Suchergebnisse

The research of first author was supported by MECD (Spain) FPU2016/00015, by MINECO (Spain) Grant MTM2015-65020-P and by Junta de Andaluc´ıa Grant FQM- 0185. Second author gratefully acknowledges support of MINECO (Spain) through grants MTM2016-76808-P, MTM2016-75196-P, and the "Severo Ochoa Programme for Centres of Excellence in R&D" (SEV-2015-0554). Third author was supported by MINECO (Spain) Grant MTM2017-88385-P, QUITEMAD+-CM (S2013/ICE- 2801) and the "Severo Ochoa Programme for Centres of Excellence in R&D" (SEV-2015-0554). ; We study the Daugavet property in tensor products of Banach spaces. We show that L1(μ)b "L1(ν) has the Daugavet property when μ and ν are purely non-atomic measures. Also, we show that Xb Y has the Daugavet property provided X and Y are L1-preduals with the Daugavet property, in particular spaces of continuous functions with this property. With the same tecniques, we also obtain consequences about roughness in projective tensor products as well as the Daugavet property of projective symmetric tensor products. ; MECD (Spain) FPU2016/00015 ; Spanish Government PGC2018-093794-B-I00 ; Junta de Andalucia A-FQM-484-UGR18 FQM-0185 ; MINECO (Spain) MTM2016-76808-P MTM2016-75196-P MTM2017-88385-P ; Severo Ochoa Programme for Centres of Excellence in RD SEV-2015-0554 ; QUITEMAD+-CM S2013/ICE-2801

Analysis of renal diffusion-weighted imaging (DWI) data to derive markers of tissue properties requires careful consideration of the type, extent, and limitations of the acquired data. Alongside data quality and general suitability for quantitative analysis, choice of diffusion model, fitting algorithm, and processing steps can have consequences for the precision, accuracy, and reliability of derived diffusion parameters. Here we introduce and discuss important steps for diffusion-weighted image processing, and in particular give example analysis protocols and pseudo-code for analysis using the apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) models. Following an overview of general principles, we provide details of optional steps, and steps for validation of results. Illustrative examples are provided, together with extensive notes discussing wider context of individual steps, and notes on potential pitfalls.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concepts and experimental procedure.

AbstractDiffusion-weighted MR Imaging is a rapidly emerging technique, that allows in-vivo mapping processes of the water diffusion in tissues. It has the potential capabilities for clinical application in breast imaging. The aim of this study was to find out the optimal b-value for calculation of ADC value for differential diagnosis of breast lesions. A total of 124 subjects (mean age 46 years) with 141 lesions were included. The protocol consists of axial T2 sequence for lesion localization and measurement and DW sequence with three sets of b-values of 0, 300, 600, and 1000 s/mm2. The mean ADC values of the breast lesions for b-values (0, 300, 600, and 1000) were 1.75 ± 0.18 × 10−3mm2/sec, 1.66 ± 0.12 × 10−3mm2/sec and 1.57 ± 0.15 × 10−3mm2/sec for the benign lesions and 1.26 ± 0.048 × 10−3mm2/sec, 1.14 ± 0.11 × 10−3mm2/sec and 0.93 ± 0.14 × 10−3mm2/sec for malignant lesions respectively. Statistical significant differences were noted on the ADC value of benign and malignant lesions among the three sets of b values (p = 0.001). ADC values of malignant lesion was significantly lower compared to benign lesions. The AUC (0.998) was substantially large for b-value of 0,600 s/mm2 with a threshold ADC cut off value of 1.28 × 10−3mm2/sec with 98.4% sensitivity, 93.2% specificity and 98.5% positive predictive value(PPV). In conclusion, diffusion weighted imaging has the ability for differential diagnosis of breast lesions with the optimal b value of 0,600 s/ mm2. DWI is a reliable tool for characterising breast lesions and may increase the overall specificity of breast MRI.

Standard MRI methods are often inadequate for identifying mild traumatic brain injury (TBI). Advances in diffusion tensor imaging now provide potential biomarkers of TBI among white matter fascicles (tracts). However, it is still unclear which tracts are most pertinent to TBI diagnosis. This study ranked fiber tracts on their ability to discriminate patients with and without TBI. We acquired diffusion tensor imaging data from military veterans admitted to a polytrauma clinic (Overall n = 109; Age: M = 47.2, SD = 11.3; Male: 88%; TBI: 67%). TBI diagnosis was based on self-report and neurological examination. Fiber tractography analysis produced 20 fiber tracts per patient. Each tract yielded four clinically relevant measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity). We applied receiver operating characteristic (ROC) analyses to identify the most diagnostic tract for each measure. The analyses produced an optimal cutpoint for each tract. We then used kappa coefficients to rate the agreement of each cutpoint with the neurologist's diagnosis. The tract with the highest kappa was most diagnostic. As a check on the ROC results, we performed a stepwise logistic regression on each measure using all 20 tracts as predictors. We also bootstrapped the ROC analyses to compute the 95% confidence intervals for sensitivity, specificity, and the highest kappa coefficients. The ROC analyses identified two fiber tracts as most diagnostic of TBI: the left cingulum (LCG) and the left inferior fronto-occipital fasciculus (LIF). Like ROC, logistic regression identified LCG as most predictive for the FA measure but identified the right anterior thalamic tract (RAT) for the MD, RD, and AD measures. These findings are potentially relevant to the development of TBI biomarkers. Our methods also demonstrate how ROC analysis may be used to identify clinically relevant variables in the TBI population.

BackgroundPervasive use of improvised explosive devices (IEDs), rocket-propelled grenades, and land mines in the recent conflicts in Iraq and Afghanistan has brought traumatic brain injury (TBI) and its impact on health outcomes into public awareness. Blast injuries have been deemed signature wounds of these wars. War-related TBI is not new, having become prevalent during WWI and remaining medically relevant in WWII and beyond. Medicine's past attempts to accurately diagnose and disentangle the pathophysiology of war-related TBI parallels current lines of inquiry and highlights limitations in methodology and attribution of symptom etiology, be it organic, psychological, or behavioral. New approaches and biomarkers are needed.PreclinicalSerological biomarkers and biomarkers of injury obtained with imaging techniques represent cornerstones in the translation between experimental data and clinical observations. Experimental models for blast related TBI and PTSD can generate critical data on injury threshold, for example for white matter injury from acceleration. Carefully verified and validated models can be evaluated with gene expression arrays and proteomics to identify new candidates for serological biomarkers. Such models can also be analyzed with diffusion MRI and microscopy in order to identify criteria for detection of diffuse white matter injuries, such as DAI (diffuse axonal injury). The experimental models can also be analyzed with focus on injury outcome in brain stem regions, such as locus coeruleus or nucleus raphe magnus that can be involved in response to anxiety changes.ClinicalMild (and some moderate) TBI can be difficult to diagnose because the injuries are often not detectable on conventional MRI or CT. There is accumulating evidence that injured brain tissues in TBI patients generate abnormal low-frequency magnetic activity (ALFMA, peaked at 1-4Hz) that can be measured and localized by magnetoencephalography (MEG). MEG imaging detects TBI abnormalities at the rates of 87% for the mild TBI, group (blast-induced plus non-blast causes) and 100% for the moderate group. Among the mild TBI patients, the rates of abnormalities are 96% and 77% for the blast and non-blast TBI groups, respectively. There is emerging evidence based on fMRI and MEG studies showing hyper-activity in the amygdala and hypo-activity in pre-frontal cortex in individuals with PTSD. MEG signal may serve as a sensitive imaging marker for mTBI, distinguishable from abnormalities generated in association with PTSD. More work is needed to fully describe physiological mechanisms of post-concussive symptoms.

Abstract
A tensor is a multidimensional array of numbers that can be used to store data, encode a computational relation and represent quantum entanglement. In this sense a tensor can be viewed as valuable resource whose transformation can lead to an understanding of structure in data, computational complexity and quantum information. In order to facilitate the understanding of this resource, we propose a family of information-theoretically constructed preorders on tensors, which can be used to compare tensors with each other and to assess the existence of transformations between them. The construction places copies of a given tensor at the edges of a hypergraph and allows transformations at the vertices. A preorder is then induced by the transformations possible in a given growing sequence of hypergraphs. The new family of preorders generalises the asymptotic restriction preorder which Strassen defined in order to study the computational complexity of matrix multiplication. We derive general properties of the preorders and their associated asymptotic notions of tensor rank and view recent results on tensor rank non-additivity, tensor networks and algebraic complexity in this unifying frame. We hope that this work will provide a useful vantage point for exploring tensors in applied mathematics, physics and computer science, but also from a purely mathematical point of view.

Erstes Kapitel: Tensoren -- Zweites Kapitel: Der einzelne Diatensor -- Drittes Kapitel: Additive Eigenschaften, additive Zerlegung des Diatensors und die auf dieselbe gegründete Transformation des Raumes -- Viertes Kapitel: Die Multiplikation der Diatensoren -- Fünftes Kapitel: Die Faktorenzerlegung und die auf dieselbe gegründete Behandlung der Transformation des Raumes -- Erstes Kapitel: Die einzelne Dyade -- Zweites Kapitel: Dyadentripel -- Drittes Kapitel: Hinweise auf Begriffserweiterungen -- Erstes Kapitel: Additive Diatensoren -- Zweites Kapitel: Derivative Operationen -- Drittes Kapitel: Vektorfelder und Diatensoren -- Viertes Kapitel: Orthogonale krummlinige Koordinaten -- Erstes Kapitel: Die Voigtsche Ableitung des Tensorbegriffes -- Zweites Kapitel: Selbständige Tensoren und Diatensoren -- Register der Definitionen.

Use of wireless signal for identification of unknown object, or technology to see-through a wall to form an image, is gaining growing interest from various fields including law enforcement and military sectors, disaster management, or even in civilian sectors such as construction sites. The great challenge in the implementation of such technology is the stochastic disturbances on wireless signal which will result in a signal with missing samples. Compressive Sensing (CS) is a powerful tool for estimating the missing samples since it can find accurate solution to largely underdetermined linear wireless signals. However, sparse models like CS can also suffer from information loss dues to stochastic lossy nature of wireless, making CS not to have accurate information for reconstruction of a signal. In this paper, we developed a theoretical and experimental framework for the mapping of obstacles by reconstructing the wireless signal based on a sparse signal. We apply tensor format to perform the computations along each mode by relaxing the tensor constraints to obtain accurate results. The proposed framework demonstrates how to take 2D signals, formulate estimate signals and produce a 3D image location in a completely unknown area inside of the obstacle (wall). ; Funding Agencies|strategic innovation programme Smart Built Environment; VinnovaVinnova; FormasSwedish Research Council Formas; Energimyndigheten

We extract the isovector tensor nucleon form factors, which play an important role in understanding the transverse spin structure of the nucleon when related to the quark helicity-flip generalized parton distributions via their first moments. We employ the light-cone QCD sum rules to leading order in QCD and include distribution amplitudes up to twist 6 in order to calculate the three tensor form factors HT, ET and . Our results agree well with those from other approaches in the low and high momentum-transfer regions. ; TÜBİTAK ; European Union ; publisher version

Background The relationship between lumbar spine posture and muscle structure is not well understood. Objectives To investigate the predictive capacity of muscle structure on lumbar spine posture in active-duty Marines. Methods Forty-three Marines were scanned in this cross-sectional study, using an upright magnetic resonance imaging scanner while standing without load and standing, sitting, and prone on elbows with body armor. Cobb, horizontal, and sacral angles were measured. Marines were then scanned while unloaded in supine using a supine magnetic resonance imaging scanner. The imaging protocol consisted of T2 intervertebral disc mapping; high-resolution, anatomical, fat-water separation, and diffusion tensor imaging to quantify disc hydration and muscle volume, fat fraction, and restricted diffusion profiles in the lumbar muscles. A stepwise multiple linear regression model was used to identify physiological measures predictive of lumbar spine posture. Results The multiple regression model demonstrated that fractional anisotropy of the erector spinae was a significant predictor of lumbar posture for 7 of 18 dependent variables measured, and explained 20% to 35% of the variance in each model. Decreased fractional anisotropy of the erector spinae predicted decreased lordosis, lumbosacral extension, and anterior pelvic tilt. Conclusion Fractional anisotropy is inversely related with muscle fiber size, which is associated with the isometric force-generating capacity of a muscle fiber. This suggests that stronger erector spinae muscles predict decreased lordosis, lumbosacral extension, and anterior pelvic tilt in a highly trained population. J Orthop Sports Phys Ther 2018;48(8):613-621. Epub 17 May 2018. doi:10.2519/jospt.2018.7865.

Background The relationship between lumbar spine posture and muscle structure is not well understood. Objectives To investigate the predictive capacity of muscle structure on lumbar spine posture in active-duty Marines. Methods Forty-three Marines were scanned in this cross-sectional study, using an upright magnetic resonance imaging scanner while standing without load and standing, sitting, and prone on elbows with body armor. Cobb, horizontal, and sacral angles were measured. Marines were then scanned while unloaded in supine using a supine magnetic resonance imaging scanner. The imaging protocol consisted of T2 intervertebral disc mapping; high-resolution, anatomical, fat-water separation, and diffusion tensor imaging to quantify disc hydration and muscle volume, fat fraction, and restricted diffusion profiles in the lumbar muscles. A stepwise multiple linear regression model was used to identify physiological measures predictive of lumbar spine posture. Results The multiple regression model demonstrated that fractional anisotropy of the erector spinae was a significant predictor of lumbar posture for 7 of 18 dependent variables measured, and explained 20% to 35% of the variance in each model. Decreased fractional anisotropy of the erector spinae predicted decreased lordosis, lumbosacral extension, and anterior pelvic tilt. Conclusion Fractional anisotropy is inversely related with muscle fiber size, which is associated with the isometric force-generating capacity of a muscle fiber. This suggests that stronger erector spinae muscles predict decreased lordosis, lumbosacral extension, and anterior pelvic tilt in a highly trained population. J Orthop Sports Phys Ther 2018;48(8):613-621. Epub 17 May 2018. doi:10.2519/jospt.2018.7865.

Tensor Properties of Solids presents the phenomenological development of solid state properties represented as matter tensors in two parts: Part I on equilibrium tensor properties and Part II on transport tensor properties.Part I begins with an introduction to tensor notation, transformations, algebra, and calculus together with the matrix representations. Crystallography, as it relates to tensor properties of crystals, completes the background treatment. A generalized treatment of solid-state equilibrium thermodynamics leads to the systematic correlation of equilibrium tensor properties. This