Patent ID: 9629612
Date: 2017-04-25
CPC Classifications: A61B,G01R

Claim:
1. A method of reconstructing an MRI or ultrasound biomedical image, based on compressed sensing, the method comprising: a) generating signals by exciting a body under examination with radiofrequency (RF) or ultrasound (US) pulses; b) acquiring image data from said generated signals, wherein said image data is acquired by pseudo-random undersampling; c) reconstructing said MRI or ultrasound biomedical image using a nonlinear iterative algorithm for minimizing an optimization function containing a term for image data sparsity in a predetermined domain and a term for finite differences of the image, with a data fidelity constraint term ensuring fidelity to the acquired image data; wherein two or more sets of image data are acquired from said generated signals, each image data set being acquired in a different undersampling scheme, or a different acquisition mode such as to make expected and unavoidable artifacts incoherent; each of the acquired image data sets is multiplied by a correction matrix, which is calculated from a mathematical model of expected artifacts according to prior knowledge of such artifacts, for adjusting said data fidelity constraint term in such a way that the data fidelity constraint term ensures fidelity of the reconstructed MRI or ultrasound biomedical image to the acquired image data sets; for each iteration of said nonlinear iterative algorithm the data sets are processed to generate a combination image which is faithful to the acquired data but not to the incoherent artifacts, thereby leading to suppression of said artifacts; wherein: the data fidelity constraint term is adjusted in such a way that the data fidelity constraint term ensures fidelity of the reconstructed MRI or ultrasound biomedical image to the corrected acquired image data as in the following equation: wherein: the index of each image data set is i, the different undersampling schemes are denoted by u the correction matrix is denoted by Δ m is the combination image; F y is k-space; λ ψ is a transform in a domain; and TV is Total Variation.