Sur la génération de schémas d'échantillonnage compressé en IRM

International audienceThis article contains two contributions. First, we describe the state-of-the-art theories in compressed sensing for Magnetic ResonanceImaging (MRI). This allows us to bring out important principles that should guide the generation of sampling patterns. Second, wedescribe an original methodology to design efficient sampling schemes. It consists of projecting a sampling density on the space of feasiblemeasures for MRI. We end up by proposing comparisons to current sampling strategies on simulated data. This illustrates the well-foundednessof our approach.Cet article a deux finalités. Premièrement, nous proposons un état de l'art des théories d'échantillonnage compressé pour l'Imagerie par résonance magnétique (IRM). Ceci nous permet de dégager quelques grands principes à suivre pour générer des schémas performants en termes de temps d'acquisition et de qualité de reconstruction. Dans une deuxième partie, nous proposons une méthodologie originale de conception de schémas qui repose sur des algorithmes de projection de densités sur des espaces de mesures. Nous proposons finalement des comparaisons avec des stratégies actuelles d'échantillonnage sur des simulations et montrons ainsi le bien-fondé de notre approche. Abstract – This article contains two contributions. First, we describe the state-of-the-art theories in compressed sensing for Magnetic Resonance Imaging (MRI). This allows us to bring out important principles that should guide the generation of sampling patterns. Second, we describe an original methodology to design efficient sampling schemes. It consists of projecting a sampling density on the space of feasible measures for MRI. We end up by proposing comparisons to current sampling strategies on simulated data. This illustrates the well-foundedness of our approach

Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors

High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex fiber configurations, albeit at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a kq-space under-sampling scheme to enable accelerated acquisitions. The inverse problem for reconstruction of the fiber orientation distribution (FOD) is regularized by a structured sparsity prior promoting simultaneously voxelwise sparsity and spatial smoothness of fiber orientation. Prior knowledge of the spatial distribution of white matter, gray matter and cerebrospinal fluid is also assumed. A minimization problem is formulated and solved via a forward-backward convex optimization algorithmic structure. Simulations and real data analysis suggest that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes, potentially enabling high spatio-angular dMRI in the clinical setting.Comment: 10 pages, 5 figures, Supplementary Material

Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing

Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue. A particular example is the planar Fabry-Perot (FP) scanner, which yields high-resolution images but takes several minutes to sequentially map the photoacoustic field on the sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: First, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP scanner and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in-vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction methods that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of PAT scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.Comment: submitted to "Physics in Medicine and Biology

A feasible and automatic free tool for T1 and ECV mapping

Purpose: Cardiac magnetic resonance (CMR) is a useful non-invasive tool for characterizing tissues and detecting myocardial fibrosis and edema. Estimation of extracellular volume fraction (ECV) using T1 sequences is emerging as an accurate biomarker in cardiac diseases associated with diffuse fibrosis. In this study, automatic software for T1 and ECV map generation consisting of an executable file was developed and validated using phantom and human data. Methods: T1 mapping was performed in phantoms and 30 subjects (22 patients and 8 healthy subjects) on a 1.5T MR scanner using the modified Look-Locker inversion-recovery (MOLLI) sequence prototype before and 15 min after contrast agent administration. T1 maps were generated using a Fast Nonlinear Least Squares algorithm. Myocardial ECV maps were generated using both pre- and post-contrast T1 image registration and automatic extraction of blood relaxation rates. Results: Using our software, pre- and post-contrast T1 maps were obtained in phantoms and healthy subjects resulting in a robust and reliable quantification as compared to reference software. Coregistration of pre- and post-contrast images improved the quality of ECV maps. Mean ECV value in healthy subjects was 24.5% ± 2.5%. Conclusions: This study demonstrated that it is possible to obtain accurate T1 maps and informative ECV maps using our software. Pixel-wise ECV maps obtained with this automatic software made it possible to visualize and evaluate the extent and severity of ECV alterations