MRI image inhomogeneity correction method based on regularization

Abstract

The invention provides an MRI image inhomogeneity correction method based on regularization. A lowpass filtering method is improved, part of an area with low grey-value difference is selected and is subjected to lowpass filtering to obtain an initial estimate for a inhomogeneous field, and then extrapolation is performed through a regularized polynomial fitting method to obtain an estimate for an inhomogeneous field of a whole image. According to the method provided by the invention, an area-of-interest is determined and is subjected to filtering, so the boundary effect in the global image filtering method can be avoided, and fitting is performed through a regularization method, thus overfitting is guaranteed not to happen under the condition of improving the fitting order.

Description

technical field [0001] The present invention relates to the technical field of nuclear magnetic resonance and image processing, in particular to a method for correcting non-uniformity of MRI images based on regularization. Background technique [0002] In the past ten years, MRI has been widely used in medical diagnosis due to its high resolution, no ionizing radiation damage, and imaging at any angle. With the demand for higher resolution scanned images, scanners have increasingly higher magnetic field strengths and finer magnetic field gradients. However, the accompanying problem is that the interference of non-uniform fields suffered by MRI images is becoming more and more serious. The non-uniform field is the deviation field caused by the non-uniformity of the transmitted spatial magnetic field or the non-uniform sensitivity of the receiving coil. This deviation field is generally assumed to be a smooth, slowly changing multiplicative deviation field, which will lead to...

AI Technical Summary

Problems solved by technology

[0007] Disadvantages of the algorithm: the second scan requires additional hardware equipment, which increases the scan time; and the displacement of the patient during the two scans causes image mismatch, which requires image registration, making errors in estimating the non-uniform field and causing artifacts

[0011] Disadvantages of the algorithm: there is aliasing between the non-uniform field and the low-frequency information of the image, and the frequency threshold of the low-pass filter is difficult to determine

[0015] Disadvantages of the algorithm: poor universality, only suitable for images of special parts

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