Deep learning networks have shown promising results in fast magnetic resonance imaging (MRI) reconstruction. In our work, we develop deep networks to further improve the quantitative and the perceptual quality of reconstruction. To begin with, we propose reconsynergynet (RSN), a network that combines the complementary benefits of independently operating on both the image and the Fourier domain. For a single-coil acquisition, we introduce deep cascade RSN (DC-RSN), a cascade of RSN blocks interleaved with data fidelity (DF) units. Secondly, we improve the structure recovery of DC-RSN for T2 weighted Imaging (T2WI) through assistance of T1 weighted imaging (T1WI), a sequence with short acquisition time. T1 assistance is provided to DC-RSN through a gradient of log feature (GOLF) fusion. Furthermore, we propose perceptual refinement network (PRN) to refine the reconstructions for better visual information fidelity (VIF), a metric highly correlated to radiologists opinion on the image quality. Lastly, for multi-coil acquisition, we propose variable splitting RSN (VS-RSN), a deep cascade of blocks, each block containing RSN, multi-coil DF unit, and a weighted average module. We extensively validate our models DC-RSN and VS-RSN for single-coil and multi-coil acquisitions and report the state-of-the-art performance. We obtain a SSIM of 0.768, 0.923, 0.878 for knee single-coil-4x, multi-coil-4x, and multi-coil-8x in fastMRI. We also conduct experiments to demonstrate the efficacy of GOLF based T1 assistance and PRN.
Friedrich-Alexander-Universität（弗里德里希-亚历山大大学）；Imperial College（帝国理工学院）；National Heart & Lung Institute, Imperial College（帝国理工学院国家心肺研究所）；Royal Brompton Hospital（皇家布朗普顿医院）
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 p…
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Department of Artificial Intelligence, Hanyang University, Seoul, South Korea（人工智能系，翰阳大学，首尔，韩国）；Department of Electronic Engineering, Hanyang University, Seoul, South Korea（电子工程系，翰阳大学，首尔，韩国）；Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea（计算机工程系，韩国外语大学， Yongin，韩国）；Division of AI Data Convergence, Hankuk University of Foreign Studies, Yongin, South Korea（AI数据融合系，韩国外语大学， Yongin，韩国）；Division of Language & AI, Hankuk University of Foreign Studies, Seoul, South Korea（语言与AI系，韩国外语大学，首尔，韩国）
Removing Motion Artifact in MRI by Using a Perceptual Loss Driven Deep Learning Framework
Institute of Magnetic Resonance and Molecular Imaging in Medicine（医学磁共振与分子成像研究所）；Shanghai Key Laboratory of Magnetic Resonance（上海磁共振重点实验室）；The First Affiliated Hospital of Naval Medical University Department of Radiology（海军医学院第一附属医院放射科）；Laboratory for Intelligent Medical Imaging（智能医学成像实验室）；Tsinghua Cross-Strait Research Institute（清华大学海峡研究院）；RadioDynamic Medical（RadioDynamic医疗）
University of Notre Dame（圣母大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）
Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions. In order to enable fast and accurate visual assistance, the temporal frames need to be segmented on-the-fly. However, state-of-the-art MRI segmentation methods are used either offline because of their high computation complexity, or in real-time but with significant accuracy loss and latency increase (causing visually noticeable lag). As such, they can hardly be adopted to assist visual guidance. In this work, inspired by a new interpretation of Independent Component Analysis …
University of Birmingham（伯明翰大学）；Imperial College London（帝国理工学院）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）
In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data. We formulate the generalized parallel compressed sensing reconstruction as an energy minimization problem, for which a variable splitting optimization method is derived. Based on this formulation we propose a novel, end-to-end trainable deep neural network architecture by unrolling the resulting iterative process of such variable splitting scheme. VS-Net is …
University of Cambridge（剑桥大学）；ShanghaiTech University（上海科技大学）；Zhejiang University of Technology（浙江工业大学）；Northwest University（西北大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）
Recent studies on T1-assisted MRI reconstruction for under-sampled images of other modalities have demonstrated the potential of further accelerating MRI acquisition of other modalities. Most of the state-of-the-art approaches have achieved improvement through the development of network architectures for fixed under-sampling patterns, without fully exploiting the complementary information between modalities. Although existing under-sampling pattern learning algorithms can be simply modified to allow the fully-sampled T1-weighted MR image to assist the pattern learning, no significant improveme…
DeepFakes are synthetic videos generated by swapping a face of an original image with the face of somebody else. In this paper, we describe our work to develop general, deep learning-based models to classify DeepFake content. We propose a novel framework for using Generative Adversarial Network (GAN)-based models, we call MRI-GAN, that utilizes perceptual differences in images to detect synthesized videos. We test our MRI-GAN approach and a plain-frames-based model using the DeepFake Detection Challenge Dataset. Our plain frames-based-model achieves 91% test accuracy and a model which uses our…
On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院Athinoula A. Martinos生物医学成像中心）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）
The acquisition of MRI images offers a trade-off in terms of acquisition time, spatial/temporal resolution and signal-to-noise ratio (SNR). Thus, for instance, increasing the time efficiency of MRI often comes at the expense of reduced SNR. This, in turn, necessitates the use of post-processing tools for noise rejection, which makes image de-noising an indispensable component of computer assistance diagnosis. In the field of MRI, a multitude of image de-noising methods have been proposed hitherto. In this paper, the application of a particular class of de-noising algorithms - known as non-loca…
Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans. This will affect further post-processing tasks such as attenuation correction in PET/MRI or radiation therapy planning. In this work, we propose the inpainting of medical images via Generative Adversarial Networks (GANs). The proposed framework incorporates two patch-based discriminator networks with additional style and perceptual losses for the inpainting of missing information in realistically detailed and contextually consistent manner. …
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual network architecture (FRnet). We also introduce a new boundary loss function to …
Evaluation of Machine Learning Reconstruction Techniques for Accelerated Brain MRI Scans
Humber River Hospital（亨伯河医院）；Toronto Western Hospital, University of Toronto（多伦多大学多伦多西区医院）；Foqus Technologies Inc.（Foqus 技术公司）；Robarts Research Institute, Western University（西安大略大学罗巴茨研究所）
Indian Institute of Technology Bombay（印度理工学院孟买分校）；Whirlpool（惠而浦公司）
Capturing high-resolution magnetic resonance (MR) images is a time consuming process, which makes it unsuitable for medical emergencies and pediatric patients. Low-resolution MR imaging, by contrast, is faster than its high-resolution counterpart, but it compromises on fine details necessary for a more precise diagnosis. Super-resolution (SR), when applied to low-resolution MR images, can help increase their utility by synthetically generating high-resolution images with little additional time. In this paper, we present a SR technique for MR images that is based on generative adversarial netwo…
MultiFlow: A unified deep learning framework for multi-vessel classification, segmentation and clustering of phase-contrast MRI validated on a multi-site single ventricle patient cohort
University of Notre Dame（圣母大学）；Guangdong Cardiovascular Institute（广东省心血管病研究所）；Guangdong Provincial People’s Hospital（广东省人民医院）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；University of Pittsburgh（匹兹堡大学）
Real-time cardiac magnetic resonance imaging (MRI) plays an increasingly important role in guiding various cardiac interventions. In order to provide better visual assistance, the cine MRI frames need to be segmented on-the-fly to avoid noticeable visual lag. In addition, considering reliability and patient data privacy, the computation is preferably done on local hardware. State-of-the-art MRI segmentation methods mostly focus on accuracy only, and can hardly be adopted for real-time application or on local hardware. In this work, we present the first hardware-aware multi-scale neural archite…
ULF-Synth: Physics-Guided Ultra-Low-Field MRI Enhancement for Pediatric Neuroimaging
Kwame Nkrumah University of Science and Technology（科拉努姆大学科学与技术学院）；University of Catania（卡塔尼亚大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Medical Artificial Intelligence Lab（医学人工智能实验室）
DINOv3-Guided Cross Fusion Framework for Semantic-aware CT generation from MRI and CBCT
Faculty of Information Technology, Monash University（墨尔本大学信息科技学院）；Institute of Artificial Intelligence, School of Computer Science, Wuhan University（武汉大学计算机科学学院人工智能研究所）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室）
MRI-to-CT Synthesis With Cranial Suture Segmentations Using A Variational Autoencoder Framework
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, USA（Sheikh Zayed儿童外科创新研究所，Children’s National医院，华盛顿特区，美国）；CIBORG Lab, Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA（CIBORG实验室，放射科，Children’s Hospital Los Angeles，洛杉矶，加利福尼亚州，美国）；Departments of Radiology and Biomedical Engineering, University of Southern California, Los Angeles, CA, USA（放射科和生物医学工程系，美国南加州大学，洛杉矶，加利福尼亚州，美国）；School of Medicine and Health Sciences, George Washington University, Washington, DC, USA（医学与健康科学学院，乔治华盛顿大学，华盛顿特区，美国）
University of California at Berkeley（加州大学伯克利分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；GE Global Research（通用电气全球研究院）；International Computer Science Institute（国际计算机科学研究所）
Purpose: To improve reconstruction fidelity of fine structures and textures in deep learning (DL) based reconstructions. Methods: A novel patch-based Unsupervised Feature Loss (UFLoss) is proposed and incorporated into the training of DL-based reconstruction frameworks in order to preserve perceptual similarity and high-order statistics. The UFLoss provides instance-level discrimination by mapping similar instances to similar low-dimensional feature vectors and is trained without any human annotation. By adding an additional loss function on the low-dimensional feature space during training, t…
Université de Sherbrooke（舍布鲁克大学）；Laboratoire Bordelais de Recherche en Informatique（波尔多计算机研究实验室）
Diffusion magnetic resonance imaging datasets suffer from low Signal-to-Noise Ratio, especially at high b-values. Acquiring data at high b-values contains relevant information and is now of great interest for microstructural and connectomics studies. High noise levels bias the measurements due to the non-Gaussian nature of the noise, which in turn can lead to a false and biased estimation of the diffusion parameters. Additionally, the usage of in-plane acceleration techniques during the acquisition leads to a spatially varying noise distribution, which depends on the parallel acceleration meth…
What neurosurgeons need to see: synthetic intra-operative MRI from ultrasound for brain-shift compensation in brain tumour surgery
Department of Neurosurgery, Neurovascular Unit, Río Hortega University Hospital, Valladolid, Spain（西班牙巴利亚多利德里奥·奥尔特加大学医院神经外科神经血管科）；Specialized Group in Biomedical Imaging and Computational Analysis (GEIBAC), Instituto de Investigación Biosanitaria de Valladolid (IBioVALL), Valladolid, Spain（西班牙巴利亚多利德生物医学研究与计算分析专业组(GEIBAC)，巴利亚多利德生物健康研究所(IBioVALL)）
Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI
Department of Radiology, West China Second University Hospital, Sichuan University（四川大学华西第二医院放射科）；School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University（清华大学医学院生物医学工程系）；Department of Radiology, Sichuan Provincial Woman’s and Children’s Hospital, The Affiliated Women’s and Children’s Hospital of Chengdu Medical College（四川省妇幼保健院放射科，成都医学院附属妇幼医院）；Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China（成都妇女儿童中央医院，电子科技大学医学院）；Department of Radiology, The Third Affiliated Hospital of Zhengzhou University（郑州大学第三附属医院放射科）；Qujing Maternal and Child Health Hospital, Qujing, China（曲靖 maternal and child health hospital, Qujing, China）
Beyond the First Read: AI-Assisted Perceptual Error Detection in Chest Radiography Accounting for Interobserver Variability
High School Student（高中学生）；PhD Candidate（博士生）；Department of Electrical and Computer Engineering University of Houston（电气与计算机工程系，休斯顿大学）；Department of Computer Science Emory University（计算机科学系，埃默里大学）；Department of Thoracic Imaging, Division of Diagnostic Imaging The University of Texas MD Anderson Cancer Center, Houston, TX（胸腔成像部，诊断影像学分会，德克萨斯大学MD安德森癌症中心，休斯顿，TX）
University of Cambridge（剑桥大学）；Royal Brompton Hospital（皇家布朗普顿医院）；Imperial College London（帝国理工学院）
Single image super-resolution (SISR) is of great importance as a low-level computer vision task. The fast development of Generative Adversarial Network (GAN) based deep learning architectures realises an efficient and effective SISR to boost the spatial resolution of natural images captured by digital cameras. However, the SISR for medical images is still a very challenging problem. This is due to (1) compared to natural images, in general, medical images have lower signal to noise ratios, (2) GAN based models pre-trained on natural images may synthesise unrealistic patterns in medical images …
pyMEAL: A Multi-Encoder Augmentation-Aware-Learning Toolbox for Robust Medical Image Translation
Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE)（香港脑心血管健康工程中心）；Department of Informatics, Sokoine University of Agriculture（农业科学学院信息系）；Department of Diagnostic Radiology, The University of Hong Kong（香港大学诊断放射科）；State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong（香港大学脑科学与认知科学国家重点实验室）；Alzheimer’s Disease Research Network, The University of Hong Kong（香港大学阿尔茨海默病研究网络）；Department of Biomedical Engineering, City University of Hong Kong（城市大学生物医学工程系）；Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院放射学与放射科学系）；City University of Hong Kong Shenzhen Research Institute（城市大学深圳研究 institute）
Reconstruct or Generate: Exploring the Spectrum of Generative Modeling for Cardiac MRI
School of Computation, Information and Technology（计算信息学院）；Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；School of Medicine, Klinikum Rechts der Isar（医学院，莱茵河右岸医院）；Department of Quantitative Biomedicine（定量生物医学系）；University of Zurich（苏黎世大学）；Department of Computing（计算系）；Imperial College London（伦敦帝国理工学院）
Deep learning networks are being developed in every stage of the MRI workflow and have provided state-of-the-art results. However, this has come at the cost of increased computation requirement and storage. Hence, replacing the networks with compact models at various stages in the MRI workflow can significantly reduce the required storage space and provide considerable speedup. In computer vision, knowledge distillation is a commonly used method for model compression. In our work, we propose a knowledge distillation (KD) framework for the image to image problems in the MRI workflow in order to…
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Department of Radiology, Duke University（杜克大学放射学系）；Department of Computer Science, Duke University（杜克大学计算机科学系）
Can Domain Experts Rely on AI Appropriately? A Case Study on AI-Assisted Prostate Cancer MRI Diagnosis
University of Chicago（芝加哥大学）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）；University of Michigan（密歇根大学）；Bagcilar Training and Research Hospital（巴格奇拉尔培训与研究医院）；Hospital of Bolzano (SABES-ASDAA)（博尔扎诺医院（SABES-ASDAA））；Radiology Associates of North Texas（北德克萨斯放射协会）；İstanbul Medipol University Hospital（伊斯坦布尔梅迪波尔大学医院）
Diffusion Bridge Models for 3D Medical Image Translation
University of California Riverside（加州大学河滨分校）；Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California（加州大学洛杉矶分校医学学院）；Information Sciences Institute, University of Southern California（加州大学洛杉矶分校信息科学研究所）
Slovak University of Technology（斯洛伐克理工大学）；Johannes Kepler University（约翰内斯·开普勒大学）
In many practical applications, deep neural networks have been typically deployed to operate as a black box predictor. Despite the high amount of work on interpretability and high demand on the reliability of these systems, they typically still have to include a human actor in the loop, to validate the decisions and handle unpredictable failures and unexpected corner cases. This is true in particular for failure-critical application domains, such as medical diagnosis. We present a novel approach to explain and support an interpretation of the decision-making process to a human expert operating…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）
Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised. To overcome this spatio-temporal trade-off, this research presents a super-resolution (SR) MRI reconstruction with prior knowledge based fine-tuning to maximise spatial information while reducing the required scan-time for dynamic MRIs. An U-Net based network with perceptual loss is trained on a benchmark dataset and fine-tuned using one subject-specific static high resolution MRI as prior know…
Mayo Clinic（梅奥诊所）；University of Minnesota（明尼苏达大学）；Google LLC（谷歌有限责任公司）
There is a growing demand for high-resolution (HR) medical images in both the clinical and research applications. Image quality is inevitably traded off with the acquisition time for better patient comfort, lower examination costs, dose, and fewer motion-induced artifacts. For many image-based tasks, increasing the apparent resolution in the perpendicular plane to produce multi-planar reformats or 3D images is commonly used. Single image super-resolution (SR) is a promising technique to provide HR images based on unsupervised learning to increase resolution of a 2D image, but there are few rep…
22. Shortening acquisition time and reducing the motion-artifact are two of the most critical issues in MRI. As a promising solution, high-quality MRI image restoration provides a new approach to achieve higher resolution without costing additional acquisition time or modification on the pulse sequences. Recently, as to the rise of deep learning, convolutional neural networks have been proposed for super-resolution (SR) image generation and motion-artifact reduction (MAR) for MRI. Recent studies suggest that using perceptual feature space loss and k space loss to capture the perceptual informa…
Imperial College London（伦敦帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；Yantai University（烟台大学）；University of Cambridge（剑桥大学）；Beijing Institute of Technology（北京理工大学）
Magnetic Resonance Imaging (MRI) is a vital component of medical imaging. When compared to other image modalities, it has advantages such as the absence of radiation, superior soft tissue contrast, and complementary multiple sequence information. However, one drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities, limiting its usage in some clinical applications when imaging time is critical. Traditional compressive sensing based MRI (CS-MRI) reconstruction can speed up MRI acquisition, but suffers from a long iterative process and noise-induce…
Technische Universität München（慕尼黑工业大学）；Harvard University（哈佛大学）；Massachusetts General Hospital（麻省总医院）
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 ($\mathbb{C}$DFNet) 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 d…
LDPM: Towards undersampled MRI reconstruction with MR-VAE and Latent Diffusion Prior
Shenzhen Technology University（深圳科技大学）；Shenzhen University（深圳大学）；Huawei（华为）；Nanjing University of Science and Technology（南京理工大学）；Qilu University of Technology（齐鲁工业大学）
The University of Hong Kong（香港大学）；Imperial College London（帝国理工学院）
Abdominal magnetic resonance imaging (MRI) provides a straightforward way of characterizing tissue and locating lesions of patients as in standard diagnosis. However, abdominal MRI often suffers from respiratory motion artifacts, which leads to blurring and ghosting that significantly deteriorate the imaging quality. Conventional methods to reduce or eliminate these motion artifacts include breath holding, patient sedation, respiratory gating, and image post-processing, but these strategies inevitably involve extra scanning time and patient discomfort. In this paper, we propose a novel deep-le…
Purpose: To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl-MRI) reconstruction that is fast, memory efficient, and scales to high-dimensional imaging. Theory and Methods: Cl-MRI methods can enable high acceleration rates and flexible sampling patterns, but their clinical application is limited by computational complexity and large memory footprint. The proposed computational procedure, HIgh-dimensional fast ConvolU-tional framework (HICU), provides fast, memory-efficient recovery of unsampled k-space points. For demonstration, HICU is applied to…
Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
Department of Mathematics and Systems Engineering（数学与系统工程系）；Department of Biomedical Engineering & Science（生物医学工程与科学系）；Florida Institute of Technology（佛罗里达理工学院）；A. A. Martinos Center for Biomedical Imaging（A.A. Martinos生物医学成像中心）；Massachusetts General Hospital/Harvard Medical School（麻省总医院/哈佛医学院）；Department of Radiology（放射科）；Embry-Riddle Aeronautical University（艾姆伯-里尔航空大学）
Recent attempts at Super-Resolution for medical images used deep learning techniques such as Generative Adversarial Networks (GANs) to achieve perceptually realistic single image Super-Resolution. Yet, they are constrained by their inability to generalise to different scale factors. This involves high storage and energy costs as every integer scale factor involves a separate neural network. A recent paper has proposed a novel meta-learning technique that uses a Weight Prediction Network to enable Super-Resolution on arbitrary scale factors using only a single neural network. In this paper, we …
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI
Center for Biomedical Imaging（生物医学成像中心）；Siemens Healthineers International AG（西门子医疗国际公司）；Lausanne University Hospital（洛桑大学医院）；University of Lausanne（洛桑大学）；sitem-insel；University of Bern（伯尔尼大学）
Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院医学人工智能研究中心）；Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences（中国科学院大学深圳先进技术学院）；Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）
Improving the image resolution and acquisition speed of magnetic resonance imaging (MRI) is a challenging problem. There are mainly two strategies dealing with the speed-resolution trade-off: (1) $k$-space undersampling with high-resolution acquisition, and (2) a pipeline of lower resolution image reconstruction and image super-resolution. However, these approaches either have limited performance at certain high acceleration factor or suffer from the error accumulation of two-step structure. In this paper, we combine the idea of MR reconstruction and image super-resolution, and work on recover…
