GM-Net: Learning Features with More Efficiency

Deep Convolutional Neural Networks (CNNs) are capable of learning unprecedentedly effective features from images. Some researchers have struggled to enhance the parameters' efficiency using grouped convolution. However, the relation between the optimal number of convolutional groups and the recognition performance remains an open problem. In this paper, we propose a series of Basic Units (BUs) and a two-level merging strategy to construct deep CNNs, referred to as a joint Grouped Merging Net (GM-Net), which can produce joint grouped and reused deep features while maintaining the feature discriminability for classification tasks. Our GM-Net architectures with the proposed BU_A (dense connection) and BU_B (straight mapping) lead to significant reduction in the number of network parameters and obtain performance improvement in image classification tasks. Extensive experiments are conducted to validate the superior performance of the GM-Net than the state-of-the-arts on the benchmark datasets, e.g., MNIST, CIFAR-10, CIFAR-100 and SVHN.Comment: 6 Pages, 5 figure

Joint Reconstruction for Single-Shot Edge Illumination Phase-Contrast Tomography (EIXPCT)

Edge illumination X-ray phase-contrast tomography (EIXPCT) is an emerging X-ray phasecontrast tomography technique for estimating the complex-valued X-ray refractive index distribution of an object with laboratory-based X-ray sources. Conventional image reconstruction approaches for EIXPCT require multiple images to be acquired at each tomographic view angle. This contributes to prolonged data-acquisition times and elevated radiation doses, which can hinder in vivo applications. In this dissertation, a new “single-shot” method without restrictive assumptions related to the object, imaging geometry or hardware is proposed for joint reconstruction (JR) of the real and imaginary-valued components of the refractive index distribution from a tomographic data set that contains only a single image acquired at each view angle. The proposed method is predicated upon a non-linear formulation of the inverse problem that is solved by use of a gradient-based optimization method. The potential usefulness of this method is validated and investigated by use of computer-simulated and experimental EIXPCT data sets. The convexity, cross-talk properties and noise properties of the JR method are also investigated. One important advantage of EIXPCT is that its flexibility enables novel flexible data-acquisition designs. In this dissertation, two aspects of data-acquisition designs are explored in two separate studies. The first study focuses on where the masks in EIXPCT should be placed during the data-acquisition process. In this study, several promising mask displacement strategies are proposed, such as the constant aperture position (CAP) strategy and the alternating aperture position (AAP) strategies covering different angular ranges. In computer-simulation studies, candidate designs are analyzed and compared in terms of image reconstruction stability and quality. Experimental data are employed to test the designs in real-world applications. All candidate designs are also compared for their implementation complexity. The tradeoff between data acquisition time and image quality is discussed. The second study focuses on a resolution-enhancement method called dithering. Dithering requires that multiple projection images per tomographic view angle are acquired as the object is moved over sub-pixel distances. The EIXPCT resolution is mainly determined by the grating period of a sample mask, but can be significantly improved by the dithering technique. However, one main drawback of dithering is the increased data-acquisition time. Motivated by the flexibility in data acquisition designs enabled by the JR method, a novel partial dithering strategy for data acquisition is proposed. In this strategy, dithering is implemented at only a subset of the tomographic view angles. This results in spatial resolution that is comparable to that of the conventional full dithering strategy where dithering is performed at every view angle, but the acquisition time is substantially decreased. The effect of dithering parameters on image resolution is explored. Finally, a bench-top EIXPCT system has been set up in the lab. The components are designed to address the need of in vivo imaging of small animal models. However, thick objects such as animals pose unique challenges for the EIXPCT system, including the potential phase-wrapping problem, limited signal sensitivity, and elevated noise. The components of the system are designed to tackle these challenges, and some initial images obtained from the system show promising potential

Improved test-retest reliability of R2∗\textit{R}_2^* and susceptibility quantification using multi-shot multi echo 3D EPI

This study aimed to evaluate the potential of 3D echo-planar imaging (EPI) for improving the reliability of $T_2^*$-weighted ($T_2^*w$) data and quantification of $\textit{R}_2^*$ decay rate and susceptibility ($\chi$) compared to conventional gradient echo (GRE)-based acquisition. Eight healthy subjects in a wide age range were recruited. Each subject received repeated scans for both GRE and EPI acquisitions with an isotropic 1 mm resolution at 3 T. Maps of $\textit{R}_2^*$ and $\chi$ were quantified and compared using their inter-scan difference to evaluate the test-retest reliability. Inter-protocol differences of $\textit{R}_2^*$ and $\chi$ between GRE and EPI were also measured voxel by voxel and in selected ROIs to test the consistency between the two acquisition methods. The quantifications of $\textit{R}_2^*$ and $\chi$ using EPI protocols showed increased test-retest reliability with higher EPI factors up to 5 as performed in the experiment and were consistent with those based on GRE. This result suggested multi-shot multi-echo 3D EPI can be a useful alternative acquisition method for $T_2^*w$ MRI and quantification of $\textit{R}_2^*$ and $\chi$ with reduced scan time, improved test-retest reliability and similar accuracy compared to commonly used 3D GRE.Comment: 18 pages, 8 figures and 1 tabl