Brain 18F-FDG PET analysis via interval-valued reconstruction: proof of concept for Alzheimer's disease diagnosis.

OBJECTIVE: We propose an innovative approach for 18F-FDG PET analysis based on an interval-valued reconstruction of 18F-FDG brain distribution. Its diagnostic performance for Alzheimer's disease (AD) diagnosis with comparison to a validated post-processing software was assessed. METHODS: Brain 18F-FDG PET data from 26 subjects were acquired in a clinical routine setting. Raw data were reconstructed using an interval-valued version of the ML-EM algorithm called NIBEM that stands for Non-Additive interval-based expectation maximization. Subject classification was obtained via interval-based statistical comparison (intersection ratio, IR) between cortical regions of interest (ROI) including parietal, temporal, and temporo-mesial cortices and a reference region, the sub-cortical grey nuclei, known not to be affected by AD. In parallel, PET images were post-processed using a validated automated software based on the computation of ROI normalized uptake ratios standard deviation (SUVr SD) with reference to a healthy control database (Siemens Scenium). Clinical diagnosis made during follow-up was considered as the gold-standard for patient classification (16 healthy controls and 10 AD patients). RESULTS: Both methods provided cortical ROI indices that were significantly different between controls and AD patients. The area under the ROC curve for control/AD classification was statistically identical (0.96 for NIBEM IR and 0.95 for Scenium SUVr SD). At the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were, respectively, 100%, 88%, 92%, 83%, and 100% for both Scenium SUVr SD and NIBEM IR methods. CONCLUSION: This preliminary study shows that interval-valued reconstruction allows self-consistent analysis of brain 18F-FDG PET data, yielding diagnostic performances that seem promising with respect to those of a commercial post-processing software based on SUVr SD analysis.

Giant extinct caiman breaks constraint on the axial skeleton of extant crocodylians.

The number of precaudal vertebrae in all extant crocodylians is remarkably conservative, with nine cervicals, 15 dorsals and two sacrals, a pattern present also in their closest extinct relatives. The consistent vertebral count indicates a tight control of axial patterning by Hox genes during development. Here we report on a deviation from this pattern based on an associated skeleton of the giant caimanine Purussaurus, a member of crown Crocodylia, and several other specimens from the Neogene of the northern neotropics. P. mirandai is the first crown-crocodylian to have three sacrals, two true sacral vertebrae and one non-pathological and functional dorsosacral, to articulate with the ilium (pelvis). The giant body size of this caiman relates to locomotory and postural changes. The iliosacral configuration, a more vertically oriented pectoral girdle, and low torsion of the femoral head relative to the condyles are hypothesized specializations for more upright limb orientation or weight support.

Interval-based reconstruction for uncertainty quantification in PET.

A new directed interval-based tomographic reconstruction algorithm, called non-additive interval based expectation maximization (NIBEM) is presented. It uses non-additive modeling of the forward operator that provides intervals instead of single-valued projections. The detailed approach is an extension of the maximum likelihood-expectation maximization algorithm based on intervals. The main motivation for this extension is that the resulting intervals have appealing properties for estimating the statistical uncertainty associated with the reconstructed activity values. After reviewing previously published theoretical concepts related to interval-based projectors, this paper describes the NIBEM algorithm and gives examples that highlight the properties and advantages of this interval valued reconstruction.

A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform.

BACKGROUND: In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1-7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses. RESULTS: We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole-plant side views, those best suited for detecting ear position. Images are segmented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth. CONCLUSIONS: The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large-scale genetic analyses of the control of reproductive growth to changes in environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform.

Non-Additive Imprecise Image Super-Resolution in a Semi-Blind Context.

The most effective superresolution methods proposed in the literature require precise knowledge of the so-called point spread function of the imager, while in practice its accurate estimation is nearly impossible. This paper presents a new superresolution method, whose main feature is its ability to account for the scant knowledge of the imager point spread function. This ability is based on representing this imprecise knowledge via a non-additive neighborhood function. The superresolution reconstruction algorithm transfers this imprecise knowledge to output by producing an imprecise (interval-valued) high-resolution image. We propose some experiments illustrating the robustness of the proposed method with respect to the imager point spread function. These experiments also highlight its high performance compared with very competitive earlier approaches. Finally, we show that the imprecision of the high-resolution interval-valued reconstructed image is a reconstruction error marker.

Toward accurate volumetry of brain aneurysms: combination of an algorithm for automatic thresholding with a 3D eraser tool.

The present study describes a new approach for aneurysm volume quantification on three-dimensional angiograms, which focuses on solving three common technical problems: the variability associated with the use of manual thresholds, the irregular morphology of some aneurysms, and the imprecision of the limits between the parent artery and the aneurysm sac. The method consists of combining an algorithm for automatic threshold determination with a spherical eraser tool that allows the user to separate the image of the aneurysm from the parent artery. The accuracy of volumetry after automatic thresholding was verified with an in vitro experiment in which 57 measurements were performed using four artificial aneurysms of known volume. The reliability of the method was compared to that obtained with the technique of ellipsoid approximation in a clinical setting of 15 real angiograms and 150 measurements performed by five different users. The mean error in the measurement of the artificial aneurysms was 7.23%. The reliability of the new approach was significantly higher than that of the ellipsoid approximation. Limits of agreement between two measurements were determined with Bland-Altman plots and ranged from -14 to 13% for complex and from -10.8 to 11.03% for simple-shaped sacs. The reproducibility was lower (>20% of variation) for small aneurysms (<70 mm³) and for those presenting a very wide neck (dome-to-neck ratio<1). The method is potentially useful in the clinical practice, since it provides relatively precise, reproducible, volume quantification. A safety coiling volume can be established in order to perform sufficient but not excessive filling of the aneurysm pouch.

NIBART: a new interval based algebraic reconstruction technique for error quantification of emission tomography images.

This article presents a new algebraic method for reconstructing emission tomography images. This approach is mostly an interval extension of the conventional SIRT algorithm. One of the main characteristic of our approach is that the reconstructed activity associated with each pixel of the reconstructed image is an interval whose length can be considered as an estimate of the impact of the random variation of the measured activity on the reconstructed image. This work aims at investigating a new methodological concept for a reliable and robust quantification of reconstructed activities in scintigraphic images.