We present new diffusion phantoms dedicated to the study and validation of high-angular-resolution diffusion imaging (HARDI) models. The phantom design permits the application of imaging parameters that are typically employed in studies of the human brain. The phantoms were made of small-diameter acrylic fibers, chosen for their high hydrophobicity and flexibility that ensured good control of the phantom geometry. The polyurethane medium was filled under vacuum with an aqueous solution that was previously degassed, doped with gadoliniumtetraazacyclododecanetetraacetic acid (Gd-DOTA), and treated by ultrasonic waves. Two versions of such phantoms were manufactured and tested. The phantom's applicability was demonstrated on an analytical Q-ball model. Numerical simulations were performed to assess the accuracy of the phantom. The phantom data will be made accessible to the community with the objective of analyzing various HARDI models. During the last decade, diffusion-weighted (DW) imaging (DWI) has become an established technique for the diagnosis of ischemia (1) and investigations of the anatomical connectivity of the human brain (2). Presently, no manufacturer delivers any phantoms dedicated to diffusion imaging, due to the complexity of their design. However, diffusion phantoms have numerous applications. They include calibration, validation of tractography algorithms, and validation of diffusion models. The phantom design should comply with the concrete application. For example, calibration requires a large region of interest (ROI) with a specific apparent diffusion coefficient (ADC), fractional anisotropy (FA), and principal orientation(s) to reduce the impact of acquisition noise on the measurements. On the other hand, to validate tractography, one would typically use a phantom made up of long bundles, similar to those found in brain white matter. To circumvent the intrinsic limitations of diffusion tensor imaging (DTI; i.e., the inability to resolve multiple fiber populations), a number of high-angular-resolution diffusion imaging (HARDI) models were introduced (3-12). They were conceived with the aim of providing an unbiased estimate of the probability density function (PDF) describing the displacements of the water molecules during a predefined time interval. Some models deliver only the radial projections of the PDF, known as the orientation distribution function (ODF). The phantoms employed in the studies of HARDI models could be adjusted to different fiber configurations (crossing, kissing, merging, and splitting), and angular distribution. Several diffusion phantom designs were proposed, based on fibrous vegetables (13), biological tissues (14), plastic capillaries (15-17), or textile fibers (18 -20). In this work, we present a novel diffusion phantom dedicated to the validation of HARDI models. We developed two versions of this phantom corresponding to 45°and 90°fi ber crossings, and used them to test the analytical Q-ball model. MATERIALS AND METHODSThe design of diffusion phantoms dedicated to st...

OBJECTIVE: To develop an automatic system that grades the severity of facial signs through 'selfies' pictures taken by women of different ages and ethnics. METHODS: 1140 women from three ethnics (African-American, Asian, Caucasian), of different ages (18-80 years old), took 'selfies' by high resolution smartphones cameras under different conditions of lighting or facial expressions. A dedicated software, was developed, based on a Convolutional Neural Network (CNN) that integrates training data from referential Skin Aging Atlases. The latter allows to an immediate quantification of the severity of nine facial signs according to the ethnicity declared by the subject. These automatic grading were confronted to those assessed by 12 trained experts and dermatologists either on 'selfies' pictures or in live conditions on a smaller cohort of women. RESULTS: The system appears weakly influenced by lighting conditions or facial expressions (coefficients of variations ranging 10-13% for most signs) and leads to global agreements with experts' assessments, even showing a better reproducibility on some facial signs. CONCLUSION: This automatic scoring system, still in development, seems offering a new quantitative approach in the quantified description of facial signs, independent from human vision, in many applications, being individual, cosmetic oriented or dermatological with regard to the follow-up of medical anti-ageing corrective strategies. a des valeurs comparables de celles des experts, voire même de meilleure reproductibilit e dans certains cas. CONCLUSION: Ce syst eme de scorage automatique, encore en d eveloppement, semble offrir une nouvelle approche dans la description quantitative de signes du visage, ind ependante de l'oeil humain, dans de nombreuses applications, comme la personnalisation, a vis ee cosm etique ou dermatologique, dans le suivi de certaines strat egies m edicales de l'antivieillissement cutan e.

Objective These were two folds: at first, to develop an automatic grading system specifically dedicated to some facial signs of men, similar to the one previously validated on women of different ethnic ancestry and second, to assess its potential in detecting and grading the possible impacts of a severe aerial urban pollution on some facial signs of Chinese men. Methods In both studies, selfie images were obtained from differently aged men. Nine facial signs were automatically graded through a specific A.I‐based algorithm and clinically assessed by a panel of experts and dermatologists. Selfie pictures were taken from individual smartphones of variable optical properties. The first study, designed for developing an automatic grading system, involved three comparable cohorts of men from three different regional ancestries (African, Asian, Caucasian, 110 each) the selfie images of which were acquired under four different lighting conditions. As a second use case study, the facial signs of two cohorts of Chinese men (101 and 100, each), differently aged, regularly exposed to very different aerial urban pollution conditions (UP) were analysed by the same algorithm, selfies being taken under only one lighting condition. Results ‐The new automatic grading system of facial signs suits well to men, showing comparable results than that the one dedicated to women and provides data in close agreement with experts’ assessments. ‐In both cases (expert’s or automatic methodology), the accuracy of the scores appeared ethnic‐dependent. ‐The applied case confirmed previous results obtained clinically, that is, that many facial signs were found of an increased severity among men exposed to a severe urban pollution, as compared to those living in a less polluted city. ‐In both studies, statistical agreements between the automatic grading system and expert’s assessments were reached. In some facial signs, the automatic grading system seems offering a slightly better accuracy than the assessments made by the experts. Conclusion Apart from some minor limitations, this A.I‐based automatic grading system, free from human intervention, performed as well as the one previously developed in women, in close agreement with expert’s assessments. In epidemiological studies, this system offers an easy, fast, affordable and confidential approach in the detection and quantification of male facial signs.

Objective To confirm the robustness and validity of an automatic scoring system, algorithm‐based, that grades the severity of nine facial signs through “selfies” smartphones pictures taken by European Caucasian women through dermatological assessments. Methods 157 Caucasian women from three countries (France, Germany, Spain), of different ages (20–75 years), took one “selfie” image by the frontal camera of their smartphones whereas local dermatologists photographed them with the back camera of the same smartphone. The same nine facial signs of these subjects were initially graded by these local dermatologists, using referential Skin Aging Atlases. All 314 “selfies” images were then further automatically analyzed by the algorithm. The severity of facial signs (wrinkles, pigmentation, ptosis, skin folds etc.) were statistically compared to the assessments made by the three dermatologists, taken as ground truth. Results Highly significant coefficients of correlation (P < 0.001) were found in the three cohorts between the grades provided by the system and those from dermatologists in live. The back camera – of a better resolution than the frontal one – seems affording slightly more significant correlations. However, although significantly correlated, the signs of vascular disorders and cheek skin pores present some disparities that are likely linked to the technical diversity of smartphones or self‐shootings, leading to lower coefficients of correlations. Conclusion This automatic scoring system offers a promising approach in the harmonization of Dermatological assessments of skin facial signs and their changes with age or the follow up of anti‐aging treatments.

We have developed a general framework which employs quantitative computed tomography (QCT) imaging and inter-subject image registration to model the three-dimensional structure of the hip, with the goal of quantifying changes in the spatial distribution of bone as it is affected by aging, drug treatment or mechanical unloading. We have adapted rigid and non-rigid inter-subject registration techniques to transform groups of hip QCT scans into a common reference space and to construct composite proximal femoral models. We have applied this technique to a longitudinal study of 16 astronauts who on average, incurred high losses of hip bone density during spaceflights of 4-6 months on the International Space Station (ISS). We compared the pre-flight and post-flight composite hip models, and observed the gradients of the bone loss distribution. We performed paired t-tests, on a voxel by voxel basis, corrected for multiple comparisons using false discovery rate (FDR), and observed regions inside the proximal femur that showed the most significant bone loss. To validate our registration algorithm, we selected the 16 pre-flight scans and manually marked 4 landmarks for each scan. After registration, the average distance between the mapped landmarks and the corresponding landmarks in the target scan was 2.56 mm. The average error due to manual landmark identification was 1.70 mm.