Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction
CEA/NeuroSpin（法国原子能与替代能源委员会神经自旋中心）；CEA（法国原子能与替代能源委员会）；CNRS（法国国家科学研究中心）；Université Paris-Saclay（巴黎萨克雷大学）；Université Paris Diderot（巴黎第七大学）；Sorbonne Paris Cité（索邦巴黎西岱大学）；Inria Saclay Ile-de-France（法国国家信息与自动化研究所萨克雷-法兰西岛分院）
Deep neural networks have recently been thoroughly investigated as a powerful tool for MRI reconstruction. There is a lack of research, however, regarding their use for a specific setting of MRI, namely non-Cartesian acquisitions. In this work, we introduce a novel kind of deep neural networks to tackle this problem, namely density compensated unrolled neural networks, which rely on Density Compensation to correct the uneven weighting of the k-space. We assess their efficiency on the publicly available fastMRI dataset, and perform a small ablation study. Our results show that the density-compensated unrolled neural networks outperform the different baselines, and that all parts of the design are needed. We also open source our code, in particular a Non-Uniform Fast Fourier transform for TensorFlow.
Klinikum Rechts der Isar, Technical University of Munich（慕尼黑工业大学伊萨尔河右岸医院）；Imperial College London（伦敦帝国理工学院）；University Hospital of Tübingen（图宾根大学医院）
Motion-compensated MR reconstruction (MCMR) is a powerful concept with considerable potential, consisting of two coupled sub-problems: Motion estimation, assuming a known image, and image reconstruction, assuming known motion. In this work, we propose a learning-based self-supervised framework for MCMR, to efficiently deal with non-rigid motion corruption in cardiac MR imaging. Contrary to conventional MCMR methods in which the motion is estimated prior to reconstruction and remains unchanged during the iterative optimization process, we introduce a dynamic motion estimation process and embed …
Interlaced R2D2 DNN Series for Scalable Non-Cartesian MRI with Sensitivity Self-calibration
Institute of Sensors, Signals and Systems, Heriot-Watt University Edinburgh, United Kingdom（传感器、信号与系统研究所，赫里奥特-沃斯大学爱丁堡分校，英国）；Department of Diagnostic Imaging and Interventional Radiology, Lausanne University and Hospital, Switzerland（诊断影像与介入放射学系，洛桑大学及医院，瑞士）
Purpose: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model for non-rigid motion correction in coronary magnetic resonance angiography (CMRA). Methods: An unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired as part of a CMRA sequence. The unrolled model incorporates a non-uniform FFT operator to perform the data consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D i…
University of California Los Angeles（加州大学洛杉矶分校）；Siemens Medical Solutions USA, Inc.（西门子医疗系统美国公司）
The recent development of deep learning combined with compressed sensing enables fast reconstruction of undersampled MR images and has achieved state-of-the-art performance for Cartesian k-space trajectories. However, non-Cartesian trajectories such as the radial trajectory need to be transformed onto a Cartesian grid in each iteration of the network training, slowing down the training process and posing inconvenience and delay during training. Multiple iterations of nonuniform Fourier transform in the networks offset the deep learning advantage of fast inference. Current approaches typically …
Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative MR imaging approach. Deep learning methods have been proposed for MRF and demonstrated improved performance over classical compressed sensing algorithms. However many of these end-to-end models are physics-free, while consistency of the predictions with respect to the physical forward model is crucial for reliably solving inverse problems. To address this, recently [1] proposed a proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within an unrolled learni…
B-FIRE: Binning-Free Diffusion Implicit Neural Representation for Hyper-Accelerated Motion-Resolved MRI
Radiation Oncology, University of California, San Francisco, California（加州大学旧金山分校放射肿瘤学系）；Radiology and Biomedical Imaging, University of California, San Francisco, California（加州大学旧金山分校放射学与生物医学成像系）；Siemens Medical Solutions USA, Inc., Cleveland, Ohio（西门子医疗解决方案美国公司，克利夫兰，俄亥俄）；Radiology at Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California（洛杉矶儿童医院放射学，美国南加州大学凯克医学院，洛杉矶，加利福尼亚）
Purpose: To develop and evaluate a free-breathing respiratory motion compensated 4D (3D+respiration) $T_2$-weighted turbo spin echo sequence with application to radiology and MR-guided radiotherapy. Methods: k-space data are continuously acquired using a rewound Cartesian acquisition with spiral profile ordering (rCASPR) to provide matching contrast to the conventional linear phase encode ordering and to sort data into multiple respiratory phases. Low-resolution respiratory-correlated 4D images were reconstructed with compressed sensing and used to estimate non-rigid deformation vector fields,…
Purpose: To improve upon Extreme MRI, a recently proposed method by Ong Et al. for reconstructing high spatiotemporal resolution, 3D non-Cartesian acquisitions by incorporating motion compensation into these reconstructions using an approach termed MoCo-MSLR. Methods: Motion compensation is challenging to incorporate into high spatiotemporal resolution reconstruction due to the memory footprint of the motion fields and the potential to lose dynamics by relying on an initial high temporal resolution, low spatial resolution reconstruction. Motivated by the work of Ong Et al. and Huttinga Et al.,…
Optimizing k-space sampling trajectories is a promising yet challenging topic for fast magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method and sampling trajectories jointly concerning image reconstruction quality in a supervised learning manner. We parameterize trajectories with quadratic B-spline kernels to reduce the number of parameters and apply multi-scale optimization, which may help to avoid sub-optimal local minima. The algorithm includes an efficient non-Cartesian unrolled neural network-based reconstruction and an accurate approximation for backpr…
Attention-aware non-rigid image registration for accelerated MR imaging
University Hospital of Tuebingen（蒂宾根大学医院）；Klinikum Rechts der Isar（右岸医院（慕尼黑工业大学附属））；Technical University of Munich（慕尼黑工业大学）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Imperial College London（伦敦帝国学院）
Physikalisch-Technische Bundesanstalt (PTB)（德国联邦物理技术研究院）；University of Innsbruck（因斯布鲁克大学）；King’s College London（伦敦国王学院）；Technical University of Berlin（柏林工业大学）
Purpose: Iterative Convolutional Neural Networks (CNNs) which resemble unrolled learned iterative schemes have shown to consistently deliver state-of-the-art results for image reconstruction problems across different imaging modalities. However, because these methodes include the forward model in the architecture, their applicability is often restricted to either relatively small reconstruction problems or to problems with operators which are computationally cheap to compute. As a consequence, they have so far not been applied to dynamic non-Cartesian multi-coil reconstruction problems. Method…
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin（查德拉家族电子与计算机工程系，德克萨斯大学奥斯汀分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Imaging Physics, MD Anderson Cancer Center, Houston, Texas, USA（影像物理，MD安德森癌症中心，休斯顿，德克萨斯州，美国）；Electrical Engineering and Computer Sciences, University of California, Berkeley（电气工程与计算机科学，加州大学伯克利分校）；Department of Diagnostic Medicine, Dell Medical School, Austin, Texas, USA（诊断医学系，德勒医学学院，奥斯汀，德克萨斯州，美国）
