Reconstruction of Dynamic Under-sampled Mri Using Self-similarity among 1d Temporal Snippets
نویسندگان
چکیده
This paper introduces a new empirical model for dynamic MRI and shows its application to MRI reconstruction. The model proposes that short 1D signals, so-called snippets, along the image’s temporal dimension are sparse under nonlinear transformation using a compact dictionary trained on the data itself. We employ this model to the problem of reconstructing dynamic abdominal MRI and validate its efficacy on a dynamic computational phantom and on an in vivo dynamic MRI sequence. We show how the approach extends and outperforms a state-of-the-art reconstruction algorithm.
منابع مشابه
Blind Compressed Sensing Dynamic MRI
Introduction: Achieving high spatio-temporal resolutions in dynamic MRI (DMRI) (eg. myocardial perfusion MRI) is often challenging due to the slow nature of MR acquisitions. Recently, several schemes that exploit the low-rank property of dynamic datasets were introduced to accelerate dynamic MRI [eg: 1-3]. These methods exploit the similarity of the voxel time profiles (intensity variations as ...
متن کاملInfluence of temporal regularization and radial undersampling factor on compressed sensing reconstruction in dynamic contrast enhanced MRI of the breast.
BACKGROUND To evaluate the influence of temporal sparsity regularization and radial undersampling on compressed sensing reconstruction of dynamic contrast-enhanced (DCE) MRI, using the iterative Golden-angle RAdial Sparse Parallel (iGRASP) MRI technique in the setting of breast cancer evaluation. METHODS DCE-MRI examinations of the breast (n = 7) were conducted using iGRASP at 3 Tesla. Images...
متن کاملk-t SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity
M. Lustig, J. M. Santos, D. L. Donoho, J. M. Pauly Electrical Engineering, Stanford University, Stanford, CA, United States, Statistics, Stanford University, Stanford, CA, United States Introduction Recently rapid imaging methods that exploit the spatial sparsity of images using under-sampled randomly perturbed spirals and non-linear reconstruction have been proposed [1,2]. These methods were i...
متن کاملNon-iterative reconstruction with a prior for undersampled radial MRI data
This paper develops an FBP-MAP (Filtered Backprojection, Maximum a Posteriori) algorithm to reconstruct MRI images from under-sampled data. An objective function is first set up for the MRI reconstruction problem with a data fidelity term and a Bayesian term. The Bayesian term is a constraint in the temporal dimension. This objective function is minimized using the calculus of variations. The p...
متن کاملk-t FASTER: Acceleration of functional MRI data acquisition using low rank constraints
PURPOSE In functional MRI (fMRI), faster sampling of data can provide richer temporal information and increase temporal degrees of freedom. However, acceleration is generally performed on a volume-by-volume basis, without consideration of the intrinsic spatio-temporal data structure. We present a novel method for accelerating fMRI data acquisition, k-t FASTER (FMRI Accelerated in Space-time via...
متن کامل