The goal of MRI reconstruction is to restore a high fidelity image from partially observed measurements. This partial view naturally induces reconstruction uncertainty that can only be reduced by acquiring additional measurements. In this paper, we present a novel method for MRI reconstruction that, at inference time, dynamically selects the measurements to take and iteratively refines the prediction in order to best reduce the reconstruction error and, thus, its uncertainty. We validate our method on a large scale knee MRI dataset, as well as on ImageNet. Results show that (1) our system successfully outperforms active acquisition baselines; (2) our uncertainty estimates correlate with error maps; and (3) our ResNet-based architecture surpasses standard pixel-to-pixel models in the task of MRI reconstruction. The proposed method not only shows high-quality reconstructions but also paves the road towards more applicable solutions for accelerating MRI.
There is a vast literature tackling the problem of undersampled MRI reconstruction. State-of-the-art solutions include both signal processing techniques (e.g. Compressed Sensing (CS)) as well as machine learning ones. On one hand, CS-based MRI reconstruction has been widely studied in the literature @cite_27 @cite_18 @cite_52 @cite_56 @cite_11 . These approaches usually result in over-smoothed reconstructions, which involve a time consuming optimization process, limiting their practical scalability. On other hand, deep learning based approaches have been introduced as a promising alternative to MRI reconstruction @cite_24 @cite_12 @cite_2 @cite_3 @cite_41 . In @cite_23 , a cascaded CNN with a consistency layer is presented to ensure measurement fidelity in dynamic cardiac MRI reconstruction. In @cite_17 , a Unet architecture @cite_51 is used to reconstruct brain images, while @cite_48 proposes a recurrent inference machine for image reconstruction. Moreover, following recent trends, architectures involving image refinement mechanisms seem to be gaining increasing attention @cite_23 @cite_32 @cite_48 . Although all previously-mentioned approaches are able to improve the reconstruction error, the human perception of the results is still not compelling. Therefore, recent works have also focused on exploring different training objectives such as adversarial losses @cite_25 @cite_20 @cite_30 to enhance the perceptual reconstruction quality @cite_32 @cite_14 .
@cite_30 @cite_41 @cite_3 @cite_2 @cite_20 @cite_18 @cite_48 @cite_52 @cite_23 @cite_17 @cite_32 @cite_56 @cite_27 @cite_25 @cite_12 @cite_14 @cite_24 @cite_51 @cite_11
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Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN , for fast and accurate CS-MRI reconstruction. The proposed model is a variant of fully-residual convolutional autoencoder and generative adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled @math -space data. In addition, our solution leverages a chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate—the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a @math image, because it is one-way deployment on a feed-forward network, and the image quality is superior even for extremely low sampling rate (as low as 10 ) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases. Recovering images from undersampled linear measurements typically leads to an ill-posed linear inverse problem, that asks for proper statistical priors. Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually "plausible" and physically "feasible" images with minimal hallucination. To cope with these challenges, we design a cascaded network architecture that unrolls the proximal gradient iterations by permeating benefits from generative residual networks (ResNet) to modeling the proximal operator. A mixture of pixel-wise and perceptual costs is then deployed to train proximals. The overall architecture resembles back-and-forth projection onto the intersection of feasible and plausible images. Extensive computational experiments are examined for a global task of reconstructing MR images of pediatric patients, and a more local task of superresolving CelebA faces, that are insightful to design efficient architectures. Our observations indicate that for MRI reconstruction, a recurrent ResNet with a single residual block effectively learns the proximal. This simple architecture appears to significantly outperform the alternative deep ResNet architecture by 2dB SNR, and the conventional compressed-sensing MRI by 4dB SNR with 100x faster inference. For image superresolution, our preliminary results indicate that modeling the denoising proximal demands deep ResNets. Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data are acquired using aggressive Cartesian undersampling. First, we show that when each 2-D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2-D compressed sensing approaches, such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Second, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10 s and, for the 2-D case, each image frame can be reconstructed in 23 ms, enabling real-time applications. Purpose: Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans. Methods: The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies. Since similarity is not guaranteed, sampling and reconstruction are adjusted during acquisition to match the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, via adaptive sampling, and in the reconstruction stage, with weighted reconstruction. In adaptive sampling, k-space sampling locations are optimized during acquisition. Weighted reconstruction uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. The approach was tested on 2D and 3D MRI scans of patients with brain tumors. Results: The longitudinal adaptive compressed sensing MRI (LACS-MRI) scheme provides reconstruction quality which outperforms other CS-based approaches for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial resolution. Compared with data sampled at the Nyquist rate, LACS-MRI exhibits signal-to-error ratio (SER) of 24.8 dB with undersampling factor of 16.6 in 3D MRI. Conclusions: The authors presented an adaptive method for image reconstruction utilizing similarity of scans in longitudinal MRI studies, where possible. The proposed approach can significantly reduce scanning time in many applications that consist of disease follow-up and monitoring of longitudinal changes in brain MRI.