@article{hollingsworth,
	doi = {10.1088/0031-9155/60/21/r297},
	year = 2015,
	month = {oct},
	publisher = {{IOP} Publishing},
	volume = {60},
	number = {21},
	pages = {R297--R322},
	author = {Kieren Grant Hollingsworth},
	title = {Reducing acquisition time in clinical {MRI} by data undersampling and compressed sensing reconstruction},
	journal = {Physics in Medicine and Biology},
	abstract = {MRI is often the most sensitive or appropriate technique for important measurements in clinical diagnosis and research, but lengthy acquisition times limit its use due to cost and considerations of patient comfort and compliance. Once an image field of view and resolution is chosen, the minimum scan acquisition time is normally fixed by the amount of raw data that must be acquired to meet the Nyquist criteria. Recently, there has been research interest in using the theory of compressed sensing (CS) in MR imaging to reduce scan acquisition times. The theory argues that if our target MR image is sparse, having signal information in only a small proportion of pixels (like an angiogram), or if the image can be mathematically transformed to be sparse then it is possible to use that sparsity to recover a high definition image from substantially less acquired data. This review starts by considering methods of k-space undersampling which have already been incorporated into routine clinical imaging (partial Fourier imaging and parallel imaging), and then explains the basis of using compressed sensing in MRI. The practical considerations of applying CS to MRI acquisitions are discussed, such as designing k-space undersampling schemes, optimizing adjustable parameters in reconstructions and exploiting the power of combined compressed sensing and parallel imaging (CS-PI). A selection of clinical applications that have used CS and CS-PI prospectively are considered. The review concludes by signposting other imaging acceleration techniques under present development before concluding with a consideration of the potential impact and obstacles to bringing compressed sensing into routine use in clinical MRI.}
}
@article{sense,
author = {Pruessmann, Klaas P. and Weiger, Markus and Scheidegger, Markus B. and Boesiger, Peter},
title = {SENSE: Sensitivity encoding for fast MRI},
journal = {Magnetic Resonance in Medicine},
volume = {42},
number = {5},
pages = {952-962},
keywords = {MRI, sensitivity encoding, SENSE, fast imaging, receiver coil array},
doi = {10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S},
abstract = {Abstract New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementary to Fourier preparation by linear field gradients. Thus, by using multiple receiver coils in parallel scan time in Fourier imaging can be considerably reduced. The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k-space sampling patterns. Special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density. For this case the feasibility of the proposed methods was verified both in vitro and in vivo. Scan time was reduced to one-half using a two-coil array in brain imaging. With an array of five coils double-oblique heart images were obtained in one-third of conventional scan time. Magn Reson Med 42:952–962, 1999. © 1999 Wiley-Liss, Inc.},
year = {1999}
}
@article{grappa,
author = {Griswold, Mark A. and Jakob, Peter M. and Heidemann, Robin M. and Nittka, Mathias and Jellus, Vladimir and Wang, Jianmin and Kiefer, Berthold and Haase, Axel},
title = {Generalized autocalibrating partially parallel acquisitions (GRAPPA)},
journal = {Magnetic Resonance in Medicine},
volume = {47},
number = {6},
pages = {1202-1210},
keywords = {parallel imaging, rapid MRI, RF coil arrays, SMASH, SENSE, PILS},
doi = {10.1002/mrm.10171},
abstract = {Abstract In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique. Magn Reson Med 47:1202–1210, 2002. © 2002 Wiley-Liss, Inc.},
year = {2002}
}

@article{cs_mri,
author = {Lustig, Michael and Donoho, David and Pauly, John M.},
title = {Sparse MRI: The application of compressed sensing for rapid MR imaging},
journal = {Magnetic Resonance in Medicine},
volume = {58},
number = {6},
pages = {1182-1195},
keywords = {compressed sensing, compressive sampling, random sampling, rapid MRI, sparsity, sparse reconstruction, nonlinear reconstruction},
doi = {10.1002/mrm.21391},
abstract = {Abstract The sparsity which is implicit in MR images is exploited to significantly undersample k-space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain–for example, in terms of spatial finite-differences or their wavelet coefficients. According to the recently developed mathematical theory of compressed-sensing, images with a sparse representation can be recovered from randomly undersampled k-space data, provided an appropriate nonlinear recovery scheme is used. Intuitively, artifacts due to random undersampling add as noise-like interference. In the sparse transform domain the significant coefficients stand out above the interference. A nonlinear thresholding scheme can recover the sparse coefficients, effectively recovering the image itself. In this article, practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference. Incoherence is introduced by pseudo-random variable-density undersampling of phase-encodes. The reconstruction is performed by minimizing the ℓ1 norm of a transformed image, subject to data fidelity constraints. Examples demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin-echo brain imaging and 3D contrast enhanced angiography. Magn Reson Med, 2007. © 2007 Wiley-Liss, Inc.},
year = {2007}
}

@article{automap,
abstract = {Image reconstruction plays a critical role in the implementation of all contemporary imaging modalities across the physical and life sciences including optical, MRI, CT, PET, and radio astronomy. During an image acquisition, the sensor encodes an intermediate representation of an object in the sensor domain, which is subsequently reconstructed into an image by an inversion of the encoding function. Image reconstruction is challenging because analytic knowledge of the inverse transform may not exist a priori, especially in the presence of sensor non-idealities and noise. Thus, the standard reconstruction approach involves approximating the inverse function with multiple ad hoc stages in a signal processing chain whose composition depends on the details of each acquisition strategy, and often requires expert parameter tuning to optimize reconstruction performance. We present here a unified framework for image reconstruction, AUtomated TransfOrm by Manifold APproximation (AUTOMAP), which recasts image reconstruction as a data-driven, supervised learning task that allows a mapping between sensor and image domain to emerge from an appropriate corpus of training data. We implement AUTOMAP with a deep neural network and exhibit its flexibility in learning reconstruction transforms for a variety of MRI acquisition strategies, using the same network architecture and hyperparameters. We further demonstrate its efficiency in sparsely representing transforms along low-dimensional manifolds, resulting in superior immunity to noise and reconstruction artifacts compared with conventional handcrafted reconstruction methods. In addition to improving the reconstruction performance of existing acquisition methodologies, we anticipate accelerating the discovery of new acquisition strategies across modalities as the burden of reconstruction becomes lifted by AUTOMAP and learned-reconstruction approaches.},
archivePrefix = {arXiv},
arxivId = {1704.08841},
author = {Zhu, Bo and Liu, Jeremiah Z. and Cauley, Stephen F. and Rosen, Bruce R. and Rosen, Matthew S.},
doi = {10.1038/nature25988},
eprint = {1704.08841},
file = {:home/balamurali/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Zhu et al. - 2018 - Image reconstruction by domain-transform manifold learning.pdf:pdf},
isbn = {1932-6203},
issn = {14764687},
journal = {Nature},
mendeley-groups = {MR reconstruction Deep},
number = {7697},
pages = {487--492},
pmid = {29565357},
publisher = {Nature Publishing Group},
title = {{Image reconstruction by domain-transform manifold learning}},
url = {http://dx.doi.org/10.1038/nature25988},
volume = {555},
year = {2018}
}
@InProceedings{eonet,
author="Eo, Taejoon
and Shin, Hyungseob
and Kim, Taeseong
and Jun, Yohan
and Hwang, Dosik",
title="Translation of 1D Inverse Fourier Transform of K-space to an Image Based on Deep Learning for Accelerating Magnetic Resonance Imaging",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018",
year="2018",
pages="241--249",
abstract="To reconstruct magnetic resonance (MR) images from undersampled Cartesian k-space data, we propose an algorithm based on two deep-learning architectures: (1) a multi-layer perceptron (MLP) that estimates a target image from 1D inverse Fourier transform (IFT) of k-space; and (2) a convolutional neural network (CNN) that estimates the target image from the estimated image of the MLP. The MLP learns the relationship between 1D IFT of undersampled k-space which is transformed along the frequency-encoding direction and the target fully-sampled image. The MLP is trained line by line rather than by a whole image, because each frequency-encoding line of the 1D IFT of k-space is not correlated with each other. It can dramatically decrease the number of parameters to be learned because the number of input/output pixels decrease from N2 to N. The next CNN learns the relationship between an estimated image of the MLP and the target fully-sampled image to reduce remaining artifacts in the image domain. The proposed deep-learning algorithm (i.e., the combination of the MLP and the CNN) exhibited superior performance over a single MLP and a single CNN. And it outperformed the comparison algorithms including CS-MRI, DL-MRI, a CNN-based algorithm (denoted as Wang's algorithm), PANO, and FDLCP in both qualitative and quantitative evaluation. Consequently, the proposed algorithm is applicable up to a sampling ratio of 25{\%} in Cartesian k-space.",
isbn="978-3-030-00928-1"
}
@InProceedings{eternet,
author="Oh, Changheun
and Kim, Dongchan
and Chung, Jun-Young
and Han, Yeji
and Park, HyunWook",
title="ETER-net: End to End MR Image Reconstruction Using Recurrent Neural Network",
booktitle="Machine Learning for Medical Image Reconstruction",
year="2018",
pages="12--20",
abstract="Recently, an end-to-end MR image reconstruction technique, called AUTOMAP, was introduced to simplify the complicated reconstruction process of MR image and to improve the quality of reconstructed MR images using deep learning. Despite the benefits of end-to-end architecture and superior quality of reconstructed MR images, AUTOMAP suffers from the large amount of training parameters required by multiple fully connected layers. In this work, we propose a new end-to-end MR image reconstruction technique based on the recurrent neural network (RNN) architecture, which can be more efficiently used for magnetic resonance (MR) image reconstruction than the convolutional neural network (CNN). We modified the RNN architecture of ReNet for image domain data to reconstruct an MR image from k-space data by utilizing recurrent cells. The proposed network reconstructs images from the k-space data with a reduced number of parameters compared with that of fully connected architectures. We present a quantitative evaluation of the proposed method for Cartesian trajectories using nMSE and SSIM. We also present preliminary images reconstructed from k-space data acquired in the radial trajectory.",
isbn="978-3-030-00129-2"
}
@InProceedings{dautomap,
author="Schlemper, Jo
and Oksuz, Ilkay
and Clough, James
and Duan, Jinming
and King, Andrew
and Schnabel, Julia
and Hajnal, Joseph
and Rueckert, Daniel",
title="dAUTOMAP: Decomposing AUTOMAP to Achieve Scalability and Enhance Performance",
booktitle="International Society of Magnetic Resonance in Medicine - ISMRM",
year="2019",
}
@INPROCEEDINGS{isbi_wang, 
author={S. {Wang} and Z. {Su} and L. {Ying} and X. {Peng} and S. {Zhu} and F. {Liang} and D. {Feng} and D. {Liang}}, 
booktitle={2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)}, 
title={Accelerating magnetic resonance imaging via deep learning}, 
year={2016}, 
volume={}, 
number={}, 
pages={514-517}, 
abstract={This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring fine structures and details but is also compatible with online constrained reconstruction methods. Experimental results on real MR data have shown encouraging performance of the proposed method for efficient and accurate imaging.}, 
keywords={Image reconstruction;Training;Acceleration;Magnetic resonance imaging;Convolution;Neural networks;Deep learning;magnetic resonance imaging;prior knowledge;convolutional neural network}, 
doi={10.1109/ISBI.2016.7493320}, 
ISSN={}, 
month={April},}
@INPROCEEDINGS{residual_conference, 
author={D. {Lee} and J. {Yoo} and J. C. {Ye}}, 
booktitle={2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)}, 
title={Deep residual learning for compressed sensing MRI}, 
year={2017}, 
volume={}, 
number={}, 
pages={15-18}, 
abstract={Compressed sensing (CS) enables significant reduction of MR acquisition time with performance guarantee. However, computational complexity of CS is usually expensive. To address this, here we propose a novel deep residual learning algorithm to reconstruct MR images from sparsely sampled k-space data. In particular, based on the observation that coherent aliasing artifacts from downsampled data has topologically simpler structure than the original image data, we formulate a CS problem as a residual regression problem and propose a deep convolutional neural network (CNN) to learn the aliasing artifacts. Experimental results using single channel and multi channel MR data demonstrate that the proposed deep residual learning outperforms the existing CS and parallel imaging algorithms. Moreover, the computational time is faster in several orders of magnitude.}, 
keywords={biomedical MRI;compressed sensing;data acquisition;image reconstruction;learning (artificial intelligence);medical image processing;neural nets;regression analysis;compressed sensing MRI;MR acquisition time;deep residual learning algorithm;MR image reconstruction;sparsely sampled k-space data;residual regression problem;deep convolutional neural network;Image reconstruction;Complexity theory;Magnetic resonance imaging;Topology;Manifolds;Machine learning;Compressed sensing MRI;deep learning;residual learning;CNN}, 
doi={10.1109/ISBI.2017.7950457}, 
ISSN={}, 
month={April},}

@ARTICLE{residual_journal, 
author={D. {Lee} and J. {Yoo} and S. {Tak} and J. C. {Ye}}, 
journal={IEEE Transactions on Biomedical Engineering}, 
title={Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks}, 
year={2018}, 
volume={65}, 
number={9}, 
pages={1985-1995}, 
abstract={Objective: Accelerated magnetic resonance (MR) image acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts from artifact corrupted images. Methods: The deep residual learning networks are composed of magnitude and phase networks that are separately trained. If both phase and magnitude information are available, the proposed algorithm can work as an iterative k-space interpolation algorithm using framelet representation. When only magnitude data are available, the proposed approach works as an image domain postprocessing algorithm. Results: Even with strong coherent aliasing artifacts, the proposed network successfully learned and removed the aliasing artifacts, whereas current parallel and CS reconstruction methods were unable to remove these artifacts. Conclusion: Comparisons using single and multiple coil acquisition show that the proposed residual network provides good reconstruction results with orders of magnitude faster computational time than existing CS methods. Significance: The proposed deep learning framework may have a great potential for accelerated MR reconstruction by generating accurate results immediately.}, 
keywords={biomedical MRI;compressed sensing;image reconstruction;image sampling;interpolation;learning (artificial intelligence);medical image processing;parallel processing;deep residual learning networks;phase networks;magnitude information;iterative k-space interpolation algorithm;magnitude data;image domain;CS reconstruction methods;CS methods;deep learning framework;accelerated MR reconstruction;accelerated MRI;Accelerated magnetic resonance image acquisition;parallel imaging;reconstruction algorithms;high computational costs;artifact corrupted images;computational time;framelet representation;Image reconstruction;Magnetic resonance imaging;Acceleration;Machine learning;Interpolation;Iterative algorithms;Compressed sensing MRI;deep convolutional framelets;deep learning;parallel imaging}, 
doi={10.1109/TBME.2018.2821699}, 
ISSN={}, 
month={Sep.},}

@article{unet_dc,
doi = {10.1088/1361-6560/aac71a},
year = 2018,
month = {jun},
publisher = {{IOP} Publishing},
volume = {63},
number = {13},
pages = {135007},
author = {Chang Min Hyun and Hwa Pyung Kim and Sung Min Lee and Sungchul Lee and Jin Keun Seo},
title = {Deep learning for undersampled {MRI} reconstruction},
journal = {Physics in Medicine {\&} Biology},
abstract = {This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting the image-folding problem dictated by the Poisson summation formula. To deal with the localization uncertainty due to image folding, a small number of low-frequency k-space data are added. Training the deep learning net involves input and output images that are pairs of the Fourier transforms of the subsampled and fully sampled k-space data. Our experiments show the remarkable performance of the proposed method; only 29 of the k-space data can generate images of high quality as effectively as standard MRI reconstruction with the fully sampled data.}
}

@INPROCEEDINGS{complex_mri,
abstract = {Undersampling the k-space data is widely adopted for acceleration of Magnetic Resonance Imaging (MRI). Current deep learning based approaches for supervised learning of MRI image reconstruction employ real-valued operations and representations by treating complex valued k-space/spatial-space as real values. In this paper, we propose complex dense fully convolutional neural network ({\$}{\$}$\backslash$mathbb {\{}C{\}}{\$}{\$}CDFNet) for learning to de-alias the reconstruction artifacts within undersampled MRI images. We fashioned a densely-connected fully convolutional block tailored for complex-valued inputs by introducing dedicated layers such as complex convolution, batch normalization, non-linearities etc. {\$}{\$}$\backslash$mathbb {\{}C{\}}{\$}{\$}CDFNet leverages the inherently complex-valued nature of input k-space and learns richer representations. We demonstrate improved perceptual quality and recovery of anatomical structures through {\$}{\$}$\backslash$mathbb {\{}C{\}}{\$}{\$}CDFNet in contrast to its real-valued counterparts.},
address = {Cham},
author = {Dedmari, Muneer Ahmad and Conjeti, Sailesh and Estrada, Santiago and Ehses, Phillip and St{\"{o}}cker, Tony and Reuter, Martin},
booktitle = {Machine Learning for Medical Image Reconstruction},
isbn = {978-3-030-00129-2},
mendeley-groups = {Journal{\_}references},
pages = {30--38},
title = {{Complex Fully Convolutional Neural Networks for MR Image Reconstruction}},
year = {2018}
}


@ARTICLE{dagan, 
author={G. {Yang} and S. {Yu} and H. {Dong} and G. {Slabaugh} and P. L. {Dragotti} and X. {Ye} and F. {Liu} and S. {Arridge} and J. {Keegan} and Y. {Guo} and D. {Firmin}}, 
journal={IEEE Transactions on Medical Imaging}, 
title={DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction}, 
year={2018}, 
volume={37}, 
number={6}, 
pages={1310-1321}, 
abstract={Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN)-based model is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U-Net based generator, which provides an end-to-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency-domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.}, 
keywords={biomedical MRI;compressed sensing;image reconstruction;image sampling;image texture;learning (artificial intelligence);medical image processing;fast acquisition;image quality;MRI images;nonlearning methods;single image;refinement learning method;U-Net based generator;end-to-end network;conventional CS-MRI reconstruction methods;DAGAN method;superior reconstruction;compressed sensing magnetic resonance imaging;conditional Generative Adversarial Networks;deep de-aliasing generative adversarial networks;fast compressed sensing MRI reconstruction;motion artefacts;contrast washout;deep learning-based strategy;aliasing artefact reduction;frequency-domain information;deep learning approaches;preserved perceptual image;Nyquist-Shannon sampling barrier;Image reconstruction;Machine learning;Acceleration;Transforms;Encoding;Compressed sensing;magnetic resonance imaging (MRI);fast MRI;deep learning;generative adversarial networks (GAN);de-aliasing;inverse problems}, 
doi={10.1109/TMI.2017.2785879}, 
ISSN={}, 
month={June},}

@ARTICLE{cyclic_loss, 
author={T. M. {Quan} and T. {Nguyen-Duc} and W. {Jeong}}, 
journal={IEEE Transactions on Medical Imaging}, 
title={Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss}, 
year={2018}, 
volume={37}, 
number={6}, 
pages={1488-1497}, 
abstract={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 fullyresidual 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 k-space data. In addition, our solution leverages a chained networkto 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 256 × 256 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.}, 
keywords={biomedical MRI;compressed sensing;feedforward neural nets;image coding;image reconstruction;image sampling;interpolation;learning (artificial intelligence);medical image processing;theoretical foundations;time-consuming MRI acquisition process;iterative numerical solvers;time-critical applications;deep neural networks;image processing;RefineGAN;fast CS-MRI reconstruction;accurate CS-MRI reconstruction;generative adversarial network;CS-MRI formulation;deeper generator;discriminator networks;cyclic data consistency loss;under-sampled k-space data;reconstruction quality;reconstruction process;feed-forward network;image quality;extremely low sampling rate;open-source MRI databases;compressed sensing magnetic resonance imaging;compressed sensing MRI reconstruction;computer vision;deep learning-based generative adversarial model;fully-residual convolutional autoencoder;interpolation;Image reconstruction;Magnetic resonance imaging;Machine learning;Gallium nitride;Training;Image quality;Databases;Compressed sensing;MRI;GAN;DiscoGAN;CycleGAN}, 
doi={10.1109/TMI.2018.2820120}, 
ISSN={}, 
month={June},}

@article{segan,
  author    = {Zhongnian Li and
               Tao Zhang and
               Daoqiang Zhang},
  title     = {{SEGAN:} Structure-Enhanced Generative Adversarial Network for Compressed
               Sensing {MRI} Reconstruction},
  journal   = {CoRR},
  volume    = {abs/1902.06455},
  year      = {2019},
  archivePrefix = {arXiv},
  eprint    = {1902.06455},
  timestamp = {Tue, 21 May 2019 18:03:38 +0200},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{recon_glgan,
author="Murugesan, Balamurali
and Vijaya Raghavan, S.
and Sarveswaran, Kaushik
and Ram, Keerthi
and Sivaprakasam, Mohanasankar",
title="Recon-GLGAN: A Global-Local Context Based Generative Adversarial Network for MRI Reconstruction",
booktitle="Machine Learning for Medical Image Reconstruction",
year="2019",
pages="3--15",
abstract="Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and soft-tissue contrast. But, the usage of MRI is limited by its slow acquisition time, which makes it expensive and causes patient discomfort. In order to accelerate the acquisition, multiple deep learning networks have been proposed. Recently, Generative Adversarial Networks (GANs) have shown promising results in MRI reconstruction. The drawback with the proposed GAN based methods is it does not incorporate the prior information about the end goal which could help in better reconstruction. For instance, in the case of cardiac MRI, the physician would be interested in the heart region which is of diagnostic relevance while excluding the peripheral regions. In this work, we show that incorporating prior information about a region of interest in the model would offer better performance. Thereby, we propose a novel GAN based architecture, Reconstruction Global-Local GAN (Recon-GLGAN) for MRI reconstruction. The proposed model contains a generator and a context discriminator which incorporates global and local contextual information from images. Our model offers significant performance improvement over the baseline models. Our experiments show that the concept of a context discriminator can be extended to existing GAN based reconstruction models to offer better performance. We also demonstrate that the reconstructions from the proposed method give segmentation results similar to fully sampled images.",
isbn="978-3-030-33843-5"
}
@ARTICLE{gan, 
author={M. {Mardani} and E. {Gong} and J. Y. {Cheng} and S. S. {Vasanawala} and G. {Zaharchuk} and L. {Xing} and J. M. {Pauly}}, 
journal={IEEE Transactions on Medical Imaging}, 
title={Deep Generative Adversarial Neural Networks for Compressive Sensing MRI}, 
year={2019}, 
volume={38}, 
number={1}, 
pages={167-179}, 
abstract={Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative adversarial networks (GAN) to model the (low-dimensional) manifold of high-quality MR images. Leveraging a mixture of least-squares (LS) GANs and pixel-wise ℓ1/ℓ2 cost, a deep residual network with skip connections is trained as the generator that learns to remove the aliasing artifacts by projecting onto the image manifold. The LSGAN learns the texture details, while the ℓ1/ℓ2 cost suppresses high-frequency noise. A discriminator network, which is a multilayer convolutional neural network (CNN), plays the role of a perceptual cost that is then jointly trained based on high-quality MR images to score the quality of retrieved images. In the operational phase, an initial aliased estimate (e.g., simply obtained by zero-filling) is propagated into the trained generator to output the desired reconstruction. This demands a very low computational overhead. Extensive evaluations are performed on a large contrast-enhanced MR dataset of pediatric patients. Images rated by expert radiologists corroborate that GANCS retrieves higher quality images with improved fine texture details compared with conventional Wavelet-based and dictionary-learning-based CS schemes as well as with deep-learning-based schemes using pixel-wise training. In addition, it offers reconstruction times of under a few milliseconds, which are two orders of magnitude faster than the current state-of-the-art CS-MRI schemes.}, 
keywords={biomedical MRI;compressed sensing;data compression;feedforward neural nets;image enhancement;image reconstruction;image representation;image resolution;image sampling;image texture;learning (artificial intelligence);medical image processing;wavelet transforms;generative adversarial neural networks;compressive sensing;linear inverse task;resource intensive computations;state-of-the-art compressed sensing analytics;image diagnostic quality;novel CS framework;generative adversarial networks;GAN;deep residual network;skip connections;aliasing artifacts;image manifold;discriminator network;multilayer convolutional neural network;perceptual cost;retrieved images;initial aliased estimate;trained generator;desired reconstruction;low computational overhead;higher quality images;improved fine texture details;CS schemes;deep-learning-based schemes;pixel-wise training;reconstruction times;current state-of-the-art CS-MRI schemes;high-frequency noise;wavelet-based schemes;undersampled magnetic resonance image reconstruction;Image reconstruction;Manifolds;Gallium nitride;Magnetic resonance imaging;Training;Generators;Neural networks;Deep learning;generative adversarial networks (GAN);convolutional neural networks (CNN);rapid reconstruction;diagnostic quality;compressed sensing (CS)}, 
doi={10.1109/TMI.2018.2858752}, 
ISSN={}, 
month={Jan},}
@InProceedings{dc_cnn,
author="Schlemper, Jo
and Caballero, Jose
and Hajnal, Joseph V.
and Price, Anthony
and Rueckert, Daniel",
title="A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction",
booktitle="Information Processing in Medical Imaging",
year="2017",
pages="647--658",
abstract="The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, in terms of reconstruction error, perceptual quality and reconstruction speed for both 3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the method proposed is approximately twice as small, allowing to preserve anatomical structures more faithfully. Using our method, each image can be reconstructed in 23 ms, which is fast enough to enable real-time applications.",
isbn="978-3-319-59050-9"
}
@InProceedings{dc_unet,
author="Sun, Liyan
and Fan, Zhiwen
and Ding, Xinghao
and Huang, Yue
and Paisley, John",
title="Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network",
booktitle="Information Processing in Medical Imaging",
year="2019",
pages="492--504",
abstract="The need for fast acquisition and automatic analysis of MRI data is growing. Although compressed sensing magnetic resonance imaging (CS-MRI) has been studied to accelerate MRI by reducing k-space measurements, current techniques overlook downstream applications such as segmentation when doing image reconstruction. In this paper, we test the utility of CS-MRI when performing automatic segmentation and propose a unified deep neural network architecture called SegNetMRI for simultaneous CS-MRI reconstruction and segmentation. SegNetMRI uses an MRI reconstruction network with multiple cascaded blocks, each containing an encoder-decoder unit and a data fidelity unit, and a parallel MRI segmentation network having the same encoder-decoder structure. The two subnetworks are pre-trained and fine-tuned with shared reconstruction encoders. The outputs are merged into the final segmentation. Our experiments show that SegNetMRI can improve both the reconstruction and segmentation performance when using compressed measurements.",
isbn="978-3-030-20351-1"
}
@INPROCEEDINGS{miccan, 
author={Q. {Huang} and D. {Yang} and P. {Wu} and H. {Qu} and J. {Yi} and D. {Metaxas}}, 
booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, 
title={MRI Reconstruction Via Cascaded Channel-Wise Attention Network}, 
year={2019}, 
volume={}, 
number={}, 
pages={1622-1626}, 
abstract={We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate. This can practically benefit patient due to reduced time of MRI scan, but it is also challenging since quality of reconstruction may be compromised. Currently, deep learning based methods dominate MRI reconstruction over traditional approaches such as Compressed Sensing, but they rarely show satisfactory performance in the case of low undersampled k-space data. One explanation is that these methods treat channel-wise features equally, which results in degraded representation ability of the neural network. To solve this problem, we propose a new model called MRI Cascaded Channel-wise Attention Network (MICCAN), highlighted by three components: (i) a variant of U-net with Channel-wise Attention (UCA) module, (ii) a long skip connection and (iii) a combined loss. Our model is able to attend to salient information by filtering irrelevant features and also concentrate on high-frequency information by enforcing low-frequency information bypassed to the final output. We conduct both quantitative evaluation and qualitative analysis of our method on a cardiac dataset. The experiment shows that our method achieves very promising results in terms of three common metrics on the MRI reconstruction with low undersampled k-space data. Code is public availablel1.1https://github.com/charwing10/isbi2019miccan.}, 
keywords={biomedical MRI;filtering theory;image reconstruction;learning (artificial intelligence);medical image processing;neural nets;low-frequency information;low undersampled k-space data;MRI reconstruction problem;low undersampled rate;MRI scan;deep learning based methods;channel-wise features;neural network;MRI cascaded channel-wise attention network;channel-wise attention module;cardiac dataset;filtering irrelevant features;deep learning-based method;Image reconstruction;Magnetic resonance imaging;Feature extraction;Deep learning;Neural networks;TV;Linear programming;reconstruction;attention;skip connection}, 
doi={10.1109/ISBI.2019.8759423}, 
ISSN={}, 
month={April},}
@InProceedings{dc-ensemble,
author="Wu, Huafeng
and Wu, Yawen
and Sun, Liyan
and Cai, Congbo
and Huang, Yue
and Ding, Xinghao",
title="A Deep Ensemble Network for Compressed Sensing MRI",
booktitle="Neural Information Processing",
year="2018",
pages="162--171",
abstract="Compressed sensing theory has been proven to accelerate magnetic resonance imaging by measuring less K-space data called CS-MRI. Conventional sparse-optimization based CS-MRI methods lack enough capacity to encode rich patterns within the MR images and the iterative optimization for sparse recovery is often time-consuming. Although the deep convolutional neural network (CNN) models have achieved the state-of-the-art performance on CS-MRI reconstruction recently, the fine structure details can be degraded due to the information loss when the network goes deep. In order to better transfer the information in lossless way, we design deep ensemble network (DEN) architecture inspired by the novel interpretation of deep neural network in ensemble respect. The DEN model is formed by cascaded basic blocks for CS-MRI. Within the blocks, information flows forward through different depth. The intermediate outputs reconstructed by each block are merged via {\$}{\$}3{\backslash}times 3{\$}{\$}convolution to generate the final reconstruction result. The experimental results show the proposed DEN model outperforms other state-of-the-art nondeep and deep CS-MRI models.",
isbn="978-3-030-04167-0"
}
@inproceedings{hybrid,
title={A Hybrid, Dual Domain, Cascade of Convolutional Neural Networks for Magnetic Resonance Image Reconstruction},
author={Souza, Roberto and Lebel, R. Marc and Frayne, Richard},
booktitle={International Conference on Medical Imaging with Deep Learning -- Full Paper Track},
address={London, United Kingdom},
year={2019},
month={08--10 Jul},
url={https://openreview.net/forum?id=HJeJx4XxlN},
abstract={Deep-learning-based magnetic resonance (MR) imaging reconstruction techniques have the potential to accelerate MR image acquisition by reconstructing in real-time clinical quality images from k-spaces sampled at rates lower than specified by the Nyquist-Shannon sampling theorem, which is known as compressed sensing. In the past few years, several deep learning network architectures have been proposed for MR compressed sensing reconstruction. After examining the successful elements in these network architectures, we propose a hybrid frequency-/image-domain cascade of convolutional neural networks intercalated with data consistency layers that is trained end-to-end for compressed sensing reconstruction of MR images. We compare our method with five recently published deep learning-based methods using MR raw data. Our results indicate that our architecture improvements were statistically significant (Wilcoxon signed-rank test, p{\ensuremath{<}}0.05). Visual assessment of the images reconstructed confirm that our method outputs images similar to the fully sampled reconstruction reference.   
},
}
@article{kikinet,
author = {Eo, Taejoon and Jun, Yohan and Kim, Taeseong and Jang, Jinseong and Lee, Ho-Joon and Hwang, Dosik},
title = {KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images},
journal = {Magnetic Resonance in Medicine},
volume = {80},
number = {5},
pages = {2188-2201},
keywords = {convolutional neural networks, cross-domain deep learning, image reconstruction, k-space completion, MRI acceleration},
doi = {10.1002/mrm.27201},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.27201},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.27201},
abstract = {Purpose To demonstrate accurate MR image reconstruction from undersampled k-space data using cross-domain convolutional neural networks (CNNs) Methods Cross-domain CNNs consist of 3 components: (1) a deep CNN operating on the k-space (KCNN), (2) a deep CNN operating on an image domain (ICNN), and (3) an interleaved data consistency operations. These components are alternately applied, and each CNN is trained to minimize the loss between the reconstructed and corresponding fully sampled k-spaces. The final reconstructed image is obtained by forward-propagating the undersampled k-space data through the entire network. Results Performances of K-net (KCNN with inverse Fourier transform), I-net (ICNN with interleaved data consistency), and various combinations of the 2 different networks were tested. The test results indicated that K-net and I-net have different advantages/disadvantages in terms of tissue-structure restoration. Consequently, the combination of K-net and I-net is superior to single-domain CNNs. Three MR data sets, the T2 fluid-attenuated inversion recovery (T2 FLAIR) set from the Alzheimer's Disease Neuroimaging Initiative and 2 data sets acquired at our local institute (T2 FLAIR and T1 weighted), were used to evaluate the performance of 7 conventional reconstruction algorithms and the proposed cross-domain CNNs, which hereafter is referred to as KIKI-net. KIKI-net outperforms conventional algorithms with mean improvements of 2.29 dB in peak SNR and 0.031 in structure similarity. Conclusion KIKI-net exhibits superior performance over state-of-the-art conventional algorithms in terms of restoring tissue structures and removing aliasing artifacts. The results demonstrate that KIKI-net is applicable up to a reduction factor of 3 to 4 based on variable-density Cartesian undersampling.},
year = {2018}
}
@InProceedings{dc_cnn_adversarial,
author="Seitzer, Maximilian
and Yang, Guang
and Schlemper, Jo
and Oktay, Ozan
and W{\"u}rfl, Tobias
and Christlein, Vincent
and Wong, Tom
and Mohiaddin, Raad
and Firmin, David
and Keegan, Jennifer
and Rueckert, Daniel
and Maier, Andreas",
title="Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018",
year="2018",
pages="232--240",
abstract="Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE) loss functions can achieve high peak signal to noise ratio, the reconstructed images are often blurry and lack sharp details, especially for higher undersampling rates. Recently, adversarial and perceptual loss functions have been shown to achieve more visually appealing results. However, it remains an open question how to (1) optimally combine these loss functions with the MSE loss function and (2) evaluate such a perceptual enhancement. In this work, we propose a hybrid method, in which a visual refinement component is learnt on top of an MSE loss-based reconstruction network. In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis. Applied on a large cardiac MRI dataset simulated with 8-fold undersampling, we demonstrate significant improvements ({\$}{\$}p<0.01{\$}{\$}) over the state-of-the-art in both a human observer study and the semantic interpretability score.",
isbn="978-3-030-00928-1"
}
@InProceedings{recursive_dilated,
	author = {Liyan Sun and Zhiwen Fan and Yue Huang and Xinghao Ding and John Paisley},
	title = {Compressed Sensing MRI Using a Recursive Dilated Network},
	conference = {AAAI Conference on Artificial Intelligence},
	year = {2018},
	keywords = {compressed sensing; magnetic resonance imaging; deep neural networks},
	abstract = {Compressed sensing magnetic resonance imaging (CS-MRI) is an active research topic in the ﬁeld of inverse problems. Conventional CS-MRI algorithms usually exploit the sparse nature of MRI in an iterative manner. These optimization-based CS-MRI methods are often time-consuming at test time, and are based on ﬁxed transform bases or shallow dictionaries, which limits modeling capacity. Recently, deep models have been introduced to the CS-MRI problem. One main challenge for CS-MRI methods based on deep learning is the trade off between model performance and network size. We propose a recursive dilated network (RDN) for CS-MRI that achieves good performance while reducing the number of network parameters. We adopt dilated convolutions in each recursive block to aggregate multi-scale information within the MRI. We also adopt a modiﬁed shortcut strategy to help features ﬂow into deeper layers. Experimental results show that the proposed RDN model achieves state-of-the-art performance in CS-MRI while using far fewer parameters than previously required.},

	url = {https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16546}
}
@article{cs_survey,
author = {Jaspan, Oren N and Fleysher, Roman and Lipton, Michael L},
title = {Compressed sensing MRI: a review of the clinical literature},
journal = {The British Journal of Radiology},
volume = {88},
number = {1056},
pages = {20150487},
year = {2015},
doi = {10.1259/bjr.20150487},
    note ={PMID: 26402216},

URL = { 
        https://doi.org/10.1259/bjr.20150487
    
},
eprint = { 
        https://doi.org/10.1259/bjr.20150487
    
}
,
    abstract = { MRI is one of the most dynamic and safe imaging techniques available in the clinic today. However, MRI acquisitions tend to be slow, limiting patient throughput and limiting potential indications for use while driving up costs. Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of k-space. This has the potential to mitigate the time-intensiveness of MRI. The limited body of research evaluating the effects of CS on MR images has been mostly positive with regards to its potential as a clinical tool. Studies have successfully accelerated MRI with this technology, with varying degrees of success. However, more must be performed before its diagnostic efficacy and benefits are clear. Studies involving a greater number radiologists and images must be completed, rating CS based on its diagnostic efficacy. Also, standardized methods for determining optimal imaging parameters must be developed. }
}

@article{mri_survey,
title = "An overview of deep learning in medical imaging focusing on MRI",
journal = "Zeitschrift für Medizinische Physik",
volume = "29",
number = "2",
pages = "102 - 127",
year = "2019",
issn = "0939-3889",
author = "Alexander Selvikvåg Lundervold and Arvid Lundervold",
keywords = "Machine learning, Deep learning, Medical imaging, MRI",
abstract = "What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging."
}


@inproceedings{dg_paper,
  title={Learning to generalize: Meta-learning for domain generalization},
  author={Li, Da and Yang, Yongxin and Song, Yi-Zhe and Hospedales, Timothy M},
  booktitle={Thirty-Second AAAI Conference on Artificial Intelligence},
  year={2018}
}

@inproceedings{Undoingbias,
  title={Undoing the Damage of Dataset Bias},
  author={Aditya Khosla and Tinghui Zhou and Tomasz Malisiewicz and Alexei A. Efros and Antonio Torralba},
  booktitle={ECCV},
  year={2012}
}

@inproceedings{domgen,
   title={Learning to Generalize: Meta-Learning for Domain Generalization},
   author={Li, Da and Yang, Yongxin and Song, Yi-Zhe and Hospedales, Timothy},
  	booktitle={AAAI Conference on Artificial Intelligence},
  	year={2018}
 }
 @article{unrolled,
  title={Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks},
  author={Dong Liang and Jing Cheng and Ziwen Ke and Leslie Ying},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.11711}
}
@ARTICLE{unrolleddeepprior,
       author = {{Diamond}, Steven and {Sitzmann}, Vincent and {Heide}, Felix and
         {Wetzstein}, Gordon},
        title = "{Unrolled Optimization with Deep Priors}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computer Vision and Pattern Recognition},
         year = "2017",
        month = "May",
          eid = {arXiv:1705.08041},
        pages = {arXiv:1705.08041},
archivePrefix = {arXiv},
       eprint = {1705.08041},
 primaryClass = {cs.CV},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2017arXiv170508041D},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{gnldecouple, 
author={Q. {Fan} and D. {Chen} and L. {Yuan} and G. {Hua} and N. {Yu} and B. {Chen}}, 
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
title={A General Decoupled Learning Framework for Parameterized Image Operators}, 
year={2019}, 
volume={}, 
number={}, 
pages={1-1}, 
keywords={Convolution;Task analysis;Image resolution;Acceleration;Image edge detection;Runtime;Fans;Image Processing and Computer Vision;Filtering;Restoration;Smoothing}, 
doi={10.1109/TPAMI.2019.2925793}, 
ISSN={1939-3539}, 
month={},}

@ARTICLE{Schmidhuber, 
author={J. {Schmidhuber}}, 
journal={Neural Computation}, 
title={Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks}, 
year={1992}, 
volume={4}, 
number={1}, 
pages={131-139}, 
keywords={}, 
doi={10.1162/neco.1992.4.1.131}, 
ISSN={0899-7667}, 
month={Jan},}

@article{metaSR,
  title={Meta-SR: A Magnification-Arbitrary Network for Super-Resolution},
  author={Hu, Xuecai and Mu, Haoyuan and Zhang, Xiangyu and Wang, Zilei  and Tan, Tieniu and Sun, Jian},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
  year={2019}
}
@INPROCEEDINGS{deepvideoSR, 
author={Y. {Jo} and S. W. {Oh} and J. {Kang} and S. J. {Kim}}, 
booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 
title={Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation}, 
year={2018}, 
volume={}, 
number={}, 
pages={3224-3232}, 
keywords={image reconstruction;image resolution;image sampling;image sequences;learning (artificial intelligence);motion compensation;motion estimation;neural nets;video coding;video signal processing;explicit motion compensation;high resolution contents;ultra high definition displays;deep learning;VSR methods;motion estimation;fundamentally different framework;end-to-end deep neural network;dynamic upsampling filters;residual image;local spatio-temporal neighborhood;sharper HR videos;deep video super-resolution network;Image resolution;Dynamics;Motion compensation;Motion estimation;Convolution;Neural networks}, 
doi={10.1109/CVPR.2018.00340}, 
ISSN={1063-6919}, 
month={June},}

@INPROCEEDINGS{learningmultipledegrad, 
author={K. {Zhang} and W. {Zuo} and L. {Zhang}}, 
booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 
title={Learning a Single Convolutional Super-Resolution Network for Multiple Degradations}, 
year={2018}, 
volume={}, 
number={}, 
pages={3262-3271}, 
keywords={convolution;feedforward neural nets;image resolution;learning (artificial intelligence);super-resolver;synthetic LR images;real LR images;super-resolution network;multiple degradations;deep convolutional neural networks;CNN-based SISR methods;low-resolution image;high-resolution image;SISR degradation process;image super-resolution;Degradation;Kernel;Spatial resolution;Noise level;Computational modeling;Training}, 
doi={10.1109/CVPR.2018.00344}, 
ISSN={1063-6919}, 
month={June},}
@ARTICLE{acdc_dataset,  author={O. {Bernard} and A. {Lalande} and C. {Zotti} and F. {Cervenansky} and X. {Yang} and P. {Heng} and I. {Cetin} and K. {Lekadir} and O. {Camara} and M. A. {Gonzalez Ballester} and G. {Sanroma} and S. {Napel} and S. {Petersen} and G. {Tziritas} and E. {Grinias} and M. {Khened} and V. A. {Kollerathu} and G. {Krishnamurthi} and M. {Rohé} and X. {Pennec} and M. {Sermesant} and F. {Isensee} and P. {Jäger} and K. H. {Maier-Hein} and P. M. {Full} and I. {Wolf} and S. {Engelhardt} and C. F. {Baumgartner} and L. M. {Koch} and J. M. {Wolterink} and I. {Išgum} and Y. {Jang} and Y. {Hong} and J. {Patravali} and S. {Jain} and O. {Humbert} and P. {Jodoin}},  journal={IEEE Transactions on Medical Imaging},   title={Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?},   year={2018},  volume={37},  number={11},  pages={2514-2525},}

@article{mrbrains_dataset,
  title={MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans},
  author={Mendrik, Adri{\"e}nne M and Vincken, Koen L and Kuijf, Hugo J and Breeuwer, Marcel and Bouvy, Willem H and De Bresser, Jeroen and Alansary, Amir and De Bruijne, Marleen and Carass, Aaron and El-Baz, Ayman and others},
  journal={Computational intelligence and neuroscience},
  volume={2015},
  pages={1},
  year={2015},
  publisher={Hindawi Publishing Corp.}
}

@Article{metasurvey,
author="Lemke, Christiane
and Budka, Marcin
and Gabrys, Bogdan",
title="Metalearning: a survey of trends and technologies",
journal="Artificial Intelligence Review",
year="2015",
month="Jun",
day="01",
volume="44",
number="1",
pages="117--130",
abstract="Metalearning attracted considerable interest in the machine learning community in the last years. Yet, some disagreement remains on what does or what does not constitute a metalearning problem and in which contexts the term is used in. This survey aims at giving an all-encompassing overview of the research directions pursued under the umbrella of metalearning, reconciling different definitions given in scientific literature, listing the choices involved when designing a metalearning system and identifying some of the future research challenges in this domain.",
issn="1573-7462",
doi="10.1007/s10462-013-9406-y",
}

@inproceedings{DFN,
author = {De Brabandere, Bert and Jia, Xu and Tuytelaars, Tinne and Van Gool, Luc},
title = {Dynamic Filter Networks},
year = {2016},
isbn = {9781510838819},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
booktitle = {Proceedings of the 30th International Conference on Neural Information Processing Systems},
pages = {667–675},
numpages = {9},
location = {Barcelona, Spain},
series = {NIPS’16}
}

@article{MRIReview,
  title={Deep MRI Reconstruction: Unrolled optimization algorithms meet neural networks},
  author={Liang, Dong and Cheng, Jing and Ke, Ziwen and Ying, Leslie},
  journal={arXiv preprint arXiv:1907.11711},
  year={2019}
}

@article{CRMRIReview,
  title={Compressed sensing MRI: a review},
  author={Geethanath, Sairam and Reddy, Rashmi and Konar, Amaresha Shridhar and Imam, Shaikh and Sundaresan, Rajagopalan and DR, Ramesh Babu and Venkatesan, Ramesh},
  journal={Critical Reviews™ in Biomedical Engineering},
  volume={41},
  number={3},
  year={2013},
  publisher={Begel House Inc.}
}

@Article{meta_survey,
author="Lemke, Christiane
and Budka, Marcin
and Gabrys, Bogdan",
title="Metalearning: a survey of trends and technologies",
journal="Artificial Intelligence Review",
year="2015",
month="Jun",
day="01",
volume="44",
number="1",
pages="117--130",
abstract="Metalearning attracted considerable interest in the machine learning community in the last years. Yet, some disagreement remains on what does or what does not constitute a metalearning problem and in which contexts the term is used in. This survey aims at giving an all-encompassing overview of the research directions pursued under the umbrella of metalearning, reconciling different definitions given in scientific literature, listing the choices involved when designing a metalearning system and identifying some of the future research challenges in this domain.",
issn="1573-7462",
doi="10.1007/s10462-013-9406-y",
url="https://doi.org/10.1007/s10462-013-9406-y"
}

@article{hypernetworks,
  title={Hypernetworks},
  author={Ha, David and Dai, Andrew and Le, Quoc V},
  journal={arXiv preprint arXiv:1609.09106},
  year={2016}
}

@ARTICLE{multicontrastMRI, 
author={Liang, Zhi-Pei and Lauterbur, Paul C}, 
journal={IEEE Signal Processing Magazine}, 
title={Principles of Magnetic Resonance Imaging: a Signal Processing Perspective [Book Review]}, 
year={2002}, 
volume={19}, 
number={4}, 
pages={86-87}, 
keywords={Book reviews;Magnetic resonance imaging;Signal processing;Biomedical signal processing;Biomedical imaging;Image reconstruction;Biomedical engineering;Image analysis;Laboratories;Spectroscopy}, 
doi={10.1109/MSP.2002.1012356}, 
ISSN={1558-0792}, 
month={July},}