization that is more dense than required. In this paper, we propose a novel method for constructing shape atlases using currents where topology of the template is preserved and deformation parameters are optimized independently of the shape parameters. We use an L1-type prior that enables us to adaptively compute sparse and low dimensional parameterization of deformations.We show an application of our method for comparing anatomical shapes of patients with Down’s syndrome and healthy controls, where the sparse parametrization of diffeomorphisms decreases the parameter dimension by one order of magnitude.



S. Durrleman, S. Allassonniere, S. Joshi. “Sparse Adaptive Parameterization of Variability in Image Ensembles,” In International Journal of Computer Vision, pp. 1--23. 2012.

ABSTRACT

This paper introduces a new parameterization of diffeomorphic deformations for the characterization of the variability in image ensembles. Dense diffeomorphic deformations are built by interpolating the motion of a finite set of control points that forms a Hamiltonian flow of self-interacting particles. The proposed approach estimates a template image representative of a given image set, an optimal set of control points that focuses on the most variable parts of the image, and template-to-image registrations that quantify the variability within the image set. The method automatically selects the most relevant control points for the characterization of the image variability and estimates their optimal positions in the template domain. The optimization in position is done during the estimation of the deformations without adding any computational cost at each step of the gradient descent. The selection of the control points is done by adding a L1 prior to the objective function, which is optimized using the FISTA algorithm.



L.K. Ha, J. Krüger, J.L.D. Comba, C.T. Silva, S. Joshi. “ISP: An Optimal Out-of-Core Image-Set Processing Streaming Architecture for Parallel Heterogeneous Systems,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 18, No. 6, pp. 838--851. 2012.
DOI: 10.1109/TVCG.2012.32

ABSTRACT

Image population analysis is the class of statistical methods that plays a central role in understanding the development, evolution and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP we introduce specially-designed streaming algorithms and data structures that provide an optimal solution for out-of-core multi-image processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets.



L.K. Ha, J. Krüger, J.L.D. Comba, C.T. Silva, S. Joshi. “ISP: An Optimal Out-of-Core Image-Set Processing Streaming Architecture for Parallel Heterogeneous Systems,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 5, pp. 838--851. 2012.
DOI: 10.1109/TVCG.2012.32



J. Hinkle, P. Muralidharan, P.T. Fletcher, S. Joshi. “Polynomial Regression on Riemannian Manifolds,” In arXiv, Vol. 1201.2395, 2012.

ABSTRACT

In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein as well as the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer's study.



Y. Hong, S. Joshi, M. Sanchez, M. Styner, M. Niethammer. “Metamorphic Geodesic Regression,” In Proceedings of Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2012, pp. 197--205. 2012.

ABSTRACT

We propose a metamorphic geodesic regression approach approximating spatial transformations for image time-series while simultaneously accounting for intensity changes. Such changes occur for example in magnetic resonance imaging (MRI) studies of the developing brain due to myelination. To simplify computations we propose an approximate metamorphic geodesic regression formulation that only requires pairwise computations of image metamorphoses. The approximated solution is an appropriately weighted average of initial momenta. To obtain initial momenta reliably, we develop a shooting method for image metamorphosis.



V. Sarkar, Brian Wang, J. Hinkle, V.J. Gonzalez, Y.J. Hitchcock, P. Rassiah-Szegedi, S. Joshi, B.J. Salter. “Dosimetric evaluation of a virtual image-guidance alternative to explicit 6 degree of freedom robotic couch correction,” In Practical Radiation Oncology, Vol. 2, No. 2, pp. 122--137. 2012.

ABSTRACT

Purpose: Clinical evaluation of a \"virtual\" methodology for providing 6 degrees of freedom (6DOF) patient set-up corrections and comparison to corrections facilitated by a 6DOF robotic couch.

Methods: A total of 55 weekly in-room image-guidance computed tomographic (CT) scans were acquired using a CT-on-rails for 11 pelvic and head and neck cancer patients treated at our facility. Fusion of the CT-of-the-day to the simulation CT allowed prototype virtual 6DOF correction software to calculate the translations, single couch yaw, and beam-specific gantry and collimator rotations necessary to effectively reproduce the same corrections as a 6DOF robotic couch. These corrections were then used to modify the original treatment plan beam geometry and this modified plan geometry was applied to the CT-of-the-day to evaluate the dosimetric effects of the virtual correction method. This virtual correction dosimetry was compared with calculated geometric and dosimetric results for an explicit 6DOF robotic couch correction methodology.

Results: A (2\%, 2mm) gamma analysis comparing dose distributions created using the virtual corrections to those from explicit corrections showed that an average of 95.1\% of all points had a gamma of 1 or less, with a standard deviation of 3.4\%. For a total of 470 dosimetric metrics (ie, maximum and mean dose statistics for all relevant structures) compared for all 55 image-guidance sessions, the average dose difference for these metrics between the plans employing the virtual corrections and the explicit corrections was -0.12\% with a standard deviation of 0.82\%; 97.9\% of all metrics were within 2\%.

Conclusions: Results showed that the virtual corrections yielded dosimetric distributions that were essentially equivalent to those obtained when 6DOF robotic corrections were used, and that always outperformed the most commonly employed clinical approach of 3 translations only. This suggests



N.P. Singh, A.Y. Wang, P. Sankaranarayanan, P.T. Fletcher, S. Joshi. “Genetic, Structural and Functional Imaging Biomarkers for Early Detection of Conversion from MCI to AD,” In Proceedings of Medical Image Computing and Computer-Assisted Intervention MICCAI 2012, Vol. 7510, pp. 132--140. 2012.
DOI: 10.1007/978-3-642-33415-3_17

ABSTRACT

With the advent of advanced imaging techniques, genotyping, and methods to assess clinical and biological progression, there is a growing need for a unified framework that could exploit information available from multiple sources to aid diagnosis and the identification of early signs of Alzheimer’s disease (AD). We propose a modeling strategy using supervised feature extraction to optimally combine highdimensional imaging modalities with several other low-dimensional disease risk factors. The motivation is to discover new imaging biomarkers and use them in conjunction with other known biomarkers for prognosis of individuals at high risk of developing AD. Our framework also has the ability to assess the relative importance of imaging modalities for predicting AD conversion. We evaluate the proposed methodology on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to predict conversion of individuals with Mild Cognitive Impairment (MCI) to AD, only using information available at baseline.

Keywords: adni



M. Szegedi, P. Rassiah-Szegedi, V. Sarkar, J. Hinkle, Brian Wang, Y.-H. Huang, H. Zhao, S. Joshi, B.J. Salter. “Tissue characterization using a phantom to validate four-dimensional tissue deformation,” In Medical Physics, Vol. 39, No. 10, pp. 6065--6070. 2012.
DOI: 10.1118/1.4747528

ABSTRACT

Purpose: This project proposes using a real tissue phantom for 4D tissue deformation reconstruction (4DTDR) and 4D deformable image registration (DIR) validation, which allows for the complete verification of the motion path rather than limited end-point to end-point of motion.

Methods: Three electro-magnetic-tracking (EMT) fiducials were implanted into fresh porcine liver that was subsequently animated in a clinically realistic phantom. The animation was previously shown to be similar to organ motion, including hysteresis, when driven using a real patient's breathing pattern. For this experiment, 4DCTs and EMT traces were acquired when the phantom was animated using both sinusoidal and recorded patient-breathing traces. Fiducial were masked prior to 4DTDR for reconstruction. The original 4DCT data (with fiducials) were sampled into 20 CT phase sets and fiducials’ coordinates were recorded, resulting in time-resolved fiducial motion paths. Measured values of fiducial location were compared to EMT measured traces and the result calculated by 4DTDR.

Results: For the sinusoidal breathing trace, 95\% of EMT measured locations were within 1.2 mm of the measured 4DCT motion path, allowing for repeatable accurate motion characterization. The 4DTDR traces matched 95\% of the EMT trace within 1.6 mm. Using the more irregular (in amplitude and frequency) patient trace, 95\% of the EMT trace points fitted both 4DCT and 4DTDR motion path within 4.5 mm. The average match of the 4DTDR estimation of the tissue hysteresis over all CT phases was 0.9 mm using a sinusoidal signal for animation and 1.0 mm using the patient trace.

Conclusions: The real tissue phantom is a tool which can be used to accurately characterize tissue deformation, helping to validate or evaluate a DIR or 4DTDR algorithm over a complete motion path. The phantom is capable of validating, evaluating, and quantifying tissue hysteresis, thereby allowing for full motion path validation.


2011


S. Durrleman, M.W. Prastawa, G. Gerig, S. Joshi. “Optimal data-driven sparse parameterization of diffeomorphisms for population analysis,” In Proceedings of the IPMI 2011 conference, Springer LNCS, Vol. 6801/2011, pp. 123--134. July, 2011.
DOI: 10.1007/978-3-642-22092-0_11
PubMed ID: 20516153



S.E. Geneser, J.D. Hinkle, R.M. Kirby, Bo Wang, B. Salter, S. Joshi. “Quantifying variability in radiation dose due to respiratory-induced tumor motion,” In Medical Image Analysis, Vol. 15, No. 4, pp. 640--649. 2011.
DOI: 10.1016/j.media.2010.07.003



L.K. Ha, J. Krüger, J. Comba, S. Joshi, C.T. Silva. “Optimal Multi-Image Processing Streaming Framework on Parallel Heterogeneous Systems,” In Proceedings of Eurographics Symposium on Parallel Graphics and Visualization 2011, Note: Awarded Best Paper!, pp. 1--10. 2011.
DOI: 10.2312/EGPGV/EGPGV11/001-010

ABSTRACT

Atlas construction is an important technique in medical image analysis that plays a central role in understanding the variability of brain anatomy. The construction often requires applying image processing operations to multiple images (often hundreds of volumetric datasets), which is challenging in computational power as well as memory requirements. In this paper we introduce MIP, a Multi-Image Processing streaming framework to harness the processing power of heterogeneous CPU/GPU systems. In MIP we introduce specially designed streaming algorithms and data structures that provides an optimal solution for out-of-core multi-image processing problems both in terms of memory usage and computational efficiency. MIP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the MIP out-of-core solution could achieve the same performance as the in-core solution. We demonstrate the efficiency of the MIP framework on synthetic and real datasets.



L.K. Ha, J. Krüger, S. Joshi, C.T. Silva. “Multi-scale Unbiased Diffeomorphic Atlas Construction on Multi-GPUs,” Vol. 1, Ch. 10, Morgan Kaufmann, pp. 42. 2011.

ABSTRACT

In this chapter, we present a high performance multi-scale 3D image processing framework to exploit the parallel processing power of multiple graphic processing units (Multi-GPUs) for medical image analysis. We developed GPU algorithms and data structures that can be applied to a wide range of 3D image processing applications and efficiently exploit the computational power and massive bandwidth offered by modern GPUs. Our framework helps scientists solve computationally intensive problems which previously required super computing power. To demonstrate the effectiveness of our framework and to compare to existing techniques, we focus our discussions on atlas construction - the application of understanding the development of the brain and the progression of brain diseases.



F. Jiao, Y. Gur, C.R. Johnson, S. Joshi. “Detection of crossing white matter fibers with high-order tensors and rank-k decompositions,” In Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI 2011), Lecture Notes in Computer Science (LNCS), Vol. 6801, pp. 538--549. 2011.
DOI: 10.1007/978-3-642-22092-0_44
PubMed Central ID: PMC3327305

ABSTRACT

Fundamental to high angular resolution diffusion imaging (HARDI), is the estimation of a positive-semidefinite orientation distribution function (ODF) and extracting the diffusion properties (e.g., fiber directions). In this work we show that these two goals can be achieved efficiently by using homogeneous polynomials to represent the ODF in the spherical deconvolution approach, as was proposed in the Cartesian Tensor-ODF (CT-ODF) formulation. Based on this formulation we first suggest an estimation method for positive-semidefinite ODF by solving a linear programming problem that does not require special parametrization of the ODF. We also propose a rank-k tensor decomposition, known as CP decomposition, to extract the fibers information from the estimated ODF. We show that this decomposition is superior to the fiber direction estimation via ODF maxima detection as it enables one to reach the full fiber separation resolution of the estimation technique. We assess the accuracy of this new framework by applying it to synthetic and experimentally obtained HARDI data.



B. Salter, B. Wang, M. Sadinski, S. Ruhnau, V. Sarkar, J. Hinkle, Y. Hitchcock, K. Kokeny, S. Joshi. “WE-E-BRC-06: Comparison of Two Methods of Contouring Internal Target Volume on Multiple 4DCT Data Sets from the Same Subjects: Maximum Intensity Projection and Combination of 10 Phases,” In Medical Physics, Vol. 38, No. 6, pp. 3820. 2011.



M. Szegedi, J. Hinkle, S. Joshi, V. Sarkar, P. Rassiah-Szegedi, B. Wang, B. Salter. “WE-E-BRC-05: Voxel Based Four Dimensional Tissue Deformation Reconstruction (4DTDR) Validation Using a Real Tissue Phantom,” In Medical Physics, Vol. 38, pp. 3819. 2011.


2010


J.J.E. Blauer, J. Cates, C.J. McGann, E.G. Kholmovski, A. Alexander, M.W. Prastawa, S. Joshi, N.F. Marrouche, R.S. MacLeod. “MRI Based Injury Characterization Immediately Following Ablation of Atrial Fibrillation,” In Computing in Cardiology, Vol. 37, pp. 165--168. 2010.
ISSN: 0276−6574