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Adaptive Correction Method for MRI Diffusion Weighted Imaging

A diffusion-weighted imaging and diffusion-weighted image technology, applied in application, image enhancement, image analysis, etc., can solve the problems of complex hardware detection device or algorithm, limited degree of artifact suppression, ADC value error, etc., so as to suppress motion artifacts. shadows, improved image quality, excellent image quality effects

Active Publication Date: 2021-03-19
ALLTECH MEDICAL SYST
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Problems solved by technology

[0005] The above two types of artifacts are very common in diffusion-weighted imaging. In addition to artifacts appearing on the diffusion-weighted composite image, they will also affect the subsequent processing results based on diffusion-weighted imaging, such as ADC value errors, diffusion tensor imaging errors, etc. , affecting the doctor's diagnosis
In order to improve the above-mentioned artifacts, on the one hand, motion detection and correction technology, radio frequency ignition detection and correction technology can be used to reduce artifacts, but this method needs to increase special hardware detection devices or the algorithm is complex and has poor reliability; on the other hand, Usually, multiple acquisition averaging techniques are used to reduce the influence of artifacts, but this method has a limited degree of artifact suppression through direct averaging

Method used

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  • Adaptive Correction Method for MRI Diffusion Weighted Imaging
  • Adaptive Correction Method for MRI Diffusion Weighted Imaging
  • Adaptive Correction Method for MRI Diffusion Weighted Imaging

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Embodiment 1

[0050] The MRI diffusion weighted imaging adaptive correction method disclosed in this embodiment includes the following steps:

[0051] Step 1, with the same scanning parameters, repeat acquisition of diffusion weighted images N times, N is a natural number, N≥3;

[0052] Step 2, constructing a correlation matrix point by point based on the original image or the compressed image: specifically includes the following steps;

[0053] Step 2.1, for any pixel point (x, y) in the image collected for the nth time, take K surrounding adjacent points to form a neighborhood vector Xn;

[0054] Step 2.2, for the images collected repeatedly for N times, each pixel corresponds to N neighborhood vectors, and the correlation between the nth vector Xn and the mth vector Xm is calculated according to formula (1);

[0055]

[0056] In formula (1), x i is the i-th element in the vector Xn, y i is the i-th element in the vector Xm, is the mean value of the vector Xn, is the mean of the...

Embodiment 2

[0070] The difference between this embodiment and Embodiment 1 is that before step 2, all collected original images are compressed using an interpolation algorithm. The advantages are that first, it can reduce the amount of calculation, and second, it can increase the signal-to-noise ratio of the input data of subsequent algorithms.

Embodiment 3

[0072] The difference between this embodiment and embodiment 1 or 2 is: as figure 1 As shown, the correlation matrix is ​​smoothed and filtered before step 3. Wherein, the smoothing filtering process includes the following steps;

[0073] Step a, from the correlation matrix R(x, y) corresponding to each pixel point (x, y), take the i-th correlation coefficient to form a matrix Ri with the same size as the image matrix;

[0074] Step b, performing two-dimensional low-pass filtering on the matrix Ri;

[0075] Step c, replace the corresponding element in R(x,y) with the filtered result;

[0076] Step d, repeat a-c until all elements in R(x,y) are processed.

[0077] Such as figure 2 As shown, as indicated by the arrow, obvious motion artifacts can be seen in the first image, resulting in complete loss of part of the signal. Such as image 3 As shown, the diffusion-weighted image synthesized by direct averaging has a limited degree of artifact suppression and poor image qua...

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Abstract

The present invention relates to an adaptive correction method for magnetic resonance diffusion weighted imaging, comprising the following steps: step 1, repeating acquisition of diffusion weighted images N times with the same scanning parameters, N≥3; step 2, based on the original image or compressed image point by point Construct a correlation matrix; step 3, perform principal component analysis on the correlation matrix after smoothing and filtering, and obtain the eigenvector corresponding to the largest eigenvalue of each correlation matrix; step 4, calculate the weight according to the eigenvector; step 5, according to Weighting performs weighted synthesis on the original image to obtain a corrected diffusion-weighted image. On the basis of multiple acquisition average technology, the present invention adopts principal component analysis method to adaptively detect and correct data from redundant data, suppress motion artifacts, radio frequency ignition artifacts, etc., and improve image quality; no need to increase Hardware device, and the image quality is better than multiple acquisition direct averaging technology.

Description

technical field [0001] The invention relates to the field of magnetic resonance imaging, in particular to an adaptive correction method for magnetic resonance diffusion weighted imaging. Background technique [0002] Diffusion Weighted Imaging (DWI) is an imaging method that non-invasively reflects the irregular thermal motion of living water molecules at the molecular level. The imaging mainly depends on the motion of water molecules rather than the proton density, T1 or T2 relaxation time. Diffusion weighted imaging is suitable for detecting the micro-dynamics and micro-structural changes of biological tissues at the level of living cells, and plays a pivotal role in the identification of benign and malignant tumors, the evaluation and prediction of curative effect. [0003] In diffusion-weighted imaging, the applied diffusion gradient is extremely sensitive to motion. Movement mainly includes the following four aspects: (1) water molecule diffusion movement; (2) patient...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T11/00
CPCG06T11/005G06T2207/10088A61B5/055
Inventor 罗海王世杰朱高杰周翔陈梅泞王超刘霞吴子岳
Owner ALLTECH MEDICAL SYST
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