Publications

2015
Liu S, Cai W, Liu S, Zhang F, Fulham M, Feng D, Pujol S, Kikinis R. Multimodal Neuroimaging Computing: The Workflows, Methods, and Platforms. Brain Inform. 2015;2 :181-95.Abstract
The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.
Ratner V, Zhu L, Kolesov I, Nedergaard M, Benveniste H, Tannenbaum A. Optimal-mass-transfer-based estimation of glymphatic transport in living brain. Proc SPIE Int Soc Opt Eng. 2015;9413.Abstract
It was recently shown that the brain-wide cerebrospinal fluid (CSF) and interstitial fluid exchange system designated the 'glymphatic pathway' plays a key role in removing waste products from the brain, similarly to the lymphatic system in other body organs(1,2). It is therefore important to study the flow patterns of glymphatic transport through the live brain in order to better understand its functionality in normal and pathological states. Unlike blood, the CSF does not flow rapidly through a network of dedicated vessels, but rather through para-vascular channels and brain parenchyma in a slower time-domain, and thus conventional fMRI or other blood-flow sensitive MRI sequences do not provide much useful information about the desired flow patterns. We have accordingly analyzed a series of MRI images, taken at different times, of the brain of a live rat, which was injected with a paramagnetic tracer into the CSF via the lumbar intrathecal space of the spine. Our goal is twofold: (a) find glymphatic (tracer) flow directions in the live rodent brain; and (b) provide a model of a (healthy) brain that will allow the prediction of tracer concentrations given initial conditions. We model the liquid flow through the brain by the diffusion equation. We then use the Optimal Mass Transfer (OMT) approach(3) to derive the glymphatic flow vector field, and estimate the diffusion tensors by analyzing the (changes in the) flow. Simulations show that the resulting model successfully reproduces the dominant features of the experimental data.
Langs G, Golland P, Ghosh SS. Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion. Med Image Comput Comput Assist Interv. 2015;9350 :313-20.Abstract

The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by a multi-atlas label fusion algorithm in a functional space. We learn the manifold of resting-state fMRI signals in each individual, and perform manifold alignment in an embedding space. We then transfer activation predictions from a source population to a target subject via multi-atlas label fusion. The cost function is derived from the aligned manifolds, so that the resulting correspondences are derived based on the similarity of intrinsic connectivity architecture. Experiments show that the resulting label fusion predicts activation evoked by various experiment conditions with higher accuracy than relying on morphological alignment. Interestingly, the distribution of this gain is distributed heterogeneously across the cortex, and across tasks. This offers insights into the relationship between intrinsic connectivity, morphology and task activation. Practically, the mechanism can serve as prior, and provides an avenue to infer task-related activation in individuals for whom only resting data is available.

Ning L, Georgiou TT, Tannenbaum A. On Matrix-Valued Monge-Kantorovich Optimal Mass Transport. IEEE Trans Automat Contr. 2015;60 (2) :373-82.Abstract
We present a particular formulation of optimal transport for matrix-valued density functions. Our aim is to devise a geometry which is suitable for comparing power spectral densities of multivariable time series. More specifically, the value of a power spectral density at a given frequency, which in the matricial case encodes power as well as directionality, is thought of as a proxy for a "matrix-valued mass density." Optimal transport aims at establishing a natural metric in the space of such matrix-valued densities which takes into account differences between power across frequencies as well as misalignment of the corresponding principle axes. Thus, our transportation cost includes a cost of transference of power between frequencies together with a cost of rotating the principle directions of matrix densities. The two endpoint matrix-valued densities can be thought of as marginals of a joint matrix-valued density on a tensor product space. This joint density, very much as in the classical Monge-Kantorovich setting, can be thought to specify the transportation plan. Contrary to the classical setting, the optimal transport plan for matrices is no longer supported on a thin zero-measure set.
Sjölund J, Szczepankiewicz F, Nilsson M, Topgaard D, Westin C-F, Knutsson H. Constrained Optimization of Gradient Waveforms for Generalized Diffusion Encoding. J Magn Reson. 2015;261 :157-68.Abstract

Diffusion MRI is a useful probe of tissue microstructure. The conventional diffusion encoding sequence, the single pulsed field gradient, has recently been challenged as more general gradient waveforms have been introduced. Out of these, we focus on q-space trajectory imaging, which generalizes the scalar b-value to a tensor valued entity. To take full advantage of its capabilities, it is imperative to respect the constraints imposed by the hardware, while at the same time maximizing the diffusion encoding strength. We provide a tool that achieves this by solving a constrained optimization problem that accommodates constraints on maximum gradient amplitude, slew rate, coil heating and positioning of radio frequency pulses. The method's efficacy and flexibility is demonstrated both experimentally and by comparison with previous work on optimization of isotropic diffusion sequences.

Wachinger C, Toews M, Langs G, Wells III WM, Golland P. Keypoint Transfer Segmentation. Inf Process Med Imaging. 2015;24 :233-45.Abstract

We present an image segmentation method that transfers label maps of entire organs from the training images to the novel image to be segmented. The transfer is based on sparse correspondences between keypoints that represent automatically identified distinctive image locations. Our segmentation algorithm consists of three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ label maps. We introduce generative models for the inference of keypoint labels and for image segmentation, where keypoint matches are treated as a latent random variable and are marginalized out as part of the algorithm. We report segmentation results for abdominal organs in whole-body CT and in contrast-enhanced CT images. The accuracy of our method compares favorably to common multi-atlas segmentation while offering a speed-up of about three orders of magnitude. Furthermore, keypoint transfer requires no training phase or registration to an atlas. The algorithm's robustness enables the segmentation of scans with highly variable field-of-view.

Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging. 2015;34 (10) :1993-2024.Abstract

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

Dalca AV, Sridharan R, Sabuncu MR, Golland P. Predictive Modeling of Anatomy with Genetic and Clinical Data. Med Image Comput Comput Assist Interv. 2015;9351 :519-26.Abstract

We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject's health based on individual genetic and clinical indicators. In contrast to classical correlation and longitudinal analysis, we focus on predicting new observations from a single subject observation. We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient's scans to the predicted subject-specific healthy anatomical trajectory.

Maier-Hein KH, Westin C-F, Shenton ME, Weiner MW, Raj A, Thomann P, Kikinis R, Stieltjes B, Pasternak O. Widespread White Matter Degeneration Preceding the Onset of Dementia. Alzheimers Dement. 2015;11 (5) :485-93.Abstract

BACKGROUND: Brain atrophy in subjects with mild cognitive impairment (MCI) introduces partial volume effects, limiting the sensitivity of diffusion tensor imaging to white matter microstructural degeneration. Appropriate correction isolates microstructural effects in MCI that might be precursors of Alzheimer's disease (AD). METHODS: Forty-eight participants (18 MCI, 15 AD, and 15 healthy controls) had magnetic resonance imaging scans and clinical evaluations at baseline and follow-up after 36 months. Ten MCI subjects were diagnosed with AD at follow-up and eight remained MCI. Free-water (FW) corrected measures on the white matter skeleton were compared between groups. RESULTS: FW corrected radial diffusivity, but not uncorrected radial diffusivity, was increased across the brain of the converted group compared with the nonconverted group (P < .05). The extent of increases was similar to that found comparing AD with controls. CONCLUSION: Partial volume elimination reveals microstructural alterations preceding dementia. These alterations may prove to be an effective and feasible early biomarker of AD.

2014
Sridharan R, Dalca AV, Golland P. An Interactive Visualization Tool for Nipype Medical Image Computing Pipelines. Int Conf Med Image Comput Comput Assist Interv. Workshop on Interactive Medical Image Computing. 2014;17.
Dalca AV, Sridharan R, Rost NS, Golland P. tipiX: Rapid Visualization of Large Image Collections. Int Conf Med Image Comput Comput Assist Interv. Workshop on Interactive Medical Image Computing. 2014;14.
Zhu L, Kolesov I, Gao Y, Kikinis R, Tannenbaum A. An Effective Interactive Medical Image Segmentation Method using Fast GrowCut. Int Conf Med Image Comput Comput Assist Interv. Workshop on Interactive Methods. 2014;17 (WS).Abstract
Segmentation of anatomical structures in medical imagery is a key step in a variety of clinical applications. Designing a generic, automated method that works for various structures and imaging modalities is a daunting task. In this paper, we present an effective interactive segmentation method that reformulates the GrowCut algorithm as a clustering problem and computes a fast, approximate solution. The method is further improved by using an efficient updating scheme requiring only local computations when new user input becomes available, making it applicable to high resolution images. The algorithm may easily be included as a user-oriented software module in any number of available medical imaging/image processing platforms such as 3D Slicer. The efficiency and effectiveness of the algorithm are demonstrated through tests on several challenging data sets where it is also compared to standard GrowCut.
Zhu MICCAI WS 2014
Kolesov I, Zhu L, Karasev P, Tannenbaum A. A Control Framework for Interactive Deformable Image Registration. Int Conf Med Image Comput Comput Assist Interv. Workshop on Interactive Methods. 2014;17 (WS). Kolesov MICCAI 2014
Farhat N. Tutorial: Preparing Data for 3-D Printing using 3D Slicer. 2014.Abstract
This tutorial demonstrates how to prepare data for 3D printing using the open source software 3D Slicer. The following topics are highlighted in the tutorial: introduction to the 3D Slicer interface, loading data into 3D Slicer, volume rendering, cropping image volumes, creating label maps, creating surface models, and saving data in file formats appropriate for 3D printing.
3D Printing Tutorial.mov
Kahn T, Jolesz FA, Lewin JS. Proceedings of the 10th Interventional MRI Symposium. 10th Interventional MRI Symposium. 2014;10 :1-85. 2014 iMRI Symposium Proceedings
Kapur T, Tempany CM, Jolesz FA. Proceedings of the 7th Image Guided Therapy Workshop. Image Guided Therapy Workshop. 2014;7 :1-60. 2014 IGT Workshop Proceedings
Chenevert TL, Malyarenko DI, Newitt D, Li X, Jayatilake M, Tudorica A, Fedorov A, Kikinis R, Liu TT, Muzi M, et al. Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling. Transl Oncol. 2014;7 (1) :65-71.Abstract
PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers. METHODS: A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create "variable signal," whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity. RESULTS: Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images. CONCLUSION: Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.
Savadjiev P, Whitford TJ, Hough ME, Clemm von Hohenberg C, Bouix S, Westin C-F, Shenton ME, Crow TJ, A J, Kubicki M. Sexually Dimorphic White Matter Geometry Abnormalities in Adolescent Onset Schizophrenia. Cereb Cortex. 2014;24 (5) :1389-96.Abstract

The normal human brain is characterized by a pattern of gross anatomical asymmetry. This pattern, known as the "torque", is associated with a sexual dimorphism: The male brain tends to be more asymmetric than that of the female. This fact, along with well-known sex differences in brain development (faster in females) and onset of psychosis (earlier with worse outcome in males), has led to the theory that schizophrenia is a disorder in which sex-dependent abnormalities in the development of brain torque, the correlate of the capacity for language, cause alterations in interhemispheric connectivity, which are causally related to psychosis (Crow TJ, Paez P, Chance SE. 2007. Callosal misconnectivity and the sex difference in psychosis. Int Rev Psychiatry. 19(4):449-457.). To provide evidence toward this theory, we analyze the geometry of interhemispheric white matter connections in adolescent-onset schizophrenia, with a particular focus on sex, using a recently introduced framework for white matter geometry computation in diffusion tensor imaging data (Savadjiev P, Kindlmann GL, Bouix S, Shenton ME, Westin CF. 2010. Local white geometry from diffusion tensor gradients. Neuroimage. 49(4):3175-3186.). Our results reveal a pattern of sex-dependent white matter geometry abnormalities that conform to the predictions of Crow's torque theory and correlate with the severity of patients' symptoms. To the best of our knowledge, this is the first study to associate geometrical differences in white matter connectivity with torque in schizophrenia.

Nguyen T, Cevidanes L, Paniagua B, Zhu H, Koerich L, De Clerck H. Use of shape correspondence analysis to quantify skeletal changes associated with bone-anchored Class III correction. Angle Orthod. 2014;84 (2) :329-36.Abstract
OBJECTIVE: To evaluate the three-dimensional (3D) skeletal changes in the mandibles of Class III patients treated with bone-anchored maxillary protraction using shape correspondence analysis. MATERIAL AND METHOD: Twenty-five consecutive patients with skeletal Class III who were between the ages of 9 and 13 years (mean age, 11.10 ± 1.1 years) were treated using Class III intermaxillary elastics and bilateral miniplates (two in the infrazygomatic crests of the maxilla and two in the anterior mandible). Cone-beam computed tomography (CBCT) was performed for each patient before initial loading (T1) and at 1 year out (T2). From the CBCT scans, 3D models were generated, registered on the anterior cranial base, and analyzed using 3D linear distances and vectors between corresponding point-based surfaces. RESULTS: Bone-anchored traction produced anteroposterior and vertical skeletal changes in the mandible. The novel application of Shape correspondence analysis showed vectors of mean (± standard deviation) distal displacement of the posterior ramus of 3.6 ± 1.4 mm, while the chin displaced backward by 0.5 ± 3.92 mm. The lower border of the mandible at the menton region was displaced downward by 2.6 ± 1.2 mm, and the lower border at the gonial region moved downward by 3.6 ± 1.4 mm. There was a downward and backward displacement around the gonial region with a mean closure of the gonial angle by 2.1°. The condyles were displaced distally by a mean of 2.6 ± 1.5 mm, and there were three distinct patterns for displacement: 44% backward, 40% backward and downward, and 16% backward and upward. CONCLUSION: This treatment approach induces favorable control of the mandibular growth pattern and can be used to treat patients with components of mandibular prognathism.
Veeraraghavan H, Miller JV. Faceted visualization of three dimensional neuroanatomy by combining ontology with faceted search. Neuroinformatics. 2014;12 (2) :245-59.Abstract
In this work, we present a faceted-search based approach for visualization of anatomy by combining a three dimensional digital atlas with an anatomy ontology. Specifically, our approach provides a drill-down search interface that exposes the relevant pieces of information (obtained by searching the ontology) for a user query. Hence, the user can produce visualizations starting with minimally specified queries. Furthermore, by automatically translating the user queries into the controlled terminology our approach eliminates the need for the user to use controlled terminology. We demonstrate the scalability of our approach using an abdominal atlas and the same ontology. We implemented our visualization tool on the opensource 3D Slicer software. We present results of our visualization approach by combining a modified Foundational Model of Anatomy (FMA) ontology with the Surgical Planning Laboratory (SPL) Brain 3D digital atlas, and geometric models specific to patients computed using the SPL brain tumor dataset.

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