Modeling and validation methods for compressed sensing and MRI

Abstract

A computer implemented method is provided that judiciously applies or manages randomness, incoherence, nonlinearity and structures involved in signal encoding or decoding. The method in a magnetic resonance imaging example comprises acquiring data samples in accordance with a k-space sampling pattern, identifying a signal structure in an assembly of the acquired data samples, and finding a resultconsistent with both the acquired data samples and the identified signal structure.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a modeling and verification method of compressed sensing and MRI. Background technique [0002] Mathematical modeling based on physics, statistics or other disciplines is the key to signal encoding and decoding. [0003] On the basis of signal model, reconstruction algorithm, random sampling and non-coherent sampling, a new class of methods is derived. This new class of methods can encode and decode one-dimensional or multi-dimensional signals, and can greatly reduce the data samples measured, transmitted and stored in the process, and can also effectively deal with the problems caused by random noise and aliasing. interference effect. Representative methods mentioned above include compressed sensing and low-rank matrix completion. [0004] Compressed sensing and low-rank matrix completion have had a large number of successful examples in various applications. In theoretica...

AI Technical Summary

Problems solved by technology

Although there have been a large number of examples to prove that compressed sensing is superior in improving the speed of MR data acquisition and improving the noise ratio, people still worry that its imaging scheme will be in a complex and subtle way in specific imaging instances. to mask some diagnostically important features

Also, when setting up compressed sensing to increase the speed of MR data acquisition, the setup process itself is complex and lacks guidance

Since the core of the compressed sensing scheme is a nonlinear operator that uses random sampling and sparse models to solve signal interference, it faces more problems in evaluating the fidelity of images than traditional imaging schemes that mainly rely on linear operators. Difficulties

This is because the scheme's response to signal, interference and encoding / decoding parameters is difficult to grasp or understand

Oftentimes due to scattered interference effects and no obvious artifacts to signal fidelity flaws, compressed sensing imaging schemes tend to produce images that appear to be "cleaner", which can sometimes be misleading

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