Publications by Year: 2014

2014
Yi Gao, Allen Tannenbaum, and Sylvain Bouix. 2014. “A Framework for Joint Image-and-Shape Analysis.” Proc SPIE Int Soc Opt Eng, 9034, Pp. 90340V.Abstract
Techniques in medical image analysis are many times used for the comparison or regression on the intensities of images. In general, the domain of the image is a given Cartesian grids. Shape analysis, on the other hand, studies the similarities and differences among spatial objects of arbitrary geometry and topology. Usually, there is no function defined on the domain of shapes. Recently, there has been a growing needs for defining and analyzing functions defined on the shape space, and a coupled analysis on both the shapes and the functions defined on them. Following this direction, in this work we present a coupled analysis for both images and shapes. As a result, the statistically significant discrepancies in both the image intensities as well as on the underlying shapes are detected. The method is applied on both brain images for the schizophrenia and heart images for atrial fibrillation patients.
Karl G Helmer, Ofer Pasternak, Eli Fredman, Ronny I Preciado, Inga K Koerte, Takeshi Sasaki, Michael Mayinger, Andrew M Johnson, Jeffrey D Holmes, Lorie A Forwell, Elaine N Skopelja, Martha E Shenton, and Paul S Echlin. 2014. “Hockey Concussion Education Project, Part 1. Susceptibility-weighted imaging study in male and female ice hockey players over a single season.” J Neurosurg, 120, 4, Pp. 864-72.Abstract
OBJECT: Concussion, or mild traumatic brain injury (mTBI), is a commonly occurring sports-related injury, especially in contact sports such as hockey. Cerebral microbleeds (CMBs), which appear as small, hypointense lesions on T₂*-weighted images, can result from TBI. The authors use susceptibility-weighted imaging (SWI) to automatically detect small hypointensities that may be subtle signs of chronic and acute damage due to both subconcussive and concussive injury. The goal was to investigate how the burden of these hypointensities changes over time, over a playing season, and postconcussion, in comparison with subjects who did not suffer a medically observed and diagnosed concussion. METHODS: Images were obtained in 45 university-level adult male and female ice hockey players before and after a single Canadian Interuniversity Sports season. In addition, 11 subjects (5 men and 6 women) underwent imaging at 72 hours, 2 weeks, and 2 months after concussion. To identify subtle changes in brain tissue and potential CMBs, nonvessel clusters of hypointensities on SWI were automatically identified, and a hypointensity burden index was calculated for all subjects at the beginning of the season (BOS), the end of the season (EOS), and at postconcussion time points (where applicable). RESULTS: A statistically significant increase in the hypointensity burden, relative to the BOS, was observed for male subjects with concussions at the 2-week postconcussion time point. A smaller, nonsignificant rise in the burden for female subjects with concussions was also observed within the same time period. There were no significant changes in burden for nonconcussed subjects of either sex between the BOS and EOS time points. However, there was a statistically significant difference in the burden between male and female subjects in the nonconcussed group at both the BOS and EOS time points, with males having a higher burden. CONCLUSIONS: This method extends the utility of SWI from the enhancement and detection of larger (> 5 mm) CMBs, which are often observed in more severe cases of TBI, to cases involving smaller lesions in which visual detection of injury is difficult. The hypointensity burden metric proposed here shows statistically significant changes over time in the male subjects. A smaller, nonsignificant increase in the burden metric was observed in the female subjects.
Ofer Pasternak, Inga K Koerte, Sylvain Bouix, Eli Fredman, Takeshi Sasaki, Michael Mayinger, Karl G Helmer, Andrew M Johnson, Jeffrey D Holmes, Lorie A Forwell, Elaine N Skopelja, Martha E Shenton, and Paul S Echlin. 2014. “Hockey Concussion Education Project, Part 2. Microstructural white matter alterations in acutely concussed ice hockey players: a longitudinal free-water MRI study.” J Neurosurg, 120, 4, Pp. 873-81.Abstract
OBJECT: Concussion is a common injury in ice hockey and a health problem for the general population. Traumatic axonal injury has been associated with concussions (also referred to as mild traumatic brain injuries), yet the pathological course that leads from injury to recovery or to long-term sequelae is still not known. This study investigated the longitudinal course of concussion by comparing diffusion MRI (dMRI) scans of the brains of ice hockey players before and after a concussion. METHODS: The 2011-2012 Hockey Concussion Education Project followed 45 university-level ice hockey players (both male and female) during a single Canadian Interuniversity Sports season. Of these, 38 players had usable dMRI scans obtained in the preseason. During the season, 11 players suffered a concussion, and 7 of these 11 players had usable dMRI scans that were taken within 72 hours of injury. To analyze the data, the authors performed free-water imaging, which reflects an increase in specificity over other dMRI analysis methods by identifying alterations that occur in the extracellular space compared with those that occur in proximity to cellular tissue in the white matter. They used an individualized approach to identify alterations that are spatially heterogeneous, as is expected in concussions. RESULTS: Paired comparison of the concussed players before and after injury revealed a statistically significant (p < 0.05) common pattern of reduced free-water volume and reduced axial and radial diffusivities following elimination of free-water. These free-water-corrected measures are less affected by partial volumes containing extracellular water and are therefore more specific to processes that occur within the brain tissue. Fractional anisotropy was significantly increased, but this change was no longer significant following the free-water elimination. CONCLUSIONS: Concussion during ice hockey games results in microstructural alterations that are detectable using dMRI. The alterations that the authors found suggest decreased extracellular space and decreased diffusivities in white matter tissue. This finding might be explained by swelling and/or by increased cellularity of glia cells. Even though these findings in and of themselves cannot determine whether the observed microstructural alterations are related to long-term pathology or persistent symptoms, they are important nonetheless because they establish a clearer picture of how the brain responds to concussion.
Takeshi Sasaki, Ofer Pasternak, Michael Mayinger, Marc Muehlmann, Peter Savadjiev, Sylvain Bouix, Marek Kubicki, Eli Fredman, Brian Dahlben, Karl G Helmer, Andrew M Johnson, Jeffrey D Holmes, Lorie A Forwell, Elaine N Skopelja, Martha E Shenton, Paul S Echlin, and Inga K Koerte. 2014. “Hockey Concussion Education Project, Part 3. White matter microstructure in ice hockey players with a history of concussion: a diffusion tensor imaging study.” J Neurosurg, 120, 4, Pp. 882-90.Abstract
OBJECT: The aim of this study was to examine the brain's white matter microstructure by using MR diffusion tensor imaging (DTI) in ice hockey players with a history of clinically symptomatic concussion compared with players without a history of concussion. METHODS: Sixteen players with a history of concussion (concussed group; mean age 21.7 ± 1.5 years; 6 female) and 18 players without a history of concussion (nonconcussed group; mean age 21.3 ± 1.8 years, 10 female) underwent 3-T DTI at the end of the 2011-2012 Canadian Interuniversity Sports ice hockey season. Tract-based spatial statistics (TBSS) was used to test for group differences in fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and the measure "trace," or mean diffusivity. Cognitive evaluation was performed using the Immediate Postconcussion Assessment and Cognitive Test (ImPACT) and the Sport Concussion Assessment Tool-2 (SCAT2). RESULTS: TBSS revealed a significant increase in FA and AD, and a significant decrease in RD and trace in several brain regions in the concussed group, compared with the nonconcussed group (p < 0.05). The regions with increased FA and decreased RD and trace included the right posterior limb of the internal capsule, the right corona radiata, and the right temporal lobe. Increased AD was observed in a small area in the left corona radiata. The DTI measures correlated with neither the ImPACT nor the SCAT2 scores. CONCLUSIONS: The results of the current study indicate that a history of concussion may result in alterations of the brain's white matter microstructure in ice hockey players. Increased FA based on decreased RD may reflect neuroinflammatory or neuroplastic processes of the brain responding to brain trauma. Future studies are needed that include a longitudinal analysis of the brain's structure and function following a concussion to elucidate further the complex time course of DTI changes and their clinical meaning.
Yi Gao, Liang-Jia Zhu, Sylvain Bouix, and Allen Tannenbaum. 2014. “Interpolation of Longitudinal Shape and Image Data via Optimal Mass Transport.” Proc SPIE Int Soc Opt Eng, 9034, Pp. 90342X.Abstract
Longitudinal analysis of medical imaging data has become central to the study of many disorders. Unfortunately, various constraints (study design, patient availability, technological limitations) restrict the acquisition of data to only a few time points, limiting the study of continuous disease/treatment progression. Having the ability to produce a sensible time interpolation of the data can lead to improved analysis, such as intuitive visualizations of anatomical changes, or the creation of more samples to improve statistical analysis. In this work, we model interpolation of medical image data, in particular shape data, using the theory of optimal mass transport (OMT), which can construct a continuous transition from two time points while preserving "mass" (e.g., image intensity, shape volume) during the transition. The theory even allows a short extrapolation in time and may help predict short-term treatment impact or disease progression on anatomical structure. We apply the proposed method to the hippocampus-amygdala complex in schizophrenia, the heart in atrial fibrillation, and full head MR images in traumatic brain injury.
Revanth Reddy Garlapati, Aditi Roy, Grand Roman Joldes, Adam Wittek, Ahmed Mostayed, Barry Doyle, Simon Keith Warfield, Ron Kikinis, Neville Knuckey, Stuart Bunt, and Karol Miller. 2014. “More accurate neuronavigation data provided by biomechanical modeling instead of rigid registration.” J Neurosurg, 120, 6, Pp. 1477-83.Abstract
It is possible to improve neuronavigation during image-guided surgery by warping the high-quality preoperative brain images so that they correspond with the current intraoperative configuration of the brain. In this paper, the accuracy of registration results obtained using comprehensive biomechanical models is compared with the accuracy of rigid registration, the technology currently available to patients. This comparison allows investigation into whether biomechanical modeling provides good-quality image data for neuronavigation for a larger proportion of patients than rigid registration. Preoperative images for 33 neurosurgery cases were warped onto their respective intraoperative configurations using both the biomechanics-based method and rigid registration. The Hausdorff distance-based evaluation process, which measures the difference between images, was used to quantify the performance of both registration methods. A statistical test for difference in proportions was conducted to evaluate the null hypothesis that the proportion of patients for whom improved neuronavigation can be achieved is the same for rigid and biomechanics-based registration. The null hypothesis was confidently rejected (p < 10(-4)). Even the modified hypothesis that fewer than 25% of patients would benefit from the use of biomechanics-based registration was rejected at a significance level of 5% (p = 0.02). The biomechanics-based method proved particularly effective in cases demonstrating large craniotomy-induced brain deformations. The outcome of this analysis suggests that nonlinear biomechanics-based methods are beneficial to a large proportion of patients and can be considered for use in the operating theater as a possible means of improving neuronavigation and surgical outcomes.
Arie Nakhmani, Ron Kikinis, and Allen Tannenbaum. 2014. “MRI Brain Tumor Segmentation and Necrosis Detection Using Adaptive Sobolev Snakes.” Proc SPIE Int Soc Opt Eng, 9034, Pp. 903442.Abstract
Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at different points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D diffusion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.
Sidong Liu, Weidong Cai, Lingfeng Wen, David Dagan Feng, Sonia Pujol, Ron Kikinis, Michael J Fulham, and Stefan Eberl. 2014. “Multi-Channel neurodegenerative pattern analysis and its application in Alzheimer's disease characterization.” Comput Med Imaging Graph, 38, 6, Pp. 436-44.Abstract
Neuroimaging has played an important role in non-invasive diagnosis and differentiation of neurodegenerative disorders, such as Alzheimer's disease and Mild Cognitive Impairment. Various features have been extracted from the neuroimaging data to characterize the disorders, and these features can be roughly divided into global and local features. Recent studies show a tendency of using local features in disease characterization, since they are capable of identifying the subtle disease-specific patterns associated with the effects of the disease on human brain. However, problems arise if the neuroimaging database involved multiple disorders or progressive disorders, as disorders of different types or at different progressive stages might exhibit different degenerative patterns. It is difficult for the researchers to reach consensus on what brain regions could effectively distinguish multiple disorders or multiple progression stages. In this study we proposed a Multi-Channel pattern analysis approach to identify the most discriminative local brain metabolism features for neurodegenerative disorder characterization. We compared our method to global methods and other pattern analysis methods based on clinical expertise or statistics tests. The preliminary results suggested that the proposed Multi-Channel pattern analysis method outperformed other approaches in Alzheimer's disease characterization, and meanwhile provided important insights into the underlying pathology of Alzheimer's disease and Mild Cognitive Impairment.
Thomas Sc Ng, Alexander P Lin, Inga K Koerte, Ofer Pasternak, HuiJun Liao, Sai Merugumala, Sylvain Bouix, and Martha E Shenton. 2014. “Neuroimaging in repetitive brain trauma.” Alzheimers Res Ther, 6, 1, Pp. 10.Abstract
Sports-related concussions are one of the major causes of mild traumatic brain injury. Although most patients recover completely within days to weeks, those who experience repetitive brain trauma (RBT) may be at risk for developing a condition known as chronic traumatic encephalopathy (CTE). While this condition is most commonly observed in athletes who experience repetitive concussive and/or subconcussive blows to the head, such as boxers, football players, or hockey players, CTE may also affect soldiers on active duty. Currently, the only means by which to diagnose CTE is by the presence of phosphorylated tau aggregations post-mortem. Non-invasive neuroimaging, however, may allow early diagnosis as well as improve our understanding of the underlying pathophysiology of RBT. The purpose of this article is to review advanced neuroimaging methods used to investigate RBT, including diffusion tensor imaging, magnetic resonance spectroscopy, functional magnetic resonance imaging, susceptibility weighted imaging, and positron emission tomography. While there is a considerable literature using these methods in brain injury in general, the focus of this review is on RBT and those subject populations currently known to be susceptible to RBT, namely athletes and soldiers. Further, while direct detection of CTE in vivo has not yet been achieved, all of the methods described in this review provide insight into RBT and will likely lead to a better characterization (diagnosis), in vivo, of CTE than measures of self-report.
Yi Gao, Liangjia Zhu, Isaiah Norton, Nathalie YR Agar, and Allen Tannenbaum. 2014. “Reconstruction and Feature Selection for Desorption Electrospray Ionization Mass Spectroscopy Imagery.” Proc SPIE Int Soc Opt Eng, 9036, Pp. 90360D.Abstract
Desorption electrospray ionization mass spectrometry (DESI-MS) provides a highly sensitive imaging technique for differentiating normal and cancerous tissue at the molecular level. This can be very useful, especially under intra-operative conditions where the surgeon has to make crucial decision about the tumor boundary. In such situations, the time it takes for imaging and data analysis becomes a critical factor. Therefore, in this work we utilize compressive sensing to perform the sparse sampling of the tissue, which halves the scanning time. Furthermore, sparse feature selection is performed, which not only reduces the dimension of data from about 10(4) to less than 50, and thus significantly shortens the analysis time. This procedure also identifies biochemically important molecules for pathological analysis. The methods are validated on brain and breast tumor data sets.
Chintan Parmar, Emmanuel Rios Velazquez, Ralph Leijenaar, Mohammed Jermoumi, Sara Carvalho, Raymond H Mak, Sushmita Mitra, Uma B Shankar, Ron Kikinis, Benjamin Haibe-Kains, Philippe Lambin, and Hugo JWL Aerts. 2014. “Robust Radiomics feature quantification using semiautomatic volumetric segmentation.” PLoS One, 9, 7, Pp. e102107.Abstract
Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.
Paul S Echlin, Andrew M Johnson, Jeffrey D Holmes, Annalise Tichenoff, Sarah Gray, Heather Gatavackas, Joanne Walsh, Tim Middlebro, Angelique Blignaut, Martin MacIntyre, Chris Anderson, Eli Fredman, Michael Mayinger, Elaine N Skopelja, Takeshi Sasaki, Sylvain Bouix, Ofer Pasternak, Karl G Helmer, Inga K Koerte, Martha E Shenton, and Lorie A Forwell. 2014. “The Sport Concussion Education Project. A brief report on an educational initiative: from concept to curriculum.” J Neurosurg, 121, 6, Pp. 1331-6.Abstract
Current research on concussion is primarily focused on injury identification and treatment. Prevention initiatives are, however, important for reducing the incidence of brain injury. This report examines the development and implementation of an interactive electronic teaching program (an e-module) that is designed specifically for concussion education within an adolescent population. This learning tool and the accompanying consolidation rubric demonstrate that significant engagement occurs in addition to the knowledge gained among participants when it is used in a school curriculum setting.
Laura L Horky, Victor H Gerbaudo, Alexander Zaitsev, Wen Plesniak, Jon Hainer, Usha Govindarajulu, Ron Kikinis, and Jörg Dietrich. 2014. “Systemic chemotherapy decreases brain glucose metabolism.” Ann Clin Transl Neurol, 1, 10, Pp. 788-98.Abstract
OBJECTIVE: Cancer patients may experience neurologic adverse effects, such as alterations in neurocognitive function, as a consequence of chemotherapy. The mechanisms underlying such neurotoxic syndromes remain poorly understood. We here describe the temporal and regional effects of systemically administered platinum-based chemotherapy on glucose metabolism in the brain of cancer patients. METHODS: Using sequential FDG-PET/CT imaging prior to and after administration of chemotherapy, we retrospectively characterized the effects of intravenously administered chemotherapy on brain glucose metabolism in a total of 24 brain regions in a homogenous cohort of 10 patients with newly diagnosed non-small-cell lung cancer. RESULTS: Significant alterations of glucose metabolism were found in response to chemotherapy in all gray matter structures, including cortical structures, deep nuclei, hippocampi, and cerebellum. Metabolic changes were also notable in frontotemporal white matter (WM) network systems, including the corpus callosum, subcortical, and periventricular WM tracts. INTERPRETATION: Our data demonstrate a decrease in glucose metabolism in both gray and white matter structures associated with chemotherapy. Among the affected regions are those relevant to the maintenance of brain plasticity and global neurologic function. This study potentially offers novel insights into the spatial and temporal effects of systemic chemotherapy on brain metabolism in cancer patients.
Michele Cavallari, Nicola Moscufo, Dominik Meier, Pawel Skudlarski, Godfrey D Pearlson, William B White, Leslie Wolfson, and Charles RG Guttmann. 2014. “Thalamic fractional anisotropy predicts accrual of cerebral white matter damage in older subjects with small-vessel disease.” J Cereb Blood Flow Metab, 34, 8, Pp. 1321-7.Abstract
White matter hyperintensities (WMHs) and lacunes are magnetic resonance imaging hallmarks of cerebral small-vessel disease, which increase the risk of stroke, cognitive, and mobility impairment. Although most studies of cerebral small-vessel disease have focused on white matter abnormalities, the gray matter (GM) is also affected, as evidenced by frequently observed lacunes in subcortical GM. Diffusion tensor imaging (DTI) is sensitive to subtle neurodegenerative changes in deep GM structures. We explored the relationship between baseline DTI characteristics of the thalamus, caudate, and putamen, and the volume and subsequent accrual of WMHs over a 4-year period in 56 community-dwelling older (⩾75 years) individuals. Baseline thalamic fractional anisotropy (FA) was an independent predictor of WMH accrual. WMH accrual also correlated with baseline lacune count and baseline WMH volume, the latter showing the strongest predictive power, explaining 27.3% of the variance. The addition of baseline thalamic FA in multivariate modeling increased this value by 70%, which explains 46.5% of the variance in WMH accrual rate. Thalamic FA might serve as a novel predictor of cerebral small-vessel disease progression in clinical settings and trials. Furthermore, our findings point to the possibility of a causal relationship between thalamic damage and the accrual of WMHs.
Tung Nguyen, Lucia Cevidanes, Beatriz Paniagua, Hongtu Zhu, Leonardo Koerich, and Hugo De Clerck. 2014. “Use of shape correspondence analysis to quantify skeletal changes associated with bone-anchored Class III correction.” Angle Orthod, 84, 2, Pp. 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.
Wei Huang, Xin Li, Yiyi Chen, Xia Li, Ming-Ching Chang, Matthew J Oborski, Dariya I Malyarenko, Mark Muzi, Guido H Jajamovich, Andriy Fedorov, Alina Tudorica, Sandeep N Gupta, Charles M Laymon, Kenneth I Marro, Hadrien A Dyvorne, James V Miller, Daniel P Barbodiak, Thomas L Chenevert, Thomas E Yankeelov, James M Mountz, Paul E Kinahan, Ron Kikinis, Bachir Taouli, Fiona Fennessy, and Jayashree Kalpathy-Cramer. 2014. “Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge.” Transl Oncol, 7, 1, Pp. 153-66.Abstract
Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.
Christian Wachinger and Polina Golland. 2014. “Atlas-based Under-segmentation.” Med Image Comput Comput Assist Interv, 17, Pt 1, Pp. 315-22.Abstract

We study the widespread, but rarely discussed, tendency of atlas-based segmentation to under-segment the organs of interest. Commonly used error measures do not distinguish between under- and over-segmentation, contributing to the problem. We explicitly quantify over- and under-segmentation in several typical examples and present a new hypothesis for the cause. We provide evidence that segmenting only one organ of interest and merging all surrounding structures into one label creates bias towards background in the label estimates suggested by the atlas. We propose a generative model that corrects for this effect by learning the background structures from the data. Inference in the model separates the background into distinct structures and consequently improves the segmentation accuracy. Our experiments demonstrate a clear improvement in several applications.

Alexandra Woolgar, Polina Golland, and Stefan Bode. 2014. “Coping With Confounds in Multivoxel Pattern Analysis: What Should We Do About Reaction Time Differences? A Comment On Todd, Nystrom & Cohen 2013.” Neuroimage, 98, Pp. 506-12.Abstract
Multivoxel pattern analysis (MVPA) is a sensitive and increasingly popular method for examining differences between neural activation patterns that cannot be detected using classical mass-univariate analysis. Recently, Todd et al. ("Confounds in multivariate pattern analysis: Theory and rule representation case study", 2013, NeuroImage 77: 157-165) highlighted a potential problem for these methods: high sensitivity to confounds at the level of individual participants due to the use of directionless summary statistics. Unlike traditional mass-univariate analyses where confounding activation differences in opposite directions tend to approximately average out at group level, group level MVPA results may be driven by any activation differences that can be discriminated in individual participants. In Todd et al.'s empirical data, factoring out differences in reaction time (RT) reduced a classifier's ability to distinguish patterns of activation pertaining to two task rules. This raises two significant questions for the field: to what extent have previous multivoxel discriminations in the literature been driven by RT differences, and by what methods should future studies take RT and other confounds into account? We build on the work of Todd et al. and compare two different approaches to remove the effect of RT in MVPA. We show that in our empirical data, in contrast to that of Todd et al., the effect of RT on rule decoding is negligible, and results were not affected by the specific details of RT modelling. We discuss the meaning of and sensitivity for confounds in traditional and multivoxel approaches to fMRI analysis. We observe that the increased sensitivity of MVPA comes at a price of reduced specificity, meaning that these methods in particular call for careful consideration of what differs between our conditions of interest. We conclude that the additional complexity of the experimental design, analysis and interpretation needed for MVPA is still not a reason to favour a less sensitive approach.
Georg Langs, Andrew Sweet, Danial Lashkari, Yanmei Tie, Laura Rigolo, Alexandra J Golby, and Polina Golland. 2014. “Decoupling Function and Anatomy in Atlases of Functional Connectivity Patterns: Language Mapping in Tumor Patients.” Neuroimage, 103, Pp. 462-75.Abstract
In this paper we construct an atlas that summarizes functional connectivity characteristics of a cognitive process from a population of individuals. The atlas encodes functional connectivity structure in a low-dimensional embedding space that is derived from a diffusion process on a graph that represents correlations of fMRI time courses. The functional atlas is decoupled from the anatomical space, and thus can represent functional networks with variable spatial distribution in a population. In practice the atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects. The method also successfully maps functional networks from a healthy population used as a training set to individuals whose language networks are affected by tumors.
Hans Knutsson and Carl-Fredrik Westin. 2014. “From Expected Propagator Distribution to Optimal Q-Space Sample Metric.” Med Image Comput Comput Assist Interv, 17, Pt 3, Pp. 217-24.Abstract
We present a novel approach to determine a local q-space metric that is optimal from an information theoreticperspective with respect to the expected signal statistics. It should be noted that the approach does not attempt to optimize the quality of a pre-defined mathematical representation, the estimator. In contrast, our suggestion aims at obtaining the maximum amount of information without enforcing a particular feature representation. Results for three significantly different average propagator distributions are presented. The results show that the optimal q-space metric has a strong dependence on the assumed distribution in the targeted tissue. In many practical cases educated guesses can be made regarding the average propagator distribution present. In such cases the presented analysis can produce a metric that is optimal with respect to this distribution. The metric will be different at different q-space locations and is defined by the amount of additional information that is obtained when adding a second sample at a given offset from a first sample. The intention is to use the obtained metric as a guide for the generation of specific efficient q-space sample distributions for the targeted tissue.

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