Publications by Year: 2007

Nobuhiko Hata, Steve Piper, Ferenc A Jolesz, Clare M Tempany, Peter M Black, Shigehiro Morikawa, Horoshi Iseki, Makoto Hashizume, and Ron Kikinis. 10/2007. “Application of Open Source Image Guided Therapy Software in MR-guided Therapies.” Med Image Comput Comput Assist Interv, 10, Pt 1, Pp. 491-8.Abstract

We present software engineering methods to provide free open-source software for MR-guided therapy. We report that graphical representation of the surgical tools, interconnectively with the tracking device, patient-to-image registration, and MRI-based thermal mapping are crucial components of MR-guided therapy in sharing such software. Software process includes a network-based distribution mechanism by multi-platform compiling tool CMake, CVS, quality assurance software DART. We developed six procedures in four separate clinical sites using proposed software engineering and process, and found the proposed method is feasible to facilitate multicenter clinical trial of MR-guided therapies. Our future studies include use of the software in non-MR-guided therapies.

Santiago Aja-Fernández, Carlos Alberola-López, and Carl-Fredrik Westin. 10/2007. “Signal LMMSE estimation from multiple samples in MRI and DT-MRI.” Med Image Comput Comput Assist Interv, 10, Pt 2, Pp. 368-75.Abstract

A method to estimate the magnitude MR data from several noisy samples is presented. It is based on the Linear Minimum Mean Squared Error (LMMSE) estimator for the Rician noise model when several scanning repetitions are available. This method gives a closed-form analytical solution that takes into account the probability distribution of the data as well as the existing level of noise, showing a better performance than methods such as the average or the median.

Simon DiMaio, Tina Kapur, Kevin Cleary, Stephen Aylward, Peter Kazanzides, Kirby Vosburgh, Randy Ellis, James Duncan, Keyvan Farahani, Heinz Lemke, Terry Peters, William Bill Lorensen, David Gobbi, John Haller, Laurence Larry Clarke, Stephen Pizer, Russell Taylor, Robert Galloway, Gabor Fichtinger, Nobuhiko Hata, Kimberly Lawson, Clare M Tempany, Ron Kikinis, and Ferenc A Jolesz. 9/2007. “Challenges in Image-guided Therapy System Design.” Neuroimage, 37 Suppl 1, Pp. S144-51.Abstract

System development for image-guided therapy (IGT), or image-guided interventions (IGI), continues to be an area of active interest across academic and industry groups. This is an emerging field that is growing rapidly: major academic institutions and medical device manufacturers have produced IGT technologies that are in routine clinical use, dozens of high-impact publications are published in well regarded journals each year, and several small companies have successfully commercialized sophisticated IGT systems. In meetings between IGT investigators over the last two years, a consensus has emerged that several key areas must be addressed collaboratively by the community to reach the next level of impact and efficiency in IGT research and development to improve patient care. These meetings culminated in a two-day workshop that brought together several academic and industrial leaders in the field today. The goals of the workshop were to identify gaps in the engineering infrastructure available to IGT researchers, develop the role of research funding agencies and the recently established US-based National Center for Image Guided Therapy (NCIGT), and ultimately to facilitate the transfer of technology among research centers that are sponsored by the National Institutes of Health (NIH). Workshop discussions spanned many of the current challenges in the development and deployment of new IGT systems. Key challenges were identified in a number of areas, including: validation standards; workflows, use-cases, and application requirements; component reusability; and device interface standards. This report elaborates on these key points and proposes research challenges that are to be addressed by a joint effort between academic, industry, and NIH participants.

Kilian M Pohl, Ron Kikinis, and William M Wells III. 7/2007. “Active Mean Fields: Solving the Mean Field Approximation in the Level Set Framework.” Inf Process Med Imaging, 20, Pp. 26-37.Abstract

We describe a new approach for estimating the posterior probability of tissue labels. Conventional likelihood models are combined with a curve length prior on boundaries, and an approximate posterior distribution on labels is sought via the Mean Field approach. Optimizing the resulting estimator by gradient descent leads to a level set style algorithm where the level set functions are the logarithm-of-odds encoding of the posterior label probabilities in an unconstrained linear vector space. Applications with more than two labels are easily accommodated. The label assignment is accomplished by the Maximum A Posteriori rule, so there are no problems of "overlap" or "vacuum". We test the method on synthetic images with additive noise. In addition, we segment a magnetic resonance scan into the major brain compartments and subcortical structures.

Lilla Zöllei, Mark Jenkinson, Samson Timoner, and William M Wells III. 7/2007. “A Marginalized MAP Approach and EM Optimization for Pair-wise Registration.” Inf Process Med Imaging, 20, Pp. 662-74.Abstract

We formalize the pair-wise registration problem in a maximum a posteriori (MAP) framework that employs a multinomial model of joint intensities with parameters for which we only have a prior distribution. To obtain an MAP estimate of the aligning transformation alone, we treat the multinomial parameters as nuisance parameters, and marginalize them out. If the prior on those is uninformative, the marginalization leads to registration by minimization of joint entropy. With an informative prior, the marginalization leads to minimization of the entropy of the data pooled with pseudo observations from the prior. In addition, we show that the marginalized objective function can be optimized by the Expectation-Maximization (EM) algorithm, which yields a simple and effective iteration for solving entropy-based registration problems. Experimentally, we demonstrate the effectiveness of the resulting EM iteration for rapidly solving a challenging intra-operative registration problem.

Neculai Archip, Olivier Clatz, Stephen Whalen, Dan Kacher, Andriy Fedorov, Andriy Kot, Nikos Chrisochoides, Ferenc A Jolesz, Alexandra Golby, Peter M Black, and Simon K Warfield. 4/2007. “Non-rigid Alignment of Pre-operative MRI, fMRI, and DT-MRI with Intra-operative MRI for Enhanced Visualization and Navigation in Image-guided Neurosurgery.” Neuroimage, 35, 2, Pp. 609-24.Abstract

OBJECTIVE: The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the pre-operative MR data and the intra-operative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) to create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI. MATERIALS AND METHODS: Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3 T prior to surgery. SPGR and T2w images were acquired with a 0.5 T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of-the-art systems based only on rigid registration. RESULTS: Alignment between pre-operative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after non-rigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p<0.001). CONCLUSIONS: We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative fMRI with intra-operative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard real-time constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon.

Raymundo Sierra, Simon P Dimaio, Jun Wada, Nobuhiko Hata, Gábor Székely, Ron Kikinis, and Ferenc A Jolesz. 1/2007. “Patient Specific Simulation and Navigation of Ventriculoscopic Interventions.” Stud Health Technol Inform, 125, Pp. 433-5.Abstract

In this paper a comprehensive framework for pre-operative planning, procedural skill training, and intraoperative navigation is presented. The goal of this system is to integrate surgical simulation with surgical planning in order to improve the individual treatment of patients. Various surgical approaches and new, more complex procedures can be assessed using a safe and objective platform that will allow the physicians to explore and discuss possible risks and benefits prior to the intervention. A simulation environment extends the pre-operative planning in a natural way, as it allows for direct evaluation of the surgical approach envisioned for each case. In addition, by providing intraoperative navigation based on this simulation, surgeons can carry out the previously optimized plan with higher precision and greater confidence.

Simon P Dimaio, Steve Pieper, Kiyoyuki Chinzei, Nobuhiko Hata, Stephen J. Haker, Daniel F Kacher, Gabor Fichtinger, Clare M Tempany, and Ron Kikinis. 1/2007. “Robot-assisted Needle Placement in Open MRI: System Architecture, Integration and Validation.” Comput Aided Surg, 12, 1, Pp. 15-24.Abstract

In prostate cancer treatment, there is a move toward targeted interventions for biopsy and therapy, which has precipitated the need for precise image-guided methods for needle placement. This paper describes an integrated system for planning and performing percutaneous procedures with robotic assistance under MRI guidance. A graphical planning interface allows the physician to specify the set of desired needle trajectories, based on anatomical structures and lesions observed in the patient's registered pre-operative and pre-procedural MR images, immediately prior to the intervention in an open-bore MRI scanner. All image-space coordinates are automatically computed, and are used to position a needle guide by means of an MRI-compatible robotic manipulator, thus avoiding the limitations of the traditional fixed needle template. Automatic alignment of real-time intra-operative images aids visualization of the needle as it is manually inserted through the guide. Results from in-scanner phantom experiments are provided.

Kilian M Pohl, Sylvain Bouix, Martha E Shenton, Eric WL Grimson, and Ron Kikinis. 2007. “Automatic Segmentation Using Non-Rigid Registration.” Med Image Comput Comput Assist Interv, 26, 9, Pp. 1201-1212.Abstract
Many neuroanatomy studies rely on brain tissue segmentations of magnetic resonance images (MRI). We present a segmentation tool, which performs this task automatically by analyzing the MRIs as well as tissue specific spatial priors. The priors are aligned to the patient through a non-rigid registration method. The segmentation itself is parameterized by an XML file making the approach easily adjustable to various segmentation problems. The tool is hidden beneath a 'one-button' user interface, which is simple to install and is applicable to a wide variety of image acquisition protocols.
Lauren J O'Donnell and Carl-Fredrik Westin. 2007. “Automatic tractography segmentation using a high-dimensional white matter atlas.” IEEE Trans Med Imaging, 26, 11, Pp. 1562-75.Abstract
We propose a new white matter atlas creation method that learns a model of the common white matter structures present in a group of subjects. We demonstrate that our atlas creation method, which is based on group spectral clustering of tractography, discovers structures corresponding to expected white matter anatomy such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, and corona radiata. The white matter clusters are augmented with expert anatomical labels and stored in a new type of atlas that we call a high-dimensional white matter atlas. We then show how to perform automatic segmentation of tractography from novel subjects by extending the spectral clustering solution, stored in the atlas, using the Nystrom method. We present results regarding the stability of our method and parameter choices. Finally we give results from an atlas creation and automatic segmentation experiment. We demonstrate that our automatic tractography segmentation identifies corresponding white matter regions across hemispheres and across subjects, enabling group comparison of white matter anatomy.
Oleg Michailovich and Allen Tannenbaum. 2007. “Blind deconvolution of medical ultrasound images: a parametric inverse filtering approach.” IEEE Trans Image Process, 16, 12, Pp. 3005-19.Abstract
The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a "hybridization" of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the "hybrid" approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolutioh algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used.
Julien Dauguet, Sharon Peled, Vladimir Berezovskii, Thierry Delzescaux, Simon K Warfield, Richard Born, and Carl-Fredrik Westin. 2007. “Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain.” Neuroimage, 37, 2, Pp. 530-8.Abstract
Since the introduction of diffusion weighted imaging (DWI) as a method for examining neural connectivity, its accuracy has not been formally evaluated. In this study, we directly compared connections that were visualized using injected neural tract tracers (WGA-HRP) with those obtained using in-vivo diffusion tensor imaging (DTI) tractography. First, we injected the tracer at multiple sites in the brain of a macaque monkey; second, we reconstructed the histological sections of the labeled fiber tracts in 3D; third, we segmented and registered the fibers (somatosensory and motor tracts) with the anatomical in-vivo MRI from the same animal; and last, we conducted fiber tracing along the same pathways on the DTI data using a classical diffusion tracing technique with the injection sites as seeds. To evaluate the performance of DTI fiber tracing, we compared the fibers derived from the DTI tractography with those segmented from the histology. We also studied the influence of the parameters controlling the tractography by comparing Dice superimposition coefficients between histology and DTI segmentations. While there was generally good visual agreement between the two methods, our quantitative comparisons reveal certain limitations of DTI tractography, particularly for regions at remote locations from seeds. We have thus demonstrated the importance of appropriate settings for realistic tractography results.
Danial Lashkari and Polina Golland. 2007. “Convex Clustering with Exemplar-Based Models.” Adv Neural Inf Process Syst, 20.Abstract
Clustering is often formulated as the maximum likelihood estimation of a mixture model that explains the data. The EM algorithm widely used to solve the resulting optimization problem is inherently a gradient-descent method and is sensitive to initialization. The resulting solution is a local optimum in the neighborhood of the initial guess. This sensitivity to initialization presents a significant challenge in clustering large data sets into many clusters. In this paper, we present a different approach to approximate mixture fitting for clustering. We introduce an exemplar-based likelihood function that approximates the exact likelihood. This formulation leads to a convex minimization problem and an efficient algorithm with guaranteed convergence to the globally optimal solution. The resulting clustering can be thought of as a probabilistic mapping of the data points to the set of exemplars that minimizes the average distance and the information-theoretic cost of mapping. We present experimental results illustrating the performance of our algorithm and its comparison with the conventional approach to mixture model clustering.
Peng Yu, Boon Thye Thomas Yeo, Ellen P Grant, Bruce Fischl, and Polina Golland. 2007. “Cortical Folding Development Study based on Over-Complete Spherical Wavelets.” Proc IEEE Int Conf Comput Vis, 2007.Abstract
We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been shown to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant under rotations of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that over-complete spherical wavelets allow us to build more stable cortical folding development models, and detect a wider array of regions of folding development in a newborn dataset.
Peng Yu, Ellen P Grant, Yuan Qi, Xiao Han, Florent Ségonne, Rudolph Pienaar, Evelina Busa, Jenni Pacheco, Nikos Makris, Randy L Buckner, Polina Golland, and Bruce Fischl. 2007. “Cortical surface shape analysis based on spherical wavelets.” IEEE Trans Med Imaging, 26, 4, Pp. 582-97.Abstract
In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, which allows us to characterize the order of development of large-scale and finer folding patterns independently. Given a limited amount of training data, we use a regularization framework to estimate the parameters of the Gompertz model to improve the prediction performance on new data. We develop an efficient method to estimate this regularized Gompertz model based on the Broyden-Fletcher-Goldfarb-Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomic information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurologic deficits in newborns.
Gordon Kindlmann, Xavier Tricoche, and Carl-Fredrik Westin. 2007. “Delineating white matter structure in diffusion tensor MRI with anisotropy creases.” Med Image Anal, 11, 5, Pp. 492-502.Abstract
Geometric models of white matter architecture play an increasing role in neuroscientific applications of diffusion tensor imaging, and the most popular method for building them is fiber tractography. For some analysis tasks, however, a compelling alternative may be found in the first and second derivatives of diffusion anisotropy. We extend to tensor fields the notion from classical computer vision of ridges and valleys, and define anisotropy creases as features of locally extremal tensor anisotropy. Mathematically, these are the loci where the gradient of anisotropy is orthogonal to one or more eigenvectors of its Hessian. We propose that anisotropy creases provide a basis for extracting a skeleton of the major white matter pathways, in that ridges of anisotropy coincide with interiors of fiber tracts, and valleys of anisotropy coincide with the interfaces between adjacent but distinctly oriented tracts. The crease extraction algorithm we present generates high-quality polygonal models of crease surfaces, which are further simplified by connected-component analysis. We demonstrate anisotropy creases on measured diffusion MRI data, and visualize them in combination with tractography to confirm their anatomic relevance.
Polina Golland, Yulia Golland, and Rafael Malach. 2007. “Detection of spatial activation patterns as unsupervised segmentation of fMRI data.” Med Image Comput Comput Assist Interv, 10, Pt 1, Pp. 110-8.Abstract
In functional connectivity analysis, networks of interest are defined based on correlation with the mean time course of a user-selected 'seed' region. In this work we propose to simultaneously estimate the optimal representative time courses that summarize the fMRI data well and the partition of the volume into a set of disjoint regions that are best explained by these representative time courses. Our approach offers two advantages. First, is removes the sensitivity of the analysis to the details of the seed selection. Second, it substantially simplifies group analysis by eliminating the need for a subject-specific threshold at which correlation values are deemed significant. This unsupervised technique generalizes connectivity analysis to situations where candidate seeds are difficult to identify reliably or are unknown. Our experimental results indicate that the functional segmentation provides a robust, anatomically meaningful and consistent model for functional connectivity in fMRI.
Jochen Von Spiczak, Eigil Samset, Simon DiMaio, Gerhard Reitmayr, Dieter Schmalstieg, Catherina Burghart, and Ron Kikinis. 2007. “Device connectivity for image-guided medical applications.” Stud Health Technol Inform, 125, Pp. 482-4.Abstract
The integration of medical devices with software applications is crucial for image-guided medical applications. This work describes a general device interface that has been designed for high-frequency streaming of multi-modal events, thus providing maximum performance and flexibility for such applications. Several sample applications and performance tests are provided to demonstrate the usability of the concept.
Jan Lesniak, Junichi Tokuda, Ron Kikinis, Catherina Burghart, and Nobuhiko Hata. 2007. “A device guidance method for organ motion compensation in MRI-guided therapy.” Phys Med Biol, 52, 21, Pp. 6427-38.Abstract
Organ motion compensation in image-guided therapy is an active area of research. However, there has been little research on motion tracking and compensation in magnetic resonance imaging (MRI)-guided therapy. In this paper, we present a method to track a moving organ in MRI and control an active mechanical device for motion compensation. The method proposed is based on MRI navigator echo tracking enhanced by Kalman filtering for noise robustness. We also developed an extrapolation scheme to resolve any discrepancies between tracking and device control sampling rates. The algorithm was tested in a simulation study using a phantom and an active mechanical tool holder. We found that the method is feasible to use in a clinical MRI scanner with sufficient accuracy (0.36 mm to 1.51 mm depending on the range of phantom motion) and is robust to noise. The method proposed may be useful in MRI-guided targeted therapy, such as focused ultrasound therapy for a moving organ.
Gordon Kindlmann, Daniel B Ennis, Ross T Whitaker, and Carl-Fredrik Westin. 2007. “Diffusion tensor analysis with invariant gradients and rotation tangents.” IEEE Trans Med Imaging, 26, 11, Pp. 1483-99.Abstract
Guided by empirically established connections between clinically important tissue properties and diffusion tensor parameters, we introduce a framework for decomposing variations in diffusion tensors into changes in shape and orientation. Tensor shape and orientation both have three degrees-of-freedom, spanned by invariant gradients and rotation tangents, respectively. As an initial demonstration of the framework, we create a tunable measure of tensor difference that can selectively respond to shape and orientation. Second, to analyze the spatial gradient in a tensor volume (a third-order tensor), our framework generates edge strength measures that can discriminate between different neuroanatomical boundaries, as well as creating a novel detector of white matter tracts that are adjacent yet distinctly oriented. Finally, we apply the framework to decompose the fourth-order diffusion covariance tensor into individual and aggregate measures of shape and orientation covariance, including a direct approximation for the variance of tensor invariants such as fractional anisotropy.