Postmortem Findings in Patients with COVID19 Using Multiple Organ Core Needle Biopsies

Introduction/Objective

The coronavirus disease 2019 (COVID19) pandemic had caused more than 500,000 deaths in the United States. Although it mainly manifests with respiratory symptoms, postmortem examination reveals that it is more of a systemic disease affecting mutliple body organs.

Methods/Case Report

Postmortem needle core biopsies from multiple organs were obtained from 9 patients who died at our institution in the months of April and May of 2020 due to a confirmed SARS-CoV-2 infection by RT-PCR testing of nasopharyngeal swabs. The core biopsies from body organs included lungs (8), liver (7), kidneys (5), heart (2), spleen (2), and brain (2). Histopathological examination was performed in conjunction with a set of special and immunohistochemical stains. Electron microscopy examination was also done in 4 cases.

Results (if a Case Study enter NA)

The cohort consisted of 6 males and 3 females with a mean age of 70.4 years (range: 68–79). The majority had comorbidities (8/9) and presented with respiratory symptoms (9/9). The most significant postmortem findings were mainly in the lungs, including alveolar hemorrhage, hyaline membranes, fibrin thrombi, intraalveolar macrophages, type-2 pneumocyte hyperplasia, and interstitial myofibroblast reaction and collagen deposition. Immunohistochemical stains showed predominance of T-lymphocytes with a mixture of CD4 and CD8 positive cells. Examination of liver showed minimal to marked microvesicular and macrovesicular steatosis and centrilobular congestion and necrosis. Tissue from kidneys revealed mild to severe acute tubular injury. Microglial activation and Alzheimer type-II astrocytosis were noted in brain, and mild white pulp depletion was seen in the spleen. Electron microscopy showed the presence of foreign bodies suspicious for viral particles ranging in size from 52.6 to 97.9 nm in 2/4 cases.

Conclusion

Our findings based on postmortem core needle biopsies confirm the observation that most severely affected patients have significant pulmonary pathology. However, other organs show findings that may lead to a better understanding of this disease. Postmortem examination will continue to be an invaluable tool for studying the pathologic manifestations of COVID-19.

A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment

Automatic shape anomaly detection in large-scale imaging data can be useful for screening suboptimal segmentations and pathologies altering the cardiac morphology without intensive manual labour. We propose a deep probabilistic model for local anomaly detection in sequences of heart shapes, modelled as point sets, in a cardiac cycle. A deep recurrent encoder-decoder network captures the spatio-temporal dependencies to predict the next shape in the cycle and thus derive the outlier points that are attributed to excessive deviations from the network prediction. A predictive mixture distribution models the inlier and outlier classes via Gaussian and uniform distributions, respectively. A Gibbs sampling Expectation-Maximisation (EM) algorithm computes soft anomaly scores of the points via the posterior probabilities of each class in the E-step and estimates the parameters of the network and the predictive distribution in the M-step. We demonstrate the versatility of the method using two shape datasets derived from: (i) one million biventricular CMR images from 20,000 participants in the UK Biobank (UKB), and (ii) routine diagnostic imaging from Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Image (M&Ms). Experiments show that the detected shape anomalies in the UKB dataset are mostly associated with poor segmentation quality, and the predicted shape sequences show significant improvement over the input sequences. Furthermore, evaluations on U-Net based shapes from the M&Ms dataset reveals that the anomalies are attributable to the underlying pathologies that affect the ventricles. The proposed model can therefore be used as an effective mechanism to sift shape anomalies in large-scale cardiac imaging pipelines for further analysis.

Shape registration with learned deformations for 3D shape reconstruction from sparse and incomplete point clouds

Shape reconstruction from sparse point clouds/images is a challenging and relevant task required for a variety of applications in computer vision and medical image analysis (e.g. surgical navigation, cardiac motion analysis, augmented/virtual reality systems). A subset of such methods, viz. 3D shape reconstruction from 2D contours, is especially relevant for computer-aided diagnosis and intervention applications involving meshes derived from multiple 2D image slices, views or projections. We propose a deep learning architecture, coined Mesh Reconstruction Network (MR-Net), which tackles this problem. MR-Net enables accurate 3D mesh reconstruction in real-time despite missing data and with sparse annotations. Using 3D cardiac shape reconstruction from 2D contours defined on short-axis cardiac magnetic resonance image slices as an exemplar, we demonstrate that our approach consistently outperforms state-of-the-art techniques for shape reconstruction from unstructured point clouds. Our approach can reconstruct 3D cardiac meshes to within 2.5-mm point-to-point error, concerning the ground-truth data (the original image spatial resolution is ∼1.8×1.8×10mm³). We further evaluate the robustness of the proposed approach to incomplete data, and contours estimated using an automatic segmentation algorithm. MR-Net is generic and could reconstruct shapes of other organs, making it compelling as a tool for various applications in medical image analysis.

Spatio-Temporal Multi-Task Learning for Cardiac MRI Left Ventricle Quantification

Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved by manual contouring of the endo- and epicardial. However, this process subjected to inter and intra-observer variability, and it is a time-consuming and tedious task. In this article, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence. We first segment cardiac LVs using an encoder-decoder network and then introduce a multitask framework to regress 11 LV indices and classify the cardiac phase, as parallel tasks during model optimization. The proposed deep learning model is based on the 3D spatio-temporal convolutions, which extract spatial and temporal features from MR images. We demonstrate the efficacy of the proposed method using cine-MR sequences of 145 subjects and comparing the performance with other state-of-the-art quantification methods. The proposed method obtained high prediction accuracy, with an average mean absolute error (MAE) of 129 mm ², 1.23 mm, 1.76 mm, Pearson correlation coefficient (PCC) of 96.4%, 87.2%, and 97.5% for LV and myocardium (Myo) cavity regions, 6 RWTs, 3 LV dimensions, and an error rate of 9.0% for phase classification. The experimental results highlight the robustness of the proposed method, despite varying degrees of cardiac morphology, image appearance, and low contrast in the cardiac MR sequences.

Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimization for Multi-Modal Cardiac Image Segmentation

Deep learning models are sensitive to domain shift phenomena. A model trained on images from one domain cannot generalise well when tested on images from a different domain, despite capturing similar anatomical structures. It is mainly because the data distribution between the two domains is different. Moreover, creating annotation for every new modality is a tedious and time-consuming task, which also suffers from high inter- and intra- observer variability. Unsupervised domain adaptation (UDA) methods intend to reduce the gap between source and target domains by leveraging source domain labelled data to generate labels for the target domain. However, current state-of-the-art (SOTA) UDA methods demonstrate degraded performance when there is insufficient data in source and target domains. In this paper, we present a novel UDA method for multi-modal cardiac image segmentation. The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces. The paper introduces an end-to-end framework that integrates: a) entropy minimization, b) output feature space alignment and c) a novel point-cloud shape adaptation based on the latent features learned by the segmentation model. We validated our method on two cardiac datasets by adapting from the annotated source domain, bSSFP-MRI (balanced Steady-State Free Procession-MRI), to the unannotated target domain, LGE-MRI (Late-gadolinium enhance-MRI), for the multi-sequence dataset; and from MRI (source) to CT (target) for the cross-modality dataset. The results highlighted that by enforcing adversarial learning in different parts of the network, the proposed method delivered promising performance, compared to other SOTA methods.

Super-Resolution of Cardiac MR Cine Imaging using Conditional GANs and Unsupervised Transfer Learning

High-resolution (HR), isotropic cardiac Magnetic Resonance (MR) cine imaging is challenging since it requires long acquisition and patient breath-hold times. Instead, 2D balanced steady-state free precession (SSFP) sequence is widely used in clinical routine. However, it produces highly-anisotropic image stacks, with large through-plane spacing that can hinder subsequent image analysis. To resolve this, we propose a novel, robust adversarial learning super-resolution (SR) algorithm based on conditional generative adversarial nets (GANs), that incorporates a state-of-the-art optical flow component to generate an auxiliary image to guide image synthesis. The approach is designed for real-world clinical scenarios and requires neither multiple low-resolution (LR) scans with multiple views, nor the corresponding HR scans, and is trained in an end-to-end unsupervised transfer learning fashion. The designed framework effectively incorporates visual properties and relevant structures of input images and can synthesise 3D isotropic, anatomically plausible cardiac MR images, consistent with the acquired slices. Experimental results show that the proposed SR method outperforms several state-of-the-art methods both qualitatively and quantitatively. We show that subsequent image analyses including ventricle segmentation, cardiac quantification, and non-rigid registration can benefit from the super-resolved, isotropic cardiac MR images, to produce more accurate quantitative results, without increasing the acquisition time. The average Dice similarity coefficient (DSC) for the left ventricular (LV) cavity and myocardium are 0.95 and 0.81, respectively, between real and synthesised slice segmentation. For non-rigid registration and motion tracking through the cardiac cycle, the proposed method improves the average DSC from 0.75 to 0.86, compared to the original resolution images.

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images

In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation. Point-wise features that comprise spatial positions (and surface normal vectors in the case of structured meshes), and their corresponding image features, are used to encode the observation and represent the underlying 3D volume. The actor and value networks are applied iteratively to estimate a sequence of transformations that enable accurate delineation of object boundaries. The proposed approach was extensively evaluated in both segmentation and registration tasks using a variety of challenging clinical datasets. Our method has fewer trainable parameters and lower computational complexity compared to the 3D U-Net, and it is independent of the volume resolution. We show that the proposed method is applicable to mono- and multi-modal segmentation tasks, achieving significant improvements over the state-of-the-art for the latter. The flexibility of the proposed framework is further demonstrated for a multi-modal registration application. As we learn to predict actions rather than a target, the proposed method is more robust compared to the 3D U-Net when dealing with previously unseen datasets, acquired using different protocols or modalities. As a result, the proposed method provides a promising multi-purpose segmentation and registration framework, particular in the context of image-guided interventions.

A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging

Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community.

0604 Comorbidities and Admission Rates in Inpatients Undergoing Sleep Studies

Introduction

Uncontrolled sleep-disordered breathing (SDB) and hypoventilation, which are common in COPD, CHF and obesity hypoventilation patients can lead to death and readmissions. It is unknown whether inpatient sleep studies to diagnose and optimize treatment improve care and prevent readmissions.

Methods

All patients > 18 years old with sleep studies while inpatient at Baystate Medical Center between October 2015 and September 2017 were included. Patient characteristics, comorbidities, sleep study diagnoses, and treatment recommendations were evaluated. Admission (inpatient or observation) and death rates were determined for 1-year before admit date and 1-year after discharge date of index admission.

Results

326 adult inpatients had 120 portable and 304 in-laboratory tests performed. Average age was 62.9±14.4, mean BMI was 37.2±12.3 and 56% were male. Principal diagnoses were CHF (50%), COPD (39%), both COPD and CHF (20%) and obesity hypoventilation (27%). 31 used PAP and 71 used oxygen prior to admission. Sleep diagnoses included OSA (73%), central sleep apnea (CSA) (29%), treatment emergent CSA (8%), hypoxia (48%), hypoventilation (41%), and normal or non-diagnostic (6%). Treatment recommendations included CPAP (25%), BiPAP (18%), BiPAP ST (3%), ASV (4%), iVAPS (22%), oxygen only (5%) and further titration (20%). The average length of stay was 11.6 ± 9.6 days. There was no difference in the percentage of patients who had an admission before or after their sleep study (53% vs 56%, respectively). In addition, no difference was seen in the median number of admissions before and after the sleep study (median=1.0, IQI=0-2, p=0.77). 90-day readmission rate was 19%. 14% died.

Conclusion

SDB, hypoxia and hypoventilation were common in inpatients evaluated with sleep studies with PAP therapy recommended in most patients. Further research is needed to determine whether inpatient testing and subsequent treatment can result in decreased readmissions and death.

Support

None

Comparative Evaluation of Fracture Resistance of Root Dentin Treated with Calendula Officinalis L. and Calcium Hydroxide as Intracanal Medicaments- An In vitro Study

Introduction: Intracanal Medicaments (ICMs) play a major role in disinfection of root canal system. The use of interappointment ICM during endodontic treatment may affect the mechanical properties of dentin which results in decreased fracture resistance of teeth. The use of synthetic medicament is associated with many limitations such as antibiotic overdose, side effects and cytotoxic reactions. In order to overcome this, recent research has been directed towards herbal ICMs with better efficacy and lesser side effects. Aim: To evaluate the effect of a novel herbal ICM Calendula officinalis L. (CO) on fracture resistance of root dentin in comparison to Calcium Hydroxide (CH). Materials and Methods: This in vitro study was conducted in the Department of Conservative Dentistry and Endodontics, SRM Dental College and Hospital, Ramapuram, Chennai, Tamil Nadu. Thirty freshly extracted single rooted human premolar teeth were selected for the in vitro study and randomly assigned into three groups: Group 1: No medication (Control group) (n=10), Group 2: CH (n=10), Group 3: CO (n=10). The samples were decoronated and biomechanical preparation was done followed by placement of respective ICMs in the root canal space, sealed with glass ionomer cement and immersed in saline for a storage period of 7 and 14 days. Each group was subdivided into 5 teeth, depending on the storage period. After each storage period, ICMs were removed and samples were subjected to fracture resistance test using universal testing machine. Data were analysed using One-way Anova followed by Tukey HSD post-hoc test with level of statistical significance set at p<0.05. Results: On evaluation of compressive strength, CO group showed more fracture resistance compared to CH group on 7th day and no statistical significant differences were seen between CO and CH groups on 14th day. Conclusion: CO can be efficiently used as an alternative to CH because of its low toxicity and increased resistance to fracture.

Registration of vascular structures using a hybrid mixture model

PURPOSE

Morphological changes to anatomy resulting from invasive surgical procedures or pathology, typically alter the surrounding vasculature. This makes it useful as a descriptor for feature-driven image registration in various clinical applications. However, registration of vasculature remains challenging, as vessels often differ in size and shape, and may even miss branches, due to surgical interventions or pathological changes. Furthermore, existing vessel registration methods are typically designed for a specific application. To address this limitation, we propose a generic vessel registration approach useful for a variety of clinical applications, involving different anatomical regions.

METHODS

A probabilistic registration framework based on a hybrid mixture model, with a refinement mechanism to identify missing branches (denoted as HdMM+) during vasculature matching, is introduced. Vascular structures are represented as 6-dimensional hybrid point sets comprising spatial positions and centerline orientations, using Student's t-distributions to model the former and Watson distributions for the latter.

RESULTS

The proposed framework is evaluated for intraoperative brain shift compensation, and monitoring changes in pulmonary vasculature resulting from chronic lung disease. Registration accuracy is validated using both synthetic and patient data. Our results demonstrate, HdMM+ is able to reduce more than [Formula: see text] of the initial error for both applications, and outperforms the state-of-the-art point-based registration methods such as coherent point drift and Student's t-distribution mixture model, in terms of mean surface distance, modified Hausdorff distance, Dice and Jaccard scores.

CONCLUSION

The proposed registration framework models complex vascular structures using a hybrid representation of vessel centerlines, and accommodates intricate variations in vascular morphology. Furthermore, it is generic and flexible in its design, enabling its use in a variety of clinical applications.

Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data

A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student's t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. The proposed approach jointly estimates the non-rigid transformations necessary to register an unbiased mean template (represented as a 7-dimensional hybrid point set comprising spatial positions, fibre orientations and FA values) to white matter regions of interest (ROIs), and approximates the joint distribution of voxel spatial positions, their associated principal diffusion axes, and FA. Specific white matter ROIs, namely, the corpus callosum and cingulum, are analysed across healthy control (HC) subjects (K = 20 samples) and patients diagnosed with mild cognitive impairment (MCI) (K = 20 samples) or Alzheimer's disease (AD) (K = 20 samples) using the proposed framework, facilitating inter-group comparisons of FA and fibre orientations. Group-wise analyses of the latter is not afforded by conventional approaches such as tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM).

Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer's disease

There is emerging evidence suggesting that Alzheimer's disease is a vascular disorder, caused by impaired cerebral perfusion, which may be promoted by cardiovascular risk factors that are strongly influenced by lifestyle. In order to develop an understanding of the exact nature of such a hypothesis, a biomechanical understanding of the influence of lifestyle factors is pursued. An extended poroelastic model of perfused parenchymal tissue coupled with separate workflows concerning subject-specific meshes, permeability tensor maps and cerebral blood flow variability is used. The subject-specific datasets used in the modelling of this paper were collected as part of prospective data collection. Two cases were simulated involving male, non-smokers (control and mild cognitive impairment (MCI) case) during two states of activity (high and low). Results showed a marginally reduced clearance of cerebrospinal fluid (CSF)/interstitial fluid (ISF), elevated parenchymal tissue displacement and CSF/ISF accumulation and drainage in the MCI case. The peak perfusion remained at 8 mm s⁻¹ between the two cases.

Group-wise similarity registration of point sets using Student's t-mixture model for statistical shape models

A probabilistic group-wise similarity registration technique based on Student's t-mixture model (TMM) and a multi-resolution extension of the same (mr-TMM) are proposed in this study, to robustly align shapes and establish valid correspondences, for the purpose of training statistical shape models (SSMs). Shape analysis across large cohorts requires automatic generation of the requisite training sets. Automated segmentation and landmarking of medical images often result in shapes with varying proportions of outliers and consequently require a robust method of alignment and correspondence estimation. Both TMM and mrTMM are validated by comparison with state-of-the-art registration algorithms based on Gaussian mixture models (GMMs), using both synthetic and clinical data. Four clinical data sets are used for validation: (a) 2D femoral heads (K= 1000 samples generated from DXA images of healthy subjects); (b) control-hippocampi (K= 50 samples generated from T1-weighted magnetic resonance (MR) images of healthy subjects); (c) MCI-hippocampi (K= 28 samples generated from MR images of patients diagnosed with mild cognitive impairment); and (d) heart shapes comprising left and right ventricular endocardium and epicardium (K= 30 samples generated from short-axis MR images of: 10 healthy subjects, 10 patients diagnosed with pulmonary hypertension and 10 diagnosed with hypertrophic cardiomyopathy). The proposed methods significantly outperformed the state-of-the-art in terms of registration accuracy in the experiments involving synthetic data, with mrTMM offering significant improvement over TMM. With the clinical data, both methods performed comparably to the state-of-the-art for the hippocampi and heart data sets, which contained few outliers. They outperformed the state-of-the-art for the femur data set, containing large proportions of outliers, in terms of alignment accuracy, and the quality of SSMs trained, quantified in terms of generalization, compactness and specificity.

Nanochitosan modified glass ionomer cement with enhanced mechanical properties and fluoride release

Conventional glass-ionomer cements (GIC) are one of the most prevalent dental restorative materials, but their use is limited by their relatively low mechanical strength. Efforts have been made to improve the mechanical properties by addition of various fillers of which nano-sized particles appears to be a promising strategy. In the current study, effect of addition of nanochitosan particles in GIC (NCH-GIC) on compressive strength, flexural strength, wear resistance and fluoride release has been evaluated and compared with conventional GIC (C-GIC). Nanochitosan was synthesized by ionic cross linking method and its particle size was found to be 110-235nm. Nanochitosan was mixed with glass ionomer powder at a concentration of 10wt.% and cement samples were prepared. NCH-GIC had significantly higher compressive strength values which could be attributed to early formation of aluminium polysalts. Similarly, flexural strength of NCH-GIC (21.26MPa) was significantly higher than C-GIC (12.67MPa). Wear resistance was also found to increase due to better integrated interface between the glass particle and polymer matrix bonding in NCH-GIC. Fluoride release was significantly higher in NCH-GIC compared to C-GIC for 7 days. It can be anticipated that addition of nanochitosan to GIC will improve the anti-cariogenic and mechanical properties for high strength applications.

Evaluation of wave delivery methodology for brain MRE: Insights from computational simulations

PURPOSE

MR elastography (MRE) of the brain is being explored as a biomarker of neurodegenerative disease such as dementia. However, MRE measures for healthy brain have varied widely. Differing wave delivery methodologies may have influenced this, hence finite element-based simulations were performed to explore this possibility.

METHODS

The natural frequencies of a series of cranial models were calculated, and MRE-associated vibration was simulated for different wave delivery methods at varying frequency, using simple isotropic viscoelastic material models for the brain. Displacement fields and the corresponding brain constitutive properties estimated by standard inversion techniques were compared across delivery methods and frequencies.

RESULTS

The delivery methods produced widely different MRE displacement fields and inversions. Furthermore, resonances at natural frequencies influenced the displacement patterns. Consequently, some delivery methods led to lower inversion errors than others, and the error on the storage modulus varied by up to 11% between methods.

CONCLUSION

Wave delivery has a considerable impact on brain MRE reliability. Assuming small variations in brain biomechanics, as recently reported to accompany neurodegenerative disease (e.g., 7% for Alzheimer's disease), the effect of wave delivery is important. Hence, a consensus should be established on a consistent methodology to ensure diagnostic and prognostic consistency. Magn Reson Med 78:341-356, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Magnetic resonance elastography of the brain: An in silico study to determine the influence of cranial anatomy

PURPOSE

Magnetic resonance elastography (MRE) of the brain has demonstrated potential as a biomarker of neurodegenerative disease such as dementia but requires further evaluation. Cranial anatomical features such as the falx cerebri and tentorium cerebelli membranes may influence MRE measurements through wave reflection and interference and tissue heterogeneity at their boundaries. We sought to determine the influence of these effects via simulation.

METHODS

MRE-associated mechanical stimulation of the brain was simulated using steady state harmonic finite element analysis. Simulations of geometrical models and anthropomorphic brain models derived from anatomical MRI data of healthy individuals were compared. Constitutive parameters were taken from MRE measurements for healthy brain. Viscoelastic moduli were reconstructed from the simulated displacement fields and compared with ground truth.

RESULTS

Interference patterns from reflections and heterogeneity resulted in artifacts in the reconstructions of viscoelastic moduli. Artifacts typically occurred in the vicinity of boundaries between different tissues within the cranium, with a magnitude of 10%-20%.

CONCLUSION

Given that MRE studies for neurodegenerative disease have reported only marginal variations in brain elasticity between controls and patients (e.g., 7% for Alzheimer's disease), the predicted errors are a potential confound to the development of MRE as a biomarker of dementia and other neurodegenerative diseases. Magn Reson Med 76:645-662, 2016. © 2015 Wiley Periodicals, Inc.

The effects of severe iron-deficiency anaemia on maternal and neonatal outcomes: A case-control study in an inner-city London hospital

This case-control study investigates the effects of severe iron-deficiency anaemia in pregnancy on maternal and neonatal outcomes in a relatively deprived inner-city population in a North London hospital. The study group comprised of 106 women with haemoglobin (Hb) < 8 g/dl at any point during pregnancy, while controls were 106 women with Hb > 11 g/dl throughout pregnancy. The study group lost an average of 80 ml more blood at delivery (p = 0.032) and had higher rates of postpartum haemorrhage than the control group (27 vs 12 patients, p = 0.012). However, anaemia did not appear to influence other maternal or neonatal outcomes; these may have been confounded by antenatal intervention with oral haematinics or blood transfusion.

A constitutive model for ballistic gelatin at surgical strain rates

This paper describes a constitutive model for ballistic gelatin at the low strain rates experienced, for example, by soft tissues during surgery. While this material is most commonly associated with high speed projectile penetration and impact investigations, it has also been used extensively as a soft tissue simulant in validation studies for surgical technologies (e.g. surgical simulation and guidance systems), for which loading speeds and the corresponding mechanical response of the material are quite different. We conducted mechanical compression experiments on gelatin specimens at strain rates spanning two orders of magnitude (~0.001-0.1s(-1)) and observed a nonlinear load-displacement history and strong strain rate-dependence. A compact and efficient visco-hyperelastic constitutive model was then formulated and found to fit the experimental data well. An Ogden type strain energy density function was employed for the elastic component. A single Prony exponential term was found to be adequate to capture the observed rate-dependence of the response over multiple strain rates. The model lends itself to immediate use within many commercial finite element packages.