Probabilistic 3D surface reconstruction from sparse MRI information
ETH Zurich（苏黎世联邦理工学院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）；Microsoft Mixed Reality and AI lab（微软混合现实与人工智能实验室）
Katarína Tóthová, Sarah Parisot, Matthew Lee, Esther Puyol-Antón, Andrew King, Marc Pollefeys, Ender Konukoglu
Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool should: be fast in prediction of accurate well localised and high resolution models, evaluate prediction uncertainty, work with as little input data as possible. Current deep learning state of the art (SOTA) 3D reconstruction methods, however, often only produce shapes of limited variability positioned in a canonical position or lack uncertainty evaluation. In this paper, we present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets whilst modelling the location of each mesh vertex through a Gaussian distribution. Prior shape information is encoded using a built-in linear principal component analysis (PCA) model. Extensive experiments on cardiac MR data show that our probabilistic approach successfully assesses prediction uncertainty while at the same time qualitatively and quantitatively outperforms SOTA methods in shape prediction. Compared to SOTA, we are capable of properly localising and orientating the prediction via the use of a spatially aware neural network.
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ segmentation from magnetic resonance (MRI) images. Our UDA approach models the co-dependency between images and their segmentation as a joint probability distribution using a new structure discriminator. The structure discriminator computes structure of interest focused adversarial loss by combining the generated pseudo MRI with probabilistic segmentations produced by a simultaneously trained segmentation sub-networ…
Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model
Washington University in St. Louis（圣路易斯华盛顿大学）；Yale University（耶鲁大学）；Western University（西安大略大学）；Lawson Health Research Institute（劳森健康研究所）；McGill University（麦吉尔大学）；Siemens Healthineers AG（西门子医疗保健公司）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Peng Cheng Laboratory（鹏城实验室）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Tsinghua University（清华大学）
Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues, i.e., where to sample and how to reconstruct. To deal with both problems simultaneously, we propose a novel end-to-end Probabilistic Under-sampling and Explicable Reconstruction neTwork, dubbed PUERT, to jointly optimize the sampling pattern and the reconstruction network. Instead of learning a deterministic mask, the proposed sampling subnet explores an optimal probabilistic sub-sampling pattern, which describes independent…
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction
Oslo University Hospital（奥斯陆大学医院）；Universitat Politècnica de València（瓦伦西亚理工大学）；Instituto Universitario de Tecnologías de la Información y Comunicaciones（信息技术与通信大学学院）
Comparative Study of Probabilistic Atlas and Deep Learning Approaches for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field Correction and Anisotropic Diffusion Pre-processing Techniques
University of Girona（赫罗纳大学）；University of Burgundy（勃艮第大学）；University of Cassino and Southern Lazio（卡西诺和南拉齐奥大学）
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have shown top performance for this task when a large amount of labeled data is available. However, the accuracy of CNNs suffers when dealing with few and/or sparsely labeled datasets. A potential solution is to leverage the information available in large public datasets in conjunction with a target dataset which only has limited labeled data. In this paper, we propose a training framework, SSL2 (self-supervised-semi-superv…
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI
School of Biomedical Engineering（生物医学工程学院）；ShanghaiTech University（上海科技大学）；King’s College London（伦敦国王学院）；St. Thomas’ hospital（圣托马斯医院）；Institute of High Performance Computing（高性能计算研究所）
Accurate and robust whole heart substructure segmentation is crucial in developing clinical applications, such as computer-aided diagnosis and computer-aided surgery. However, segmentation of different heart substructures is challenging because of inadequate edge or boundary information, the complexity of the background and texture, and the diversity in different substructures' sizes and shapes. This article proposes a framework for multi-class whole heart segmentation employing non-rigid registration-based probabilistic atlas incorporating the Bayesian framework. We also propose a non-rigid r…
It has been recently shown that incorporating priori knowledge significantly improves the performance of basic compressive sensing based approaches. We have managed to successfully exploit this idea for recovering a matrix as a summation of a Low-rank and a Sparse component from compressive measurements. When applied to the problem of construction of 4D Cardiac MR image sequences in real-time from highly under-sampled $k-$space data, our proposed method achieves superior reconstruction quality compared to the other state-of-the-art methods.
We propose to learn a probabilistic motion model from a sequence of images. Besides spatio-temporal registration, our method offers to predict motion from a limited number of frames, useful for temporal super-resolution. The model is based on a probabilistic latent space and a novel temporal dropout training scheme. This enables simulation and interpolation of realistic motion patterns given only one or any subset of frames of a sequence. The encoded motion also allows to be transported from one subject to another without the need of inter-subject registration. An unsupervised generative defor…
Magnetic resonance imaging (MRI) is one of the most commonly applied tests in neurology and neurosurgery. However, the utility of MRI is largely limited by its long acquisition time, which might induce many problems including patient discomfort and motion artifacts. Acquiring fewer k-space sampling is a potential solution to reducing the total scanning time. However, it can lead to severe aliasing reconstruction artifacts and thus affect the clinical diagnosis. Nowadays, deep learning has provided new insights into the sparse reconstruction of MRI. In this paper, we present a new approach to t…
Baidu Research（百度研究院）；University of Technology Sydney（悉尼科技大学）
Existing image segmentation networks mainly leverage large-scale labeled datasets to attain high accuracy. However, labeling medical images is very expensive since it requires sophisticated expert knowledge. Thus, it is more desirable to employ only a few labeled data in pursuing high segmentation performance. In this paper, we develop a data augmentation method for one-shot brain magnetic resonance imaging (MRI) image segmentation which exploits only one labeled MRI image (named atlas) and a few unlabeled images. In particular, we propose to learn the probability distributions of deformations…
Peking University（北京大学）；Academy for Advanced Interdisciplinary Studies（前沿交叉学科研究院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
We propose a novel and unified method, measurement-conditioned denoising diffusion probabilistic model (MC-DDPM), for under-sampled medical image reconstruction based on DDPM. Different from previous works, MC-DDPM is defined in measurement domain (e.g. k-space in MRI reconstruction) and conditioned on under-sampling mask. We apply this method to accelerate MRI reconstruction and the experimental results show excellent performance, outperforming full supervision baseline and the state-of-the-art score-based reconstruction method. Due to its generative nature, MC-DDPM can also quantify the unce…
University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology for the screening and diagnosis of frequently occurring cancers. However image quality may suffer by long acquisition times for MRIs due to patient motion, as well as result in great patient discomfort. Reducing MRI acquisition time can reduce patient discomfort and as a result reduces motion artifacts from the acquisition process. Compressive sensing strategies, when applied to MRI, have been demonstrated to be effective at decreasing acquisition times significantly by sparsely sampling the \emph{k}-space during the acqu…
University College London（伦敦大学学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；MIT（麻省理工学院）；Technical University of Denmark（丹麦技术大学）
Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed independently in neuroimaging pipelines. However, some brain structures (e.g., globus pallidus, thalamus and its nuclei) can be extracted more accurately by fusing the two modalities. Following the framework of Bayesian segmentation with probabilistic atlases and unsupervised appearance modeling, we present here a novel algorithm to jointly segment multi-modal sMRI/dMRI data. We propose a hierarchical likelihood term for the dMRI defined on the unit ball, which combines the Beta and Dimroth-Scheidegger-Watson distrib…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Human Technopole（人类技术园）；German Centre for Neurodegenerative Disease（德国神经退行性疾病中心）；Centre for Behavioural Brain Sciences（行为脑科学中心）
Computed tomography and magnetic resonance imaging are two widely used clinical imaging modalities for non-invasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse sampling. However, such undersampled data leads to lower resolution and introduces artefacts. Several techniques, including deep learning based methods, have been proposed to reconstruct such data. However, the undersampled reconstruction problem for these two modalit…
Xiamen University（厦门大学）；Xiamen University of Technology（厦门理工学院）；United Imaging Research Institute of Intelligent Imaging（联影智能影像研究院）；Nanjing Medical University（南京医科大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful convolutional neural network and perform 2D convolution since many magnetic resonance images or their corresponding k-space are in 2D. In this work, we present a new approach that explores the 1D convolution, making the deep network much easier to be trained and generalized. We further integrate the 1D convolution into the proposed deep network, named as One-dimensional Deep Low-rank and Sparse network (ODLS), which unrolls the …
University College London（伦敦大学学院）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Allen Institute for Brain Science（艾伦脑科学研究所）；Massachusetts Institute of Technology（麻省理工学院）
Nonlinear registration of 2D histological sections with corresponding slices of MRI data is a critical step of 3D histology reconstruction. This task is difficult due to the large differences in image contrast and resolution, as well as the complex nonrigid distortions produced when sectioning the sample and mounting it on the glass slide. It has been shown in brain MRI registration that better spatial alignment across modalities can be obtained by synthesizing one modality from the other and then using intra-modality registration metrics, rather than by using mutual information (MI) as metric…
University College London（伦敦大学学院）；Basque Center on Cognition, Brain and Language（巴斯克认知、大脑与语言中心）；University of Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Technical University of Denmark（丹麦技术大学）
The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI. In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the…
Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal（神经多极实验室，生物医学工程学院，蒙特利尔理工学院）；École Centrale de Lyon（里昂中央理工学院）；Mila - Quebec AI Institute（魁北克人工智能研究所）；Functional Neuroimaging Unit, CRIUGM, University of Montreal（功能神经影像单元，CRIUGM，蒙特利尔大学）；Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health（转化神经放射学部门，国家神经疾病与中风研究所，国家卫生研究院）；Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel（神经学转化成像（ThINk）巴塞尔，生物医学工程系，医学院，巴塞尔大学医院和巴塞尔大学）；Neurologic Clinic and Policlinic, Departments of Medicine, University Hospital Basel, Switzerland（神经科诊所和多科诊所，医学院，巴塞尔大学医院，瑞士）；Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Switzerland（临床神经免疫学和神经科学巴塞尔研究中心（RC2NB），巴塞尔大学医院和巴塞尔大学，瑞士）；National Institute of Neurological Disorders and Stroke, National Institutes of Health（国家神经疾病与中风研究所，国家卫生研究院）；Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada（圣朱斯特医院研究中心，蒙特利尔大学，蒙特利尔，魁北克，加拿大）；Quantitative MRI core facility, NINDS, NIH（定量MRI核心设施，NINDS，NIH）；Experimental Immunotherapeutics Unit, Division of Neuroimmunology and Neurovirology, NINDS, NIH（实验免疫治疗单元，神经免疫学和神经病毒学部门，NINDS，NIH）
This work addresses the problem of estimating proton density and T1 maps from two partially sampled K-space scans such that the total acquisition time remains approximately the same as a single scan. Existing multi parametric non linear curve fitting techniques require a large number (8 or more) of echoes to estimate the maps resulting in prolonged (clinically infeasible) acquisition times. Our simulation results show that our method yields very accurate and robust results from only two partially sampled scans (total scan time being the same as a single echo MRI). We model PD and T1 maps to be…
Nanyang Technological University（南洋理工大学）；Swinburne University of Technology（斯威本科技大学）
MR image sparsity/compressibility has been widely exploited for imaging acceleration with the development of compressed sensing. A sparsity-based approach to rigid-body motion correction is presented for the first time in this paper. A motion is sought after such that the compensated MR image is maximally sparse/compressible among the infinite candidates. Iterative algorithms are proposed that jointly estimate the motion and the image content. The proposed method has a lot of merits, such as no need of additional data and loose requirement for the sampling sequence. Promising results are prese…
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院Martinos生物医学影像中心）；Massachusetts Institute of Technology（麻省理工学院）；Cornell University（康奈尔大学）；University College London（伦敦大学学院）
Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning to implement segmentation tools that are computationally efficient at test time. However, most of these strategies rely on learning from manually annotated images. These supervised deep learning methods are therefore sensitive to the intensity profiles in the training dataset. To…
Current deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs. However, they implicitly make unrealistic assumptions of static $q$-space sampling during training and reconstruction. Further, such approaches can restrict downstream usage of variably sampled DWIs for usages including the estimation of microstructural indices or tractography. We propose a generative adversarial translation framework for high-quality DWI synthesis with arbitrary $q$-space…
CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI
Department of Biomedical Engineering, National University of Singapore, Singapore（新加坡国立大学生物医学工程系）；School of Automation, Southeast University, Nanjing, China（东南大学自动化学院）；Department of Medicine, National University of Singapore, Singapore（新加坡国立大学医学系）；Department of Cardiology, National University Heart Centre Singapore, Singapore（新加坡国立心脏中心心内科部）
本文提出 CineMesh4D，一种端到端的 4D 全心脏重建方法，通过跨域映射从多视角 2D cine MRI 直接重建患者特定的全心脏网格，提升重建质量和运动一致性。
Second Affiliated Hospital of Xiamen Medical College（厦门医学院附属第二医院）；Neusoft Medical System（东软医疗系统）
Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time. Although imaging can be accelerated by sparse sampling and parallel imaging, achieving promising reconstruction images with a fast reconstruction speed remains a challenge. Recently, deep learning approaches have attracted a lot of attention for its encouraging reconstruction results but without a proper interpretability. In this letter, to enable high-quality image reconstruction for the parallel magnetic resonance imaging, we design the network structure from the p…
Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for estimating high-dimensional distributions, in particular Variational Autoencoders (VAEs), opened up new avenues for probabilistic modeling. Modelling of volumetric data has remained a challenge, however, because constraints on available computation and training data make it difficult effectively leverage VAEs, which are well-developed for 2D images. We propose a method to model 3D MR brain volumes distribution by combi…
Microsoft Research（微软研究院）；University of Pennsylvania（宾夕法尼亚大学）
Accelerated MRI reconstructs images of clinical anatomies from sparsely sampled signal data to reduce patient scan times. While recent works have leveraged deep learning to accomplish this task, such approaches have often only been explored in simulated environments where there is no signal corruption or resource limitations. In this work, we explore augmentations to neural network MRI image reconstructors to enhance their clinical relevancy. Namely, we propose a ConvNet model for detecting sources of image artifacts that achieves a classifier $F_2$ score of 79.1%. We also demonstrate that tra…
University of Oxford（牛津大学）；Siemens Healthineers（西门子医疗）
Magnetic Resonance Imaging (MRI) has excellent soft tissue contrast but is hindered by an inherently slow data acquisition process. Compressed sensing, which reconstructs sparse signals from incoherently sampled data, has been widely applied to accelerate MRI acquisitions. Compressed sensing MRI requires one or more model parameters to be tuned, which is usually done by hand, giving sub-optimal tuning in general. To address this issue, we build on previous work by the authors on the single-coil Variable Density Approximate Message Passing (VDAMP) algorithm, extending the framework to multiple …
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Maryland Dental School（马里兰大学牙医学院）；MGH Institute of Health Professions（麻省总医院健康职业学院）；Massachusetts Eye and Ear Infirmary（麻省眼耳医院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Muscle coordination patterns of lingual behaviors are synergies generated by deforming local muscle groups in a variety of ways. Functional units are functional muscle groups of local structural elements within the tongue that compress, expand, and move in a cohesive and consistent manner. Identifying the functional units using tagged-Magnetic Resonance Imaging (MRI) sheds light on the mechanisms of normal and pathological muscle coordination patterns, yielding improvement in surgical planning, treatment, or rehabilitation procedures. Here, to mine this information, we propose a matrix factori…
University College London（伦敦大学学院）；Technion -- Israel Institute of Technology（以色列理工学院）；Heriot-Watt University（赫瑞-瓦特大学）
Medical imaging tasks often involve multiple contrasts, such as T1- and T2-weighted magnetic resonance imaging (MRI) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities. In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage an available guidance contrast to restore the target contrast. Our approach consists of three stages: coupled dictionary learning, coupled sparse denoising, and $k$-space consistency enforcing. The first stage learns a group of dictionaries…
This paper considers the problem of undersampled MRI reconstruction. We propose a novel Transformer-based framework for directly processing signal in k-space, going beyond the limitation of regular grids as ConvNets do. We adopt an implicit representation of k-space spectrogram, treating spatial coordinates as inputs, and dynamically query the sparsely sampled points to reconstruct the spectrogram, i.e. learning the inductive bias in k-space. To strike a balance between computational cost and reconstruction quality, we build the decoder with hierarchical structure to generate low-resolution an…
Local2Mesh: Spatially Localized Contour-to-Mesh for Left Ventricular Reconstruction from Sparse 2D Cardiac MRI
Department of Health Technology and Informatics, The Hong Kong Polytechnic University（香港理工大学健康科技与信息学系）；Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham（诺丁汉大学医学院诺丁汉生物医学研究中心）
Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks. However, undersampling during MRI acquisition as well as the overparameterized and non-transparent nature of deep learning (DL) leaves substantial uncertainty about the accuracy of DL reconstruction. With this in mind, this study aims to quantify the uncertainty in image recovery with DL models. To this end, we first leverage variational autoencoders (VAEs) to develop a probabilistic reconstruction scheme that maps out (low-quality) short scans with aliasing artifacts to the diagnostic-quality ones. The VA…
University of Florida（佛罗里达大学）；Georgia State University（佐治亚州立大学）
We propose a novel deep neural network architecture by mapping the robust proximal gradient scheme for fast image reconstruction in parallel MRI (pMRI) with regularization function trained from data. The proposed network learns to adaptively combine the multi-coil images from incomplete pMRI data into a single image with homogeneous contrast, which is then passed to a nonlinear encoder to efficiently extract sparse features of the image. Unlike most of existing deep image reconstruction networks, our network does not require knowledge of sensitivity maps, which can be difficult to estimate acc…
Zhejiang University（浙江大学）；Shandong Medical Imaging Research Institute（山东医学影像研究所）；Shandong University（山东大学）；Lanzhou University（兰州大学）；Siemens Healthineers Ltd.（西门子医疗有限公司）；Beijing Institute of Technology（北京理工大学）
Diffusion magnetic resonance imaging (dMRI) is an important tool in characterizing tissue microstructure based on biophysical models, which are complex and highly non-linear. Resolving microstructures with optimization techniques is prone to estimation errors and requires dense sampling in the q-space. Deep learning based approaches have been proposed to overcome these limitations. Motivated by the superior performance of the Transformer, in this work, we present a learning-based framework based on Transformer, namely, a Microstructure Estimation Transformer with Sparse Coding (METSC) for dMRI…
Ambiguity is inevitable in medical images, which often results in different image interpretations (e.g. object boundaries or segmentation maps) from different human experts. Thus, a model that learns the ambiguity and outputs a probability distribution of the target, would be valuable for medical applications to assess the uncertainty of diagnosis. In this paper, we propose a powerful generative model to learn a representation of ambiguity and to generate probabilistic outputs. Our model, named Coordinate Quantization Variational Autoencoder (CQ-VAE) employs a discrete latent space with an int…
University of Central Florida（中佛罗里达大学）；Kyungpook National University（庆北国立大学）
Medical image segmentation being a substantial component of image processing plays a significant role to analyze gross anatomy, to locate an infirmity and to plan the surgical procedures. Segmentation of brain Magnetic Resonance Imaging (MRI) is of considerable importance for the accurate diagnosis. However, precise and accurate segmentation of brain MRI is a challenging task. Here, we present an efficient framework for segmentation of brain MR images. For this purpose, Gabor transform method is used to compute features of brain MRI. Then, these features are classified by using four different …
End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
School of Computer and Communication Science, EPFL（计算机与通信科学学院，瑞士联邦理工学院）；ELLIS Institute Finland, Aalto University, Finland（芬兰ELLIS研究所，阿尔托大学，芬兰）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Center for Interventional MRI, CHUV（介入MRI中心，CHUV）
Indian Institute of Information Technology (IIITM-K)（印度信息技术学院（IIITM-K））
Compressed sensing (CS) methods in magnetic resonance imaging (MRI) offer rapid acquisition and improved image quality but require iterative reconstruction schemes with regularization to enforce sparsity. Regardless of the difficulty in obtaining a fast numerical solution, the total variation (TV) regularization is a preferred choice due to its edge-preserving and structure recovery capabilities. While many approaches have been proposed to overcome the non-differentiability of the TV cost term, an iterative shrinkage based formulation allows recovering an image through recursive application of…
University of Minnesota（明尼苏达大学）；Sungkyunkwan University（成均馆大学）
Recent studies show that deep learning (DL) based MRI reconstruction outperforms conventional methods, such as parallel imaging and compressed sensing (CS), in multiple applications. Unlike CS that is typically implemented with pre-determined linear representations for regularization, DL inherently uses a non-linear representation learned from a large database. Another line of work uses transform learning (TL) to bridge the gap between these two approaches by learning linear representations from data. In this work, we combine ideas from CS, TL and DL reconstructions to learn deep linear convol…
Xiamen University（厦门大学）；Xiamen University of Technology（厦门理工学院）；Neusoft Medical System（东软医疗系统）
The boom of non-uniform sampling and compressed sensing techniques dramatically alleviates the lengthy data acquisition problem of magnetic resonance imaging. Sparse reconstruction, thanks to its fast computation and promising performance, has attracted researchers to put numerous efforts on it and has been adopted in commercial scanners. To perform sparse reconstruction, choosing a proper algorithm is essential in providing satisfying results and saving time in tuning parameters. The pFISTA, a simple and efficient algorithm for sparse reconstruction, has been successfully extended to parallel…
We formulate a physics-informed compressed sensing (PICS) method for the reconstruction of velocity fields from noisy and sparse phase-contrast magnetic resonance signals. The method solves an inverse Navier-Stokes boundary value problem, which permits us to jointly reconstruct and segment the velocity field, and at the same time infer hidden quantities such as the hydrodynamic pressure and the wall shear stress. Using a Bayesian framework, we regularize the problem by introducing a priori information about the unknown parameters in the form of Gaussian random fields. This prior information is…
