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.
Introducing BrainPrint, a compact and discriminative representation of anatomical structures in the brain. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the 2D and 3D Laplace-Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. We derive a robust classifier for this representation that identifies the subject in a new scan, based on a database of brain scans. In an example dataset containing over 3000 MRI scans, we show that BrainPrint captures unique information about the subject's anatomy and permits to correctly classify a scan with an accuracy of over 99.8%. All processing steps for obtaining the compact representation are fully automated making this processing framework particularly attractive for handling large datasets.
Intensity-based image registration requires resampling images on a common grid to evaluate the similarity function. The uncertainty of interpolation varies across the image, depending on the location of resampled points relative to the base grid. We propose to perform Bayesian inference with Gaussian processes, where the covariance matrix of the Gaussian process posterior distribution estimates the uncertainty in interpolation. The Gaussian process replaces a single image with a distribution over images that we integrate into a generative model for registration. Marginalization over resampled images leads to a new similarity measure that includes the uncertainty of the interpolation. We demonstrate that our approach increases the registration accuracy and propose an efficient approximation scheme that enables seamless integration with existing registration methods.
Many studies have observed altered neurofunctional and structural organization in the aging brain. These observations from functional neuroimaging studies show a shift in brain activity from the posterior to the anterior regions with aging (PASA model), as well as a decrease in cortical thickness, which is more pronounced in the frontal lobe followed by the parietal, occipital, and temporal lobes (retrogenesis model). However, very little work has been done using diffusion MRI (dMRI) with respect to examining the structural tissue alterations underlying these neurofunctional changes in the gray matter. Thus, for the first time, we propose to examine gray matter changes using diffusion MRI in the context of aging. In this work, we propose a novel dMRI based measure of gray matter "heterogeneity" that elucidates these functional and structural models (PASA and retrogenesis) of aging from the viewpoint of diffusion MRI. In a cohort of 85 subjects (all males, ages 15-55 years), we show very high correlation between age and "heterogeneity" (a measure of structural layout of tissue in a region-of-interest) in specific brain regions. We examine gray matter alterations by grouping brain regions into anatomical lobes as well as functional zones. Our findings from dMRI data connects the functional and structural domains and confirms the "retrogenesis" hypothesis of gray matter alterations while lending support to the neurofunctional PASA model of aging in addition to showing the preservation of paralimbic areas during healthy aging.
Deformable image registration is used increasingly in image-guided interventions and other applications. However, validation and characterization of registration performance remain areas that require further study. We propose an analysis methodology for deriving tolerance limits on the initial conditions for deformable registration that reliably lead to a successful registration. This approach results in a concise summary of the probability of registration failure, while accounting for the variability in the test data. The (β, γ) tolerance limit can be interpreted as a value of the input parameter that leads to successful registration outcome in at least 100β% of cases with the 100γ% confidence. The utility of the methodology is illustrated by summarizing the performance of a deformable registration algorithm evaluated in three different experimental setups of increasing complexity. Our examples are based on clinical data collected during MRI-guided prostate biopsy registered using publicly available deformable registration tool. The results indicate that the proposed methodology can be used to generate concise graphical summaries of the experiments, as well as a probabilistic estimate of the registration outcome for a future sample. Its use may facilitate improved objective assessment, comparison and retrospective stress-testing of deformable.
Tumor associated seizures (TAS) are common and cause significant morbidity. Both imaging and gene expression features play significant roles in determining TAS, with strong interactions between them. We describe gene expression imaging tools which allow mapping of brain regions where gene expression has significant influence on TAS, and apply these methods to study 77 patients who underwent surgical evaluation for supratentorial glioblastomas. Tumor size and location were measured from MRI scans. A 9-set gene expression profile predicting long-term survivors was obtained from RNA derived from formalin-fixed paraffin embedded tissue. A total of 32 patients (42%) experienced preoperative TAS. Tumor volume was smaller (31.1 vs. 58.8 cubic cm, p<0.001) and there was a trend toward median survival being higher (48.4 vs. 32.7 months, p=0.055) in patients with TAS. Although the expression of only OLIG2 was significantly lower in patients with TAS in a groupwise analysis, gene expression imaging analysis revealed regions with significantly lower expression of OLIG2 and RTN1 in patients with TAS. Gene expression imaging is a powerful technique that demonstrates that the influence of gene expression on TAS is highly region specific. Regional variability should be evaluated with any genomic or molecular markers of solid brain lesions.
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.
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.
When multiple persons speak simultaneously, it may be difficult for the listener to direct attention to correct sound objects among conflicting ones. This could occur, for example, in an emergency situation in which one hears conflicting instructions and the loudest, instead of the wisest, voice prevails. Here, we used cortically-constrained oscillatory MEG/EEG estimates to examine how different brain regions, including caudal anterior cingulate (cACC) and dorsolateral prefrontal cortices (DLPFC), work together to resolve these kinds of auditory conflicts. During an auditory flanker interference task, subjects were presented with sound patterns consisting of three different voices, from three different directions (45° left, straight ahead, 45° right), sounding out either the letters "A" or "O". They were asked to discriminate which sound was presented centrally and ignore the flanking distracters that were phonetically either congruent (50%) or incongruent (50%) with the target. Our cortical MEG/EEG oscillatory estimates demonstrated a direct relationship between performance and brain activity, showing that efficient conflict resolution, as measured with reduced conflict-induced RT lags, is predicted by theta/alpha phase coupling between cACC and right lateral frontal cortex regions intersecting the right frontal eye fields (FEF) and DLPFC, as well as by increased pre-stimulus gamma (60-110 Hz) power in the left inferior fontal cortex. Notably, cACC connectivity patterns that correlated with behavioral conflict-resolution measures were found during both the pre-stimulus and the pre-response periods. Our data provide evidence that, instead of being only transiently activated upon conflict detection, cACC is involved in sustained engagement of attentional resources required for effective sound object selection performance.
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.
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.
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.
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.
RESULTS: Significant group differences were found between first episode patients and controls for FA (p=0.0001), RD (p=0.001) and trace (p=0.006).
CONCLUSION: These findings suggest abnormalities in the fornix in the early stages of schizophrenia, and further suggest that white matter abnormalities, which are apparent in the early course of the disease, may reflect myelin disturbances.
Thomas L Chenevert, Dariya I Malyarenko, David Newitt, Xin Li, Mohan Jayatilake, Alina Tudorica, Andriy Fedorov, Ron Kikinis, Tiffany Ting Liu, Mark Muzi, Matthew J Oborski, Charles M Laymon, Xia Li, Yankeelov Thomas, Kalpathy-Cramer Jayashree, James M Mountz, Paul E Kinahan, Daniel L Rubin, Fiona Fennessy, Wei Huang, Nola Hylton, and Brian D Ross. 2014. “Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling.” Transl Oncol, 7, 1, Pp. 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.
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.
PURPOSE: Reperfusion therapy enables effective treatment of ischemic stroke presenting within 4-6 hours. However, tissue progression from ischemia to infarction is variable, and some patients benefit from treatment up until 24 hours. Improved imaging techniques are needed to identify these patients. Here, it was hypothesized that time dependence in diffusion MRI may predict tissue outcome in ischemic stroke. METHODS: Diffusion MRI data were acquired with multiple diffusion times in five non-reperfused patients at 2, 9, and 100 days after stroke onset. Maps of "rate of kurtosis change" (k), mean kurtosis, ADC, and fractional anisotropy were derived. The ADC maps defined lesions, normal-appearing tissue, and the lesion tissue that would either be infarcted or remain viable by day 100. Diffusion parameters were compared (1) between lesions and normal-appearing tissue, and (2) between lesion tissue that would be infarcted or remain viable. RESULTS: Positive values of k were observed within stroke lesions on day 2 (P = .001) and on day 9 (P = .023), indicating diffusional exchange. On day 100, high ADC values indicated infarction of 50 ± 20% of the lesion volumes. Tissue infarction was predicted by high k values both on day 2 (P = .026) and on day 9 (P = .046), by low mean kurtosis values on day 2 (P = .043), and by low fractional anisotropy values on day 9 (P = .029), but not by low ADC values. CONCLUSIONS: Diffusion time dependence predicted tissue outcome in ischemic stroke more accurately than the ADC, and may be useful for predicting reperfusion benefit.
In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes an upper bound on the joint entropy of the distribution that generates the joint image data. Further, we derive the congealing method for groupwise registration by optimizing the profile likelihood in closed form, and using coordinate ascent, or iterative model refinement. We also describe a method for feature based registration in the same framework and demonstrate it on groupwise tractographic registration. In the second part of the article, we propose an approach to deep metric registration that implements maximum likelihood registration using deep discriminative classifiers. We show further that this approach can be used for maximum profile likelihood registration to discharge the need for well-registered training data, using iterative model refinement. We demonstrate that the method succeeds on a challenging registration problem where the standard mutual information approach does not perform well.
Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.
Using positron emission tomography, we recently demonstrated elevated brain levels of the 18kDa translocator protein (TSPO), a glial activation marker, in chronic low back pain (cLBP) patients, compared to healthy controls (HC). Here, we first sought to replicate the original findings in an independent cohort (15 cLBP, 37.8±12.5 y/o; 18 HC, 48.2±12.8 y/o). We then trained random forest (RF) machine learning algorithms based on TSPO imaging features combining discovery and replication cohorts (totaling 25 cLBP, 42.4±13.2 y/o; 27 HC, 48.9±12.6 y/o), in order to explore whether image features other than the mean contain meaningful information that might contribute to the discrimination of cLBP patients and HC. Feature importance was ranked usind SHapley Additive exPlanations (SHAP) values, and the classification performance (in terms of AUC values) of classifiers containing only the mean, other features, or all features was compared using the DeLong test. Both region-of-interest (ROI) and voxelwise analyses replicated the original observation of thalamic TSPO signal elevations in cLBP patients compared to HC (p's<0.05). The RF-based analyses revealed that while the mean is a discriminating feature, other features demonstrate similar level of importance, including the maximum, kurtosis and entropy.Our observations suggest that thalamic neuroinflammatory signal is a reproducible and discriminating feature for cLBP, further supporting a role for glial activation in human chronic low back pain, and the exploration of neuroinflammation as a therapeutic target for chronic pain. This work further shows that TSPO signal contains a richness of information that the simple mean might fail to capture completely.
PURPOSE: Diffusion-weighted MRI is sensitive to incoherent tissue motion, which may confound the measured signal and subsequent analysis. We propose a "motion-compensated" gradient waveform design for tensor-valued diffusion encoding that negates the effects bulk motion and incoherent motion in the ballistic regime. METHODS: Motion compensation was achieved by constraining the magnitude of gradient waveform moment vectors. The constraint was incorporated into a numerical optimization framework, along with existing constraints that account for b-tensor shape, hardware restrictions, and concomitant field gradients. We evaluated the efficacy of encoding and motion compensation in simulations, and we demonstrated the approach by linear and planar b-tensor encoding in a healthy heart in vivo. RESULTS: The optimization framework produced asymmetric motion-compensated waveforms that yielded b-tensors of arbitrary shape with improved efficiency compared with previous designs for tensor-valued encoding, and equivalent efficiency to previous designs for linear (conventional) encoding. Technical feasibility was demonstrated in the heart in vivo, showing vastly improved data quality when using motion compensation. The optimization framework is available online in open source. CONCLUSION: Our gradient waveform design is both more flexible and efficient than previous methods, facilitating tensor-valued diffusion encoding in tissues in which motion would otherwise confound the signal. The proposed design exploits asymmetric encoding times, a single refocusing pulse or multiple refocusing pulses, and integrates compensation for concomitant gradient effects throughout the imaging volume.