Novel Moessbauer experiment in a rotating system and the extra-energy shift between emission and absorption lines

We present the results of a novel Mossbauer experiment in a rotating system, implemented recently in Istanbul University, which yields the coefficient k=0.69+/-0.02 within the frame of the expression for the relative energy shift between emission and absorption lines dE/E=ku2/c2. This result turned out to be in a quantitative agreement with an experiment achieved earlier on the subject matter (A.L. Kholmetskii et al. 2009 Phys. Scr. 79 065007), and once again strongly pointed to the inequality k>0.5, revealed originally in (A.L. Kholmetskii et al. 2008 Phys. Scr. 77, 035302 (2008)) via the re-analysis of Kundig experiment (W. Kundig. Phys. Rev. 129, 2371 (1963)). A possible explanation of the deviation of the coefficient k from the relativistic prediction k=0.5 is discussed.Comment: 21 pages, 8 figures, 3 table

White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET

The accurate segmentation of brain tissues in Magnetic Resonance (MR) images is an important step for detection and treatment planning of brain diseases. Among other brain tissues, Gray Matter, White Matter and Cerebrospinal Fluid are commonly segmented for Alzheimer diagnosis purpose. Therefore, different algorithms for segmenting these tissues in MR image scans have been proposed over the years. Nowadays, with the trend of deep learning, many methods are trained to learn important features and extract information from the data leading to very promising segmentation results. In this work, we propose an effective approach to segment three tissues in 3D Brain MR images based on B-UNET. The method is implemented by using the Bitplane method in each convolution of the UNET model. We evaluated the proposed method using two public databases with very promising results. (c) Springer Nature Switzerland AG 2019

A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction

The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs. In this work, we propose a deep learning combined with gradient boosting machine framework to solve this task. We train a convolutional neural network to compress the high dimensional MRI data and learn meaningful image features by predicting the 123 continuous-valued derived data provided with each MRI. These extracted features are then used to train a gradient boosting machine that predicts the residualized fluid intelligence score. Our approach achieved mean square error (MSE) scores of 18.4374, 68.7868, and 96.1806 for the training, validation, and test set respectively.Comment: Challenge in Adolescent Brain Cognitive Development Neurocognitive Predictio

Mineral maturity and crystallinity index are distinct characteristics of bone mineral

The purpose of this study was to test the hypothesis that mineral maturity and crystallinity index are two different characteristics of bone mineral. To this end, Fourier transform infrared microspectroscopy (FTIRM) was used. To test our hypothesis, synthetic apatites and human bone samples were used for the validation of the two parameters using FTIRM. Iliac crest samples from seven human controls and two with skeletal fluorosis were analyzed at the bone structural unit (BSU) level by FTIRM on sections 2–4 lm thick. Mineral maturity and crystallinity index were highly correlated in synthetic apatites but poorly correlated in normal human bone. In skeletal fluorosis, crystallinity index was increased and maturity decreased, supporting the fact of separate measurement of these two parameters. Moreover, results obtained in fluorosis suggested that mineral characteristics can be modified independently of bone remodeling. In conclusion, mineral maturity and crystallinity index are two different parameters measured separately by FTIRM and offering new perspectives to assess bone mineral traits in osteoporosis

Nonlinear Markov Random Fields Learned via Backpropagation

Although convolutional neural networks (CNNs) currently dominate competitions on image segmentation, for neuroimaging analysis tasks, more classical generative approaches based on mixture models are still used in practice to parcellate brains. To bridge the gap between the two, in this paper we propose a marriage between a probabilistic generative model, which has been shown to be robust to variability among magnetic resonance (MR) images acquired via different imaging protocols, and a CNN. The link is in the prior distribution over the unknown tissue classes, which are classically modelled using a Markov random field. In this work we model the interactions among neighbouring pixels by a type of recurrent CNN, which can encode more complex spatial interactions. We validate our proposed model on publicly available MR data, from different centres, and show that it generalises across imaging protocols. This result demonstrates a successful and principled inclusion of a CNN in a generative model, which in turn could be adapted by any probabilistic generative approach for image segmentation.Comment: Accepted for the international conference on Information Processing in Medical Imaging (IPMI) 2019, camera ready versio

Gastrointestinal stromal tumour of the duodenum in childhood: a rare case report

<p>Abstract</p> <p>Background</p> <p>Gastrointestinal stromal tumours (GISTs) are uncommon primary mesenchymal tumours of the gastrointestinal tract mostly observed in the adults. Duodenal GISTs are relatively rare in adults and it should be regarded as exceptional in childhood. In young patients duodenal GISTs may be a source of potentially lethal haemorrhage and this adds diagnostic and therapeutic dilemmas to the concern about the long-term outcome.</p> <p>Case presentation</p> <p>A 14-year-old boy was referred to our hospital with severe anaemia due to recurrent episodes of upper gastrointestinal haemorrhage. Endoscopy, small bowel series, scintigraphy and video capsule endoscopy previously done elsewhere were negative. Shortly after the admission, the patient underwent emergency surgery for severe recurrence of the bleeding. At surgery, a 4 cm solid mass arising from the wall of the fourth portion of the duodenum was identified. The invasion and the erosion of the duodenal mucosa was confirmed by intra-operative pushed duodenoscopy. The mass was resected by a full-thickness duodenal wall excision with adequate grossly free margins. Immunohistochemical analysis of the specimen revealed to be positive for CD117 (c-KIT protein) consistent with a diagnosis of GIST. The number of mitoses was < 5/50 HPF. Mutational analysis for c-KIT/PDGFRA tyrosine kinase receptor genes resulted in a wildtype pattern. The patient had an uneventful course and he has remained disease-free during two years of follow-up.</p> <p>Conclusion</p> <p>Duodenal GISTs in children are very rare and may present with massive bleeding. Cure can be achieved by complete surgical resection, but even in the low-aggressive tumours the long-term outcome may be unpredictable.</p

Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast

Partial voluming (PV) is arguably the last crucial unsolved problem in Bayesian segmentation of brain MRI with probabilistic atlases. PV occurs when voxels contain multiple tissue classes, giving rise to image intensities that may not be representative of any one of the underlying classes. PV is particularly problematic for segmentation when there is a large resolution gap between the atlas and the test scan, e.g., when segmenting clinical scans with thick slices, or when using a high-resolution atlas. In this work, we present PV-SynthSeg, a convolutional neural network (CNN) that tackles this problem by directly learning a mapping between (possibly multi-modal) low resolution (LR) scans and underlying high resolution (HR) segmentations. PV-SynthSeg simulates LR images from HR label maps with a generative model of PV, and can be trained to segment scans of any desired target contrast and resolution, even for previously unseen modalities where neither images nor segmentations are available at training. PV-SynthSeg does not require any preprocessing, and runs in seconds. We demonstrate the accuracy and flexibility of the method with extensive experiments on three datasets and 2,680 scans. The code is available at https://github.com/BBillot/SynthSeg.Comment: accepted for MICCAI 202

High-spin States in \u3csup\u3e191, 193\u3c/sup\u3eAu and \u3csup\u3e192\u3c/sup\u3ePt: Evidence for Oblate Deformation and Triaxial Shapes

High-spin states of 191, 193Au and 192Pt have been populated in the 186W(11B, xn) and 186W(11B, p4n) reactions, respectively, at a beam energy of 68 MeV and their γ decay was studied using the YRAST Ball detector array at the Wright Nuclear Structure Laboratory at Yale University. The level scheme of 193Au has been extended up to Iπ = 55/2+. New transitions were observed also in 191Au and 192Pt. Particle-plus-Triaxial-Rotor (PTR) and Total Routhian Surface (TRS) calculations were performed to determine the equilibrium deformations of the Au isotopes. The predictions for oblate deformations in these nuclei are in agreement with the experimental data. Development of nonaxial shapes is discussed within the framework of the PTR model