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: Diffusion encoding with asymmetric gradient waveforms is appealing because the asymmetry provides superior efficiency. However, concomitant gradients may cause a residual gradient moment at the end of the waveform, which can cause significant signal error and image artifacts. The purpose of this study was to develop an asymmetric waveform designs for tensor-valued diffusion encoding that is not sensitive to concomitant gradients. METHODS: The "Maxwell index" was proposed as a scalar invariant to capture the effect of concomitant gradients. Optimization of "Maxwell-compensated" waveforms was performed in which this index was constrained. Resulting waveforms were compared to waveforms from literature, in terms of the measured and predicted impact of concomitant gradients, by numerical analysis as well as experiments in a phantom and in a healthy human brain. RESULTS: Maxwell-compensated waveforms with Maxwell indices below 100 (mT/m) ms showed negligible signal bias in both numerical analysis and experiments. By contrast, several waveforms from literature showed gross signal bias under the same conditions, leading to a signal bias that was large enough to markedly affect parameter maps. Experimental results were accurately predicted by theory. CONCLUSION: Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient diffusion encoding that negates the effects of concomitant fields while enabling arbitrary shapes of the b-tensor. This waveform design is especially useful in combination with strong gradients, long encoding times, thick slices, simultaneous multi-slice acquisition, and large FOVs.
Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and its collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach and describe the brain deformation in mechanical terms (such as displacements, strains, and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading, and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady-state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.
Jean-Jacques Lemaire, Antonio De Salles, Guillaume Coll, Youssef El Ouadih, Rémi Chaix, Jérôme Coste, Franck Durif, Nikos Makris, and Ron Kikinis. 8/2019. “MRI Atlas of the Human Deep Brain.” Front Neurol, 10, Pp. 851.Abstract
Mastering detailed anatomy of the human deep brain in clinical neurosciences is challenging. Although numerous pioneering works have gathered a large dataset of structural and topographic information, it is still difficult to transfer this knowledge into practice, even with advanced magnetic resonance imaging techniques. Thus, classical histological atlases continue to be used to identify structures for stereotactic targeting in functional neurosurgery. Physicians mainly use these atlases as a template co-registered with the patient's brain. However, it is possible to directly identify stereotactic targets on MRI scans, enabling personalized targeting. In order to help clinicians directly identify deep brain structures relevant to present and future medical applications, we built a volumetric MRI atlas of the deep brain (MDBA) on a large scale (infra millimetric). Twelve hypothalamic, 39 subthalamic, 36 telencephalic, and 32 thalamic structures were identified, contoured, and labeled. Nineteen coronal, 18 axial, and 15 sagittal MRI plates were created. Although primarily designed for direct labeling, the anatomic space was also subdivided in twelfths of AC-PC distance, leading to proportional scaling in the coronal, axial, and sagittal planes. This extensive work is now available to clinicians and neuroscientists, offering another representation of the human deep brain ([https://hal.archives-ouvertes.fr/] [hal-02116633]). The atlas may also be used by computer scientists who are interested in deciphering the topography of this complex region.
Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (US) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy US. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. To improve accuracy of registration, we use high-dimensional texture attributes instead of image intensities and propose to replace the standard difference-based attribute matching with correlation-based attribute matching. We also present a strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images. We optimize key parameters across independent MR-iUS brain tumor datasets acquired at three different institutions, with a total of 43 tumor patients and 758 corresponding landmarks to validate the registration algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, our algorithm was able to reduce landmark errors prior to registration in three data sets (5.37 ± 4.27, 4.18 ± 1.97 and 6.18 ± 3.38 mm, respectively) to a consistently low level (2.28 ± 0.71, 2.08 ± 0.37 and 2.24 ± 0.78 mm, respectively). Our algorithm is compared to 15 other algorithms that have been previously tested on MR-iUS registration and it is competitive with the state-of-the-art on multiple datasets. We show that our algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). We further characterized landmark errors according to brain regions and tumor types, a topic so far missing in the literature. We found that landmark errors were higher in high-grade than low-grade glioma patients, and higher in tumor regions than in other brain regions.
PURPOSE: In image-guided surgery for glioma removal, neurosurgeons usually plan the resection on images acquired before surgery and use them for guidance during the subsequent intervention. However, after the surgical procedure has begun, the preplanning images become unreliable due to the brain shift phenomenon, caused by modifications of anatomical structures and imprecisions in the neuronavigation system. To obtain an updated view of the resection cavity, a solution is to collect intraoperative data, which can be additionally acquired at different stages of the procedure in order to provide a better understanding of the resection. A spatial mapping between structures identified in subsequent acquisitions would be beneficial. We propose here a fully automated segmentation-based registration method to register ultrasound (US) volumes acquired at multiple stages of neurosurgery. METHODS: We chose to segment sulci and falx cerebri in US volumes, which remain visible during resection. To automatically segment these elements, first we trained a convolutional neural network on manually annotated structures in volumes acquired before the opening of the dura mater and then we applied it to segment corresponding structures in different surgical phases. Finally, the obtained masks are used to register US volumes acquired at multiple resection stages. RESULTS: Our method reduces the mean target registration error (mTRE) between volumes acquired before the opening of the dura mater and during resection from 3.49 mm (± 1.55 mm) to 1.36 mm (± 0.61 mm). Moreover, the mTRE between volumes acquired before opening the dura mater and at the end of the resection is reduced from 3.54 mm (± 1.75 mm) to 2.05 mm (± 1.12 mm). CONCLUSION: The segmented structures demonstrated to be good candidates to register US volumes acquired at different neurosurgical phases. Therefore, our solution can compensate brain shift in neurosurgical procedures involving intraoperative US data.