ChronoMID—Cross-modal neural networks for 3-D temporal medical imaging data

ChronoMID—neural networks for temporally-varying, hence Chrono, Medical Imaging Data—makes the novel application of cross-modal convolutional neural networks (X-CNNs) to the medical domain. In this paper, we present multiple approaches for incorporating temporal information into X-CNNs and compare their performance in a case study on the classification of abnormal bone remodelling in mice. Previous work developing medical models has predominantly focused on either spatial or temporal aspects, but rarely both. Our models seek to unify these complementary sources of information and derive insights in a bottom-up, data-driven approach. As with many medical datasets, the case study herein exhibits deep rather than wide data; we apply various techniques, including extensive regularisation, to account for this. After training on a balanced set of approximately 70000 images, two of the models—those using difference maps from known reference points—outperformed a state-of-the-art convolutional neural network baseline by over 30pp (> 99% vs. 68.26%) on an unseen, balanced validation set comprising around 20000 images. These models are expected to perform well with sparse data sets based on both previous findings with X-CNNs and the representations of time used, which permit arbitrarily large and irregular gaps between data points. Our results highlight the importance of identifying a suitable description of time for a problem domain, as unsuitable descriptors may not only fail to improve a model, they may in fact confound it

Uncertainty inequalities on groups and homogeneous spaces via isoperimetric inequalities

We prove a family of $L^p$ uncertainty inequalities on fairly general groups and homogeneous spaces, both in the smooth and in the discrete setting. The crucial point is the proof of the $L^1$ endpoint, which is derived from a general weak isoperimetric inequality.Comment: 17 page

Chronic cerebrospinal venous insufficiency in patients with multiple sclerosis

BACKGROUND: The extracranial venous outflow routes in clinically defined multiple sclerosis (CDMS) have never been investigated. METHODS: Sixty-five patients affected by CDMS, and 235 controls composed, respectively, of healthy subjects, healthy subjects older than CDMS patients, patients affected by other neurological diseases, and older controls not affected by neurological diseases but scheduled for venography (HAV-C), blindly underwent a combined transcranial and extracranial Color-Doppler high-resolution examination (TCCS-ECD) aimed at detecting at least two of five parameters of anomalous venous outflow. According to the TCCS-ECD screening, patients and HAV-C further underwent selective venography of the azygous and jugular venous system with venous pressure measurement. RESULTS: CDMS and TCCS-ECD venous outflow anomalies were dramatically associated (OR 43, 95% CI 29-65, p<0.0001). Subsequently, venography demonstrated in CDMS, and not in controls, the presence of multiple severe extracranial stenosis, affecting the principal cerebrospinal venous segments; it configures a picture of chronic cerebrospinal venous insufficiency (CCSVI) with four different patterns of distribution of stenosis and substitute circle. Moreover, relapsing-remitting and secondary progressive courses were associated to CCSVI patterns significantly different from those of primary progressive (p<0.0001). Finally, the pressure gradient measured across the venous stenosies was slightly but significantly higher. CONCLUSION: CDMS is strongly associated with CCSVI, a picture never been described so far, characterized by abnormal venous haemodynamics determined by extracranial multiple venous strictures of unknown origin. The location of venous obstructions plays a key role in determining the clinical course of the disease

Effect of radiation-induced damage of trabecular bone tissue evaluated using indentation and digital volume correlation

Exposure to X-ray radiation for an extended amount of time can cause damage to the bone tissue and therefore affect its mechanical properties. Specifically, high-resolution X-ray Computed Tomography (XCT), in both synchrotron and lab-based systems, has been employed extensively for evaluating bone micro-to-nano architecture. However, to date, it is still unclear how long exposures to X-ray radiation affect the mechanical properties of trabecular bone, particularly in relation to lab-XCT systems. Indentation has been widely used to identify local mechanical properties such as hardness and elastic modulus of bone and other biological tissues. The purpose of this study is therefore, to use indentation and XCT-based investigative tools such as digital volume correlation (DVC) to assess the microdamage induced by long exposure of trabecular bone tissue to X-ray radiation and how this affects its local mechanical properties. Trabecular bone specimens were indented before and after X-ray exposures of 33 and 66 h, where variation of elastic modulus was evaluated at every stage. The resulting elastic modulus was decreased, and micro-cracks appeared in the specimens after the first long X-ray exposure and crack formation increased after the second exposure. High strain concentration around the damaged tissue exceeding 1% was also observed from DVC analysis. The outcomes of this study show the importance of designing appropriate XCT-based experiments in lab systems to avoid degradation of the bone tissue mechanical properties due to radiation and these results will help to inform future studies that require long X-ray exposure for in situ experiments or generation of reliable subject-specific computational models

Biomechanical assessment of vertebrae with lytic metastases with subject-specific finite element models

The assessment of risk of vertebral fracture in patients with lytic metastases is challenging, due to the complexity in modelling the mechanical properties of this heterogeneous material. Currently clinical assessment of patients at high risk of fracture is based on the Spinal Instability Neoplastic Score (SINS), which however in many cases does not provide clear guidelines. The goal of this study was to develop a computational approach to provide a comparative biomechanical assessment of vertebrae with lytic lesions with respect to the adjacent controls and highlight the critical vertebrae. The computed tomography images of the thoracolumbar spine of eight patients suffering of vertebral lytic metastases with SINS between 7 and 12 (indeterminate unstable) were analysed. For each patient one or two vertebrae with lytic lesions were modelled and the closest vertebrae without lesions were considered as control. Metastatic vertebrae (N = 12) and controls (N = 18) were converted to subject-specific, heterogeneous, isotropic, nonlinear finite element models for simulating uniaxial compression. Densitometric and mechanical properties were computed for each vertebra. In average, similar mechanical properties were found for vertebrae with lytic lesions and controls (e.g. ultimate force equal to 6.2 ± 2.7 kN for vertebrae with lytic lesions and to 6.2 ± 3.0 kN for control vertebrae). Only in three patients the vertebrae with lytic lesions were found to be mechanically weaker (−19% to −75% difference for ultimate stress) than the controls. In conclusion, in this study we presented an approach to estimate the mechanical competence of vertebrae with lytic metastases. It remains to be investigated in a clinical study if this method, together with the SINS, can better classify patients with vertebrae with lytic lesions at high risk of fracture

The application of digital volume correlation (DVC) to evaluate strain predictions generated by finite element models of the osteoarthritic humeral head

Continuum-level finite element models (FEMs) of the humerus offer the ability to evaluate joint replacement designs preclinically; however, experimental validation of these models is critical to ensure accuracy. The objective of the current study was to quantify experimental full-field strain magnitudes within osteoarthritic (OA) humeral heads by combining mechanical loading with volumetric microCT imaging and digital volume correlation (DVC). The experimental data was used to evaluate the accuracy of corresponding FEMs. Six OA humeral head osteotomies were harvested from patients being treated with total shoulder arthroplasty and mechanical testing was performed within a microCT scanner. MicroCT images (33.5 µm isotropic voxels) were obtained in a pre- and post-loaded state and BoneDVC was used to quantify full-field experimental strains (≈ 1 mm nodal spacing, accuracy = 351 µstrain, precision = 518 µstrain). Continuum-level FEMs with two types of boundary conditions (BCs) were simulated: DVC-driven and force-driven. Accuracy of the FEMs was found to be sensitive to the BC simulated with better agreement found with the use of DVC-driven BCs (slope = 0.83, r2 = 0.80) compared to force-driven BCs (slope = 0.22, r2 = 0.12). This study quantified mechanical strain distributions within OA trabecular bone and demonstrated the importance of BCs to ensure the accuracy of predictions generated by corresponding FEMs

Non-invasive prediction of the mouse tibia mechanical properties from microCT images: comparison between different finite element models

New treatments for bone diseases require testing in animal models before clinical translation, and the mouse tibia is among the most common models. In vivo micro-Computed Tomography (microCT)-based micro-Finite Element (microFE) models can be used for predicting the bone strength non-invasively, after proper validation against experimental data. Different modelling techniques can be used to estimate the bone properties, and the accuracy associated with each is unclear. The aim of this study was to evaluate the ability of different microCT-based microFE models to predict the mechanical properties of the mouse tibia under compressive load. Twenty tibiae were microCT scanned at 10.4 µm voxel size and subsequently compressed at 0.03 mm/s until failure. Stiffness and failure load were measured from the load–displacement curves. Different microFE models were generated from each microCT image, with hexahedral or tetrahedral mesh, and homogeneous or heterogeneous material properties. Prediction accuracy was comparable among models. The best correlations between experimental and predicted mechanical properties, as well as lower errors, were obtained for hexahedral models with homogeneous material properties. Experimental stiffness and predicted stiffness were reasonably well correlated (R2 = 0.53–0.65, average error of 13–17%). A lower correlation was found for failure load (R2 = 0.21–0.48, average error of 9–15%). Experimental and predicted mechanical properties normalized by the total bone mass were strongly correlated (R2 = 0.75–0.80 for stiffness, R2 = 0.55–0.81 for failure load). In conclusion, hexahedral models with homogeneous material properties based on in vivo microCT images were shown to best predict the mechanical properties of the mouse tibia

Whole genome SNP-associated signatures of local adaptation in honeybees of the Iberian Peninsula

The availability of powerful high-throughput genomic tools, combined with genome scans, has helped identifying genes and genetic changes responsible for environmental adaptation in many organisms, including the honeybee. Here, we resequenced 87 whole genomes of the honeybee native to Iberia and used conceptually different selection methods (Samβada, LFMM, PCAdapt, iHs) together with in sillico protein modelling to search for selection footprints along environmental gradients. We found 670 outlier SNPs, most of which associated with precipitation, longitude and latitude. Over 88.7% SNPs laid outside exons and there was a significant enrichment in regions adjacent to exons and UTRs. Enrichment was also detected in exonic regions. Furthermore, in silico protein modelling suggests that several non-synonymous SNPs are likely direct targets of selection, as they lead to amino acid replacements in functionally important sites of proteins. We identified genomic signatures of local adaptation in 140 genes, many of which are putatively implicated in fitness-related functions such as reproduction, immunity, olfaction, lipid biosynthesis and circadian clock. Our genome scan suggests that local adaptation in the Iberian honeybee involves variations in regions that might alter patterns of gene expression and in protein-coding genes, which are promising candidates to underpin adaptive change in the honeybee.John C. Patton, Phillip San Miguel, Paul Parker, Rick Westerman, University of Purdue, resequenced the 87 whole genomes of IHBs. Jose Rufino provided computational resources at IPB. Analyses were performed using the computational resources at the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX), Uppsala University. DH was supported by a PhD scholarship (SFRH/BD/84195/2012) from the Portuguese Science Foundation (FCT). MAP is a member of and receives support from the COST Action FA1307 (SUPER-B). This work was supported by FCT through the programs COMPETE/QREN/EU (PTDC/BIA-BEC/099640/2008) and the 2013-2014 BiodivERsA/FACCE-JPI (joint call for research proposals, with the national funders FCT, Portugal, CNRS, France, and MEC, Spain) to MAP

Are CT-Based Finite Element Model Predictions of Femoral Bone Strengthening Clinically Useful?

Purpose of Review: This study reviews the available literature to compare the accuracy of areal bone mineral density derived from dual X-ray absorptiometry (DXA-aBMD) and of subject-specific finite element models derived from quantitative computed tomography (QCT-SSFE) in predicting bone strength measured experimentally on cadaver bones, as well as their clinical accuracy both in terms of discrimination and prediction. Based on this information, some basic cost-effectiveness calculations are performed to explore the use of QCT-SSFE instead of DXA-aBMD in (a) clinical studies with femoral strength as endpoint, (b) predictor of the risk of hip fracture in low bone mass patients. Recent Findings: Recent improvements involving the use of smooth-boundary meshes, better anatomical referencing for proximal-only scans, multiple side-fall directions, and refined boundary conditions increase the predictive accuracy of QCT-SSFE. Summary: If these improvements are adopted, QCT-SSFE is always preferable over DXA-aBMD in clinical studies with femoral strength as the endpoint, while it is not yet cost-effective as a hip fracture risk predictor, although pathways that combine both QCT-SSFE and DXA-aBMD are promising