Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI

Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution

In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.Comment: Accepted paper at MICCAI 201

Deformation and stress of a composite-metal assembly

Compliant structures, e.g. automobile body panel and airplane wing box are widely used. A compliant structure consists of one or more flexible parts, and these parts share the mating features among them. Because of process-induced deformation and part-to-part variations, external forces are applied during the assembly process and the parts are deformed. As a result, the final assembly is pre-stressed and its geometrical shape may deviate from the designed shape. Therefore, the assembly variation and residual stress need to be analysed in order to evaluate the structure performance. In this study, a new approach based on response surface methodology is developed. A number of organised virtual experiments are conducted with the aid of finite element analysis and regression models are fitted to the resulting data. These regression models relate part variations to assembly variation and residual stress. Monte Carlo simulation can be conveniently done using these simple regression models. The effectiveness of this method was illustrated using a composite–metal assembly. It is shown that the method presented in this paper provides a practical and reliable solution to the analysis of compliant structures

Truncated Schwinger-Dyson Equations and Gauge Covariance in QED3

We study the Landau-Khalatnikov-Fradkin transformations (LKFT) in momentum space for the dynamically generated mass function in QED3. Starting from the Landau gauge results in the rainbow approximation, we construct solutions in other covariant gauges. We confirm that the chiral condensate is gauge invariant as the structure of the LKFT predicts. We also check that the gauge dependence of the constituent fermion mass is considerably reduced as compared to the one obtained directly by solving SDE.Comment: 17 pages, 11 figures. v3. Improved and Expanded. To appear in Few Body System

Digital PCR methods improve detection sensitivity and measurement precision of low abundance mtDNA deletions

Mitochondrial DNA (mtDNA) mutations are a common cause of primary mitochondrial disorders, and have also been implicated in a broad collection of conditions, including aging, neurodegeneration, and cancer. Prevalent among these pathogenic variants are mtDNA deletions, which show a strong bias for the loss of sequence in the major arc between, but not including, the heavy and light strand origins of replication. Because individual mtDNA deletions can accumulate focally, occur with multiple mixed breakpoints, and in the presence of normal mtDNA sequences, methods that detect broad-spectrum mutations with enhanced sensitivity and limited costs have both research and clinical applications. In this study, we evaluated semi-quantitative and digital PCR-based methods of mtDNA deletion detection using double-stranded reference templates or biological samples. Our aim was to describe key experimental assay parameters that will enable the analysis of low levels or small differences in mtDNA deletion load during disease progression, with limited false-positive detection. We determined that the digital PCR method significantly improved mtDNA deletion detection sensitivity through absolute quantitation, improved precision and reduced assay standard error

Training Auto-encoder-based Optimizers for Terahertz Image Reconstruction

Terahertz (THz) sensing is a promising imaging technology for a wide variety of different applications. Extracting the interpretable and physically meaningful parameters for such applications, however, requires solving an inverse problem in which a model function determined by these parameters needs to be fitted to the measured data. Since the underlying optimization problem is nonconvex and very costly to solve, we propose learning the prediction of suitable parameters from the measured data directly. More precisely, we develop a model-based autoencoder in which the encoder network predicts suitable parameters and the decoder is fixed to a physically meaningful model function, such that we can train the encoding network in an unsupervised way. We illustrate numerically that the resulting network is more than 140 times faster than classical optimization techniques while making predictions with only slightly higher objective values. Using such predictions as starting points of local optimization techniques allows us to converge to better local minima about twice as fast as optimization without the network-based initialization.Comment: This is a pre-print of a conference paper published in German Conference on Pattern Recognition (GCPR) 201

Survivin gene silencing sensitizes prostate cancer cells to selenium growth inhibition

<p>Abstract</p> <p>Background</p> <p>Prostate cancer is a leading cause of cancer-related death in men worldwide. Survivin is a member of the inhibitor of apoptosis (IAP) protein family that is expressed in the majority of human tumors including prostate cancer, but is barely detectable in terminally differentiated normal cells. Downregulation of survivin could sensitize prostate cancer cells to chemotherapeutic agents <it>in vitro </it>and <it>in vivo</it>. Selenium is an essential trace element. Several studies have shown that selenium compounds inhibit the growth of prostate cancer cells. The objective of this study is to investigate whether survivin gene silencing in conjunction with selenium treatment could enhance the therapeutic efficacy for prostate cancer and to elucidate the underlying mechanisms.</p> <p>Methods</p> <p>Expression of survivin was analyzed in a collection of normal and malignant prostatic tissues by immunohistochemical staining. <it>In vitro </it>studies were conducted in PC-3M, C4-2B, and 22Rv1 prostate cancer cells. The effect of selenium on survivin expression was analyzed by Western blotting and semi-quantitative RT-PCR. Survivin gene knockdown was carried out by transfecting cells with a short hairpin RNA (shRNA) designed against survivin. Cell proliferation was quantitated by the 3-(4,5-Dimethylthiazol-2-yl)- 2,5-Diphenyltetrazolium Bromide (MTT) assay and apoptosis by propidium iodide staining followed by flow cytometry analysis. Finally, <it>in vivo </it>tumor growth assay was performed by establishing PC-3M xenograft in nude mice and monitoring tumor growth following transfection and treatment.</p> <p>Results</p> <p>We found that survivin was undetectable in normal prostatic tissues but was highly expressed in prostate cancers. Survivin knockdown or selenium treatment inhibited the growth of prostate cancer cells, but the selenium effect was modest. In contrast to what have been observed in other cell lines, selenium treatment had little or no effect on survivin expression in several androgen-independent prostate cancer cell lines. Survivin knockdown sensitized these cells to selenium growth inhibition and apoptosis induction. In nude mice bearing PC-3M xenografts, survivin knockdown synergizes with selenium in inhibiting tumor growth.</p> <p>Conclusions</p> <p>Selenium could inhibit the growth of hormone-refractory prostate cancer cells both <it>in vitro </it>and <it>in vivo</it>, but the effects were modest. The growth inhibition was not mediated by downregulating survivin expression. Survivin silencing greatly enhanced the growth inhibitory effects of selenium.</p

Local antiferromagnetic exchange and collaborative Fermi surface as key ingredients of high temperature superconductors

Cuprates, ferropnictides and ferrochalcogenides are three classes of unconventional high-temperature superconductors, who share similar phase diagrams in which superconductivity develops after a magnetic order is suppressed, suggesting a strong interplay between superconductivity and magnetism, although the exact picture of this interplay remains elusive. Here we show that there is a direct bridge connecting antiferromagnetic exchange interactions determined in the parent compounds of these materials to the superconducting gap functions observed in the corresponding superconducting materials. High superconducting transition temperature is achieved when the Fermi surface topology matches the form factor of the pairing symmetry favored by local magnetic exchange interactions. Our result offers a principle guide to search for new high temperature superconductors.Comment: 12 pages, 5 figures, 1 table, 1 supplementary materia