Deformable Registration through Learning of Context-Specific Metric Aggregation

We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Furthermore, such hand-crafted combination can only happen at global scale (entire volume) and therefore will not be able to account for the different tissue properties. We propose a learning algorithm for estimating these parameters locally, conditioned to the data semantic classes. The objective function of our formulation is a special case of non-convex function, difference of convex function, which we optimize using the concave convex procedure. As a proof of concept, we show the impact of our approach on three challenging datasets for different anatomical structures and modalities.Comment: Accepted for publication in the 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017), in conjunction with MICCAI 201

Phase Coexistence Near a Morphotropic Phase Boundary in Sm-doped BiFeO3 Films

We have investigated heteroepitaxial films of Sm-doped BiFeO3 with a Sm-concentration near a morphotropic phase boundary. Our high-resolution synchrotron X-ray diffraction, carried out in a temperature range of 25C to 700C, reveals substantial phase coexistence as one changes temperature to crossover from a low-temperature PbZrO3-like phase to a high-temperature orthorhombic phase. We also examine changes due to strain for films greater or less than the critical thickness for misfit dislocation formation. Particularly, we note that thicker films exhibit a substantial volume collapse associated with the structural transition that is suppressed in strained thin films

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

Prior-based Coregistration and Cosegmentation

We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation. Our approach deforms all volumes towards consensus, taking into account image similarities and label consistency. Our pipeline can incorporate any classifier and similarity metric. Results on two datasets, containing annotations of challenging brain structures, demonstrate the potential of our method.Comment: The first two authors contributed equall

Suppression of Magnetic Phase Separation in Epitaxial SrCoOx Films

Using pulsed laser deposition and a unique fast quenching method, we have prepared SrCoOx epitaxial films on SiTiO3 substrates. As electrochemical oxidation increases the oxygen content from x = 2.75 to 3.0, the films tend to favor the discrete magnetic phases seen in bulk samples for the homologous series SrCoO(3-n/8) (n = 0, 1, 2). Unlike bulk samples, 200nm thick films remain single phase throughout the oxidation cycle. 300 nm films can show two simultaneous phases during deoxidation. These results are attributed to finite thickness effects and imply the formation of ordered regions larger than approximately 300 nm.Comment: The following article has been submitted to Applied Physics Letters. After it is published, it will be found at http://apl.aip.or

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

Hydrostatic pressure effects on the electrical transport properties of Pr0.5Sr0.5MnO3

We studied single-crystalline Pr0.5Sr0.5MnO3 by means of measurements of magnetic susceptibility and specific heat at ambient pressure (P), and electrical resistivity (r) in hydrostatic pressures up to 2 GPa. This material displays ferromagnetic (FM) order, with Curie temperature TC ~ 255 K. A crystallographic transformation from I4/mcm to Fmmm is accompanied by the onset of antiferromagnetism (AFM), with Neel temperature TN ~ 161 K. The effect of pressure is to lower TC, and raise TN at the approximate rates of -3.2 K/GPa, and 14.2 K/GPa, respectively. Although the value of TN increases with P, due to the enhancement of the superexchange interactions, the AFM-Fmmm state is progressively suppressed, as pressure stabilizes the FM-I4/mcm phase to lower temperatures. The r vs T data suggest that the AFM phase should be completely suppressed near 2.4 GPa.Comment: 17 pages, 7 figure