Delayed gastric emptying and reduced postprandial small bowel water content of equicaloric whole meal bread versus rice meals in healthy subjects: novel MRI insights

BACKGROUND/OBJECTIVES: Postprandial bloating is a common symptom in patients with functional gastrointestinal (GI) diseases. Whole meal bread (WMB) often aggravates such symptoms though the mechanisms are unclear. We used magnetic resonance imaging (MRI) to monitor the intragastric fate of a WMB meal (11% bran) compared to a rice pudding (RP) meal. SUBJECTS/METHODS: 12 healthy volunteers completed this randomised crossover study. They fasted overnight and after an initial MRI scan consumed a glass of orange juice with a 2267 kJ WMB or an equicaloric RP meal. Subjects underwent serial MRI scans every 45 min up to 270 min to assess gastric volumes and small bowel water content and completed a GI symptom questionnaire. RESULTS: The MRI intragastric appearance of the two meals was markedly different. The WMB meal formed a homogeneous dark bolus with brighter liquid signal surrounding it. The RP meal separated into an upper, liquid layer and a lower particulate layer allowing more rapid emptying of the liquid compared to solid phase (sieving). The WMB meal had longer gastric half emptying times (132±8 min) compared to the RP meal (104±7 min), P<0.008. The WMB meal was associated with markedly reduced MRI-visible small bowel free mobile water content compared to the RP meal, P<0.0001. CONCLUSIONS: WMB bread forms a homogeneous bolus in the stomach which inhibits gastric sieving and hence empties slower than the equicaloric rice meal. These properties may explain why wheat causes postprandial bloating and could be exploited to design foods which prolong satiation

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website

Intensity-based robust similarity for multimodal image registration

International audienceThis paper proposes a new intensity-based similarity metric that can be used for the registration of multimodal images. It combines the robust estimation with both the forward and inverse transformation to reduce the negative effects of outliers in the images. For this purpose, we firstly employ the multiresolution technique to downsample the original images, then resort to the simulated annealing method to initialize the transformation parameters at the coarsest resolution. Finally the Powell method is utilized to obtain the optimal transformation parameters at each resolution. In our experiments, the new method is compared to other popular similarity measures, on the synthetic data as well as the real data, and the experimental results are encouraging