An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization.

Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.

Fluoroscopic-Guided Radial Endobronchial Ultrasound Without Guide Sheath For Peripheral Pulmonary Lesions: A Safe And Efficient Combination.

BACKGROUND: Several guidelines recommend computed tomography scans for populations with high-risk for lung cancer. The number of individuals evaluated for peripheral pulmonary lesions (PPL) will probably increase, and with it non-surgical biopsies. Associating a guidance method with a target confirmation technique has been shown to achieve the highest diagnostic yield, but the utility of bronchoscopy with radial probe endobronchial ultrasound using fluoroscopy as guidance without a guide sheath has not been reported. METHODS: We conducted a retrospective analysis of bronchoscopy with radial probe endobronchial ultrasound using fluoroscopy procedures for the investigation of PPL performed by experienced bronchoscopists with no specific previous training in this particular technique. Operator learning curves and radiological predictors were assessed for all consecutive patients examined during the first year of application of the technique. RESULTS: Fifty-one PPL were investigated. Diagnostic yield and visualization yield were 72.5 and 82.3% respectively. The diagnostic yield was 64.0% for PPL ≤20mm, and 80.8% for PPL>20mm. No false-positive results were recorded. The learning curve of all diagnostic tools showed a DY of 72.7% for the first sub-group of patients, 81.8% for the second, 72.7% for the third, and 81.8% for the last. CONCLUSION: Bronchoscopy with radial probe endobronchial ultrasound using fluoroscopy as guidance is safe and simple to perform, even without specific prior training, and diagnostic yield is high for PPL>and ≤20mm. Based on these findings, this method could be introduced as a first-line procedure for the investigation of PPL, particularly in centers with limited resources.

Is altitude training an efficient treatment for obesity?

Worldwide, about half the adult population is considered overweight as defined by a body mass index (BMI - calculated by body weight divided by height squared) ratio in excess of 25 kg.m-2. Of these individuals, half are clinically obese (with a BMI in excess of 30) and these numbers are still increasing, notably in developing countries such as those of the Middle East region. Obesity is a disorder characterised by increased mass of adipose tissue (excessive fat accumulation) that is the result of a systemic imbalance between food intake and energy expenditure. Although factors such as family history, sedentary lifestyle, urbanisation, income and family diet patterns determine obesity prevalence, the main underlying causes are poor knowledge about food choice and lack of physical activity3. Current obesity treatments include dietary restriction, pharmacological interventions and ultimately, bariatric surgery. The beneficial effects of physical activity on weight loss through increased energy expenditure and appetite modulation are also firmly established. Another viable option to induce a negative energy balance, is to incorporate hypoxia per se or combine it with exercise in an individual's daily schedule. This article will present recent evidence suggesting that combining hypoxic exposure and exercise training might provide a cost-effective strategy for reducing body weight and improving cardio-metabolic health in obese individuals. The efficacy of this approach is further reinforced by epidemiological studies using large-scale databases, which evidence a negative relationship between altitude of habitation and obesity. In the United States, for instance, obesity prevalence is inversely associated with altitude of residence and urbanisation, after adjusting for temperature, diet, physical activity, smoking and demographic factors.

Efficient total variation algorithm for fetal MRI reconstruction

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.