The Nyquist-Shannon theorem states that the information accessible by discrete Fourier protocols saturates when the sampling rate reaches twice the bandwidth of the detected continuous time signal. This maximum rate (the NS-limit) plays a prominent role in Magnetic Resonance Imaging (MRI). Nevertheless, reconstruction methods other than Fourier analysis can extract useful information from data oversampled with respect to the NS-limit, given that relevant prior knowledge is available. Here we present PhasE-Constrained OverSampled MRI (PECOS), a method that exploits data oversampling in combination with prior knowledge of the physical interactions between electromagnetic fields and spins in MRI systems. In PECOS, highly oversampled-in-time k-space data are fed into a phase-constrained variant of Kaczmarz's algebraic reconstruction algorithm, where prior knowledge of the expected spin contributions to the signal is codified into an encoding matrix. PECOS can be used for scan acceleration in relevant scenarios by oversampling along frequency-encoded directions, which is innocuous in MRI systems under reasonable conditions. We find situations in which the reconstruction quality can be higher than with NS-limited acquisitions and traditional Fourier reconstruction. Besides, we compare the performance of a variety of encoding pulse sequences as well as image reconstruction protocols, and find that accelerated spiral trajectories in k-space combined with algebraic reconstruction techniques are particularly advantageous. The proposed sampling and reconstruction method is able to improve image quality for fully-sampled k-space trajectories, while allowing accelerated or undersampled acquisitions without regularization or signal extrapolation to unmeasured regions.
Balanced steady-state free precession (bSSFP) imaging enables high scan efficiency in MRI, but differs from conventional sequences in terms of elevated sensitivity to main field inhomogeneity and nonstandard T2/T1-weighted tissue contrast. To address these limitations, multiple bSSFP images of the same anatomy are commonly acquired with a set of different RF phase-cycling increments. Joint processing of phase-cycled acquisitions serves to mitigate sensitivity to field inhomogeneity. Recently phase-cycled bSSFP acquisitions were also leveraged to estimate relaxation parameters based on explicit…
Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data
Department of Imaging Neuroscience, University College London（影像神经科学系，伦敦大学学院）；Institute of Cognitive Neuroscience, University College London（认知神经科学研究所，伦敦大学学院）
Purpose: The purpose of this work is to investigate the hypothesis that uniform sampling measurements that are endowed with antipodal symmetry play an important role when the raw data and image data are related through the Fourier relationship as in q-space diffusion MRI and 3D radial MRI. Currently, it is extremely challenging to generate large uniform antipodally symmetric point sets suitable for 3D radial MRI. A novel approach is proposed to solve this important and long-standing problem. Methods: The proposed method is based upon constrained centroidal Voronoi tessellations of the upper he…
Constrained estimation of rotational invariants of the cumulant expansion (RICE) for rapid tensor-valued diffusion MRI
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根大学附属综合医院，弗里德里希·亚历山大·埃尔朗根-纽伦堡大学）；University Hospital Heidelberg（海德堡大学附属医院）
Magnetic Resonance Imaging (MRI) is considered today the golden-standard modality for soft tissues. The long acquisition times, however, make it more prone to motion artifacts as well as contribute to the relatively high costs of this examination. Over the years, multiple studies concentrated on designing reduced measurement schemes and image reconstruction schemes for MRI, however, these problems have been so far addressed separately. On the other hand, recent works in optical computational imaging have demonstrated growing success of the simultaneous learning-based design of the acquisition …
Magnetic Resonance Imaging (MRI) has long been considered to be among "the gold standards" of diagnostic medical imaging. The long acquisition times, however, render MRI prone to motion artifacts, let alone their adverse contribution to the relative high costs of MRI examination. Over the last few decades, multiple studies have focused on the development of both physical and post-processing methods for accelerated acquisition of MRI scans. These two approaches, however, have so far been addressed separately. On the other hand, recent works in optical computational imaging have demonstrated gro…
Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuning
Chandra Family Department of Electrical and Computer Engineering（查克拉家族电气与计算机工程系）；Department of Imaging Physics（成像物理系）；Dell Medical School Department of Neurology（德莱尔医学院神经学系）；Dell Medical School Department of Neurosurgery（德莱尔医学院神经外科系）；Dell Medical School Department of Diagnostic Medicine（德莱尔医学院诊断医学系）；Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究院）
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction
Xiamen University（厦门大学）；Fudan University（复旦大学）；Philips Healthcare（飞利浦医疗保健）；Siemens Healthineers Ltd.（西门子医疗有限公司）；United Imaging Research Institute of Intelligent Imaging（联合智能影像研究所）；Nanjing First Hospital（南京市第一医院）；Shandong Aoxin Medical Technology Company（山东奥新医疗科技有限公司）；The First Affiliated Hospital of Xiamen University（厦门大学附属第一医院）；Zhongshan Hospital Affiliated to Xiamen University（厦门大学附属中山医院）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Ruijin Hospital, Shanghai Jiaotong University School of Medicine（上海交通大学医学院附属瑞金医院）；The First Affiliated Hospital of Kunming Medical University（昆明医科大学第一附属医院）；Xiamen University of Technology（厦门理工学院）；Imperial College London（帝国理工学院）
Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data
UCL Great Ormond Street Institute of Child Health, University College London（伦敦大学学院大奥蒙德街儿童健康研究所）；Great Ormond Street Hospital（大奥蒙德街医院）；Lund University（隆德大学）
In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic ('stick-like') diffusion. Second, the 'density' of tissue components may be confounded by non-diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with 'b-tensor encoding' and multiple echo times in both healthy brain and white matter lesions. Results showed that m…
Purpose: To investigate motion compensated, self-supervised, model based deep learning (MBDL) as a method to reconstruct free breathing, 3D Pulmonary ultrashort echo time (UTE) acquisitions. Theory and Methods: A self-supervised eXtra Dimension MBDL architecture (XD-MBDL) was developed that combined respiratory states to reconstruct a single high-quality 3D image. Non-rigid, GPU based motion fields were incorporated into this architecture by estimating motion fields from a low resolution motion resolved (XD-GRASP) iterative reconstruction. Motion Compensated XD-MBDL was evaluated on lung UTE d…
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes i…
ALMA: a mathematics-driven approach for determining tuning parameters in generalized LASSO problems, with applications to MRI
University of Bologna, Department of Mathematics, Bologna, Italy（博洛尼亚大学数学系）；Lausanne University Hospital and University of Lausanne（洛桑大学医院和洛桑大学）；Department of Diagnostic and Interventional Radiology, Lausanne, Switzerland（诊断与介入放射学部，洛桑，瑞士）
SIREM: Speech-Informed MRI Reconstruction with Learned Sampling
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃森哲-埃尔朗根-纽伦堡大学模式识别实验室）；Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根大学医院放射学研究所）；Institut für Informationsverarbeitung, Leibniz Universität Hannover（汉诺威莱比锡大学信息处理研究所）；Department of Radiology, Harvard Medical School and Massachusetts General Hospital（哈佛医学院放射科和麻省总医院）
University Medical Center Göttingen（哥廷根大学医学中心）；German Centre for Cardiovascular Research (DZHK)（德国心血管研究中心）；University of California, Berkeley（加州大学伯克利分校）
Purpose: To develop an ESPIRiT-based method to estimate coil sensitivities with image phase as a building block for efficient and robust image reconstruction with phase constraints. Theory and Methods: ESPIRiT is a new framework for calibration of the coil sensitivities and reconstruction in parallel Magnetic Resonance Imaging (MRI). Applying ESPIRiT to a combined set of physical and virtual conjugate coils (VCC-ESPIRiT) implicitly exploits conjugate symmetry in k-space similar to VCC-GRAPPA. Based on this method, a new post-processing step is proposed for the explicit computation of coil sens…
