We propose a novel l1l2-norm inverse solver for estimating the sources of EEG/MEG signals. Based on the standard l1-norm inverse solver, the proposed sparse distributed inverse solver integrates the l1-norm spatial model with a temporal model of the source signals in order to avoid unstable activation patterns and "spiky" reconstructed signals often produced by the original solvers. The joint spatio-temporal model leads to a cost function with an l1l2-norm regularizer whose minimization can be reduced to a convex second-order cone programming problem and efficiently solved using the interior-point method. Validation with simulated and real MEG data shows that the proposed solver yields source time course estimates qualitatively similar to those obtained through dipole fitting, but without the need to specify the number of dipole sources in advance. Furthermore, the l1l2-norm solver achieves fewer false positives and a better representation of the source locations than the conventional l2 minimum-norm estimates.
Publications by Year: 2008
Poynton C, Jenkinson M, Whalen S, Golby AJ, Wells W. Fieldmap-free retrospective registration and distortion correction for EPI-based functional imaging. Med Image Comput Comput Assist Interv. 2008;11(Pt 2):271–9.
We describe a method for correcting the distortions present in echo planar images (EPI) and registering the EPI to structural MRI. A fieldmap is predicted from an air / tissue segmentation of the MRI using a perturbation method and subsequently used to unwarp the EPI data. Shim and other missing parameters are estimated by registration. We obtain results that are similar to those obtained using fieldmaps, however neither fieldmaps, nor knowledge of shim coefficients is required.
Balci SK, Sabuncu MR, Yoo J, Ghosh SS, Whitfield-Gabrieli S, Gabrieli J, Golland P. Prediction of Successful Memory Encoding from fMRI Data. Med Image Comput Comput Assist Interv. 2008;2008(11):97–104.
In this work, we explore the use of classification algorithms in predicting mental states from functional neuroimaging data. We train a linear support vector machine classifier to characterize spatial fMRI activation patterns. We employ a general linear model based feature extraction method and use the t-test for feature selection. We evaluate our method on a memory encoding task, using participants’ subjective prediction about learning as a benchmark for our classifier. We show that the classifier achieves better than random predictions and the average accuracy is close to subject’s own prediction performance. In addition, we validate our tool on a simple motor task where we demonstrate an average prediction accuracy of over 90%. Our experiments demonstrate that the classifier performance depends significantly on the complexity of the experimental design and the mental process of interest.
We explore unsupervised, hypothesis-free methods for fMRI analysis in two different types of experiments. First, we employ clustering to identify large-scale functionally homogeneous systems. We formulate a generative mixture model, derive the EM algorithm and apply it to delineate functional systems. We also investigate spectral clustering in application to this problem and demonstrate that both methods give rise to similar partitions of the brain based on resting state fMRI data. Second, we demonstrate how to extend this approach to include information about the experimental protocol. Specifically, we formulate a mixture model in the space of possible profiles of brain response to stimuli. In both applications, our methods confirm previously known results in brain mapping and point to new research directions for exploratory analysis of fMRI data.
We present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output is a small number of template images that represent different modes in a population. This is in contrast with traditional approaches that assume a single template to construct atlases. We validate and explore the algorithm in two experiments. First, we employ iCluster to partition a data set of 416 whole brain MR volumes of subjects aged 18-96 years into three sub-groups, which mainly correspond to age groups. The templates reveal significant structural differences across these age groups that confirm previous findings in aging research. In the second experiment, we run iCluster on a group of 30 patients with dementia and 30 age-matched healthy controls. The algorithm produced three modes that mainly corresponded to a sub-population of healthy controls, a sub-population of patients with dementia and a mixture group that contained both types. These results suggest that the algorithm can be used to discover sub-populations that correspond to interesting structural or functional "modes".
We present a method for discovering patterns of activation observed through fMIRI in experiments with multiple stimuli/tasks. We introduce an explicit parameterization for the profiles of activation and represent fMRI time courses as such profiles using linear regression estimates. Working in the space of activation profiles, we design a mixture model that finds the major activation patterns along with their localization maps and derive an algorithm for fitting the model to the fMRI data. The method enables functional group analysis independent of spatial correspondence among subjects. We validate this model in the context of category selectivity in the visual cortex, demonstrating good agreement with prior findings based on hypothesis-driven methods.
BACKGROUND: We developed an image-guided robot system to provide mechanical assistance for skull base drilling, which is performed to gain access for some neurosurgical interventions, such as tumour resection. The motivation for introducing this robot was to improve safety by preventing the surgeon from accidentally damaging critical neurovascular structures during the drilling procedure. METHODS: We integrated a Stealthstation navigation system, a NeuroMate robotic arm with a six-degree-of-freedom force sensor, and the 3D Slicer visualization software to allow the robotic arm to be used in a navigated, cooperatively-controlled fashion by the surgeon. We employed virtual fixtures to constrain the motion of the robot-held cutting tool, so that it remained in the safe zone that was defined on a preoperative CT scan. RESULTS: We performed experiments on both foam skull and cadaver heads. The results for foam blocks cut using different registrations yielded an average placement error of 0.6 mm and an average dimensional error of 0.6 mm. We drilled the posterior porus acusticus in three cadaver heads and concluded that the robot-assisted procedure is clinically feasible and provides some ergonomic benefits, such as stabilizing the drill. We obtained postoperative CT scans of the cadaver heads to assess the accuracy and found that some bone outside the virtual fixture boundary was cut. The typical overcut was 1-2 mm, with a maximum overcut of about 3 mm. CONCLUSIONS: The image-guided cooperatively-controlled robot system can improve the safety and ergonomics of skull base drilling by stabilizing the drill and enforcing virtual fixtures to protect critical neurovascular structures. The next step is to improve the accuracy so that the overcut can be reduced to a more clinically acceptable value of about 1 mm.
Maddah M, Zöllei L, Grimson EL, Wells WM III. Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories using Dirichlet Distribution. Proc Workshop Math Methods Biomed Image Analysis. 2008;2008:1–7.
In this work, we describe a white matter trajectory clustering algorithm that allows for incorporating and appropriately weighting anatomical information. The influence of the anatomical prior reflects confidence in its accuracy and relevance. It can either be defined by the user or it can be inferred automatically. After a detailed description of our novel clustering framework, we demonstrate its properties through a set of preliminary experiments.
In non-rigid registration, the tradeoff between warp regularization and image fidelity is typically determined empirically. In atlas-based segmentation, this leads to a probabilistic atlas of arbitrary sharpness: weak regularization results in well-aligned training images and a sharp atlas; strong regularization yields a "blurry" atlas. In this paper, we employ a generative model for the joint registration and segmentation of images. The atlas construction process arises naturally as estimation of the model parameters. This framework allows the computation of unbiased atlases from manually labeled data at various degrees of "sharpness", as well as the joint registration and segmentation of a novel brain in a consistent manner. We study the effects of the tradeoff of atlas sharpness and warp smoothness in the context of cortical surface parcellation. This is an important question because of the increasingly availability of atlases in public databases, and the development of registration algorithms separate from the atlas construction process. We find that the optimal segmentation (parcellation) corresponds to a unique balance of atlas sharpness and warp regularization, yielding statistically significant improvements over the FreeSurfer parcellation algorithm. Furthermore, we conclude that one can simply use a single atlas computed at an optimal sharpness for the registration-segmentation of a new subject with a pre-determined, fixed, optimal warp constraint. The optimal atlas sharpness and warp smoothness can be determined by probing the segmentation performance on available training data. Our experiments also suggest that segmentation accuracy is tolerant up to a small mismatch between atlas sharpness and warp smoothness.
Wisco JJ, Rosene DL, Killiany RJ, Moss MB, Warfield SK, Egorova S, Wu Y, Liptak Z, Warner J, Guttmann CRG. A rhesus monkey reference label atlas for template driven segmentation. J Med Primatol. 2008;37(5):250–60.
BACKGROUND: We have acquired dual-echo spin-echo (DE SE) MRI data of the rhesus monkey brain since 1994 as part of an ongoing study of normal aging. To analyze these legacy data for regional volume changes, we have created a reference label atlas for the Template Driven Segmentation (TDS) algorithm. METHODS: The atlas was manually created from DE SE legacy MRI data of one behaviorally normal, young, male rhesus monkey and consisted of 14 regions of interest (ROI’s). We analyzed the reproducibility and validity of the TDS algorithm using the atlas relative to manual segmentation. RESULTS: ROI volumes were comparable between the two segmentation methodologies, except TDS overestimated the volume of basal ganglia regions. Both methodologies were highly reproducible, but TDS had lower sensitivity and comparable specificity. CONCLUSIONS: TDS segmentation calculates accurate volumes for most ROI’s. Sensitivity will be improved in future studies through the acquisition of higher quality data.