Robust Deep Compressive Sensing with Recurrent-Residual Structural Constraints

Existing deep compressive sensing (CS) methods either ignore adaptive online optimization or depend on costly iterative optimizer during reconstruction. This work explores a novel image CS framework with recurrent-residual structural constraint, termed as R$^2$CS-NET. The R$^2$CS-NET first progressively optimizes the acquired samplings through a novel recurrent neural network. The cascaded residual convolutional network then fully reconstructs the image from optimized latent representation. As the first deep CS framework efficiently bridging adaptive online optimization, the R$^2$CS-NET integrates the robustness of online optimization with the efficiency and nonlinear capacity of deep learning methods. Signal correlation has been addressed through the network architecture. The adaptive sensing nature further makes it an ideal candidate for color image CS via leveraging channel correlation. Numerical experiments verify the proposed recurrent latent optimization design not only fulfills the adaptation motivation, but also outperforms classic long short-term memory (LSTM) architecture in the same scenario. The overall framework demonstrates hardware implementation feasibility, with leading robustness and generalization capability among existing deep CS benchmarks

STRUCTURED ILLUMINATION MICROSCOPE IMAGE RECONSTRUCTION USING UNROLLED PHYSICS-INFORMED GENERATIVE ADVERSARIAL NETWORK (UPIGAN)

In three-dimensional structured illumination microscopy (3D-SIM) where the images are taken from the object through the point spread function (PSF) of the imaging system, data acquisition can result in images taken under undesirable aberrations that contribute to a model mismatch. The inverse imaging problem in 3D-SIM has been solved using a variety of conventional model-based techniques that can be computationally intensive. Deep learning (DL) approaches, as opposed to traditional restoration methods, tackle the issue without access to the analytical model. This research aims to provide an unrolled physics-informed generative adversarial network (UPIGAN) for the reconstruction of 3D-SIM images utilizing data samples of mitochondria and lysosomes obtained from a 3D-SIM system. This design makes use of the benefits of physics knowledge in the unrolling step. Moreover, the GAN employs a Residual Channel Attention super-resolution deep neural network (DNN) in its generator architecture. The results indicate that the addition of both physics-informed unrolling and GAN incorporation yield improvements in reconstructed results compared to the regular DL approach

Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images

We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules when using convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed AG models are evaluated on a variety of tasks, including medical image classification and segmentation. For classification, we demonstrate the use case of AGs in scan plane detection for fetal ultrasound screening. We show that the proposed attention mechanism can provide efficient object localisation while improving the overall prediction performance by reducing false positives. For segmentation, the proposed architecture is evaluated on two large 3D CT abdominal datasets with manual annotations for multiple organs. Experimental results show that AG models consistently improve the prediction performance of the base architectures across different datasets and training sizes while preserving computational efficiency. Moreover, AGs guide the model activations to be focused around salient regions, which provides better insights into how model predictions are made. The source code for the proposed AG models is publicly available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging with Deep Learning). arXiv admin note: substantial text overlap with arXiv:1804.03999, arXiv:1804.0533

Compressive MRI with deep convolutional and attentive models

Since its advent in the last century, Magnetic Resonance Imaging (MRI) has demonstrated a significant impact on modern medicine and spectroscopy and witnessed widespread use in medical imaging and clinical practice, owing to the flexibility and excellent ability in viewing anatomical structures. Although it provides a non-invasive and ionizing radiation-free tool to create images of the anatomy of the human body being inspected, the long data acquisition process hinders its growth and development in time-critical applications. To shorten the scanning time and reduce the discomfort of patients, the sampling process can be accelerated by leaving out an amount of sampling steps and performing image reconstruction from a subset of measurements. However, the images created with under-sampled signals can suffer from strong aliasing artifacts which unfavorably affect the quality of diagnosis and treatment. Compressed sensing (CS) methods were introduced to alleviate the aliasing artifacts by reconstructing an image from the observed measurements via model-based optimization algorithms. Despite achieved success, the sparsity prior assumed by CS methods can be difficult to hold in real-world practice and challenging to capture complex anatomical structures. The iterative optimization algorithms are often computationally expensive and time-consuming, against the speed demand of modern MRI. Those factors limit the quality of reconstructed images and put restrictions on the achievable acceleration rates. This thesis mainly focuses on developing deep learning-based methods, specifically using modern over-parametrized models, for MRI reconstruction, by leveraging the powerful learning ability and representation capacity of such models. Firstly, we introduce an attentive selection generative adversarial network to achieve fine-grained reconstruction by performing large-field contextual information integration and attention selection mechanism. To incorporate domain-specific knowledge into the reconstruction procedure, an optimization-inspired deep cascaded framework is proposed with a novel deep data consistency block to leverage domain-specific knowledge and an adaptive spatial attention selection module to capture the correlations among high-resolution features, aiming to enhance the quality of recovered images. To efficiently utilize the contextual information hidden in the spatial dimensions, a novel region-guided channel-wise attention network is introduced to incorporate the spatial semantics into a channel-based attention mechanism, demonstrating a light-weight and flexible design to attain improved reconstruction performance. Secondly, a coil-agnostic reconstruction framework is introduced to solve the unknown forward process problem in parallel MRI reconstruction. To avoid the estimation of sensitivity maps, a novel data aggregation consistency block is proposed to approximately perform the data consistency enforcement without resorting to coil sensitivity information. A locality-aware spatial attention module is devised and embedded into the reconstruction pipeline to enhance the model performance by capturing spatial contextual information via data-adaptive kernel prediction. It is demonstrated by experiments that the proposed coil-agnostic method is robust and resilient to different machine configurations and outperforms other sensitivity estimation-based methods. Finally, the research work focusing on dynamic MRI reconstruction is presented. We introduce an optimization-inspired deep cascaded framework to recover a sequence of MRI images, which utilizes a novel mask-guided motion feature incorporation method to explicitly extract and incorporate the motion information into the reconstruction iterations, showing to better preserve the dynamic content. A spatio-temporal Fourier neural block is proposed and embedded into the network to improve the model performance by efficiently retrieving useful information in both spatial and temporal domains. It is demonstrated that the devised framework surpasses other competing methods and can generalize well on other reconstruction models and unseen data, validating its transferability and generalization capacity

Neural networks-based regularization for large-scale medical image reconstruction

In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks (NNs) and cascaded NNs have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging modalities. However, the fact that these approaches employ the application of the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volumes at once, which for some applications is computationally infeasible. In this work, we follow a different reconstruction strategy by strictly separating the application of the NN, the regularization of the solution and the consistency with the measured data. The regularization is given in the form of an image prior obtained by the output of a previously trained NN which is used in a Tikhonov regularization framework. By doing so, more complex and sophisticated network architectures can be used for the removal of the artefacts or noise than it is usually the case in iterative NNs. Due to the large scale of the considered problems and the resulting computational complexity of the employed networks, the priors are obtained by processing the images or volumes as patches or slices. We evaluated the method for the cases of 3D cone-beam low dose CT and undersampled 2D radial cine MRI and compared it to a total variation-minimization-based reconstruction algorithm as well as to a method with regularization based on learned overcomplete dictionaries. The proposed method outperformed all the reported methods with respect to all chosen quantitative measures and further accelerates the regularization step in the reconstruction by several orders of magnitude

FISTA-Net: Learning A Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging

Inverse problems are essential to imaging applications. In this paper, we propose a model-based deep learning network, named FISTA-Net, by combining the merits of interpretability and generality of the model-based Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and tuning-free advantages of the data-driven neural network. By unfolding the FISTA into a deep network, the architecture of FISTA-Net consists of multiple gradient descent, proximal mapping, and momentum modules in cascade. Different from FISTA, the gradient matrix in FISTA-Net can be updated during iteration and a proximal operator network is developed for nonlinear thresholding which can be learned through end-to-end training. Key parameters of FISTA-Net including the gradient step size, thresholding value and momentum scalar are tuning-free and learned from training data rather than hand-crafted. We further impose positive and monotonous constraints on these parameters to ensure they converge properly. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can optimize parameters for different imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep learning methods and exhibits good generalization ability over other competitive learning-based approaches under different noise levels.Comment: 11 pages