New lesion segmentation for multiple sclerosis brain images with imaging and lesion-aware augmentation

Multiple sclerosis (MS) is an inflammatory and demyelinating neurological disease of the central nervous system. Image-based biomarkers, such as lesions defined on magnetic resonance imaging (MRI), play an important role in MS diagnosis and patient monitoring. The detection of newly formed lesions provides crucial information for assessing disease progression and treatment outcome. Here, we propose a deep learning-based pipeline for new MS lesion detection and segmentation, which is built upon the nnU-Net framework. In addition to conventional data augmentation, we employ imaging and lesion-aware data augmentation methods, axial subsampling and CarveMix, to generate diverse samples and improve segmentation performance. The proposed pipeline is evaluated on the MICCAI 2021 MS new lesion segmentation challenge (MSSEG-2) dataset. It achieves an average Dice score of 0.510 and F1 score of 0.552 on cases with new lesions, and an average false positive lesion number nFP of 0.036 and false positive lesion volume VFP of 0.192 mm3 on cases with no new lesions. Our method outperforms other participating methods in the challenge and several state-of-the-art network architectures

Quantum memory for non-stationary light fields based on controlled reversible inhomogeneous broadening

We propose a new method for efficient storage and recall of non-stationary light fields, e.g. single photon time-bin qubits, in optically dense atomic ensembles. Our approach to quantum memory is based on controlled, reversible, inhomogeneous broadening. We briefly discuss experimental realizations of our proposal.Comment: 4 page

Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank

In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment is unfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) images to the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics (heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factors including acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage (i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of the stacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slice motion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastolic cardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involved in UKBB CMR acquisition and for the ones who use the dataset for research purposes

Semi-analytical solution to the second-order wave loads on a vertical cylinder in bi-chromatic bi-directional waves

A complete solution is presented for the second-order wave loads experienced by a 15 uniform vertical cylinder in bi-chromatic bi-directional waves. The solution is obtained 16 based on the introduction of an assisting radiation potential without explicitly 17 evaluating the second-order diffraction potential. The semi-analytical formulation for 18 calculating the wave loads is provided and an efficient numerical technique is 19 developed to treat the oscillatory free-surface integral that appears in the force 20 formulation. After validating the present solution by comparing with the predictions 21 based on other methods, numerical studies are conducted for different combinations of 22 incident wave frequencies and wave headings, and the influence of frequencies and 23 headings of dual waves on the second-order wave loads is investigated. In addition, by 24 expressing the second-order wave loads in a power expansion with respect to the wave 25 frequency difference and wave heading difference which are both assumed to be small, 26 approximations on the calculation of wave loads are developed. The accuracy of 27 different approximations is assessed by comparing the approximate results with those 28 based on the complete solution

A Population of Radio-loud Narrow Line Seyfert 1 Galaxies with Blazar-like Properties?

(abridged) We present a comprehensive study of a sample of 23 genuine radio-loud NLS1 galaxies which have the radio-loudness parameters greater than 100. The radio sources of the sample are ubiquitously compact. A significant fraction of these objects show interesting radio to X-ray properties that are unusual to most of the previously known radio-loud NLS1 AGN, but are reminiscent of blazars. These include flat radio spectra, large amplitude flux and spectral variability, compact VLBI cores, very high brightness temperatures derived from variability, enhanced optical emission in excess of the normal ionising continuum, flat X-ray spectra, and blazar-like SEDs. We interpret them as evidence for the postulated blazar nature of these very radio-loud NLS1 AGN, which might possess at least moderately relativistic jets. Intrinsically, some of the objects have relatively low radio power and would have been classified as radio-intermediate AGN. The black hole masses are estimated to be within 10^{6-8}Msun, and the inferred Eddington ratios are around unity. The results imply that radio-loud AGN may be powered by black holes with moderate masses (10^{6-7}Msun) accreting at high rates. We find that a significant fraction of the objects, despite having strong emission lines, resemble high-energy peaked BL Lacs (HBL) in their SED. Given the peculiarities of blazar-like NLS1 galaxies, questions arise as to whether they are plain downsizing extensions of normal radio-loud AGN, or whether they form a previously unrecognised population.Comment: Comments: 29 pages, 16 figures, 4 tables, accepted for publication in Ap

Stratified decision forests for accurate anatomical landmark localization in cardiac images

Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy