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.

Effect of Antecolic versus Retrocolic Gastroenteric Reconstruction after Pancreaticoduodenectomy on Delayed Gastric Emptying: A Meta-Analysis of Six Randomized Controlled Trials.

BACKGROUND: One of the most frequent complications of pancreaticoduodenectomy (PD) is delayed gastric emptying (DGE). The study aim was to evaluate the impact of the type of gastro/duodenojejunal reconstruction (antecolic vs. retrocolic) after PD on DGE incidence. METHODS: A systematic review was made according to the PRISMA guidelines. Randomized controlled trials (RCTs) comparing antecolic vs. retrocolic reconstruction were included irrespective of the PD techniques. A meta-analysis was then performed. RESULTS: Six RCTs were included for a total of 588 patients. The overall quality was good. General risk of bias was low. DGE was not statistically significantly different between the antecolic and retrocolic group (OR 0.6, 95% CI 0.31-1.16, p = 0.13). The other main surgery-related complications (pancreatic fistula, hemorrhage, intra-abdominal abscess, bile leak and wound infection) were not dependent on the reconstruction route (OR 0.84, 95% CI 0.41-1.70, p = 0.63). No statistically significant difference in terms of length of hospital stay was found between the 2 groups. There was also no difference of DGE incidence if only pylorus-preserving PD was considered and between the DGE grades A, B or C. CONCLUSION: This meta-analysis shows that antecolic reconstruction after PD is not superior to retrocolic reconstruction in terms of DGE.

Objective assessment of low contrast detectability in computed tomography with Channelized Hotelling Observer.

PURPOSE: Iterative algorithms introduce new challenges in the field of image quality assessment. The purpose of this study is to use a mathematical model to evaluate objectively the low contrast detectability in CT. MATERIALS AND METHODS: A QRM 401 phantom containing 5 and 8 mm diameter spheres with a contrast level of 10 and 20 HU was used. The images were acquired at 120 kV with CTDIvol equal to 5, 10, 15, 20 mGy and reconstructed using the filtered back-projection (FBP), adaptive statistical iterative reconstruction 50% (ASIR 50%) and model-based iterative reconstruction (MBIR) algorithms. The model observer used is the Channelized Hotelling Observer (CHO). The channels are dense difference of Gaussian channels (D-DOG). The CHO performances were compared to the outcomes of six human observers having performed four alternative forced choice (4-AFC) tests. RESULTS: For the same CTDIvol level and according to CHO model, the MBIR algorithm gives the higher detectability index. The outcomes of human observers and results of CHO are highly correlated whatever the dose levels, the signals considered and the algorithms used when some noise is added to the CHO model. The Pearson coefficient between the human observers and the CHO is 0.93 for FBP and 0.98 for MBIR. CONCLUSION: The human observers' performances can be predicted by the CHO model. This opens the way for proposing, in parallel to the standard dose report, the level of low contrast detectability expected. The introduction of iterative reconstruction requires such an approach to ensure that dose reduction does not impair diagnostics.