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Our hypothesis is that UDA using diffusion-weighted images, generated with a unified model, offers a promising and reliable strategy for enhancing the performance of supervised learning models in multi-site prostate lesion detection, especially when various b-values are present. This retrospective study included data from 5,150 patients (14,191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for multi-site PCa detection. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual DW images acquired using various b-values, to align with the style of images acquired using b-values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1,692 test cases (2,393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 (p<.001), respectively, for PI-RADS>=3, and 0.77 and 0.80 (p<.001) for PI-RADS>=4 PCa lesions. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 (p<.001) for PI-RADS>=3, and 0.50 and 0.77 (p<.001) for PI-RADS>=4 PCa lesions. The results indicate the proposed UDA with generated images improved the performance of SL methods in multi-site PCa lesion detection across datasets with various b values, especially for images acquired with significant deviations from the PI-RADS recommended DWI protocol (e.g. with an extremely high b-value)., Comment: Accept at Radiology: Artificial Intelligence. Journal reference and external DOI will be added once published

A series of tetragonal Fe$_{1-x}$Co${_x}$Se single crystals with a complete Co doping range (0$\leq$x$\leq$0.52) up to its solid solubility limit in FeSe have been grown by an eutectic AlCl${_3}$/KCl molten salt method. The typical lateral size of as-grown Fe$_{1-x}$Co${_x}$Se single crystals is 1$-$5 mm. The chemical composition and homogeneity of the crystals was examined by both inductively coupled plasma atomic emission spectroscopy and energy dispersive spectrometer. X-ray diffraction analysis demonstrates that the crystal lattice parameters $a$ and $c$ are both linearly decreased with increasing Co doping level x. In the whole doping range, all the samples show metallic behaviour in contrast to a metal insulator transition of Cu-doped FeSe according to the resistivity measurements

An eutectic AlCl$_3$/KCl molten salt method in a horizontal configuration was employed to grow millimeter-sized and composition homogeneous CuFeSe$_2$ single crystals due to the continuous growth process in a temperature gradient induced solution convection. The typical as-grown CuFeSe$_2$ single crystals in cubic forms are nearly 1.6$\times$1.2$\times$1.0 mm3 in size. The chemical composition and homogeneity of the crystals was examined by both inductively coupled plasma atomic emission spectroscopy and energy dispersive spectrometer with Cu:Fe:Se = 0.96:1.00:1.99 consistent with the stoichiometric composition of CuFeSe$_2$. The magnetic measurements suggest a ferrimagnetic or weak ferromagnetic transition below T$_C$ = 146 K and the resistivity reveals a semiconducting behavior and an abrupt increase below T$_C$.

The oscillating temperature technique was successfully employed to grow large and composition homogeneous CaFeAsF single crystals due to the reduction of nuclei number by temperature oscillation. The largest as-grown CaFeAsF single crystal was nearly 8 mm in lateral size and 0.27 mm in thickness which is far larger than previously reported in all dimensions. The full-width-at-half maximum (FWHM) of x-ray rocking curve is as small as 0.163{\deg} indicating the high quality of CaFeAsF single crystals.

The discovery of EuFeAs2, currently the only charge-neutral parent phase of the 112-type iron-pnictide system, provides a new platform for the study of elemental doping effects on magnetism and superconductivity (SC). In this study, a series of polycrystalline EuFe1-yCoyAs2 and Eu0.9Pr0.1Fe1-yCoyAs2 samples are synthesized through solid-state reaction, and the evolutions of SC and magnetism with Co doping in EuFeAs2 and Eu0.9Pr0.1FeAs2 are investigated by electrical transport and magnetic susceptibility measurements. For EuFe1-yCoyAs2, the Eu-related antiferromagnetic (AFM) transition around 40 K is barely affected by Co doping, while the Fe-related spin density wave (SDW) transition temperature drops rapidly. Meanwhile, SC is induced by a trace amount of Co doping, with a highest transition temperature Tc ~ 28 K found in EuFe0.9Co0.1As2. For the Eu0.9Pr0.1Fe1-yCoyAs2 series, the magnetism and superconductivity show similar evolutions upon Co doping, and the highest Tc is enhanced to 30.6 K with an optimum doping level y ~ 0.07. Our results shed light on the competition between SC and SDW with Co doping in the 112-type EuFeAs2 system., Comment: 10 pages, 5 figures

Computed tomography (CT) is a popular medical imaging modality in clinical applications. At the same time, the x-ray radiation dose associated with CT scans raises public concerns due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of Low-Dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality. In this paper, we propose a novel 3D noise reduction method, called Structure-sensitive Multi-scale Generative Adversarial Net (SMGAN), to improve the LDCT image quality. Specifically, we incorporate three-dimensional (3D) volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve structural and texture information from normal-dose CT (NDCT) images, and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more detailed information, and outperforms competing methods., Comment: IEEE Access 2018

Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients. The reduction of CT radiation dose, however, compromises the signal-to-noise ratio, and may compromise the image quality and the diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in low-dose CT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN). This article introduces a Contracting Path-based Convolutional Encoder-decoder (CPCE) network in 2D and 3D configurations within the GAN framework for low-dose CT denoising. A novel feature of our approach is that an initial 3D CPCE denoising model can be directly obtained by extending a trained 2D CNN and then fine-tuned to incorporate 3D spatial information from adjacent slices. Based on the transfer learning from 2D to 3D, the 3D network converges faster and achieves a better denoising performance than that trained from scratch. By comparing the CPCE with recently published methods based on the simulated Mayo dataset and the real MGH dataset, we demonstrate that the 3D CPCE denoising model has a better performance, suppressing image noise and preserving subtle structures., Comment: To be published in the IEEE TMI

In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transform theory, and promises to improve the performance of the GAN. The perceptual loss compares the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN helps migrate the data noise distribution from strong to weak. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task, is capable of not only reducing the image noise level but also keeping the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.

Regularization methods are commonly used in X-ray CT image reconstruction. Different regularization methods reflect the characterization of different prior knowledge of images. In a recent work, a new regularization method called a low-dimensional manifold model (LDMM) is investigated to characterize the low-dimensional patch manifold structure of natural images, where the manifold dimensionality characterizes structural information of an image. In this paper, we propose a CT image reconstruction method based on the prior knowledge of the low-dimensional manifold of CT image. Using the clinical raw projection data from GE clinic, we conduct comparisons for the CT image reconstruction among the proposed method, the simultaneous algebraic reconstruction technique (SART) with the total variation (TV) regularization, and the filtered back projection (FBP) method. Results show that the proposed method can successfully recover structural details of an imaging object, and achieve higher spatial and contrast resolution of the reconstructed image than counterparts of FBP and SART with TV.

Increasing use of CT in modern medical practice has raised concerns over associated radiation dose. Reduction of radiation dose associated with CT can increase noise and artifacts, which can adversely affect diagnostic confidence. Denoising of low-dose CT images on the other hand can help improve diagnostic confidence, which however is a challenging problem due to its ill-posed nature, since one noisy image patch may correspond to many different output patches. In the past decade, machine learning based approaches have made quite impressive progress in this direction. However, most of those methods, including the recently popularized deep learning techniques, aim for minimizing mean-squared-error (MSE) between a denoised CT image and the ground truth, which results in losing important structural details due to over-smoothing, although the PSNR based performance measure looks great. In this work, we introduce a new perceptual similarity measure as the objective function for a deep convolutional neural network to facilitate CT image denoising. Instead of directly computing MSE for pixel-to-pixel intensity loss, we compare the perceptual features of a denoised output against those of the ground truth in a feature space. Therefore, our proposed method is capable of not only reducing the image noise levels, but also keeping the critical structural information at the same time. Promising results have been obtained in our experiments with a large number of CT images.