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 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.
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.
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.
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.
Chintan Parmar, Emmanuel Rios Velazquez, Ralph Leijenaar, Mohammed Jermoumi, Sara Carvalho, Raymond H Mak, Sushmita Mitra, B, Ron Kikinis, Benjamin Haibe-Kains, Philippe Lambin, and Hugo J W L Aerts. 2014. Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One, 9, 7, Pp. e102107.
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.
Michele Cavallari, Nicola Moscufo, Dominik Meier, Pawel Skudlarski, Godfrey D Pearlson, William B White, Leslie Wolfson, and Charles R G 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.
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.
J Fitzsimmons, H M Hamoda, T Swisher, D Terry, G Rosenberger, L J Seidman, J Goldstein, R Mesholam-Gately, T. Petryshen, J Wojcik, R. Kikinis, and M Kubicki. 2014. Diffusion tensor imaging study of the fornix in first episode schizophrenia and in healthy controls. Schizophr Res, 156, 2-3, Pp. 157-60.
BACKGROUND: The fornix is a compact bundle of white matter fibers that project from the hippocampus to the mamillary bodies and septal nuclei. Its association with memory, as well as with symptoms in schizophrenia, has been reported in chronic schizophrenia. The purpose of this study is to determine whether or not fornix abnormalities are evident at the onset of schizophrenia. METHODS: Diffusion tensor imaging (DTI) and DT tractography were used to evaluate the fornix in 21 patients with first episode schizophrenia (16 males/5 females) and 22 healthy controls (13 males/9 females). Groups were matched on age, gender, parental socioeconomic status, education and handedness. Fractional anisotropy (FA), a measure of white matter integrity, radial diffusivity (RD), thought to reflect myelin integrity, trace, a possible marker of atrophy or cell loss, and axial diffusivity (AD), thought to reflect axonal integrity, were averaged over the entire tract extracted by means of DT tractography, and used to investigate fornix abnormalities in first episode schizophrenia compared with healthy controls.
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.
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.
Farah Naaz, Julia H Chariker, and John R Pani. 2014. Computer-Based Learning: Graphical Integration of Whole and Sectional Neuroanatomy Improves Long-Term Retention. Cogn Instr, 32, 1, Pp. 44-64.
A study was conducted to test the hypothesis that instruction with graphically integrated representations of whole and sectional neuroanatomy is especially effective for learning to recognize neural structures in sectional imagery (such as MRI images). Neuroanatomy was taught to two groups of participants using computer graphical models of the human brain. Both groups learned whole anatomy first with a three-dimensional model of the brain. One group then learned sectional anatomy using two-dimensional sectional representations, with the expectation that there would be transfer of learning from whole to sectional anatomy. The second group learned sectional anatomy by moving a virtual cutting plane through the three-dimensional model. In tests of long-term retention of sectional neuroanatomy, the group with graphically integrated representation recognized more neural structures that were known to be challenging to learn. This study demonstrates the use of graphical representation to facilitate a more elaborated (deeper) understanding of complex spatial relations.
M G Crabb, J L Davidson, R Little, P. Wright, A R Morgan, C A Miller, J H Naish, G J M Parker, R. Kikinis, H McCann, and W R B Lionheart. 2014. Mutual Information as a Measure of Image Quality for 3-D Dynamic Lung Imaging with EIT. Physiol Meas, 35, 5, Pp. 863-79.
We report on a pilot study of dynamic lung electrical impedance tomography (EIT) at the University of Manchester. Low-noise EIT data at 100 frames per second were obtained from healthy male subjects during controlled breathing, followed by magnetic resonance imaging (MRI) subsequently used for spatial validation of the EIT reconstruction. The torso surface in the MR image and electrode positions obtained using MRI fiducial markers informed the construction of a 3D finite element model extruded along the caudal-distal axis of the subject. Small changes in the boundary that occur during respiration were accounted for by incorporating the sensitivity with respect to boundary shape into a robust temporal difference reconstruction algorithm. EIT and MRI images were co-registered using the open source medical imaging software, 3D Slicer. A quantitative comparison of quality of different EIT reconstructions was achieved through calculation of the mutual information with a lung-segmented MR image. EIT reconstructions using a linear shape correction algorithm reduced boundary image artefacts, yielding better contrast of the lungs, and had 10% greater mutual information compared with a standard linear EIT reconstruction.
Vikram Appia, Anthony Yezzi, Chesnal Arepalli, Tracy Faber, Arthur Stillman, and Allen Tannenbaum. 2014. A complete system for automatic extraction of left ventricular myocardium from CT images using shape segmentation and contour evolution. IEEE Trans Image Process, 23, 3, Pp. 1340-51.
The left ventricular myocardium plays a key role in the entire circulation system and an automatic delineation of the myocardium is a prerequisite for most of the subsequent functional analysis. In this paper, we present a complete system for an automatic segmentation of the left ventricular myocardium from cardiac computed tomography (CT) images using the shape information from images to be segmented. The system follows a coarse-to-fine strategy by first localizing the left ventricle and then deforming the myocardial surfaces of the left ventricle to refine the segmentation. In particular, the blood pool of a CT image is extracted and represented as a triangulated surface. Then, the left ventricle is localized as a salient component on this surface using geometric and anatomical characteristics. After that, the myocardial surfaces are initialized from the localization result and evolved by applying forces from the image intensities with a constraint based on the initial myocardial surface locations. The proposed framework has been validated on 34-human and 12-pig CT images, and the robustness and accuracy are demonstrated.
Takeshi Asami, Sang Hyuk Lee, Sylvain Bouix, Yogesh Rathi, Thomas J Whitford, Margaret Niznikiewicz, Paul Nestor, Robert W McCarley, Martha E Shenton, and Marek Kubicki. 2014. Cerebral white matter abnormalities and their associations with negative but not positive symptoms of schizophrenia. Psychiatry Res, 222, 1-2, Pp. 52-9.
Although diffusion tensor imaging (DTI) studies have reported fractional anisotropy (FA) abnormalities in multiple white matter (WM) regions in schizophrenia, relationship between abnormal FA and negative symptoms has not been fully explored. DTI data were acquired from twenty-four patients with chronic schizophrenia and twenty-five healthy controls. Regional brain abnormalities were evaluated by conducting FA comparisons in the cerebral and each lobar WMs between groups. Focal abnormalities were also evaluated with a voxel-wise tract specific method. Associations between structural WM changes and negative symptoms were assessed using the Scale for the Assessment of Negative Symptoms (SANS). The patient group showed decreased FA in the cerebrum, especially in the frontal lobe, compared with controls. A voxel-wise analysis showed FA decreases in almost all WM tracts in schizophrenia. Correlation analyses demonstrated negative relationships between FA in the cerebrum, particularly in the left hemisphere, and SANS global and global rating scores (Anhedonia-Asociality, Attention, and Affective-Flattening), and also associations between FA of left frontal lobe and SANS global score, Anhedonia-Asociality, and Attention. This study demonstrates that patients with chronic schizophrenia evince widespread cerebral FA abnormalities and that these abnormalities, especially in the left hemisphere, are associated with negative symptoms.