Atlas Space Research Articles

Diffusion MRI harmonization via personalized template mapping.

One fundamental challenge in diffusion magnetic resonance imaging (dMRI) harmonization is to disentangle the contributions of scanner-related effects from the variable brain anatomy for the observed imaging signals. Conventional harmonization methods rely on establishing an atlas space to resolve anatomical variability and generate a unified inter-site mapping function. However, this approach is limited in accounting for the misalignment of neuroanatomy that still widely persists even after registration, especially in regions close to cortical boundaries. To overcome this challenge, we propose a personalized framework in this paper to more effectively address the confounding from the misalignment of neuroanatomy in dMRI harmonization. Instead of using a common template representing site-effects for all subjects, the main novelty of our method is the adaptive computation of personalized templates for both source and target scanning sites to estimate the inter-site mapping function. We integrate our method with the rotation invariant spherical harmonics (RISH) features to achieve the harmonization of dMRI signals between sites. In our experiments, the proposed approach is applied to harmonize the dMRI data acquired from two scanning platforms: Siemens Prisma and GE MR750 from the Adolescent Brain Cognitive Development dataset and compared with a state-of-the-art method based on RISH features. Our results indicate that the proposed harmonization framework achieves superior performance not only in reducing inter-site variations due to scanner differences but also in preserving sex-related biological variability in original cohorts. Moreover, we assess the impact of harmonization on the estimation of fiber orientation distributions and show the robustness of the personalized harmonization procedure in preserving the fiber orientation of original dMRI signals.

Learning joint surface reconstruction and segmentation, from brain images to cortical surface parcellation.

Reconstructing and segmenting cortical surfaces from MRI is essential to a wide range of brain analyses. However, most approaches follow a multi-step slow process, such as a sequential spherical inflation and registration, which requires considerable computation times. To overcome the limitations arising from these multi-steps, we propose SegRecon, an integrated end-to-end deep learning method to jointly reconstruct and segment cortical surfaces directly from an MRI volume in one single step. We train a volume-based neural network to predict, for each voxel, the signed distances to multiple nested surfaces and their corresponding spherical representation in atlas space. This is, for instance, useful for jointly reconstructing and segmenting the white-to-gray-matter interface and the gray-matter-to-CSF (pial) surface. We evaluate the performance of our surface reconstruction and segmentation method with a comprehensive set of experiments on the MindBoggle, ABIDE and OASIS datasets. Our reconstruction error is found to be less than 0.52 mm and 0.97 mm in terms of average Hausdorff distance to the FreeSurfer generated surfaces. Likewise, the parcellation results show over 4% improvements in average Dice with respect to FreeSurfer, in addition to an observed drastic speed-up from hours to seconds of computation on a standard desktop station.

Motor control similarity between speakers saying "a souk" using inverse atlas tongue modeling.

Finite element models (FEM) of the tongue have facilitated speech studies through analysis of internal muscle forces indirectly derived from imaging data. In this work, we build a uniform hexahedral FEM of a tongue atlas constructed from magnetic resonance imaging data of a healthy population. The FEM is driven by inverse internal tongue tissue kinematics of speakers temporally aligned and deformed into the same atlas space, while performing the speech task "a souk" allowing muscle activation predictions. This work aims to investigate the commonalities in tongue motor strategies in the articulation of "a souk" predicted by the inverse tongue atlas model. Our findings report variability among five speakers for estimated muscle activations with a similarity index using a dynamic time warp function. Two speakers show similarity index > 0.9 and two others < 0.7 with respect to a reference speaker for most tongue muscles. The relative motion tracking error of the model is less than 2% which is promising for speech study applications.

A comprehensive array of gray matter labels for the MIITRA atlas: Interoperability with complementary atlases

AbstractBackgroundA comprehensive array of gyral‐based,cytoarchitecture‐based,and functional connectivity‐based gray matter labels were constructed in the Multichannel Illinois Institute of Technology &amp; Rush university Aging (MIITRA) atlas space in order to enhance the functionality of the atlas and its interoperability with complementary atlases.MethodT1w images from the 400 older adults included in the construction of the MIITRA atlas (50%male;64.9‐98.9 years of age) were processed with Freesurfer’s standard recon‐all pipeline. The Freesurfer outputs for all images were manually edited. Parcellations from Yeo,Desikan‐Killiany,HCP‐MMP,Brainnetome,Brodmann,Destrieux,Campbell,Flechsig,Mindboggle,Kleist,Smith,and EconomoCT atlases were projected onto the surface of each image and then converted to volumetric labels. The T1w images were also nonlinearly registered using ANTs‐SyN to the MNI152‐6thgen,MNI‐Colin,and the MNI‐2009b templates, and volumetric labels from Harvard‐Oxford,Julich,AAL3,Buckner,CoBrALab,CAREN,Striatum‐subdivision and Hippocampal‐subfields atlases were warped to the space of each image. The ANTs‐derived transformations applied on individual T1w images to build the MIITRA T1w template were used to map the corresponding gray matter labels from raw space to exact physical locations in the final MIITRA space. The performance of the MIITRA gray matter labels in segmentation of the gray matter of a separate group of 100 older adult individuals was evaluated in terms of label overlap, geometry, and dissimilarity when comparing the MIITRA labels warped to each individual’s space, to the respective reference labels from the source atlases.ResultExamples of the gray matter labels in MIITRA space are shown in Figure1. The measures of overlap between the warped and reference labels for each set of labels were generally high, with an average Dice coefficient of 0.78±0.3, average Jaccard coefficient of 0.59±0.27(Fig.2), sensitivity of 0.74±0.1, and specificity of 0.86±0.35(Fig.3). The label geometry showed a high correlation both in terms of the average volume of each label(correlation coefficient = 0.997,p‐value&lt;10−10) and in terms of the average surface area of each label(correlation coefficient = 0.991,p‐value&lt;10−10)(Fig.4). The values of dissimilarity were low, with an average volume error of 0.38±0.2(Fig.5).ConclusionThe gray matter labels in the MIITRA, in combination with the high‐resolution T1w template of the atlas, allowed segmentation of the gray matter of older adults that is in good agreement with the reference labels from the source atlases.

Traditional Village Building Extraction Based on Improved Mask R-CNN: A Case Study of Beijing, China

As an essential material carrier of cultural heritage, the accurate identification and effective monitoring of buildings in traditional Chinese villages are of great significance to the sustainable development of villages. However, along with rapid urbanization in recent years, many towns have experienced problems such as private construction, hollowing out, and land abuse, destroying the traditional appearance of villages. This study combines deep learning technology and UAV remote sensing to propose a high-precision extraction method for conventional village architecture. Firstly, this study constructs the first sample database of traditional village architecture based on UAV remote sensing orthophotos of eight representative villages in Beijing, combined with fine classification; secondly, in the face of the diversity and complexity of the built environment in traditional villages, we use the Mask R-CNN instance segmentation model as the basis and Path Aggregate Feature Pyramid Network (PAFPN) and Atlas Space Pyramid Pool (ASPP) as the main strategies to enhance the backbone model for multi-scale feature extraction and fusion, using data increment and migration learning as auxiliary means to overcome the shortage of labeled data. The results showed that some categories could achieve more than 91% accuracy, with average precision, recall, F1-score, and Intersection over Union (IoU) values reaching 71.3% (+7.8%), 81.9% (+4.6%), 75.7% (+6.0%), and 69.4% (+8.5%), respectively. The application practice in Hexi village shows that the method has good generalization ability and robustness, and has good application prospects for future traditional village conservation.

Quantifying Velopharyngeal Motion Variation in Speech Sound Production Using an Audio-Informed Dynamic MRI Atlas.

New developments in dynamic magnetic resonance imaging (MRI) facilitate high-quality data acquisition of human velopharyngeal deformations in real-time speech. With recently established speech motion atlases, group analysis is made possible via spatially and temporally aligned datasets in the atlas space from a desired population of interest. In practice, when analyzing motion characteristics from various subjects performing a designated speech task, it is observed that different subjects' velopharyngeal deformation patterns could vary during the pronunciation of the same utterance, regardless of the spatial and temporal alignment of their MRI. Since such variation can be subtle, identification and extraction of unique patterns out of these high-dimensional datasets is a challenging task. In this work, we present a method that computes and visualizes subtle deformation variation patterns as principal components of a subject group's dynamic motion fields in the atlas space. Coupled with the real-time speech audio recordings during image acquisition, the key time frames that contain maximum speech variations are identified by the principal components of temporally aligned audio waveforms, which in turn inform the temporal location of the maximum spatial deformation variation. Henceforth, the motion fields between the key frames and the reference frame for each subject are computed and warped into the common atlas space, enabling a direct extraction of motion variation patterns via quantitative analysis. The method was evaluated on a dataset of twelve healthy subjects. Subtle velopharyngeal motion differences were visualized quantitatively to reveal pronunciation-specific patterns among different subjects.

Unsupervised Registration Refinement for Generating Unbiased Eye Atlas.

With the confounding effects of demographics across large-scale imaging surveys, substantial variation is demonstrated with the volumetric structure of orbit and eye anthropometry. Such variability increases the level of difficulty to localize the anatomical features of the eye organs for populational analysis. To adapt the variability of eye organs with stable registration transfer, we propose an unbiased eye atlas template followed by a hierarchical coarse-to-fine approach to provide generalized eye organ context across populations. Furthermore, we retrieved volumetric scans from 1842 healthy patients for generating an eye atlas template with minimal biases. Briefly, we select 20 subject scans and use an iterative approach to generate an initial unbiased template. We then perform metric-based registration to the remaining samples with the unbiased template and generate coarse registered outputs. The coarse registered outputs are further leveraged to train a deep probabilistic network, which aims to refine the organ deformation in unsupervised setting. Computed tomography (CT) scans of 100 de-identified subjects are used to generate and evaluate the unbiased atlas template with the hierarchical pipeline. The refined registration shows the stable transfer of the eye organs, which were well-localized in the high-resolution (0.5 mm3) atlas space and demonstrated a significant improvement of 2.37% Dice for inverse label transfer performance. The subject-wise qualitative representations with surface rendering successfully demonstrate the transfer details of the organ context and showed the applicability of generalizing the morphological variation across patients.

Articulation differences of /s/ observed using inverse tongue atlas modeling

Depending on the vowel context, the consonant /s/ can be articulated by engaging different functional units of the tongue. An inverse finite element (FE) tongue model comprising hexahedral elements generated from a 4D statistical MRI atlas of 22 speakers performing the speech tasks “a geese” (tongue moving forwards) and “a souk” (tongue moving backwards) is used to study this articulatory behavior. The model uses a state-of-the-art inverse tracking controller to simulate the motion of internal tissue points of the different speakers deformed into the atlas space. Motion tracking is successfully carried out by minimizing the L2-norm of velocity error of FEM nodes using the Cottle–Dantzig algorithm. The results show that for “a-geese,” the utterance of /s/ in context of vowel /i/ showed increase in activation of tongue protruder muscles such as genioglossus posterior, floor muscle geniohyoid by ∼5% more than in the case of “a-souk.” In “a-souk” relative activity increased in the tongue retractor muscles mid- and anterior-genioglossus, and superior longitudinal by ∼4%. Our findings are consistent with subject-specific state-of-the-art models and with articulatory expectations. The inverse atlas tongue model can be further used to estimate such articulation behavior on extended datasets of subjects performing more variations of speech tasks.

Automatic detection and segmentation of postoperative cerebellar damage based on normalization.

Surgical resection is the gold standard in the treatment of pediatric posterior fossa tumors. However, surgical damage is often unavoidable and its association with postoperative complications is not well understood. A reliable localization and measure of cerebellar damage is fundamental to study the relationship between the damaged cerebellar regions and postoperative neurological outcomes. Existing cerebellum normalization methods are likely to fail on postoperative scans, therefore current approaches to measure postoperative damage rely on manual labelling. In this work, we develop a robust algorithm to automatically detect and measure cerebellum damage in postoperative 3D T1 magnetic resonance imaging (MRI). In our approach, normal brain tissues are first segmented using a Bayesian algorithm customized for postoperative scans. Next, the cerebellum is isolated by nonlinear registration of a whole-brain template to the native space. The isolated cerebellum is then normalized into the spatially unbiased atlas (SUIT) space using anatomical information derived from the previous step. Finally, the damage is detected in the atlas space by comparing the normalized cerebellum and the SUIT template. We evaluated our damage detection tool on postoperative scans of 153 patients with medulloblastoma based on inspection by human experts. We also designed a simulation to evaluate performance without human intervention and with an explicitly controlled and defined ground truth. Our results show that the approach performs adequately under various realistic conditions. We develop an accurate, robust, and fully automatic localization and measurement of cerebellar damage in the atlas space using postoperative MRI.

Understanding the role of amyloid on cerebral microvasculature in Alzheimer’s disease: A pre‐clinical Intra Voxel Incoherent Motion (IVIM) study

AbstractBackgroundVascular perturbation is emerging as an important contribution to Alzheimer’s disease (AD) trajectory. However, the role of microvascular alterations in pathophysiological processes remains unclear. We hypothesized a direct link between β‐amyloid (Aβ) and the cerebral microvasculature, leading to vascular stress, deteriorated blood‐brain barrier, and impaired microcirculation. The early detection of changes in the microvasculature can be used as an early biomarker to study underlying disease mechanisms. This study used intra‐voxel incoherent motion (IVIM) technique to quantitatively map functional capillaries related to blood volume fraction as surrogate marker for microvasculature alterations in an Alzheimer’s disease mouse model.MethodMale 5xFAD mice at 6‐months‐old and age‐matched littermates were scanned using high‐resolution IVIM optimized DWI. The DWI and anatomical images were acquired on a horizontal bore 9.4T Biospec micro‐MRI system equipped with a 1H cryogenic coil. 2D T2‐weightd images were acquired with a voxel size of 60x60x300 µm3 and DWIs were acquired with a voxel size of 120x120x300 µm3. Hippocampal subfields (CA1, CA2, CA3, DG, and Subiculum) in (Badhwar hippocampal) atlas space were transformed to individual T2W space and then linearly transformed to DWI space. IVIM maps (Apparent diffusion coefficient [D], pseudo‐diffusion coefficient [D*], and perfusion fraction [fIVIM]) were calculated (voxel‐wise) using a bi‐exponential IVIM model. The mean fIVIM of individual subfields were used for group‐wise comparison, and the Hedges’g were used for effect‐size analysis.ResultOur preliminary results showed regional differences in microvascular perfusion fraction between control (n=6) and 5xFAD (n=8) mice at 6 months of age. fIVIM was significantly lower in DG (p&lt;0.05; Hedge’s g =‐1.4; 95% CI: ‐2.552, ‐0.202) and in Subiculum (p&lt;0.05; Hedge’s g=‐1.3; 95% CI: ‐2.54, ‐0.194) of 5xFAD compared to wild‐type mice. The fIVIM remains unchanged in other subfields. The decreased fIVIM suggested that deficits in the capillary network of DG and Subiculum develop earlyConclusionThese findings support the sensitivity of IVIM to detect circulatory deficits in the hippocampal microvasculature associated with Aβ pathology. The IVIM may provide new insight into cerebral small vessel health in an early AD stage without a contrast agent administration and can be readily translated to the clinic.

Combining high‐resolution ex vivo MRI and histopathology to identify medial temporal lobe atrophy patterns specific to tau pathology in Alzheimer’s Disease

AbstractBackgroundTau pathology in the medial temporal lobe (MTL) is closely linked to neurodegeneration, and is an early pathological change associated with Alzheimer’s disease (AD). MRI measures of MTL neurodegeneration have proven to be sensitive to change during preclinical AD. Current measures are confounded by the presence of non‐AD pathologies (e.g., TDP‐43, ageing). Here, we combine ex vivo imaging with histopathological ratings of tau and TDP‐43 to identify fine‐grained MTL atrophy patterns specific to tau. Such an analysis could be used to define MTL “hotspots” where in vivo measures of neurodegeneration are expected to be strongly associated with tau, potentially enabling the development of biomarkers that are more effective during early AD clinical trials.MethodEx vivo MRI scans (0.2x0.2x0.2mm3, 9.4T) of human MTL specimens were combined using a customized registration approach to construct a 3D atlas. Using serial histology available in a subset of specimens (n = 11), MTL subregions in the atlas were labelled based on cytoarchitecture (Ravikumar et al., 2021) (Figure 1A). To perform thickness analysis, 29 specimens containing a primary diagnosis of AD or primary age‐related tauopathy were registered to the atlas (13 specimens were registered to the atlas during atlas construction, and 16 were registered to the atlas after it was constructed) (Figure 1B, Table 1). Using histopathology measures of tau and TDP‐43 pathology (based on contralateral sampling), we investigated the association between tau and thickness by fitting a linear model (with age/TDP‐43 as covariates) at each point along the MTL and SRLM surface.ResultPointwise thickness analysis reveals significant atrophy patterns in the transentorhinal region and SRLM. When excluding age from the model, stronger tau associations are observed in the SRLM, entorhinal cortex, and extending further towards Brodmann Area 35 (Figure 2).ConclusionOur findings are consistent with early Braak stages but suggest that covarying for age may be obscuring some associations due to tau since age and tau burden have a highly correlated relationship (Figure 3). In future work, quantitative maps of NFT burden will be mapped from serial pathology images into atlas space, allowing us to characterize NFT distribution in 3D (Yushkevich et al., 2021).

Differential vulnerability of hippocampal microstructure in 5xFAD mouse model of Alzheimer’s Disease: A dMRI study

AbstractBackgroundHippocampal subfields exhibit differential vulnerabilities to Alzheimer’s disease (AD) associated pathologies. These pathological processes extensively attenuate the structural and functional interconnectivities of the subfields. Abnormal accumulation of beta‐amyloid (Aβ) is considered a primary hallmark of AD. Histopathological studies suggest region‐specific accumulation of Aβ in hippocampal subfields. The aim of the current study is to understand the subfield‐specific in‐vivo microstructural changes in hippocampus due to Aβ load in 2.5 months old 5xFAD mouse model of AD.MethodTwo and a half month old male 5xFAD and age‐matched littermate controls were used in this study. DWI and anatomical images were acquired on a horizontal bore 9.4T Biospec micro‐MRI system equipped with a H1 cryogenic surface coil. 2D T2‐weighted anatomical images were acquired using the following parameters: TE/TR=43.67/7500 ms, voxel size=117x117x250 µm3 and number of slices=20. DWIs were acquired using multi‐shot dual‐spin‐echo EPI sequence using following parameters: TE/TR=34.78/3000 ms, voxel size=117x117x250 µm3, number of slices=20, 3 b‐values (650, 1200 and 2500) s/mm2, 5 b0 per shell, 12 diffusion encoding direction for b=650, 60 for b=1200 and 80 for b=2500 s/mm2. T2‐W images were nonlinearly registered to Badhwar hippocampal atlas. Hippocampal subfields (CA1, CA2, CA3, DG and Subiculum) in atlas space were transformed to individual T2W space and then linearly transformed to DWI space. Multi‐compartment microstructural imaging was performed using Cortical‐NODDI. Volume fraction of isotropic water diffusivity (VFISO) and intracellular volume fraction (VFIC) maps were calculated. The regional mean values (VFIC and VFISO) of individual subfields were extracted and used in general linear model for group‐comparisons.ResultThe results of the study suggest group‐level differences between control and 5xFAD mice in cornu ammonis regions. Compared to 5xFAD, VFIC in littermate control mice was significantly higher in CA1 (p&lt;0.05; d=1.47), CA2 (p&lt;0.05; d=1.11) and CA3 (p&lt;0.05; d=1.54). VFISO in DG was significantly lower in littermate control compared to 5xFAD mice (p&lt;0.05; d=1.31).ConclusionThe study demonstrates the importance of utilizing tissue‐specific microstructural imaging in detecting Aβ associated microstructural alterations in multiple hippocampal subfields. This study demonstrates that Aβ alters the hippocampal microstructure. study also suggests on the differential effect of Aβ pathology on hippocampal microstructure.

A Semi-Automated Workflow for Brain Slice Histology Alignment, Registration, and Cell Quantification (SHARCQ).

Tools for refined cell-specific targeting have significantly contributed to understanding the characteristics and dynamics of distinct cellular populations by brain region. While advanced cell-labeling methods have accelerated the field of neuroscience, specifically in brain mapping, there remains a need to quantify and analyze the data. Here, by modifying a toolkit that localizes electrodes to brain regions (SHARP-Track; Slice Histology Alignment, Registration, and Probe-Track analysis), we introduce a post-imaging analysis tool to map histological images to established mouse brain atlases called SHARCQ (Slice Histology Alignment, Registration, and Cell Quantification). The program requires MATLAB, histological images, and either a manual or automatic cell count of the unprocessed images. SHARCQ simplifies the post-imaging analysis pipeline with a step-by-step GUI. We demonstrate that SHARCQ can be applied for a variety of mouse brain images, regardless of histology technique. In addition, SHARCQ rectifies discrepancies in mouse brain region borders between atlases by allowing the user to select between the Allen Brain Atlas or the digitized and modified Franklin-Paxinos Atlas for quantifying cell counts by region. SHARCQ produces quantitative and qualitative data, including counts of brain-wide region populations and a 3D model of registered cells within the atlas space. In summary, SHARCQ was designed as a neuroscience post-imaging analysis tool for cell-to-brain registration and quantification with a simple, accessible interface. All code is open-source and available for download (https://github.com/wildrootlab/SHARCQ).

Abstract 127: Anatomical Predictors Of Malignant Cerebral Edema Using Voxel-based Lesion Symptom Mapping

Introduction: Prior publications indicate an increased risk of developing malignant cerebral edema in acute ischemic stroke patients with temporal lobe involvement. We examined on a voxel-by-voxel basis whether topographic locations of baseline diffusion and perfusion weighted MRI lesions could predict subsequent need for treatment of malignant cerebral edema with either decompressive hemicraniectomy (DHC) or hyperosmolar therapy (HT). Methods: We used a registry of 898 patients evaluated for acute treatment for suspected large vessel occlusion (LVO) stroke. Fifty-nine cases, receiving either DHC and/or HT and having sufficient data for evaluation, were manually matched with 59 controls for age, lesion size, and Thrombolysis in Cerebral Infarction (TICI) score. Binary masks of ADC + Tmax &gt;6s lesions generated from automated RAPID software output were created. Lesions were co-registered to standard MNI atlas space. Voxel-based lesion symptom mapping (Version 2.55) was used to generate statistical maps of lesion contribution to malignant cerebral edema formation. Maps were thresholded to P&lt;0.01 on basis of cluster size and permutation method. Hemispheres were combined to increase statistical power. Results and Conclusions: 118 patients were analyzed. After controlling for age, TICI score, and lesion volume, only punctate regions of the parieto-occipital lobe were found to be mildly predictive of the need for either DHC or HT (T-scores 2.5-3, p&lt;0.01). There does not appear to be any significant topographic region of the brain involved on baseline diffusion-perfusion MRI that predicts subsequent need for treatment of malignant cerebral edema in patients with LVO stroke.

Tools for efficient analysis of neurons in a 3D reference atlas of whole mouse spinal cord

SummaryTo fill the prevailing gap in methodology for whole spinal cord (SC) analysis, we have (1) designed scaffolds (SpineRacks) that facilitate efficient and ordered cryo-sectioning of the entire SC in a single block, (2) constructed a 3D reference atlas of adult mouse SC, and (3) developed software (SpinalJ) to register images of sections and for standardized analysis of cells and projections in atlas space. We have verified mapping accuracies for known neurons and demonstrated the usefulness of this platform to reveal unknown neuronal distributions. Together, these tools provide high-throughput analyses of whole mouse SC and enable direct comparison of 3D spatial information between animals and studies.

CASE SERIES OF CHORDOMA IN THREE DIFFERENT LOCATIONS: CLIVAL, CERVICAL SPINE, AND SACROCOCCYGEAL

Background: Chordoma is a relatively rare malignant, slow growing, and locally aggressive tumour that arise from embryonic remnants of the primitive notochord. It accounts for 4% of all primary bone tumours. Chordomas can present a diagnostic challenge due to the rare occurrence and a tendency to involve any region within the craniospinal axis. Case Presentation: First case, 25-y-o female with cephalgia. CT scan shows lytic clival bone destruction. MRI show lobulated isointense mass on T1-WI, hyperintense on T2-WI and heterogenous contrast-enhancement. The mass extending to the many structures, destructing the Atlas, infiltrating perivertebral and epidural space. The patient received endoscopic tumour excision and confirmed as chordoma. Second case, 50-y-o male with tetraparesis, urinary and bowel incontinence. CT scan shows destruction of the first and second cervical spine. MRI shows the same characteristics as the first case. The mass compressing spinal cord, infiltrating the right perivertebral and orophrayngeal space. The patient received laminectomy and confirmed as chordoma. Third case, 50-y-o male with large soft tissue bulging at gluteal region. CT scan 3D surface rendering shows destruction of the first sacral spine until coccygeal. MRI shows the same characteristics as the first and second case. The mass infiltrating gluteal and piriform muscles, encasing left internal iliac artery. The mass further pathologically confirmed as chordoma. Conclusion: Patients might experience different symptoms. Imaging studies classically show a destructive bone mass with lobular growth, high water content (high T2 signal intensity), foci of haemorrhage and calci?cation. Differential diagnosis are also depend on the location. Physaliferous cells is the hallmark of chordoma. Surgical intervention is the recommended treatment modality.

Common patterns of variation between femoral and tibial cartilage maps and baseline features from the osteoarthritis initiative

Common patterns of variation between femoral and tibial cartilage maps and baseline features from the osteoarthritis initiative

Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks.

Spatiotemporal (4D) cortical surface atlas during infancy plays an important role for surface-based visualization, normalization and analysis of the dynamic early brain development. Conventional atlas construction methods typically rely on classical group-wise registration on sub-populations and ignore longitudinal constraints, thus having three main issues: 1) constructing templates at discrete time points; 2) resulting in longitudinal inconsistency among different age's atlases; and 3) taking extremely long runtime. To address these issues, in this paper, we propose a fast unsupervised learning-based surface atlas construction framework incorporating longitudinal constraints to enforce the within-subject temporal correspondence in the atlas space. To well handle the difficulties of learning large deformations, we propose a multi-level multimodal spherical registration network to perform cortical surface registration in a coarse-to-fine manner. Thus, only small deformations need to be estimated at each resolution level using the registration network, which further improves registration accuracy and atlas quality. Our constructed 4D infant cortical surface atlas based on 625 longitudinal scans from 291 infants is temporally continuous, in contrast to the state-of-the-art UNC 4D Infant Surface Atlas, which only provides the atlases at a few discrete sparse time points. By evaluating the intra- and inter-subject spatial normalization accuracy after alignment onto the atlas, our atlas demonstrates more detailed and fine-grained cortical patterns, thus leading to higher accuracy in surface registration.

Potential precision of terrain measurement using space lidars

Laser remote sensing technologies are being actively developed and used, for instance, for modelling the terrain of the surface of the Earth, the Moon and other planets of the Solar System. Theoretical aspect of these projects is underdeveloped and not related to the measurement precision assessment. The article studies the calculation of the potential precision of range measurement and presents a method of calculation of the a space-based lidar parameters based on the condition of providing the set range measurement precision. It produces the equations that connect the potentially achievable precision of terrain (range) measurement and the receiver output signal-to-noise ratio to the lidar parameters and the parameters that determine the external conditions of such measurement. These equations form the foundation for a method of determining the lidar parameters and the requirements acting as the input data for a lidar development. It demonstrates that the space lidar altimeters are rationally associated with the term of the “energy space resolution” as the minimum size of the area of the terrain under resolution under the established range measurement error. The calculations carried out with the said method made it possible to assess the precision parameters of the currently used ATLAS space lidar. According to our data, a lidar is capable of providing the range measurement precision of several centimetres, provided that the objects are located within the area of several tens of cm2. The collected results bring the conclusion of the convergence of the results of the lidar precision calculation, carried out with the developed method and using the data published by NASA.

Abstract WP90: Voxel-Based Lesion Symptom Mapping (VLSM) of NIH Stroke Scale Subscore Deficits

Background: Studies identifying brain regions required to preserve specific neurologic functions after stroke typically analyze infarct volume or location. Lesion topography studies have found that, independent of volume, involvement of specific regions have greater impact on clinical outcome than others, suggesting location-determined differences in eloquence. However, most anatomy-based studies define location broadly. VLSM is an imaging analysis technique that establishes a relationship between precise lesion location and clinical deficit. A group of patients with a symptom (e.g. aphasia) is analyzed; every voxel on each patient’s MRI is evaluated for lesion presence or absence. Each voxel is assigned a t-statistic, and a statistical map of all lesioned voxels is generated to pinpoint regions most strongly associated with the deficit; a high t-score indicates a lesion in that voxel has a significant effect on the specified symptom. We generated VLSM maps for 6 NIHSS subscores (aphasia, right- and left-limb weakness, and sensory loss) in a novel characterization to be used in future research. Methods: 172 acute ischemic stroke (AIS) patients with large vessel occlusions (LVOs) were analyzed. Binary masks of infarcts on D5 FLAIR sequences were created. Lesions were coregistered to standard MNI atlas space. VLSM V2.55 was used to generate statistical maps of lesion contribution to clinical deficit. Maps were thresholded to p&lt;0.001 on basis of cluster size and permutation method. A symptom reference area was defined as a region on which voxels had a t-score &gt;3.14. Results: VLSM maps with voxelwise thresholds of p&lt;0.001 were generated for 6 NIHSS categories (examples in figure). Conclusions: VLSM successfully generated unique maps of 6 NIHSS subscore deficits. These maps will be used to study patients presenting with perfusion/diffusion mismatch to predict the potential for resolution of specific deficits with reperfusion therapy and aid in prognostication.