Showing papers by "Philips published in 2016"

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

Abstract: Background: As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective: To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods: A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results: The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions: A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. [J Med Internet Res 2016;18(12):e323]

A Novel Algorithm for Remote Photoplethysmography: Spatial Subspace Rotation

Abstract: In this paper, we propose a conceptually novel algorithm, namely “Spatial Subspace Rotation” (2SR), that improves the robustness of remote photoplethysmography. Based on the assumption of 1) spatially redundant pixel-sensors of a camera, and 2) a well-defined skin mask, our core idea is to estimate a spatial subspace of skin-pixels and measure its temporal rotation for pulse extraction, which does not require skin-tone or pulse-related priors in contrast to existing algorithms. The proposed algorithm is thoroughly assessed on a benchmark dataset containing 54 videos, which includes challenges of various skin-tones, body-motions in complex illuminance conditions, and pulse-rate recovery after exercise. The experimental results show that given a well-defined skin mask, 2SR outperforms the popular ICA-based approach and two state-of-the-art algorithms (CHROM and PBV). When comparing the pulse frequency spectrum, 2SR improves on average the SNR of ICA by 2.22 dB, CHROM by 1.56 dB, and PBV by 1.95 dB. When comparing the instant pulse-rate, 2SR improves on average the Pearson correlation and precision of ICA by 47% and 65%, CHROM by 22% and 23%, and PBV by 21% and 39%. ANOVA confirms the significant improvement of 2SR in peak-to-peak accuracy. The proposed 2SR algorithm is very simple to use and extend, i.e., the implementation only requires a few lines MATLAB code.

New principle for measuring arterial blood oxygenation, enabling motion-robust remote monitoring.

Abstract: Finger-oximeters are ubiquitously used for patient monitoring in hospitals worldwide. Recently, remote measurement of arterial blood oxygenation (SpO2) with a camera has been demonstrated. Both contact and remote measurements, however, require the subject to remain static for accurate SpO2 values. This is due to the use of the common ratio-of-ratios measurement principle that measures the relative pulsatility at different wavelengths. Since the amplitudes are small, they are easily corrupted by motion-induced variations. We introduce a new principle that allows accurate remote measurements even during significant subject motion. We demonstrate the main advantage of the principle, i.e. that the optimal signature remains the same even when the SNR of the PPG signal drops significantly due to motion or limited measurement area. The evaluation uses recordings with breath-holding events, which induce hypoxemia in healthy moving subjects. The events lead to clinically relevant SpO2 levels in the range 80–100%. The new principle is shown to greatly outperform current remote ratio-of-ratios based methods. The mean-absolute SpO2-error (MAE) is about 2 percentage-points during head movements, where the benchmark method shows a MAE of 24 percentage-points. Consequently, we claim ours to be the first method to reliably measure SpO2 remotely during significant subject motion.

Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds

Abstract: The goal of the 2016 PhysioNet/CinC Challenge is the development of an algorithm to classify normal/abnormal heart sounds. A total of 124 time-frequency features were extracted from the phonocardiogram (PCG) and input to a variant of the AdaBoost classifier. A second classifier using convolutional neural network (CNN) was trained using PCGs cardiac cycles decomposed into four frequency bands. The final decision rule to classify normal/abnormal heart sounds was based on an ensemble of classifiers combining the outputs of AdaBoost and the CNN. The algorithm was trained on a training dataset (normal= 2575, abnormal= 665) and evaluated on a blind test dataset. Our classifier ensemble approach obtained the highest score of the competition with a sensitivity, specificity, and overall score of 0.9424, 0.7781, and 0.8602, respectively.

Making sense of big data in health research: Towards an EU action plan

Abstract: Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of “big data” for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.

Neural Paraphrase Generation with Stacked Residual LSTM Networks

Abstract: In this paper, we propose a novel neural approach for paraphrase generation. Conventional paraphrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles. To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation. Our primary contribution is a stacked residual LSTM network, where we add residual connections between LSTM layers. This allows for efficient training of deep LSTMs. We evaluate our model and other state-of-the-art deep learning models on three different datasets: PPDB, WikiAnswers, and MSCOCO. Evaluation results demonstrate that our model outperforms sequence to sequence, attention-based, and bi-directional LSTM models on BLEU, METEOR, TER, and an embedding-based sentence similarity metric.

Double-Negative Mechanical Metamaterials Displaying Simultaneous Negative Stiffness and Negative Poisson's Ratio Properties

Abstract: A scalable mechanical metamaterial simultaneously displaying negative stiffness and negative Poisson's ratio responses is presented Interlocking hexagonal subunit assemblies containing 3 alternative embedded negative stiffness (NS) element types display Poisson's ratio values of -1 and NS values over two orders of magnitude (-14 N mm-1 to -160 N mm-1 ), in good agreement with model predictions

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine: Brussels, Belgium. 15-18 March 2016

Abstract: [This corrects the article DOI: 10.1186/s13054-016-1208-6.].

Phytochrome A and B function antagonistically to regulate cold tolerance via abscisic acid-dependent jasmonate signaling.

Abstract: Light signaling and phytohormones both influence plant growth, development, and stress responses; however, cross talk between these two signaling pathways in response to cold remains underexplored. Here, we report that far-red light (FR) and red light (R) perceived by phytochrome A (phyA) and phyB positively and negatively regulated cold tolerance, respectively, in tomato (Solanum lycopersicum), which were associated with the regulation of levels of phytohormones such as abscisic acid (ABA) and jasmonic acid (JA) and transcript levels of ABA- and JA-related genes and the C-REPEAT BINDING FACTOR (CBF) stress signaling pathway genes. A reduction in the R/FR ratio did not alter cold tolerance, ABA and JA accumulation, and transcript levels of ABA- and JA-related genes and the CBF pathway genes in phyA mutant plants; however, those were significantly increased in wild-type and phyB plants with the reduction in the R/FR ratio. Even though low R/FR treatments did not confer cold tolerance in ABA-deficient (notabilis [not]) and JA-deficient (prosystemin-mediated responses2 [spr2]) mutants, it up-regulated ABA accumulation and signaling in the spr2 mutant, with no effect on JA levels and signaling in the not mutant. Foliar application of ABA and JA further confirmed that JA functioned downstream of ABA to activate the CBF pathway in light quality-mediated cold tolerance. It is concluded that phyA and phyB function antagonistically to regulate cold tolerance that essentially involves FR light-induced activation of phyA to induce ABA signaling and, subsequently, JA signaling, leading to an activation of the CBF pathway and a cold response in tomato plants.

Transthoracic 3D Echocardiographic Left Heart Chamber Quantification Using an Automated Adaptive Analytics Algorithm.

Abstract: Objectives The goal of this study was to test the feasibility and accuracy of an automated algorithm that simultaneously quantifies 3-dimensional (3D) transthoracic echocardiography (TTE)-derived left atrial (LA) and left ventricular (LV) volumes and left ventricular ejection fraction (LVEF). Conventional manual 3D TTE tracings and cardiac magnetic resonance (CMR) images were used as a reference for comparison. Background Cardiac chamber quantification from 3D TTE is superior to 2D TTE measurements. However, integration of 3D quantification into clinical practice has been limited by time-consuming workflow and the need for 3D expertise. A novel automated software was developed that provides LV and LA volumetric quantification from 3D TTE datasets that reflect real-life manual 3-dimensional echocardiography measurements and values comparable to CMR. Methods A total of 159 patients were studied in 2 separate protocols. In protocol 1, 94 patients underwent 3D TTE imaging (EPIQ, iE33, X5-1, Philips Healthcare, Andover, Massachusetts) covering the left atrium and left ventricle. LA and LV volumes and LVEF were obtained using the automated software (HeartModel, Philips Healthcare) with and without contour correction, and compared with the averaged manual 3D volumetric measurements from 3 readers. In protocol 2, automated measurements from 65 patients were compared with a CMR reference. The Pearson correlation coefficient, Bland-Altman analysis, and paired Student t tests were used to assess inter-technique agreement. Results Correlations between the automated and manual 3D TTE measurements were strong (r = 0.87 to 0.96). LVEF was underestimated and automated LV end-diastolic, LV end-systolic, and LA volumes were overestimated compared with manual measurements. Agreement between the automated analysis and CMR was also strong (r = 0.84 to 0.95). Test–retest variability was low. Conclusions Automated simultaneous quantification of LA and LV volumes and LVEF is feasible and requires minimal 3D software analysis training. The automated measurements are not only comparable to manual measurements but also to CMR. This technique is highly reproducible and timesaving, and it therefore promises to facilitate the integration of 3D TTE-based left-heart chamber quantification into clinical practice.

Depression and anxiety as predictors of mortality among heart failure patients : systematic review and meta-analysis

Abstract: Several studies suggest that psychological factors are associated with negative outcomes and in particular higher mortality rates among heart failure (HF) patients. We aimed to evaluate the effect sizes of depression and anxiety on all-cause mortality in HF patients. We conducted a systematic review according to the PRISMA methodology. We searched for studies on depression or anxiety effects on all-cause mortality among HF patients published up to June 2015. A number of 26 and 6 articles met inclusion criteria for depression (total 80,627 patients) and anxiety (total 17,214 patients), respectively. The effect estimates were pooled using random-effect meta-analysis. Depression has significant and moderately heterogeneous effect on all-cause mortality (HR = 1.57; 95%CI 1.30-1.89, p < 0.001); adjustment for confounders led to a similar effect estimate (HR = 1.40; 95%CI 1.22-1.60; p < 0.001). Larger studies and higher study prevalence of depression were associated with smaller effect size. The effect of anxiety on mortality outcome was small and not conclusive given the low number of studies (n = 6) (HR = 1.02; 95% CI 1.00-1.04, p < 0.05). This systematic review and meta-analysis suggests that depression is an important and independent predictor of all-cause mortality among HF patients, while anxiety does not appear to have a strong effect. Further research is recommended toward the detection and treatment of depression.

Metallic nanostructures for efficient LED lighting

Abstract: Light-emitting diodes (LEDs) are driving a shift toward energy-efficient illumination. Nonetheless, modifying the emission intensities, colors and directionalities of LEDs in specific ways remains a challenge often tackled by incorporating secondary optical components. Metallic nanostructures supporting plasmonic resonances are an interesting alternative to this approach due to their strong light-matter interaction, which facilitates control over light emission without requiring external secondary optical components. This review discusses new methods that enhance the efficiencies of LEDs using nanostructured metals. This is an emerging field that incorporates physics, materials science, device technology and industry. First, we provide a general overview of state-of-the-art LED lighting, discussing the main characteristics required of both quantum wells and color converters to efficiently generate white light. Then, we discuss the main challenges in this field as well as the potential of metallic nanostructures to circumvent them. We review several of the most relevant demonstrations of LEDs in combination with metallic nanostructures, which have resulted in light-emitting devices with improved performance. We also highlight a few recent studies in applied plasmonics that, although exploratory and eminently fundamental, may lead to new solutions in illumination.

Wall Enhancement of the Intracranial Aneurysms Revealed by Magnetic Resonance Vessel Wall Imaging Using Three-Dimensional Turbo Spin-Echo Sequence with Motion-Sensitized Driven-Equilibrium: A Sign of Ruptured Aneurysm?

Abstract: Wall enhancement of saccular cerebral aneurysms has not been researched sufficiently. Our purpose of this study was to investigate the incidence of aneurysmal wall enhancement by the three-dimensional turbo spin-echo sequence with motion-sensitized driven equilibrium (MSDE-3D-TSE) imaging after gadolinium injection. We retrospectively reviewed the pre- and postcontrast MSDE-3D-TSE images of 117 consecutive patients with intracranial aneurysms from September 2011 to July 2013. A total of 61 ruptured and 83 unruptured aneurysms of 61 patients with subarachnoid hemorrhage (SAH) and 56 non-SAH patients were enrolled in this study. We evaluated the wall enhancement of each aneurysm on postcontrast MSDE-3D-TSE images compared with precontrast images. We classified the aneurysmal wall enhancement into three groups as “Strong enhancement,” “Faint enhancement,” and “No enhancement.” “Strong/Faint enhancement” of the aneurysm was detected in 73.8/24.6 % of the ruptured aneurysms and 4.8/13.3 % of the unruptured aneurysms. “No enhancement” was found in 1.6 % of the ruptured aneurysms and 81.9 % of the unruptured aneurysms. By magnetic resonance vessel wall imaging using the MSDE-3D-TSE sequence, wall enhancement was frequently observed on ruptured aneurysms. Therefore, aneurysmal wall enhancement may be an indicator of the ruptured condition, which is useful information for managing patients with SAH.

Incremental value of quantitative CMR including parametric mapping for the diagnosis of acute myocarditis

Abstract: Aim Cardiac magnetic resonance (CMR) can visualize inflammatory tissue changes in acute myocarditis. Several quantitative image-derived parameters have been described to enhance the diagnostic value of CMR, but no direct comparison of these techniques is available. Methods and results A total of 34 patients with suspected acute myocarditis and 50 control subjects underwent CMR. CMR protocol included quantitative assessment of T1 relaxation times using modified Look-Locker inversion recovery (MOLLI) and shortened MOLLI (ShMOLLI) acquisition schemes, extracellular volume fraction (ECV), T2 relaxation times, and longitudinal strain. Established Lake-Louise criteria (LLC) consisting of T2-weighted signal intensity ratio (T2-ratio), early gadolinium enhancement ratio (EGEr), and late gadolinium enhancement (LGE) were assessed. Receiver operating characteristics analysis was performed to compare diagnostic performance. Areas under the curve of native T1 (MOLLI: 0.95; ShMOLLI: 0.92) and T2 relaxation times (0.92) were higher compared with those of the other CMR parameters (T2-ratio: 0.71, EGEr: 0.71, LGE: 0.87, LLC: 0.90, ECV MOLLI: 0.77, ECV ShMOLLI: 0.80, longitudinal strain: 0.83). Combined with LGE, each native mapping technique outperformed the diagnostic performance of LLC ( P < 0.01, respectively). A combination of native parameters (T1, T2, and longitudinal strain) significantly increased the diagnostic performance of CMR compared with LLC without need of contrast media application (0.99 vs. 0.90; P = 0.008). Conclusion In patients suspected of having acute myocarditis, diagnostic performance of CMR can be improved by implementation of quantitative CMR parameters. Especially, native mapping techniques have the potential to replace current LLC. ClinicalTrials.gov number NCT02299856.

Artifacts at Cardiac CT: Physics and Solutions

Abstract: This article illustrates common artifacts encountered at cardiac CT, details the physics behind their origins, and describes methods to minimize or eliminate these artifacts.

Automatic planning of head and neck treatment plans.

Abstract: Treatment planning is time-consuming and the outcome depends on the person performing the optimization. A system that automates treatment planning could potentially reduce the manual time required for optimization and could also provide a method to reduce the variation between persons performing radiation dose planning (dosimetrist) and potentially improve the overall plan quality. This study evaluates the performance of the Auto-Planning module that has recently become clinically available in the Pinnacle3 radiation therapy treatment planning system. Twenty-six clinically delivered head and neck treatment plans were reoptimized with the Auto-Planning module. Comparison of the two types of treatment plans were performed using DVH metrics and a blinded clinical evaluation by two senior radiation oncologists using a scale from one to six. Both evaluations investigated dose coverage of target and dose to healthy tissues. Auto-Planning was able to produce clinically acceptable treatment plans in all 26 cases. Target coverages in the two types of plans were similar, but automatically generated plans had less irradiation of healthy tissue. In 94% of the evaluations, the autoplans scored at least as high as the previously delivered clinical plans. For all patients, the Auto-Planning tool produced clinically acceptable head and neck treatment plans without any manual intervention, except for the initial target and OAR delineations. The main benefit of the method is the likely improvement in the overall treatment quality since consistent, high-quality plans are generated which even can be further optimized, if necessary. This makes it possible for the dosimetrist to focus more time on difficult dose planning goals and to spend less time on the more tedious parts of the planning process.

Cerebral Glioma Grading Using Bayesian Network with Features Extracted from Multiple Modalities of Magnetic Resonance Imaging

Abstract: Many modalities of magnetic resonance imaging (MRI) have been confirmed to be of great diagnostic value in glioma grading. Contrast enhanced T1-weighted imaging allows the recognition of blood-brain barrier breakdown. Perfusion weighted imaging and MR spectroscopic imaging enable the quantitative measurement of perfusion parameters and metabolic alterations respectively. These modalities can potentially improve the grading process in glioma if combined properly. In this study, Bayesian Network, which is a powerful and flexible method for probabilistic analysis under uncertainty, is used to combine features extracted from contrast enhanced T1-weighted imaging, perfusion weighted imaging and MR spectroscopic imaging. The networks were constructed using K2 algorithm along with manual determination and distribution parameters learned using maximum likelihood estimation. The grading performance was evaluated in a leave-one-out analysis, achieving an overall grading accuracy of 92.86% and an area under the curve of 0.9577 in the receiver operating characteristic analysis given all available features observed in the total 56 patients. Results and discussions show that Bayesian Network is promising in combining features from multiple modalities of MRI for improved grading performance.

Parallel computation with molecular-motor-propelled agents in nanofabricated networks

Abstract: The combinatorial nature of many important mathematical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers. There have been significant efforts in conceiving parallel-computation approaches in the past, for example: DNA computation, quantum computation, and microfluidics-based computation. However, these approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective. Here, we report the foundations of an alternative parallel-computation system in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. Exploring the network in a parallel fashion using a large number of independent, molecular-motor-propelled agents then solves the mathematical problem. This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation. We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance {2, 5, 9} of the subset sum problem, which is a benchmark NP-complete problem. Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.

Surgical Navigation Technology Based on Augmented Reality and Integrated 3D Intraoperative Imaging: A Spine Cadaveric Feasibility and Accuracy Study.

Abstract: STUDY DESIGN A cadaveric laboratory study. OBJECTIVE The aim of this study was to assess the feasibility and accuracy of thoracic pedicle screw placement using augmented reality surgical navigation (ARSN). SUMMARY OF BACKGROUND DATA Recent advances in spinal navigation have shown improved accuracy in lumbosacral pedicle screw placement but limited benefits in the thoracic spine. 3D intraoperative imaging and instrument navigation may allow improved accuracy in pedicle screw placement, without the use of x-ray fluoroscopy, and thus opens the route to image-guided minimally invasive therapy in the thoracic spine. METHODS ARSN encompasses a surgical table, a motorized flat detector C-arm with intraoperative 2D/3D capabilities, integrated optical cameras for augmented reality navigation, and noninvasive patient motion tracking. Two neurosurgeons placed 94 pedicle screws in the thoracic spine of four cadavers using ARSN on one side of the spine (47 screws) and free-hand technique on the contralateral side. X-ray fluoroscopy was not used for either technique. Four independent reviewers assessed the postoperative scans, using the Gertzbein grading. Morphometric measurements of the pedicles axial and sagittal widths and angles, as well as the vertebrae axial and sagittal rotations were performed to identify risk factors for breaches. RESULTS ARSN was feasible and superior to free-hand technique with respect to overall accuracy (85% vs. 64%, P < 0.05), specifically significant increases of perfectly placed screws (51% vs. 30%, P < 0.05) and reductions in breaches beyond 4 mm (2% vs. 25%, P < 0.05). All morphometric dimensions, except for vertebral body axial rotation, were risk factors for larger breaches when performed with the free-hand method. CONCLUSION ARSN without fluoroscopy was feasible and demonstrated higher accuracy than free-hand technique for thoracic pedicle screw placement. LEVEL OF EVIDENCE N/A.

Tuning the Lattice Parameter of InxZnyP for Highly Luminescent Lattice-Matched Core/Shell Quantum Dots.

Abstract: Colloidal quantum dots (QDs) show great promise as LED phosphors due to their tunable narrow-band emission and ability to produce high-quality white light. Currently, the most suitable QDs for lighting applications are based on cadmium, which presents a toxicity problem for consumer applications. The most promising cadmium-free candidate QDs are based on InP, but their quality lags much behind that of cadmium based QDs. This is not only because the synthesis of InP QDs is more challenging than that of Cd-based QDs, but also because the large lattice parameter of InP makes it difficult to grow an epitaxial, defect-free shell on top of such material. Here, we propose a viable approach to overcome this problem by alloying InP nanocrystals with Zn2+ ions, which enables the synthesis of InxZnyP alloy QDs having lattice constant that can be tuned from 5.93 A (pure InP QDs) down to 5.39 A by simply varying the concentration of the Zn precursor. This lattice engineering allows for subsequent strain-free, epitaxia...

Differentiation of high-grade and low-grade diffuse gliomas by intravoxel incoherent motion MR imaging

Abstract: BACKGROUND Our aim was to assess the diagnostic performance of intravoxel incoherent motion (IVIM) MR imaging for differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs). METHODS Forty-five patients with diffuse glioma (age 50.9 ± 20.4 y; 26 males, 19 females) were assessed with IVIM imaging using 13 b-values (0-1000 s/mm(2)) at 3T. The perfusion fraction (f), true diffusion coefficient (D), and pseudo-diffusion coefficient (D*) were calculated by fitting the bi-exponential model. The apparent diffusion coefficient (ADC) was obtained with 2 b-values (0 and 1000 s/mm(2)). Relative cerebral blood volume was measured by the dynamic susceptibility contrast method. Two observers independently measured D, ADC, D*, and f, and these measurements were compared between the LGG group (n = 16) and the HGG group (n = 29). RESULTS Both D (1.26 ± 0.37 mm(2)/s in LGG, 0.94 ± 0.19 mm(2)/s in HGG; P < .001) and ADC (1.28 ± 0.35 mm(2)/s in LGG, 1.03 ± 0.19 mm(2)/s in HGG; P < .01) were lower in the HGG group. D was lower than ADC in the LGG (P < .05) and HGG groups (P < .0001). D* was not different between the groups. The f-values were significantly larger in HGG (17.5 ± 6.3%) than in LGG (5.8 ± 3.8%; P < .0001) and correlated with relative cerebral blood volume (r = 0.85; P < .0001). Receiver operating characteristic analyses showed areas under curve of 0.95 with f, 0.78 with D, 0.73 with ADC, and 0.60 with D*. CONCLUSION IVIM imaging is useful in differentiating HGGs from LGGs.

Therapeutic targeting of oxygen-sensing prolyl hydroxylases abrogates ATF4-dependent neuronal death and improves outcomes after brain hemorrhage in several rodent models

Abstract: Disability or death due to intracerebral hemorrhage (ICH) is attributed to blood lysis, liberation of iron, and consequent oxidative stress. Iron chelators bind to free iron and prevent neuronal death induced by oxidative stress and disability due to ICH, but the mechanisms for this effect remain unclear. We show that the hypoxia-inducible factor prolyl hydroxylase domain (HIF-PHD) family of iron-dependent, oxygen-sensing enzymes are effectors of iron chelation. Molecular reduction of the three HIF-PHD enzyme isoforms in the mouse striatum improved functional recovery after ICH. A low-molecular-weight hydroxyquinoline inhibitor of the HIF-PHD enzymes, adaptaquin, reduced neuronal death and behavioral deficits after ICH in several rodent models without affecting total iron or zinc distribution in the brain. Unexpectedly, protection from oxidative death in vitro or from ICH in vivo by adaptaquin was associated with suppression of activity of the prodeath factor ATF4 rather than activation of an HIF-dependent prosurvival pathway. Together, these findings demonstrate that brain-specific inactivation of the HIF-PHD metalloenzymes with the blood-brain barrier–permeable inhibitor adaptaquin can improve functional outcomes after ICH in several rodent models.

In vivo measurement of skin surface strain and sub-surface layer deformation induced by natural tissue stretching.

Abstract: Stratum corneum and epidermal layers change in terms of thickness and roughness with gender, age and anatomical site. Knowledge of the mechanical and tribological properties of skin associated with these structural changes are needed to aid in the design of exoskeletons, prostheses, orthotics, body mounted sensors used for kinematics measurements and in optimum use of wearable on-body devices. In this case study, optical coherence tomography (OCT) and digital image correlation (DIC) were combined to determine skin surface strain and sub-surface deformation behaviour of the volar forearm due to natural tissue stretching. The thickness of the epidermis together with geometry changes of the dermal-epidermal junction boundary were calculated during change in the arm angle, from flexion (90°) to full extension (180°). This posture change caused an increase in skin surface Lagrange strain, typically by 25% which induced considerable morphological changes in the upper skin layers evidenced by reduction of epidermal layer thickness (20%), flattening of the dermal-epidermal junction undulation (45-50% reduction of flatness being expressed as Ra and Rz roughness profile height change) and reduction of skin surface roughness Ra and Rz (40-50%). The newly developed method, DIC combined with OCT imaging, is a powerful, fast and non-invasive methodology to study structural skin changes in real time and the tissue response provoked by mechanical loading or stretching.

User-Independent Recognition of Sports Activities From a Single Wrist-Worn Accelerometer: A Template-Matching-Based Approach

Abstract: Goal : To investigate the accuracy of template matching for classifying sports activities using the acceleration signal recorded with a wearable sensor. Methods : A population of 29 normal weight and 19 overweight subjects was recruited to perform eight common sports activities, while body movement was measured using a triaxial accelerometer placed at the wrist. User- and axis-independent acceleration signal templates were automatically extracted to represent each activity category and recognize activity types. Five different similarity measures between example signals and templates were compared: Euclidean distance, dynamic time warping (DTW), derivative DTW, correlation and an innovative index, and combining distance and correlation metrics ( Rce ). Template-based activity recognition was compared to statistical-learning classifiers, such as Naive Bayes, decision tree, logistic regression (LR), and artificial neural network (ANN) trained using time- and frequency-domain signal features. Each algorithm was tested on data from a holdout group of 15 normal weight and 19 overweight subjects. Results : The Rce index outperformed other template-matching metrics by achieving recognition rate above 80% for the majority of the activities. Template matching showed robust classification accuracy when tested on unseen data and in case of limited training examples. LR and ANN achieved the highest overall recognition accuracy $\sim$ 85% but showed to be more vulnerable to misclassification error than template matching on overweight subjects’ data. Conclusion : Template matching can be used to classify sports activities using the wrist acceleration signal. Significance : Automatically extracted template prototypes from the acceleration signal may be used to enhance accuracy and generalization properties of statistical-learning classifiers.

Liver Stiffness Measurements with MR Elastography: Agreement and Repeatability across Imaging Systems, Field Strengths, and Pulse Sequences.

Abstract: Purpose To prospectively assess agreement and repeatability of magnetic resonance (MR) elastography liver stiffness measurements across imager manufacturers, field strengths, and pulse sequences. Materials and Methods This prospective cross-sectional study was approved by the institutional review board; informed consent was obtained from all subjects. On the basis of an a priori power calculation, 24 volunteer adult subjects underwent MR elastography with four MR imaging systems (two vendors) and multiple pulse sequences (two-dimensional [2D] gradient-echo [GRE] imaging, 2D spin-echo [SE] echo-planar imaging, and three-dimensional [3D] SE echo-planar imaging). Each sequence was performed twice in each patient with each imaging system. Intraclass correlation coefficients (ICCs) were used to assess agreement and repeatability. P < .05 was considered indicative of a statistically significant difference. Results Pairwise ICCs were 0.67-0.82 and 0.62-0.83 for agreement between pulse sequences across manufacturers (n = 4) and field strengths (n = 5), respectively. ICCs were 0.45-0.90 for pairwise agreement between sequences while fixing manufacturer and field strength (n = 8). Test-retest repeatability across the various manufacturer, field strength, and pulse sequence combinations (n = 10) was excellent (ICCs, 0.77-0.94). The overall ICC for all manufacturer, field strength, and sequence combinations (n = 10) was 0.68 (95% confidence interval [CI]: 0.55, 0.82). ICC according to field strength was 0.78 (95% CI: 0.67, 0.88) at 1.5 T (n = 5) and 0.64 (95% CI: 0.49, 0.78) at 3.0 T (n = 5). ICCs according to vendor were 0.83 (95% CI: 0.73, 0.91) (n = 4) and 0.65 (95% CI: 0.51, 0.79) (n = 6). Average patient level variance was 0.042 kPa, with a coefficient of variation of 10.7%. Conclusion MR elastography is a reliable method for assessing liver stiffness, with small amounts of variability between imager manufacturers, field strengths, and pulse sequences. © RSNA, 2016.

Can PPG be used for HRV analysis

Abstract: Heart rate variability (HRV) represents one of the most promising markers of the autonomic nervous system (ANS) regulation. However, it requires the acquisition of the ECG signal in order to reliably detect the RR intervals, which is not always easily and comfortably available in personal health applications. Additionally, due to progress in single spot optical sensors, photoplethysmography (PPG) is an interesting alternative for heartbeat interval measurements, since it is a more convenient and a less intrusive measurement technique. Driven by the technological advances in such sensors, wrist-worn devices are becoming a commodity, and the interest in the assessment of HRV indexes from the PPG analysis (pulse rate variability — PRV) is rising. In this study, we investigate the hypothesis of using PRV features as surrogates for HRV indexes, in three different contexts: healthy subjects at rest, healthy subjects after physical exercise and subjects with cardiovascular diseases (CVD). Additionally, we also evaluate which are the characteristic points better suited for PRV analysis in these contexts, i.e. the PPG waveform characteristic points leading to the PRV features that present the best estimates of HRV (correlation and error analysis). The achieved results suggest that the PRV can be often used as an alternative for HRV analysis in healthy subjects, with significant correlations above 82%, for both time and frequency features. Contrarily, in the post-exercise and CVD subjects, time and (most importantly) frequency domain features shall be used with caution (mean correlations ranging from 68% to 88%).

Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.

Abstract: PURPOSE To investigate the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in capturing breast lesion heterogeneity and determine which ADC metric may help best differentiate benign from malignant breast mass lesions at 3.0T magnetic resonance imaging (MRI). MATERIALS AND METHODS We retrospectively included 101 women with breast mass lesions (benign:malignant = 36:65) who underwent 3.0T diffusion-weighted imaging (DWI) and subsequently had histopathologic confirmation. ADC histogram parameters, including the mean, minimum, maximum, 10th/25th/50th/75th/90th percentile, skewness, kurtosis, and entropy ADCs, were derived for the whole-lesion volume in each patient. Mann-Whitney U-test, univariate and multivariate logistic regression, area under the receiver-operating characteristic curve (Az ), intraclass correlation coefficient (ICC), and Bland-Altman test were used for statistical analysis. RESULTS Mean, minimum, maximum, and 10th/25th/50th/75th/90th percentile ADCs were significantly lower (all P < 0.0001), while skewness and entropy ADCs were significantly higher (P < 0.001 and P = 0.001, respectively) in malignant lesions compared with benign ones. The Az values of minimum and 25th percentile ADCs were significantly higher than that of mean ADC (P = 0.0194 and P = 0.0154, respectively) or that of median ADC (P = 0.0300 and P = 0.0401, respectively), indicating that minimum and 25th percentile ADCs may be more accurate for lesion discrimination. Multivariate logistic regression showed that the minimum ADC was the unique independent predictor of breast malignancy. Minimum and 25th percentile ADCs had excellent interobserver agreement (ICC = 0.943 and 0.989, respectively; narrow width of 95% limits of agreement). CONCLUSION These results suggest that whole-lesion ADC histogram analysis may facilitate the differentiation between benign and malignant breast mass lesions.