An Eulerian PDE approach for computing tissue thickness

Automated measurement and grading of knee cartilage thickness: a deep learning-based approach

Background

Knee cartilage is the most crucial structure in the knee, and the reduction of cartilage thickness is a significant factor in the occurrence and development of osteoarthritis. Measuring cartilage thickness allows for a more accurate assessment of cartilage wear, but this process is relatively time-consuming. Our objectives encompass using various DL methods to segment knee cartilage from MRIs taken with different equipment and parameters, building a DL-based model for measuring and grading knee cartilage, and establishing a standardized database of knee cartilage thickness.

Methods

In this retrospective study, we selected a mixed knee MRI dataset consisting of 700 cases from four datasets with varying cartilage thickness. We employed four convolutional neural networks-UNet, UNet++, ResUNet, and TransUNet-to train and segment the mixed dataset, leveraging an extensive array of labeled data for effective supervised learning. Subsequently, we measured and graded the thickness of knee cartilage in 12 regions. Finally, a standard knee cartilage thickness dataset was established using 291 cases with ages ranging from 20 to 45 years and a Kellgren-Lawrence grading of 0.

Results

The validation results of network segmentation showed that TransUNet performed the best in the mixed dataset, with an overall dice similarity coefficient of 0.813 and an Intersection over Union of 0.692. The model's mean absolute percentage error for automatic measurement and grading after segmentation was 0.831. The experiment also yielded standard knee cartilage thickness, with an average thickness of 1.98 mm for the femoral cartilage and 2.14 mm for the tibial cartilage.

Conclusion

By selecting the best knee cartilage segmentation network, we built a model with a stronger generalization ability to automatically segment, measure, and grade cartilage thickness. This model can assist surgeons in more accurately and efficiently diagnosing changes in patients' cartilage thickness.

Computing thickness of irregularly-shaped thin walls using a locally semi-implicit scheme with extrapolation to solve the Laplace equation: Application to the right ventricle

Cardiac Magnetic Resonance (CMR) Imaging is currently considered the gold standard imaging modality in cardiology. However, it is accompanied by a tradeoff between spatial resolution and acquisition time. Providing accurate measures of thin walls relative to the image resolution may prove challenging. One such anatomical structure is the cardiac right ventricle. Methods for measuring thickness of wall-like anatomical structures often rely on the Laplace equation to provide point-to-point correspondences between both boundaries. This work presents limex, a novel method to solve the Laplace equation using ghost nodes and providing extrapolated values, which is tested on three different datasets: a mathematical phantom, a set of biventricular segmentations from CMR images of ten pigs and the database used at the RV Segmentation Challenge held at MICCAI'12. Thickness measurements using the proposed methodology are more accurate than state-of-the-art methods, especially with the coarsest image resolutions, yielding mean L1 norms of the error between 43.28% and 86.52% lower than the second-best methods on the different test datasets. It is also computationally affordable. Limex has outperformed other state-of-the-art methods in classifying RV myocardial segments by their thickness.

CartiMorph: A framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient ρ∈[0.82,0.97]), surface area (ρ∈[0.82,0.98]) and volume (ρ∈[0.89,0.98]) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.

Transmural fibre orientations based on Laplace-Dirichlet-Rule-Based-Methods and their influence on human heart simulations

It is well known that the orthotropic tissue structure decisively influences the mechanical and electrical properties of the heart. Numerous approaches to compute the orthotropic tissue structure in computational heart models have been developed in the past decades. In this study, we investigate to what extent different Laplace-Dirichlet-Rule-Based-Methods (LDRBMs) influence the local orthotropic tissue structure and thus the electromechanical behaviour of the subsequent cardiac simulation. In detail, we are utilising three Laplace-Dirichlet-Rule-Based-Methods and compare: (i) the local myofibre orientation; (ii) important global characteristics (ejection fraction, peak pressure, apex shortening, myocardial volume reduction, fractional wall thickening); (iii) local characteristics (active fibre stress, fibre strain). We observe that the orthotropic tissue structures for the three LDRBMs show significant differences in the local myofibre orientation. The global characteristics myocardial volume reduction and peak pressure are rather insensitive to a change in local myofibre orientation, while the ejection fraction is moderately influenced by the different LDRBMs. Moreover, the apical shortening and fractional wall thickening exhibit a sensitive behaviour to a change in the local myofibre orientation. The highest sensitivity can be observed for the local characteristics.

Evolving Mural Defects, Dilatation, and Biomechanical Dysfunction in Angiotensin II-Induced Thoracic Aortopathies

BACKGROUND

Thoracic aortopathy associates with extracellular matrix remodeling and altered biomechanical properties. We sought to quantify the natural history of thoracic aortopathy in a common mouse model and to correlate measures of wall remodeling such as aortic dilatation or localized mural defects with evolving microstructural composition and biomechanical properties of the wall.

METHODS

We combined a high-resolution multimodality imaging approach (panoramic digital image correlation and optical coherence tomography) with histopathologic examinations and biaxial mechanical testing to correlate spatially, for the first time, macroscopic mural defects and medial degeneration within the ascending aorta with local changes in aortic wall composition and mechanical properties.

RESULTS

Findings revealed strong correlations between local decreases in elastic energy storage and increases in circumferential material stiffness with increasing proximal aortic diameter and especially mural defect size. Mural defects tended to exhibit a pronounced biomechanical dysfunction that is driven by an altered organization of collagen and elastic fibers.

CONCLUSIONS

While aneurysmal dilatation is often observed within particular segments of the aorta, dissection and rupture initiate as highly localized mechanical failures. We show that wall composition and material properties are compromised in regions of local mural defects, which further increases the dilatation and overall structural vulnerability of the wall. Identification of therapies focused on promoting robust collagen accumulation may protect the wall from these vulnerabilities and limit the incidence of dissection and rupture.

Characterization of surface motion patterns in highly deformable soft tissue organs from dynamic MRI: An application to assess 4D bladder motion

BACKGROUND AND OBJECTIVES

Dynamic Magnetic Resonance Imaging (MRI) may capture temporal anatomical changes in soft tissue organs with high-contrast but the obtained sequences usually suffer from limited volume coverage which makes the high-resolution reconstruction of organ shape trajectories a major challenge in temporal studies. Because of the variability of abdominal organ shapes across time and subjects, the objective of the present study is to go towards 3D dense velocity measurements to fully cover the entire surface and to extract meaningful features characterizing the observed organ deformations and enabling clinical action or decision.

METHODS

We present a pipeline for characterization of bladder surface dynamics during deep respiratory movements. For a compact shape representation, the reconstructed temporal volumes were first used to establish subject-specific dynamical 4D mesh sequences using the large deformation diffeomorphic metric mapping (LDDMM) framework. Then, we performed a statistical characterization of organ dynamics from mechanical parameters such as mesh elongations and distortions. Since we refer to organs as non-flat surfaces, we have also used the mean curvature change as metric to quantify surface evolution. However, the numerical computation of curvature is strongly dependant on the surface parameterization (i.e. the mesh resolution). To cope with this dependency, we employed a non-parametric method for surface deformation analysis. Independent of parameterization and minimizing the length of the geodesic curves, it stretches smoothly the surface curves towards a sphere by minimizing a Dirichlet energy. An Eulerian PDE approach is used to derive a shape descriptor from the curve-shortening flow. Intercorrelations between individuals' motion patterns are computed using the Laplace-Beltrami Operator (LBO) eigenfunctions for spherical mapping.

RESULTS

Application to extracting characterization correlation curves for locally-controlled simulated shape trajectories demonstrates the stability of the proposed shape descriptor. Its usability was shown on MRI acquired for seven healthy participants for which the bladder was highly deformed by maximum of inspiration. As expected, the study showed that deformations occured essentially on the top lateral regions.

CONCLUSION

Promising results were obtained, showing the organ in its 3D complexity during deformation due to strain conditions. Smooth genus-0 manifold reconstruction from sparse dynamic MRI data is employed to perform a statistical shape analysis for the determination of bladder deformation.

Scleral Lens Thickness and Corneal Edema Under Open Eye Conditions

PURPOSE

To examine the relationship between lens thickness and central corneal edema during short-term open-eye scleral lens wear, and to compare these empirical edema measurements with theoretical modelling.

METHODS

Nine participants (mean age 30 years) with normal corneas wore scleral lenses {Dk 141×10-11 cm3 O2 [cm]/([sec] [cm2] [mm Hg])} under open-eye conditions on separate days with nominal center thicknesses of 150, 300, 600, and 1,200 μm. Epithelial, stromal, and total corneal edema were measured using high-resolution optical coherence tomography immediately after lens application and after 90 min of wear, before lens removal.

RESULTS

Central corneal edema was primarily stromal in nature and increased with increasing central lens thickness. The mean±standard error total corneal edema was 1.14±0.22%, 1.36±0.26%, 1.74±0.30%, and 2.13±0.24% for the 150, 300, 600, and 1,200 μm lenses, respectively. A significant difference in stromal and total corneal edema was observed between the 1,200 and 150 μm thickness lenses only (both P<0.05). Theoretical modelling overestimated the magnitude of central corneal edema and the influence of central lens thickness when the scleral lens Dk/t was less than 20.

CONCLUSION

Scleral lens-induced central corneal edema during short-term open-eye lens wear increases with increasing central lens thickness. Theoretical models overestimated the effect of increasing scleral lens thickness upon central corneal edema for higher lens thickness values (lens Dk/t<20) when controlling for initial central fluid reservoir thickness.

Comparing Accuracies of Length-Type Geographic Atrophy Growth Rate Metrics Using Atrophy-Front Growth Modeling

Purpose

To compare the accuracies of the previously proposed square-root-transformed and perimeter-adjusted metrics for estimating length-type geographic atrophy (GA) growth rates.

Design

Cross-sectional and simulation-based study.

Participants

Thirty-eight eyes with GA from 27 patients.

Methods

We used a previously developed atrophy-front growth model to provide analytical and numerical evaluations of the square-root-transformed and perimeter-adjusted growth rate metrics on simulated and semisimulated GA growth data.

Main Outcome Measures

Comparison of the accuracies of the square-root-transformed and perimeter-adjusted metrics on simulated and semisimulated GA growth data.

Results

Analytical and numerical evaluations showed that the accuracy of the perimeter-adjusted metric is affected minimally by baseline lesion area, focality, and circularity over a wide range of GA growth rates. Average absolute errors of the perimeter-adjusted metric were approximately 20 times lower than those of the square-root-transformed metrics, per evaluation on a semisimulated dataset with growth rate characteristics matching clinically observed data.

Conclusions

Length-type growth rates have an intuitive, biophysical interpretation that is independent of lesion geometry, which supports their use in clinical trials of GA therapeutics. Taken in the context of prior studies, our analyses suggest that length-type GA growth rates should be measured using the perimeter-adjusted metric, rather than square-root-transformed metrics.

A Transmural Path Model Improves the Definition of the Orthotropic Tissue Structure in Heart Simulations

In the past decades, the structure of the heart, human as well as other species, has been explored in a detailed way, e.g., via histological studies or diffusion tensor magnetic resonance imaging. Nevertheless, the assignment of the characteristic orthotropic structure in a patient-specific finite element model remains a challenging task. Various types of rule-based models, which define the local fiber and sheet orientation depending on the transmural depth, have been developed. However, the correct assessment of the transmural depth is not trivial. Its accuracy has a substantial influence on the overall mechanical and electrical properties in rule-based models. The main purpose of this study is the development of a finite element-based approach to accurately determine the transmural depth on a general unstructured grid. Instead of directly using the solution of the Laplace problem as the transmural depth, we make use of a well-established model for the assessment of the transmural thickness. It is based on two hyperbolic first-order partial differential equations for the definition of a transmural path, whereby the transmural thickness is defined as the arc length of this path. Subsequently, the transmural depth is determined based on the position on the transmural path. Originally, the partial differential equations were solved via finite differences on structured grids. In order to circumvent the need of two grids and mapping between the structured (to determine the transmural depth) and unstructured (electromechanical heart simulation) grids, we solve the equations directly on the same unstructured tetrahedral mesh. We propose a finite-element-based discontinuous Galerkin approach. Based on the accurate transmural depth, we assign the local material orientation of the orthotropic tissue structure in a usual fashion. We show that this approach leads to a more accurate definition of the transmural depth. Furthermore, for the left ventricle, we propose functions for the transmural fiber and sheet orientation by fitting them to literature-based diffusion tensor magnetic resonance imaging data. The proposed functions provide a distinct improvement compared to existing rules from the literature.

Cobiveco: Consistent biventricular coordinates for precise and intuitive description of position in the heart - with MATLAB implementation

Ventricular coordinates are widely used as a versatile tool for various applications that benefit from a description of local position within the heart. However, the practical usefulness of ventricular coordinates is determined by their ability to meet application-specific requirements. For regression-based estimation of biventricular position, for example, a symmetric definition of coordinate directions in both ventricles is important. For the transfer of data between different hearts as another use case, the consistency of coordinate values across different geometries is particularly relevant. To meet these requirements, we compare different approaches to compute coordinates and present Cobiveco, a symmetric, consistent and intuitive biventricular coordinate system that builds upon existing coordinate systems, but overcomes some of their limitations. A novel one-way transfer error is introduced to assess the consistency of the coordinates. Normalized distances along bijective trajectories between two boundaries were found to be superior to solutions of Laplace's equation for defining coordinate values, as they show better linearity in space. Evaluation of transfer and linearity errors on 36 patient geometries revealed a more than 4-fold improvement compared to a state-of-the-art method. Finally, we show two application examples underlining the relevance for cardiac data processing. Cobiveco MATLAB code is available under a permissive open-source license.

Time-efficient three-dimensional transmural scar assessment provides relevant substrate characterization for ventricular tachycardia features and long-term recurrences in ischemic cardiomyopathy

Delayed gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging requires novel and time-efficient approaches to characterize the myocardial substrate associated with ventricular arrhythmia in patients with ischemic cardiomyopathy. Using a translational approach in pigs and patients with established myocardial infarction, we tested and validated a novel 3D methodology to assess ventricular scar using custom transmural criteria and a semiautomatic approach to obtain transmural scar maps in ventricular models reconstructed from both 3D-acquired and 3D-upsampled-2D-acquired LGE-CMR images. The results showed that 3D-upsampled models from 2D LGE-CMR images provided a time-efficient alternative to 3D-acquired sequences to assess the myocardial substrate associated with ischemic cardiomyopathy. Scar assessment from 2D-LGE-CMR sequences using 3D-upsampled models was superior to conventional 2D assessment to identify scar sizes associated with the cycle length of spontaneous ventricular tachycardia episodes and long-term ventricular tachycardia recurrences after catheter ablation. This novel methodology may represent an efficient approach in clinical practice after manual or automatic segmentation of myocardial borders in a small number of conventional 2D LGE-CMR slices and automatic scar detection.

Causes of altered ventricular mechanics in hypertrophic cardiomyopathy: an in-silico study

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is typically caused by mutations in sarcomeric genes leading to cardiomyocyte disarray, replacement fibrosis, impaired contractility, and elevated filling pressures. These varying tissue properties are associated with certain strain patterns that may allow to establish a diagnosis by means of non-invasive imaging without the necessity of harmful myocardial biopsies or contrast agent application. With a numerical study, we aim to answer: how the variability in each of these mechanisms contributes to altered mechanics of the left ventricle (LV) and if the deformation obtained in in-silico experiments is comparable to values reported from clinical measurements.

METHODS

We conducted an in-silico sensitivity study on physiological and pathological mechanisms potentially underlying the clinical HCM phenotype. The deformation of the four-chamber heart models was simulated using a finite-element mechanical solver with a sliding boundary condition to mimic the tissue surrounding the heart. Furthermore, a closed-loop circulatory model delivered the pressure values acting on the endocardium. Deformation measures and mechanical behavior of the heart models were evaluated globally and regionally.

RESULTS

Hypertrophy of the LV affected the course of strain, strain rate, and wall thickening-the root-mean-squared difference of the wall thickening between control (mean thickness 10 mm) and hypertrophic geometries (17 mm) was >10%. A reduction of active force development by 40% led to less overall deformation: maximal radial strain reduced from 26 to 21%. A fivefold increase in tissue stiffness caused a more homogeneous distribution of the strain values among 17 heart segments. Fiber disarray led to minor changes in the circumferential and radial strain. A combination of pathological mechanisms led to reduced and slower deformation of the LV and halved the longitudinal shortening of the LA.

CONCLUSIONS

This study uses a computer model to determine the changes in LV deformation caused by pathological mechanisms that are presumed to underlay HCM. This knowledge can complement imaging-derived information to obtain a more accurate diagnosis of HCM.

Landmark-free morphometric analysis of knee osteoarthritis using joint statistical models of bone shape and articular space variability

Purpose: Osteoarthritis (OA) is a common degenerative disease involving a variety of structural changes in the affected joint. In addition to narrowing of the articular space, recent studies involving statistical shape analysis methods have suggested that specific bone shapes might be associated with the disease. We aim to investigate the feasibility of using the recently introduced framework of functional shapes (Fshape) to extract morphological features of OA that combine shape variability of articular surfaces of the tibia (or femur) together with the changes of the joint space. Approach: Our study uses a dataset of three-dimensional cone-beam CT volumes of 17 knees without OA and 17 knees with OA. Each knee is then represented as an object (Fshape) consisting of a triangulated tibial (or femoral) articular surface and a map of joint space widths (JSWs) measured at the points of this surface (joint space map, JSM). We introduce a generative atlas model to estimate a template (mean) Fshape of the sample population together with template-centered variables that model the transformations from the template to each subject. This approach has two potential advantages compared with other statistical shape modeling methods that have been investigated in knee OA: (i) Fshapes simultaneously consider the variability in bone shape and JSW, and (ii) Fshape atlas estimation is based on a diffeomorphic transformation model of surfaces that does not require a priori landmark correspondences between the subjects. The estimated atlas-to-subject Fshape transformations were used as input to principal component analysis dimensionality reduction combined with a linear support vector machine (SVM) classifier to identify the morphological features of OA. Results: Using tibial articular surface as the shape component of the Fshape, we found leave-one-out cross validation scores of ≈ 91.18 % for the classification based on the bone surface transformations alone, ≈ 91.18 % for the classification based on the residual JSM, and ≈ 85.29 % for the classification using both Fshape components. Similar results were obtained using femoral articular surfaces. The discriminant directions identified in the statistical analysis were associated with medial narrowing of the joint space, steeper intercondylar eminence, and relative deepening of the medial tibial plateau. Conclusions: The proposed approach provides an integrated framework for combined statistical analysis of shape and JSPs. It can successfully extract features correlated to OA that appear consistent with previous studies in the field. Although future large-scale study is necessary to confirm the significance of these findings, our results suggest that the functional shape methodology is a promising new tool for morphological analysis of OA and orthopedics data in general.

Two-Year Follow-Up Study of the Relationship Between Brain Structure and Cognitive Control Function Across the Adult Lifespan

Age-related decline in cognitive control and general slowing are prominent phenomena in aging research. These declines in cognitive functions have been shown to also involve age-related decline in brain structure. However, most evidence in support of these associations is based on cross-sectional data. Therefore, the aim of this study is to contrast cross-sectional and longitudinal analyses to re-examine if the relationship between age-related brain structure and cognitive function are similar between the two approaches. One hundred and two participants completed two sessions with an average interval of 2 years. All participants were assessed by questionnaires, a series of cognitive tasks, and they all underwent neuroimaging acquisition. The main results of this study show that the majority of the conclusions regarding age effect in cognitive control function and processing speed in the literature can be replicated based on the cross-sectional data. Conversely, when we followed up individuals over an average interval of 2 years, then we found much fewer significant relationships between age-related change in gray matter structure of the cognitive control network and age-related change in cognitive control function. Furthermore, there was no "initial age" effect in the relationships between age-related changes in brain structure and cognitive function. This finding suggests that the "aging" relationship between brain structure and cognitive function over a short period of time are independent of "initial age" difference at time point 1. The result of this study warrants the importance of longitudinal research for aging studies to elucidate actual aging processes on cognitive control function.

Targeting 5-HT2B Receptor Signaling Prevents Border Zone Expansion and Improves Microstructural Remodeling After Myocardial Infarction

BACKGROUND

Myocardial infarction (MI) induces an intense injury response that ultimately generates a collagen-dominated scar. Although required to prevent ventricular rupture, the fibrotic process is often sustained in a manner detrimental to optimal recovery. Cardiac myofibroblasts are the cells tasked with depositing and remodeling collagen and are a prime target to limit the fibrotic process after MI. Serotonin 2B receptor (5-HT2B) signaling has been shown to be harmful in a variety of cardiopulmonary pathologies and could play an important role in mediating scar formation after MI.

METHODS

We used 2 pharmacological antagonists to explore the effect of 5-HT2B inhibition on outcomes after MI and characterized the histological and microstructural changes involved in tissue remodeling. Inducible 5-HT2B ablation driven by Tcf21MCM and PostnMCM was used to evaluate resident cardiac fibroblast- and myofibroblast-specific contributions of 5-HT2B, respectively. RNA sequencing was used to motivate subsequent in vitro analyses to explore cardiac fibroblast phenotype.

RESULTS

5-HT2B antagonism preserved cardiac structure and function by facilitating a less fibrotic scar, indicated by decreased scar thickness and decreased border zone area. 5-HT2B antagonism resulted in collagen fiber redistribution to thinner collagen fibers that were more anisotropic, enhancing left ventricular contractility, whereas fibrotic tissue stiffness was decreased, limiting the hypertrophic response of uninjured cardiomyocytes. Using a tamoxifen-inducible Cre, we ablated 5-HT2B from Tcf21-lineage resident cardiac fibroblasts and saw similar improvements to the pharmacological approach. Tamoxifen-inducible Cre-mediated ablation of 5-HT2B after onset of injury in Postn-lineage myofibroblasts also improved cardiac outcomes. RNA sequencing and subsequent in vitro analyses corroborate a decrease in fibroblast proliferation, migration, and remodeling capabilities through alterations in Dnajb4 expression and Src phosphorylation.

CONCLUSIONS

Together, our findings illustrate that 5-HT2B expression in either cardiac fibroblasts or activated myofibroblasts directly contributes to excessive scar formation, resulting in adverse remodeling and impaired cardiac function after MI.

Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning

Background: It was difficult to distinguish the cartilage thinning of an entire knee joint and to track the evolution of cartilage morphology alongside ages in the general population, which was of great significance for studying osteoarthritis until big imaging data and artificial intelligence are fused. The purposes of our study are (1) to explore the cartilage thickness in anatomical regions of the knee joint among a large collection of healthy knees, and (2) to investigate the relationship between the thinning pattern of the cartilages and the increasing ages. Methods: In this retrospective study, 2,481 healthy knees (subjects ranging from 15 to 64 years old, mean age: 35 ± 10 years) were recruited. With magnetic resonance images of knees acquired on a 3-T superconducting scanner, we automatically and precisely segmented the cartilage via deep learning and calculated the cartilage thickness in 14 anatomical regions. The thickness readings were compared using ANOVA by considering the factors of age, sex, and side. We further tracked the relationship between the thinning pattern of the cartilage thickness and the increasing ages by regression analysis. Results: The cartilage thickness was always thicker in the femur than corresponding regions in the tibia (p < 0.05). Regression analysis suggested cartilage thinning alongside ages in all regions (p < 0.05) except for medial and lateral anterior tibia in both females and males (p > 0.05). The thinning speed of men was faster than women in medial anterior and lateral anterior femur, yet slower in the medial patella (p < 0.05). Conclusion: We established the calculation method of cartilage thickness using big data and deep learning. We demonstrated that cartilage thickness differed across individual regions in the knee joint. Cartilage thinning alongside ages was identified, and the thinning pattern was consistent in the tibia while inconsistent in patellar and femoral between sexes. These findings provide a potential reference to detect cartilage anomaly.

Substantially thinner internal granular layer and reduced molecular layer surface in the cerebellar cortex of the Tc1 mouse model of down syndrome - a comprehensive morphometric analysis with active staining contrast-enhanced MRI

Down Syndrome is a chromosomal disorder that affects the development of cerebellar cortical lobules. Impaired neurogenesis in the cerebellum varies among different types of neuronal cells and neuronal layers. In this study, we developed an imaging analysis framework that utilizes gadolinium-enhanced ex vivo mouse brain MRI. We extracted the middle Purkinje layer of the mouse cerebellar cortex, enabling the estimation of the volume, thickness, and surface area of the entire cerebellar cortex, the internal granular layer, and the molecular layer in the Tc1 mouse model of Down Syndrome. The morphometric analysis of our method revealed that a larger proportion of the cerebellar thinning in this model of Down Syndrome resided in the inner granule cell layer, while a larger proportion of the surface area shrinkage was in the molecular layer.

Mitral valve leaflet response to ischaemic mitral regurgitation: from gene expression to tissue remodelling

Ischaemic mitral regurgitation (IMR), a frequent complication following myocardial infarction (MI), leads to higher mortality and poor clinical prognosis if untreated. Accumulating evidence suggests that mitral valve (MV) leaflets actively remodel post MI, and this remodelling increases both the severity of IMR and the occurrence of MV repair failures. However, the mechanisms of extracellular matrix maintenance and modulation by MV interstitial cells (MVICs) and their impact on MV leaflet tissue integrity and repair failure remain largely unknown. Herein, we sought to elucidate the multiscale behaviour of IMR-induced MV remodelling using an established ovine model. Leaflet tissue at eight weeks post MI exhibited significant permanent plastic radial deformation, eliminating mechanical anisotropy, accompanied by altered leaflet composition. Interestingly, no changes in effective collagen fibre modulus were observed, with MVICs slightly rounder, at eight weeks post MI. RNA sequencing indicated that YAP-induced genes were elevated at four weeks post MI, indicating elevated mechanotransduction. Genes related to extracellular matrix organization were downregulated at four weeks post MI when IMR occurred. Transcriptomic changes returned to baseline by eight weeks post MI. This multiscale study suggests that IMR induces plastic deformation of the MV with no functional damage to the collagen fibres, providing crucial information for computational simulations of the MV in IMR.

Modeling Complex Deformations of the Sigmoid Colon Between External Beam Radiation Therapy and Brachytherapy Images of Cervical Cancer

PURPOSE

In this study, we investigated registration methods for estimating the large interfractional sigmoid deformations that occur between external beam radiation therapy (EBRT) and brachytherapy (BT) for cervical cancer.

METHODS AND MATERIALS

Sixty-three patients were retrospectively analyzed. The sigmoid colon was delineated on 2 computed tomography images acquired during EBRT (without applicator) and BT (with applicator) for each patient. Five registration approaches were compared to propagate the contour of the sigmoid from BT to EBRT anatomies: rigid registration, commercial hybrid (ANAtomically CONstrained Deformation Algorithm), controlling ROI surface projection of RayStation, and the classical and constrained symmetrical thin-plate spline robust point matching (sTPS-RPM) methods. Deformation of the sigmoid due to insertion of the BT applicator was reported. Registration performance was compared by using the Dice similarity coefficient (DSC), distance to agreement, and Hausdorff distance. The 2 sTPS-RPM methods were compared by using surface triangle quality criteria between deformed surfaces. Using the deformable approaches, the BT dose of the sigmoid was deformed toward the EBRT anatomy. The displacement and discrepancy between the deformable methods to propagate the planned D1cm³ and D2cm³ of the sigmoid from BT to EBRT anatomies were reported for 55 patients.

RESULTS

Large and complex deformations of the sigmoid were observed for each patient. Rigid registration resulted in poor sigmoid alignment with a mean DSC of 0.26. Using the contour to drive the deformation, ANAtomically CONstrained Deformation Algorithm was able to slightly improve the alignment of the sigmoid with a mean DSC of 0.57. Using only the sigmoid surface as controlling ROI, the mean DSC was improved to 0.79. The classical and constrained sTPS-RPM methods provided mean DSCs of 0.95 and 0.96, respectively, with an average inverse consistency error <1 mm. The constrained sTPS-RPM provided more realistic deformations and better surface topology of the deformed sigmoids. The planned mean (range) D1cm³ and D2cm³ of the sigmoid were 13.4 Gy (1-24.1) and 12.2 Gy (1-21.5) on the BT anatomy, respectively. Using the constrained sTPS-RPM to deform the sigmoid from BT to EBRT anatomies, these hotspots had a mean (range) displacement of 27.1 mm (6.8-81).

CONCLUSIONS

Large deformations of the sigmoid were observed between the EBRT and BT anatomies, suggesting that the D1cm³ and D2cm³ of the sigmoid would unlikely to be at the same position throughout treatment. The proposed constrained sTPS-RPM seems to be the preferred approach to manage the large deformation due to BT applicator insertion. Such an approach could be used to map the EBRT dose to the BT anatomy for personalized BT planning optimization.

4D in vivo quantification of ankle joint space width using dynamic MRI

Spatio-temporal evolution of joint space width (JSW) during motion is of great importance to help with making early treatment plans for degenerative joint diseases like osteoarthritis (OA). These diseases can affect people of all ages leading to an acceleration of joint degeneration and to limitations in the activities of daily living. However, only a few studies have attempted to quantify the JSW from moving joints. In this paper, we present a generic pipeline to accurately determine the changes of the JSW during the joint motion cycle. The key idea is to combine spatial information of static MRI with temporal information of low-resolution (LR) dynamic MRI sequences via an intensity-based registration framework, leading to a high-resolution (HR) temporal reconstruction of the joint. This allows the temporal JSW to be measured in the HR domain using an Eulerian approach for solving partial differential equations (PDEs) inside a deforming inter-bone area where the HR reconstructed bone segmentations are considered as temporal Dirichlet boundaries. The proposed approach has been applied and evaluated on in vivo MRI data of five healthy children to non-invasively quantify the spatio-temporal evolution of the JSW of the ankle (tibiotalar joint) during the entire dorsi-plantar flexion motion cycle. Promising results were obtained, showing that this pipeline can be useful to perform large-scale studies containing subjects with OA for different joints like ankle and knee.

Comorbid amyloid-β pathology affects clinical and imaging features in VCD

INTRODUCTION

To date, the clinical relevance of comorbid amyloid-β (Aβ) pathology in patients with vascular cognitive disorders (VCD) is largely unknown.

METHODS

We included 218 VCD patients with available cerebrospinal fluid Aβ₄₂ levels. Patients were divided into Aβ+ mild-VCD (n = 84), Aβ- mild-VCD (n = 68), Aβ+ major-VCD (n = 31), and Aβ- major-VCD (n = 35). We measured depression with the Geriatric Depression Scale, cognition with a neuropsychological test battery and derived white matter hyperintensities (WMH) and gray matter atrophy from MRI.

RESULTS

Aβ- patients showed more depressive symptoms than Aβ+. In the major-VCD group, Aβ- patients performed worse on attention (P = .02) and executive functioning (P = .008) than Aβ+. We found no cognitive differences in patients with mild VCD. In the mild-VCD group, Aβ- patients had more WMH than Aβ+ patients, whereas conversely, in the major-VCD group, Aβ+ patients had more WMH. Atrophy patterns did not differ between Aβ+ and Aβ- VCD group.

DISCUSSION

Comorbid Aβ pathology affects the manifestation of VCD, but effects differ by severity of VCD.

A robust computational framework for estimating 3D Bi-Atrial chamber wall thickness

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. The atrial wall thickness (AWT) can potentially improve our understanding of the mechanism underlying atrial structure that drives AF and provides important clinical information. However, most existing studies for estimating AWT rely on ruler-based measurements performed on only a few selected locations in 2D or 3D using digital calipers. Only a few studies have developed automatic approaches to estimate the AWT in the left atrium, and there are currently no methods to robustly estimate the AWT of both atrial chambers. Therefore, we have developed a computational pipeline to automatically calculate the 3D AWT across bi-atrial chambers and extensively validated our pipeline on both ex vivo and in vivo human atria data. The atrial geometry was first obtained by segmenting the atrial wall from the MRIs using a novel machine learning approach. The epicardial and endocardial surfaces were then separated using a multi-planar convex hull approach to define boundary conditions, from which, a Laplace equation was solved numerically to automatically separate bi-atrial chambers. To robustly estimate the AWT in each atrial chamber, coupled partial differential equations by coupling the Laplace solution with two surface trajectory functions were formulated and solved. Our pipeline enabled the reconstruction and visualization of the 3D AWT for bi-atrial chambers with a relative error of 8% and outperformed existing algorithms by >7%. Our approach can potentially lead to improved clinical diagnosis, patient stratification, and clinical guidance during ablation treatment for patients with AF.

Cadherin-11 blockade reduces inflammation-driven fibrotic remodeling and improves outcomes after myocardial infarction

Over one million Americans experience myocardial infarction (MI) annually, and the resulting scar and subsequent cardiac fibrosis gives rise to heart failure. A specialized cell-cell adhesion protein, cadherin-11 (CDH11), contributes to inflammation and fibrosis in rheumatoid arthritis, pulmonary fibrosis, and aortic valve calcification but has not been studied in myocardium after MI. MI was induced by ligation of the left anterior descending artery in mice with either heterozygous or homozygous knockout of CDH11, wild-type mice receiving bone marrow transplants from Cdh11-deficient animals, and wild-type mice treated with a functional blocking antibody against CDH11 (SYN0012). Flow cytometry revealed significant CDH11 expression in noncardiomyocyte cells after MI. Animals given SYN0012 had improved cardiac function, as measured by echocardiogram, reduced tissue remodeling, and altered transcription of inflammatory and proangiogenic genes. Targeting CDH11 reduced bone marrow-derived myeloid cells and increased proangiogenic cells in the heart 3 days after MI. Cardiac fibroblast and macrophage interactions increased IL-6 secretion in vitro. Our findings suggest that CDH11-expressing cells contribute to inflammation-driven fibrotic remodeling after MI and that targeting CDH11 with a blocking antibody improves outcomes by altering recruitment of bone marrow-derived cells, limiting the macrophage-induced expression of IL-6 by fibroblasts and promoting vascularization.

Fast personalized electrophysiological models from computed tomography images for ventricular tachycardia ablation planning

Aims

Clinical application of patient-specific cardiac computer models requires fast and robust processing pipelines that can be seamlessly integrated into clinical workflows. We aim at building such a pipeline from computed tomography (CT) images to personalized cardiac electrophysiology (EP) model. The simulation output could be useful in the context of post-infarct ventricular tachycardia (VT) radiofrequency ablation (RFA) planning for pre-operative targets prediction.

Methods and results

The support for model personalization is a patient-specific virtual three-dimensional heart obtained from CT images. Here, the scar is identified as thinning of the myocardial wall on automatically computed thickness maps. We then use an Eikonal model of wave front propagation with reduced velocity in the damaged areas. An image-based vessel enhancement algorithm can automatically identify VT isthmuses. The personalized model is used for virtual pacing. We obtained a very fast pipeline that enables simulations in only a few minutes. It is fully automated starting from the semi-automated image segmentation phase. The computational time frame is compatible with the construction of a virtual pacing tool. In this tool, onset points and an optional directional block could be interactively selected. The directional block is a simple way to model tissue refractoriness. Output activation maps are compared with EP data acquired pre-operatively. We show that this framework allows the reproduction of recorded re-entrant VT activation patterns.

Conclusion

Our simulation framework has an application in VT RFA intervention planning. It could be used to guide EP explorations and even predict ablation targets pre-operatively. This could reduce intervention duration and improve success rate.

Periventricular magnetisation transfer ratio abnormalities in multiple sclerosis improve after alemtuzumab

BACKGROUND

In multiple sclerosis (MS), disease effects on magnetisation transfer ratio (MTR) increase towards the ventricles. This periventricular gradient is evident shortly after first symptoms and is independent of white matter lesions.

OBJECTIVE

To explore if alemtuzumab, a peripherally acting disease-modifying treatment, modifies the gradient's evolution, and whether baseline gradients predict on-treatment relapses.

METHODS

Thirty-four people with relapsing-remitting MS underwent annual magnetic resonance imaging (MRI) scanning (19 receiving alemtuzumab (four scans each), 15 untreated (three scans each)). The normal-appearing white matter was segmented into concentric bands. Gradients were measured over the three bands nearest the ventricles. Mixed-effects models adjusted for age, gender, relapse rate, lesion number and brain parenchymal fraction compared the groups' baseline gradients and evolution.

RESULTS

Untreated, the mean MTR gradient increased (+0.030 pu/band/year) but decreased following alemtuzumab (-0.045 pu/band/year, p = 0.037). Within the alemtuzumab group, there were no significant differences in baseline lesion number (p = 0.568) nor brain parenchymal fraction (p = 0.187) between those who relapsed within 4 years (n = 4) and those who did not (n = 15). However, the baseline gradient was significantly different (p = 0.020).

CONCLUSION

Untreated, abnormal periventricular gradients worsen with time, but appear reversible with peripheral immunotherapy. Baseline gradients - but not lesion loads or brain volumes - may predict on-treatment relapses. Larger confirmatory studies are required.

Local variations in material and structural properties characterize murine thoracic aortic aneurysm mechanics

We recently developed an approach to characterize local nonlinear, anisotropic mechanical properties of murine arteries by combining biaxial extension-distension testing, panoramic digital image correlation, and an inverse method based on the principle of virtual power. This experimental-computational approach was illustrated for the normal murine abdominal aorta assuming uniform wall thickness. Here, however, we extend our prior approach by adding an optical coherence tomography (OCT) imaging system that permits local reconstructions of wall thickness. This multimodality approach is then used to characterize spatial variations of material and structural properties in ascending thoracic aortic aneurysms (aTAA) from two genetically modified mouse models (fibrillin-1 and fibulin-4 deficient) and to compare them with those from angiotensin II-infused apolipoprotein E-deficient and wild-type control ascending aortas. Local values of stored elastic energy and biaxial material stiffness, computed from spatial distributions of the best fit material parameters, varied significantly with circumferential position (inner vs. outer curvature, ventral vs. dorsal sides) across genotypes and treatments. Importantly, these data reveal an inverse relationship between material stiffness and wall thickness that underlies a general linear relationship between stiffness and wall stress across aTAAs. OCT images also revealed sites of advanced medial degeneration, which were captured by the inverse material characterization. Quantification of histological data further provided high-resolution local correlations among multiple mechanical metrics and wall microstructure. This is the first time that such structural defects and local properties have been characterized mechanically, which can better inform computational models of aortopathy that seek to predict where dissection or rupture may initiate.

Ambulatory systolic blood pressure and obesity are independently associated with left ventricular hypertrophic remodeling in children

BACKGROUND

Children with obesity have hypertrophic cardiac remodeling. Hypertension is common in pediatric obesity, and may independently contribute to hypertrophy. We hypothesized that both the degree of obesity and ambulatory blood pressure (ABP) would independently associate with measures of hypertrophic cardiac remodeling in children.

METHODS

Children, aged 8-17 years, prospectively underwent cardiovascular magnetic resonance (CMR) and ABP monitoring. Left ventricular (LV) mass indexed to height2.7 (LVMI), myocardial thickness and end-diastolic volume were quantified from a 3D LV model reconstructed from cine balanced steady state free precession images. Categories of remodeling were determined based on cutoff values for LVMI and mass/volume. Principal component analysis was used to define a "hypertrophy score" to study the continuous relationship between concentric hypertrophy and ABP.

RESULTS

Seventy-two children were recruited, and 68 of those (37 healthy weight and 31 obese/overweight) completed both CMR and ABP monitoring. Obese/overweight children had increased LVMI (27 ± 4 vs 22 ± 3 g/m2.7, p < 0.001), myocardial thickness (5.6 ± 0.9 vs 4.9 ± 0.7 mm, p < 0.001), mass/volume (0.69 ± 0.1 vs 0.61 ± 0.06, p < 0.001), and hypertrophy score (1.1 ± 2.2 vs -0.96 ± 1.1, p < 0.001). Thirty-five percent of obese/overweight children had concentric hypertrophy. Ambulatory hypertension was observed in 26% of the obese/overweight children and none of the controls while masked hypertension was observed in 32% of the obese/overweight children and 16% of the controls. Univariate linear regression showed that BMI z-score, systolic BP (24 h, day and night), and systolic load correlated with LVMI, thickness, mass/volume and hypertrophy score, while 24 h and nighttime diastolic BP and load also correlated with thickness and mass/volume. Multivariate analysis showed body mass index z-score and systolic blood pressure were both independently associated with left ventricular mass index (β=0.54 [p < 0.001] and 0.22 [p = 0.03]), thickness (β=0.34 [p < 0.001] and 0.26 [p = 0.001]) and hypertrophy score (β=0.47 and 0.36, both p < 0.001).

CONCLUSIONS

In children, both the degree of obesity and ambulatory blood pressures are independently associated with measures of cardiac hypertrophic remodeling, however the correlations were generally stronger for the degree of obesity. This suggests that interventions targeted at weight loss or obesity-associated co-morbidities including hypertension may be effective in reversing or preventing cardiac remodeling in obese children.

Knee cartilage segmentation and thickness computation from ultrasound images

Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result. When LSLSM was applied to 80 datasets, the qualitative segmentation assessment indicates a substantial agreement with Cohen's κ coefficient of 0.73. The quantitative validation metrics of Dice similarity coefficient and Hausdorff distance have average values of 0.91 ± 0.01 and 6.21 ± 0.59 pixels, respectively. These satisfactory segmentation results are making the true thickness between two interfaces of the cartilage possible to be computed based on the segmented images. The measured cartilage thickness ranged from 1.35 to 2.42 mm with an average value of 1.97 ± 0.11 mm, reflecting the robustness of the segmentation algorithm to various cartilage thickness. These results indicate a potential application of the methods described for assessment of cartilage degeneration where changes in the cartilage thickness can be quantified over time by comparing the true thickness at a certain time interval.

Right Ventricular Strain, Torsion, and Dyssynchrony in Healthy Subjects Using 3D Spiral Cine DENSE Magnetic Resonance Imaging

Mechanics of the left ventricle (LV) are important indicators of cardiac function. The role of right ventricular (RV) mechanics is largely unknown due to the technical limitations of imaging its thin wall and complex geometry and motion. By combining 3D Displacement Encoding with Stimulated Echoes (DENSE) with a post-processing pipeline that includes a local coordinate system, it is possible to quantify RV strain, torsion, and synchrony. In this study, we sought to characterize RV mechanics in 50 healthy individuals and compare these values to their LV counterparts. For each cardiac frame, 3D displacements were fit to continuous and differentiable radial basis functions, allowing for the computation of the 3D Cartesian Lagrangian strain tensor at any myocardial point. The geometry of the RV was extracted via a surface fit to manually delineated endocardial contours. Throughout the RV, a local coordinate system was used to transform from a Cartesian strain tensor to a polar strain tensor. It was then possible to compute peak RV torsion as well as peak longitudinal and circumferential strain. A comparable analysis was performed for the LV. Dyssynchrony was computed from the standard deviation of regional activation times. Global circumferential strain was comparable between the RV and LV (-18.0% for both) while longitudinal strain was greater in the RV (-18.1% vs. -15.7%). RV torsion was comparable to LV torsion (6.2 vs. 7.1 degrees, respectively). Regional activation times indicated that the RV contracted later but more synchronously than the LV. 3D spiral cine DENSE combined with a post-processing pipeline that includes a local coordinate system can resolve both the complex geometry and 3D motion of the RV.

Relationship of grey and white matter abnormalities with distance from the surface of the brain in multiple sclerosis

OBJECTIVE

To assess the association between proximity to the inner (ventricular and aqueductal) and outer (pial) surfaces of the brain and the distribution of normal appearing white matter (NAWM) and grey matter (GM) abnormalities, and white matter (WM) lesions, in multiple sclerosis (MS).

METHODS

67 people with relapse-onset MS and 30 healthy controls were included in the study. Volumetric T1 images and high-resolution (1 mm³) magnetisation transfer ratio (MTR) images were acquired and segmented into 12 bands between the inner and outer surfaces of the brain. The first and last bands were discarded to limit partial volume effects with cerebrospinal fluid. MTR values were computed for all bands in supratentorial NAWM, cerebellar NAWM and brainstem NA tissue, and deep and cortical GM. Band WM lesion volumes were also measured.

RESULTS

Proximity to the ventricular surfaces was associated with progressively lower MTR values in the MS group but not in controls in supratentorial and cerebellar NAWM, brainstem NA and in deep and cortical GM. The density of WM lesions was associated with proximity to the ventricles only in the supratentorial compartment, and no link was found with distance from the pial surfaces.

CONCLUSIONS

In MS, MTR abnormalities in NAWM and GM are related to distance from the inner and outer surfaces of the brain, and this suggests that there is a common factor underlying their spatial distribution. A similar pattern was not found for WM lesions, raising the possibility that different factors promote their formation.

Multi-template analysis of human perirhinal cortex in brain MRI: Explicitly accounting for anatomical variability

RATIONAL

The human perirhinal cortex (PRC) plays critical roles in episodic and semantic memory and visual perception. The PRC consists of Brodmann areas 35 and 36 (BA35, BA36). In Alzheimer's disease (AD), BA35 is the first cortical site affected by neurofibrillary tangle pathology, which is closely linked to neural injury in AD. Large anatomical variability, manifested in the form of different cortical folding and branching patterns, makes it difficult to segment the PRC in MRI scans. Pathology studies have found that in ~97% of specimens, the PRC falls into one of three discrete anatomical variants. However, current methods for PRC segmentation and morphometry in MRI are based on single-template approaches, which may not be able to accurately model these discrete variants METHODS: A multi-template analysis pipeline that explicitly accounts for anatomical variability is used to automatically label the PRC and measure its thickness in T2-weighted MRI scans. The pipeline uses multi-atlas segmentation to automatically label medial temporal lobe cortices including entorhinal cortex, PRC and the parahippocampal cortex. Pairwise registration between label maps and clustering based on residual dissimilarity after registration are used to construct separate templates for the anatomical variants of the PRC. An optimal path of deformations linking these templates is used to establish correspondences between all the subjects. Experimental evaluation focuses on the ability of single-template and multi-template analyses to detect differences in the thickness of medial temporal lobe cortices between patients with amnestic mild cognitive impairment (aMCI, n=41) and age-matched controls (n=44).

RESULTS

The proposed technique is able to generate templates that recover the three dominant discrete variants of PRC and establish more meaningful correspondences between subjects than a single-template approach. The largest reduction in thickness associated with aMCI, in absolute terms, was found in left BA35 using both regional and summary thickness measures. Further, statistical maps of regional thickness difference between aMCI and controls revealed different patterns for the three anatomical variants.

Tissue thickness calculation in ocular optical coherence tomography

Thickness measurements derived from optical coherence tomography (OCT) images of the eye are a fundamental clinical and research metric, since they provide valuable information regarding the eye's anatomical and physiological characteristics, and can assist in the diagnosis and monitoring of numerous ocular conditions. Despite the importance of these measurements, limited attention has been given to the methods used to estimate thickness in OCT images of the eye. Most current studies employing OCT use an axial thickness metric, but there is evidence that axial thickness measures may be biased by tilt and curvature of the image. In this paper, standard axial thickness calculations are compared with a variety of alternative metrics for estimating tissue thickness. These methods were tested on a data set of wide-field chorio-retinal OCT scans (field of view (FOV) 60° x 25°) to examine their performance across a wide region of interest and to demonstrate the potential effect of curvature of the posterior segment of the eye on the thickness estimates. Similarly, the effect of image tilt was systematically examined with the same range of proposed metrics. The results demonstrate that image tilt and curvature of the posterior segment can affect axial tissue thickness calculations, while alternative metrics, which are not biased by these effects, should be considered. This study demonstrates the need to consider alternative methods to calculate tissue thickness in order to avoid measurement error due to image tilt and curvature.

Cortical thickness and sulcal depth: insights on development and psychopathology in paediatric epilepsy

BACKGROUND

The relationship between cortical thickness (CThick) and sulcal depth (SDepth) changes across brain regions during development. Epilepsy youth have CThick and SDepth abnormalities and prevalent psychiatric disorders.

AIMS

This study compared the CThick-SDepth relationship in children with focal epilepsy with typically developing children (TDC) and the role played by seizure and psychopathology variables.

METHOD

A surface-based, computational high-resolution three-dimesional (3D) magnetic resonance image analytic technique compared regional CThick-SDepth relationships in 42 participants with focal epilepsy and 46 TDC (6-16 years) imaged in a 1.5 Tesla scanner. Psychiatric interviews administered to each participant yielded psychiatric diagnoses. Parents provided seizure-related information.

RESULTS

The TDC group alone demonstrated a significant negative medial fronto-orbital CThick-SDepth correlation. Focal epilepsy participants with but not without psychiatric diagnoses showed significant positive pre-central and post-central CThick-SDepth associations not found in TDC. Although the history of prolonged seizures was significantly associated with the post-central CThick-SDepth correlation, it was unrelated to the presence/absence of psychiatric diagnoses.

CONCLUSIONS

Abnormal CThick-SDepth pre-central and post-central associations might be a psychopathology biomarker in paediatric focal epilepsy.

DECLARATION INTEREST

None.

COPYRIGHT AND USAGE

© 2015 The Royal College of Psychiatrists. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence.

Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data

Magnetic resonance imaging (MRI) has become an important imaging technique for quantifying the spatial location and magnitude/direction of longitudinal cartilage morphology changes in patients with osteoarthritis (OA). Although several analytical methods, such as subregion-based analysis, have been developed to refine and improve quantitative cartilage analyses, they can be suboptimal due to two major issues: the lack of spatial correspondence across subjects and time and the spatial heterogeneity of cartilage progression across subjects. The aim of this paper is to present a statistical method for longitudinal cartilage quantification in OA patients, while addressing these two issues. The 3D knee image data is preprocessed to establish spatial correspondence across subjects and/or time. Then, a Gaussian hidden Markov model (GHMM) is proposed to deal with the spatial heterogeneity of cartilage progression across both time and OA subjects. To estimate unknown parameters in GHMM, we employ a pseudo-likelihood function and optimize it by using an expectation-maximization (EM) algorithm. The proposed model can effectively detect diseased regions in each OA subject and present a localized analysis of longitudinal cartilage thickness within each latent subpopulation. Our GHMM integrates the strengths of two standard statistical methods including the local subregion-based analysis and the ordered value approach. We use simulation studies and the Pfizer longitudinal knee MRI dataset to evaluate the finite sample performance of GHMM in the quantification of longitudinal cartilage morphology changes. Our results indicate that GHMM significantly outperforms several standard analytical methods.

Voxel-based dipole orientation constraints for distributed current estimation

Distributed electroencephalography source localization is a highly ill-posed problem. With measurements on the order of 10(2), and unknowns in the range of 10(4)-10(5), the range of feasible solutions is quite large. One approach to reducing ill-posedness is to intelligently reduce the number of unknowns. Restricting solutions to gray matter is one approach. A further step is to use the anatomy of each patient to identify and constrain the orientation of the dipole within each voxel. While dipole orientation constraints for cortical patch-based approaches have been proposed, to our knowledge, no solutions for full volumetric localizations have been presented. Patch techniques account for patch surface area, but place dipoles only on the surface, rather than throughout the cortex. Variability in human cortical thickness means that thicker regions of cortex will potentially contribute more to the EEG signal, and should be accounted for in modeling. Additionally, patch models require cortical surface identification techniques, which can separate them from the extensive literature on voxel-based MR image processing, and require additional adaptation to incorporate more complex information. We present a volumetric approach for computing voxel-based distributed estimates of cortical activity with constrained dipole orientations. Using a tissue thickness estimation approach, we obtain estimates of the cortical surface normal at each voxel. These let us constrain the inverse problem, and yield localizations with reduced spatial blurring and better identification of signal magnitude within the cortex. This is demonstrated for a series of simulated and experimental data using patient-specific bioelectric models.

Characterization of 3D joint space morphology using an electrostatic model (with application to osteoarthritis)

Joint space morphology can be indicative of the risk, presence, progression, and/or treatment response of disease or trauma. We describe a novel methodology of characterizing joint space morphology in high-resolution 3D images (e.g. cone-beam CT (CBCT)) using a model based on elementary electrostatics that overcomes a variety of basic limitations of existing 2D and 3D methods. The method models each surface of a joint as a conductor at fixed electrostatic potential and characterizes the intra-articular space in terms of the electric field lines resulting from the solution of Gauss' Law and the Laplace equation. As a test case, the method was applied to discrimination of healthy and osteoarthritic subjects (N = 39) in 3D images of the knee acquired on an extremity CBCT system. The method demonstrated improved diagnostic performance (area under the receiver operating characteristic curve, AUC > 0.98) compared to simpler methods of quantitative measurement and qualitative image-based assessment by three expert musculoskeletal radiologists (AUC = 0.87, p-value = 0.007). The method is applicable to simple (e.g. the knee or elbow) or multi-axial joints (e.g. the wrist or ankle) and may provide a useful means of quantitatively assessing a variety of joint pathologies.

Thickness network features for prognostic applications in dementia

Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer's disease but not its interregional covariation of thickness. We present novel features based on the inter-regional covariation of cortical thickness. Initially, the cortical labels of each subject are partitioned into small patches (graph nodes) by spatial k-means clustering. A graph is then constructed by establishing a link between 2 nodes if the difference in thickness between the nodes is below a certain threshold. From this binary graph, a thickness network is computed using nodal degree, betweenness, and clustering coefficient measures. Fusing them with multiple kernel learning, it is observed that thickness network features discriminate mild cognitive impairment (MCI) converters from controls (CN) with an area under curve (AUC) of 0.83, 74% sensitivity and 76% specificity on a large subset obtained from the Alzheimer's Disease Neuroimaging Initiative data set. A comparison of predictive utility in Alzheimer's disease and/or CN classification (AUC of 0.92, 80% sensitivity [SENS] and 90% specificity [SPEC]), in discriminating CN from MCI (converters and nonconverters combined; AUC of 0.75, SENS and SPEC of 64% and 73%, respectively) and in discriminating between MCI nonconverters and MCI converters (AUC of 0.68, SENS and SPEC of 65% and 64%) is also presented. ThickNet features as defined here are novel, can be derived from a single magnetic resonance imaging scan, and demonstrate the potential for the computer-aided prognostic applications.

Biomechanics and inflammation in atherosclerotic plaque erosion and plaque rupture: implications for cardiovascular events in women

Objective

Although plaque erosion causes approximately 40% of all coronary thrombi and disproportionally affects women more than men, its mechanism is not well understood. The role of tissue mechanics in plaque rupture and regulation of mechanosensitive inflammatory proteins is well established, but their role in plaque erosion is unknown. Given obvious differences in morphology between plaque erosion and rupture, we hypothesized that inflammation in general as well as the association between local mechanical strain and inflammation known to exist in plaque rupture may not occur in plaque erosion. Therefore, our objective was to determine if similar mechanisms underlie plaque rupture and plaque erosion.

Methods and Results

We studied a total of 74 human coronary plaque specimens obtained at autopsy. Using lesion-specific computer modeling of solid mechanics, we calculated the stress and strain distribution for each plaque and determined if there were any relationships with markers of inflammation. Consistent with previous studies, inflammatory markers were positively associated with increasing strain in specimens with rupture and thin-cap fibroatheromas. Conversely, overall staining for inflammatory markers and apoptosis were significantly lower in erosion, and there was no relationship with mechanical strain. Samples with plaque erosion most closely resembled those with the stable phenotype of thick-cap fibroatheromas.

Conclusions

In contrast to classic plaque rupture, plaque erosion was not associated with markers of inflammation and mechanical strain. These data suggest that plaque erosion is a distinct pathophysiological process with a different etiology and therefore raises the possibility that a different therapeutic approach may be required to prevent plaque erosion.

Interaction between neuroanatomical and psychological changes after mindfulness-based training

Several cross-sectional studies have documented neuroanatomical changes in individuals with a long history of meditation, while a few evidences are available about the interaction between neuroanatomical and psychological changes even during brief exposure to meditation. Here we analyzed several morphometric indexes at both cortical and subcortical brain level, as well as multiple psychological dimensions, before and after a brief -8 weeks- Mindfulness Based Stress Reduction (MBSR) training program, in a group of 23 meditation naïve-subjects compared to age-gender matched subjects. We found a significant cortical thickness increase in the right insula and the somatosensory cortex of MBSR trainees, coupled with a significant reduction of several psychological indices related to worry, state anxiety, depression and alexithymia. Most importantly, an interesting correlation between the increase in right insula thickness and the decrease in alexithymia levels during the MBSR training were observed. Moreover, a multivariate pattern classification approach allowed to identify a cluster of regions more responsive to MBSR training across subjects. Taken together, these findings documented the significant impact of a brief MBSR training on brain structures, as well as stressing the idea of MBSR as a valuable tool for alexithymia modulation, also originally providing a plausible neurobiological evidence of a major role of right insula into mediating the observed psychological changes.