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Purpose: It is challenging for radiologists and gynecologists to identify the type of ovarian lesions by reading magnetic resonance (MR) images. Recently developed convolutional neural networks (CNNs) have made great progress in computer vision, but their architectures still need modification if they are used in processing medical images. This study aims to improve the feature extraction capability of CNNs, thus promoting the diagnostic performance in discriminating between benign and malignant ovarian lesions. Methods: We introduce a feature fusion architecture and insert the attention models in the neural network. The features extracted from different middle layers are integrated with reoptimized spatial and channel weights. We add a loss function to constrain the additional probability vector generated from the integrated features, thus guiding the middle layers to emphasize useful information. We analyzed 159 lesions imaged by dynamic contrast‐enhanced MR imaging (DCE‐MRI), including 73 benign lesions and 86 malignant lesions. Senior radiologists selected and labeled the tumor regions based on the pathology reports. Then, the tumor regions were cropped into 7494 nonoverlapping image patches for training and testing. The type of a single tumor was determined by the average probability scores of the image patches belonging to it. Results: We implemented fivefold cross‐validation to characterize our proposed method, and the distribution of performance matrics was reported. For all the test image patches, the average accuracy of our method is 70.5% with an average area under the curve (AUC) of 0.785, while the baseline is 69.4% and 0.773, and for the diagnosis of single tumors, our model achieved an average accuracy of 82.4% and average AUC of 0.916, which were better than the baseline (81.8% and 0.899). Moreover, we evaluated the performance of our proposed method utilizing different CNN backbones and different attention mechanisms. Conclusions: The texture features extracted from different middle layers are crucial for ovarian lesion diagnosis. Our proposed method can enhance the feature extraction capabilities of different layers of the network, thereby improving diagnostic performance. [ABSTRACT FROM AUTHOR]

Tensor singular value decomposition (t‐SVD) provides a novel way to decompose a tensor. It has been employed mostly in recovering missing tensor entries from the observed tensor entries. The problem of applying t‐SVD to recover tensors from limited coefficients in any given ortho‐normal basis is addressed. We prove that an n × n × n3 tensor with tubal‐rank r can be efficiently reconstructed by minimising its tubal nuclear norm from its O(rn3n log2(n3n)) randomly sampled coefficients w.r.t any given ortho‐normal basis. In our proof, we extend the matrix coherent conditions to tensor coherent conditions. We first prove the theorem belonging to the case of Fourier‐type basis under certain coherent conditions. Then, we prove that our results hold for any ortho‐normal basis meeting the conditions. Our work covers the existing t‐SVD‐based tensor completion problem as a special case. We conduct numerical experiments on random tensors and dynamic magnetic resonance images (d‐MRI) to demonstrate the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Purpose: Recent studies have witnessed that self‐attention modules can better solve the vision understanding problems by capturing long‐range dependencies. However, there are very few works designing a lightweight self‐attention module to improve the quality of MRI reconstruction. Furthermore, it can be observed that several important self‐attention modules (e.g., the non‐local block) cause high computational complexity and need a huge number of GPU memory when the size of the input feature is large. The purpose of this study is to design a lightweight yet effective spatial orthogonal attention module (SOAM) to capture long‐range dependencies, and develop a novel spatial orthogonal attention generative adversarial network, termed as SOGAN, to achieve more accurate MRI reconstruction. Methods: We first develop a lightweight SOAM, which can generate two small attention maps to effectively aggregate the long‐range contextual information in vertical and horizontal directions, respectively. Then, we embed the proposed SOAMs into the concatenated convolutional autoencoders to form the generator of the proposed SOGAN. Results: The experimental results demonstrate that the proposed SOAMs improve the quality of the reconstructed MR images effectively by capturing long‐range dependencies. Besides, compared with state‐of‐the‐art deep learning‐based CS‐MRI methods, the proposed SOGAN reconstructs MR images more accurately, but with fewer model parameters. Conclusions: The proposed SOAM is a lightweight yet effective self‐attention module to capture long‐range dependencies, thus, can improve the quality of MRI reconstruction to a large extent. Besides, with the help of SOAMs, the proposed SOGAN outperforms the state‐of‐the‐art deep learning‐based CS‐MRI methods. [ABSTRACT FROM AUTHOR]

Total variation (TV) regularization is a technique commonly utilized to promote sparsity of image in gradient domain. In this article, we address the problem of MR brain image reconstruction from highly undersampled Fourier measurements. We define the Moreau enhanced function of  L1 norm, and introduce the minmax‐concave TV (MCTV) penalty as a regularization term for MR brain image reconstruction. MCTV strongly induces the sparsity in gradient domain, and fits the frame of fast algorithms (eg, ADMM) for solving optimization problems. Although MCTV is non‐convex, the cost function in each iteration step can maintain convexity by specifying the relative nonconvexity parameter properly. Experimental results demonstrate the superior performance of the proposed method in comparison with standard TV as well as non‐local TV minimization method, which suggests that MCTV may have promising applications in the field of neuroscience in the future. [ABSTRACT FROM AUTHOR]

It has always been challenging to reconstruct magnetic resonance (MR) images from a limited set of k‐space data due to the ill‐posed nature. An effective way to compensate for the data incompleteness is through the use of the sparsity‐based regularisation. Recent work in image processing suggests that exploiting structured sparsity may lead to improved results. In this study, this idea is explored in combination with additional support prior of the MR images in the analysis context. Put differently, the authors propose a highly effective regulariser constraining group sparsity of the analysis coefficients within the pre‐estimated cosupport. A two‐stage iterative algorithm is developed and proceeds by alternatively calling its two key components: image reconstruction and cosupport detection. The feasibility of the proposed method is demonstrated for individual and multiple T1/T2‐weighted MR images. Simulation results show considerable improvement of their method compared with the methods using structured sparsity and support knowledge in the synthesis context and other related reconstruction methods. [ABSTRACT FROM AUTHOR]

ABSTRACT Compressed sensing (CS) enables to reconstruct MR images from highly undersampled k-space data by exploiting the sparsity which is implicit in the images. In this article, an MR image [ABSTRACT FROM AUTHOR]

ABSTRACT This article addresses the problem of reconstructing a magnetic resonance image from highly undersampled data, which frequently arises in accelerated magnetic resonance imaging. We propose to impose sparsity of first and second order difference sparse coefficients within the complement of the known support. Second order variation is involved to overcome blocky effects and support information is used to reduce the sampling rate further. The resulting optimization problem consists of a data fidelity term and first-second order variation terms penalizing entries within the complement of the known support. The efficient split Bregman algorithm is used to solve the problem. Reconstruction results from magnetic resonance imaging data corresponding to different sampling rates are shown to illustrate the performance of the proposed method. Then, we also assess the tolerance of the new method to noise briefly. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 277-284, 2015 [ABSTRACT FROM AUTHOR]

The problem of estimating rapidly time‐varying channels is considered to be one of the key challenges in high mobility orthogonal frequency division multiplexing (OFDM) systems. In such scenarios, fast time variation within OFDM symbol duration requires overloaded measurements for estimation. By exploiting the inherent sparsity of wireless channels, the authors cast the channel estimation as a compressive sensing (CS) problem to reduce the required pilot symbols. Different from the existing CS‐based estimators which mainly focus on the diagonal matrix model and treat the inter‐carrier interference as additive noise, the proposed methods are designed for the non‐diagonal matrix, a more precise representation of fast fading channels. To handle this more complex channel model, an iterative estimation scheme is presented, which adopts the recently introduced modified‐CS algorithm. In addition, a more simplified scheme is also designed by utilising a reasonable approximation of the system model. Compared to the former method, it has a reduced computational complexity with limited performance degradation. The simulation results demonstrate that the two proposed CS‐based methods are robust to large Doppler spreading and have better performance than conventional CS‐based estimators for fast time‐varying channels in OFDM systems. [ABSTRACT FROM AUTHOR]