CN116152253B - Cardiac magnetic resonance mapping quantification method, system and storage medium - Google Patents
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Abstract
The invention belongs to the technical field of medical image processing, and particularly relates to a method, a system and a storage medium for quantifying cardiac magnetic resonance mapping. The method comprises the following steps: step 1, inputting original data of a heart magnetic resonance mapping image, respectively acquiring original images of a left ventricular basal layer, a middle layer and a cardiac apex layer, and executing subsequent steps on at least one layer of the original images of the basal layer, the middle layer and the cardiac apex layer; step 2, preprocessing the original image, and determining a general target area of left ventricle cardiac muscle based on contour recognition; step 3, obtaining an accurate target area of the segmentation target through connected domain analysis; step 4, segmenting left ventricular myocardium based on the accurate target region; and 5, calculating the average value of the original image pixels of each segmented region. The invention further provides a system for realizing the method. The method has high accuracy and repeatability of the quantized result and good application prospect.
Description
Technical Field
The invention belongs to the technical field of medical image processing, and particularly relates to a method, a system and a storage medium for quantifying cardiac magnetic resonance mapping.
Background
In recent years, the application of cardiac magnetic resonance mapping techniques in clinical environments has steadily increased, mapping being the quantification of one or more magnetic resonance contrast driving factors (i.e. relaxation times) in each pixel of an image. These relaxation times or relaxation parameters are quantifiable properties of the tissue in a magnetic field and are strongly dependent on the physiological properties of the tissue. Because mapping images are no longer dependent on some specific scan parameters (e.g., radio frequency coil proximity, receiver chain efficiency, or magnetic field inhomogeneity), this makes mapping imaging a great advantage over qualitative imaging. The quantified relaxation time also reduces inter-observer variability, allows for dynamic tracking of tissue parameters of patients, and allows for comparison of tissue parameters between individual patients. These mapping images rely solely on physical and biological interactions and are therefore highly reproducible in theory.
According to the standard post-processing guidelines for cardiac magnetic resonance images published by the international cardiac magnetic resonance society (Society for Cardiovascular Magnetic Resonance, SCMR) in 2020, the existing post-processing analysis methods of mapping images include visual analysis and manual quantitative analysis, no specific recommended or preferred post-processing software package exists at present, and an analyst should receive training of local standard or selected post-processing software package. Visual analysis, i.e. visual inspection of the general image, aims at detecting image artifacts and verifying the diagnostic image quality, but does not allow to obtain quantitative data from the mapping image. In manual quantitative analysis, guidelines recommend a conservative delineation of a region of interest at the ventricular septum of the mid-layer short axis layer at the time of measuring global or diffuse myocardial lesions to reduce the effects of magnetically sensitive artifacts from adjacent tissues. To accurately assess diffuse disease, focal lesions should be excluded from the region of interest of the mapping image. For focal lesions, it is recommended to delineate additional regions of interest outside the abnormal region found by visual analysis, while the region of interest range <20 pixels should be avoided. In actual clinical applications and studies, doctors (researchers) often divide the left ventricular myocardium into 16 segments according to the american heart association (American Heart Association, AHA) -16 segment division method, and measure the average of the whole or segment myocardium in mapping images.
The above-mentioned post-processing method of the existing cardiac magnetic resonance image mainly has the following problems: the mapping image needs to be imported into a special commercial software package for cardiac imaging, then the inner/outer membrane of the left ventricle and/or the region of interest are manually sketched, the measurement method is long in time consumption, certain difference among observers exists, and the measured data also needs to be manually output, so that the clinical application and popularization of the technology are greatly limited. These problems are effectively solved if the above-described segmentation and quantization processes can be automatically implemented in computer software. However, due to the specificity of cardiac anatomy and interference of image background noise (especially myotrabeculae, pericardial tissue in close proximity to the endocardium/epicardium), automatic identification of the contours of the target region in the cardiac magnetic resonance mapping image (e.g., left ventricular endocardial/epicardium, right ventricular insertion) is still not accurate enough, and there is currently a lack of related research on how to implement accurate full-automatic processing of cardiac magnetic resonance mapping images, there is still a need in the art to develop a method and system capable of automatically segmenting and quantifying cardiac magnetic resonance mapping images using a computer.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method, a system and a storage medium for quantifying a cardiac magnetic resonance mapping image, which realize the purpose of automatically segmenting and quantifying the cardiac magnetic resonance mapping image by using a computer.
A method for quantifying cardiac magnetic resonance, comprising the steps of:
step 1, inputting original data of a heart magnetic resonance mapping image, respectively acquiring original images of a basal layer, a middle layer and a cardiac apex layer, and executing subsequent steps on at least one of the original images of the basal layer, the middle layer and the cardiac apex layer;
step 2, preprocessing the original image, and determining a general target area of left ventricle cardiac muscle based on contour recognition;
step 3, obtaining an accurate target area of the segmentation target through connected domain analysis;
step 4, segmenting based on the accurate target area;
and 5, calculating the average value of the original image pixels of each segmented region.
Preferably, the format of the original data is a DICOM file.
Preferably, step 2 specifically includes:
step 2.1, converting the original image into a gray scale image;
step 2.2, filtering background noise and foreground noise;
step 2.3, drawing a reference circle with the radius of 1/4 width by using the center point of the image;
step 2.4, detecting the outline of the whole image, traversing the detected outline, and determining that the outline with the center of gravity in the reference circle is an alternative outline;
step 2.5, obtaining an alternative contour with the largest area as a target contour;
step 2.6, drawing extreme points of the target contour;
and 2.7, acquiring the approximate target area according to the extreme point.
Preferably, step 2.2 specifically includes:
step 2.2.1, filtering background noise through threshold analysis and median filtering;
step 2.2.2, filtering background noise through connected domain analysis;
step 2.2.3, inverting the image, analyzing and filtering the image through a connected domain to Jing Zaoyin;
step 2.2.4, performing image inversion again;
the approximate target area is rectangular in shape; step 2.7 specifically includes:
step 2.7.1, taking the extreme left point as the center point c (x 0 , y 0 )；
And 2.7.2, calculating an upper left point and a lower right point of the approximate target area by taking half of the vertical distance between the upper point and the lower point as a reference value d, wherein the calculation formula is as follows:
upper left dot coordinates: lt (x) 1 , y 1 ) = (x 0 - 0.5*d, y 0 - 0.8*d)；
Lower right dot coordinates: rb (x) 2 , y 2 ) = (x 0 + r, y 0 + 0.8*d)；
Step 2.7.3, the approximate target area is rendered.
Preferably, step 3 specifically includes:
step 3.1, preprocessing through threshold analysis;
step 3.2, removing the connected domain with small area through connected domain analysis, and leaving at least 3 connected domains as candidate regions;
step 3.3, positioning the left ventricle through contour detection;
step 3.4, positioning the front insertion part of the right ventricle by adopting a convex hull algorithm;
step 3.5, performing image inversion;
step 3.6, filtering background noise through at least one method of threshold analysis, median filtering, corrosion and expansion;
and 3.7, obtaining a connected domain with the largest area as the accurate target area through connected domain analysis.
Preferably, step 4 specifically includes:
step 4.1, performing contour repair on the endocardium and epicardium of the left ventricle, wherein the contour repair method comprises the following steps: filling faults through morphological operation, and deleting bloated;
step 4.2, drawing an epicardial contour ring;
step 4.3, drawing a star-shaped scattered ray uniform segmentation contour ring based on the center of gravity of the left ventricular chamber and the front insertion part of the right ventricle;
step 4.4, performing corrosion operation on each segmented region;
step 4.5, starting from the right ventricular anterior insertion, numbering the center of gravity of each segment of the region in a clockwise direction.
Preferably, in step 4, the original image of the basal layer or the middle layer is divided into 6 segments, and the original image of the apical layer is divided into 4 segments.
Preferably, step 5 specifically includes:
step 5.1, obtaining all pixel points of each section of area after segmentation, and coloring each section of area by using different colors;
step 5.2, mapping the coordinate position of each pixel point to the original image to obtain a pixel value;
and 5.3, calculating the pixel average value of each section of area according to the pixel value of the original image of each section of area.
The invention also provides a heart magnetic resonance mapping image quantification system, which comprises:
the input module is used for inputting the original data of the heart magnetic resonance mapping image;
the calculation module is used for processing the original data according to the heart magnetic resonance mapping image quantification method;
and the output module is used for outputting the processing result of the calculation module.
The present invention also provides a computer-readable storage medium having stored thereon: a computer program for implementing the above-mentioned cardiac magnetic resonance mapping quantification method.
The invention designs the flow of the automatic quantification method of the heart magnetic resonance mapping image for the first time, and can realize the purpose of automatically processing the heart magnetic resonance mapping image by using a computer. The problem of long time consumption caused by the fact that the current myocardial mapping image quantitative measurement needs to manually delineate the endocardial/epicardial of the left ventricle and manually position an inter-ventricular inserting part is solved, and full automation of measurement is realized; the operation is simple and quick, the reliability and the repeatability of the obtained result are high, and the problem of large difference of manual measurement data caused by different proficiency of analysts is solved. Therefore, the invention has good application prospect.
It should be apparent that, in light of the foregoing, various modifications, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
The above-described aspects of the present invention will be described in further detail below with reference to specific embodiments in the form of examples. It should not be understood that the scope of the above subject matter of the present invention is limited to the following examples only. All techniques implemented based on the above description of the invention are within the scope of the invention.
Drawings
Fig. 1 is a schematic flow chart of acquiring a rough target area in embodiment 1 of the present invention.
Fig. 2 is a flow chart of acquiring a precise target area in embodiment 1 of the present invention.
Fig. 3 is a flow chart of contour segmentation and data extraction in embodiment 1 of the present invention.
Fig. 4 is a preprocessing step of an original image. A. DICOM image gray scale; B. filtering background noise through threshold analysis and median filtering; C. filtering background noise through connected domain analysis; D. and after the image is inverted, the foreground noise is filtered through connected domain analysis. In the figure, the units of the abscissa and the ordinate are pixels.
Fig. 5 is a view of acquiring a rough target area according to an extreme point. In the figure, the units of the abscissa and the ordinate are pixels.
Fig. 6 is an illustration of acquisition of candidate regions based on connected domain analysis. A. Inverting the image; B. removing the small-area connected domain; C. an alternative region is acquired. In the figure, the units of the abscissa and the ordinate are pixels.
Fig. 7 is a diagram of the positioning of the anterior insertion portion of the right ventricle using a convex hull algorithm. In the figure, the units of the abscissa and the ordinate are pixels.
Fig. 8 is a diagram of filtering background noise. A. Filtering the background noise by median filtering; C. filtering background noise by corrosion; D. the background noise is filtered by dilation. In the figure, the units of the abscissa and the ordinate are pixels.
Fig. 9 is a view of acquiring a maximum area connected domain as a precise target area. In the figure, the units of the abscissa and the ordinate are pixels.
Fig. 10 illustrates contour patching of a target area. A. Filling faults; B. removing bloating; C. the left ventricular myocardium contour ring is drawn. In the figure, the units of the abscissa and the ordinate are pixels.
Fig. 11 is a segmented left ventricular myocardial contour ring. A. Drawing star-shaped scattered rays; B. dividing the contour ring; C. and acquiring the gravity center of each section of region. In the figure, the units of the abscissa and the ordinate are pixels.
Fig. 12 is a view of capturing and coloring pixel points of each segment of region. In the figure, the units of the abscissa and the ordinate are pixels.
Detailed Description
It should be noted that, in the embodiments, algorithms of steps such as data acquisition, transmission, storage, and processing, which are not specifically described, and hardware structures, circuit connections, and the like, which are not specifically described may be implemented through the disclosure of the prior art.
Example 1 cardiac magnetic resonance mapping image quantification method and System
The present embodiment provides a system for automatically segmenting and quantifying cardiac magnetic resonance mapping images, the system comprising:
the input module is used for inputting the original data of the heart magnetic resonance mapping image;
the computing module is used for processing the original data by a heart magnetic resonance mapping image quantification method;
and the output module is used for outputting the processing result of the calculation module.
The workflow of the algorithm in the computing module is shown in fig. 1-3, and fig. 4-12 illustrate an exemplary operation with a DICOM file for a set of myocardium T1 mapping, and specifically includes the following steps:
step 1, inputting original data of a heart magnetic resonance mapping image, respectively acquiring original images of a basal layer, a middle layer and a cardiac apex layer, and respectively executing subsequent steps on the original images of the basal layer, the middle layer and the cardiac apex layer; in the subsequent step, the original image of the basal layer or the middle layer is divided into 6 segments, and the original image of the apical layer is divided into 4 segments;
specific:
step 1.1, the raw data of a set of cardiac magnetic resonance mapping images to be measured (in this embodiment, a set of cardiac muscle T1 mapping images) are exported in DICOM form and classified into a folder, which includes three DICOM files of basal layer, middle layer and apex layer.
Step 1.2, reading the Slice Location of each DICOM file by using the pydicom library, and sorting each DICOM file in reverse order by the Slice Location, so as to determine the position (i.e. basal layer, middle layer and apex layer) of each DICOM.
Step 2, preprocessing the original image, and determining a general target area of left ventricle cardiac muscle based on contour recognition; the approximate target area is rectangular in shape;
specific:
step 2.1, converting the original image into a gray scale image;
step 2.2, filtering background noise and foreground noise;
step 2.2 specifically comprises:
step 2.2.1, filtering background noise through threshold analysis and median filtering (figure 4);
step 2.2.2, filtering background noise by connected domain analysis (fig. 4);
step 2.2.3, image inversion, pre-filtration Jing Zaoyin by connected domain analysis (fig. 4);
step 2.2.4, performing image inversion again;
step 2.3, drawing a reference circle with the radius of 1/4 width by using the center point of the image;
step 2.4, detecting the outline of the whole image, traversing the detected outline, and determining that the outline with the center of gravity in the reference circle is an alternative outline;
step 2.5, obtaining an alternative contour with the largest area as a target contour;
step 2.6, drawing extreme points of the target contour;
step 2.7, obtaining the approximate target area according to the extreme points (fig. 5);
step 2.7 specifically includes:
step 2.7.1, taking the extreme left point as the center point c (x 0 , y 0 )；
And 2.7.2, calculating an upper left point and a lower right point of the approximate target area by taking half of the vertical distance between the upper point and the lower point as a reference value d, wherein the calculation formula is as follows:
upper left dot coordinates: lt (x) 1 , y 1 ) = (x 0 - 0.5*d, y 0 - 0.8*d)；
Lower right dot coordinates: rb (x) 2 , y 2 ) = (x 0 + r, y 0 + 0.8*d)；
Step 2.7.3, the approximate target area is rendered.
Step 3, obtaining an accurate target area of the segmentation target through connected domain analysis;
specific:
step 3.1, preprocessing through threshold analysis;
step 3.2, removing the connected domain with small area through connected domain analysis, and leaving at least 3 connected domains as candidate regions (fig. 6);
step 3.3, positioning the left ventricle through contour detection; specific: acquiring the profiles of the first 3 connected domains with the largest area in the alternative area, and then positioning the left ventricle through the extreme lower point and the extreme left point of the connected domains;
step 3.4, positioning the right ventricle front insertion part by adopting a convex hull algorithm (fig. 7);
step 3.5, performing image inversion;
step 3.6, filtering background noise through threshold analysis, median filtering, erosion and dilation (fig. 8);
and step 3.7, acquiring a connected domain with the largest area as the precise target area through connected domain analysis (figure 9).
Step 4, segmenting based on the accurate target area;
specific:
step 4.1, performing contour repair on the endocardium and epicardium of the left ventricle, wherein the contour repair method comprises the following steps: filling the fault by morphological operations, deleting the bloating (fig. 10);
step 4.2, drawing a left ventricular myocardial contour ring (figure 10);
step 4.3, drawing a star-shaped scattered ray uniform segmentation contour ring based on the center of gravity of the left ventricular chamber and the front insertion part of the right ventricle (figure 11);
step 4.4, performing corrosion operation on each segmented region;
step 4.5, starting from the right ventricular anterior insertion, numbering the center of gravity of each segment of the region in a clockwise direction.
And 5, calculating the average value of the original image pixels of each segmented region.
Specific:
step 5.1, obtaining all pixel points of each segmented area after segmentation, and coloring each segmented area with different colors (fig. 12);
step 5.2, mapping the coordinate position of each pixel point to the original image to obtain a pixel value;
and 5.3, calculating the pixel average value of each section of area according to the pixel value of the original image of each section of area.
Taking 50 healthy people (150 DICOM original images in total) as an example for verification, the success rate of accurately identifying the target area reaches 91.3%.
The method can realize the purpose of automatically processing the heart magnetic resonance mapping image by using a computer, has the advantages of extremely high accuracy and high repeatability and reduces the workload of doctors, and has good application prospect.
Claims (7)
1. The heart magnetic resonance mapping quantification method is characterized by comprising the following steps of:
step 1, inputting original data of a heart magnetic resonance mapping image, respectively acquiring original images of a left ventricular basal layer, a middle layer and a cardiac apex layer, and executing subsequent steps on at least one layer of the original images of the basal layer, the middle layer and the cardiac apex layer;
step 2, preprocessing the original image, and determining a target area of left ventricle cardiac muscle based on contour recognition;
step 3, obtaining an accurate target area of the segmentation target through connected domain analysis;
step 4, segmenting based on the accurate target area;
step 5, calculating the pixel average value of the original image of each segmented region;
the step 4 specifically comprises the following steps:
step 4.1, performing contour repair on endocardium and epicardium of the left ventricle, wherein the contour repair method comprises the following steps: filling faults through morphological operation, and deleting bloated;
step 4.2, drawing a contour ring of the left ventricular myocardium;
step 4.3, drawing a star-shaped scattered ray uniform segmentation contour ring based on the center of gravity of the left ventricular chamber and the front insertion part of the right ventricle;
step 4.4, performing corrosion operation on each segmented region;
step 4.5, numbering the center of gravity of each segment of area in a clockwise direction from the front insertion part of the right ventricle;
in the step 4, 6-segment division is carried out on the original image of the basal layer or the original image of the middle layer, and 4-segment division is carried out on the original image of the apical layer;
the step 5 specifically comprises the following steps:
step 5.1, obtaining all pixel points of each section of area after segmentation, and coloring each section of area by using different colors;
step 5.2, mapping the coordinate position of each pixel point to the original image to obtain a pixel value;
and 5.3, calculating the pixel average value of each section of area according to the pixel value of the original image of each section of area.
2. The method for quantifying cardiac magnetic resonance according to claim 1, wherein the raw data is in the form of a DICOM file.
3. The method for quantifying cardiac magnetic resonance according to claim 1, wherein step 2 specifically comprises:
step 2.1, converting the original image into a gray scale image;
step 2.2, filtering background noise and foreground noise;
step 2.3, drawing a reference circle with the radius of 1/4 width by using the center point of the image;
step 2.4, detecting the outline of the whole image, traversing the detected outline, and determining that the outline with the center of gravity in the reference circle is an alternative outline;
step 2.5, obtaining an alternative contour with the largest area as a target contour;
step 2.6, drawing extreme points of the target contour;
and 2.7, acquiring the target area according to the extreme point.
4. A method of quantifying cardiac magnetic resonance as claimed in claim 3, wherein step 2.2 comprises:
step 2.2.1, filtering background noise through threshold analysis and median filtering;
step 2.2.2, filtering background noise through connected domain analysis;
step 2.2.3, inverting the image, analyzing and filtering the image through a connected domain to Jing Zaoyin;
step 2.2.4, performing image inversion again;
the shape of the target area is rectangular; step 2.7 specifically includes:
step 2.7.1, taking the extreme left point as the center point c (x 0 , y 0 )；
And 2.7.2, calculating an upper left point and a lower right point of the target area by taking half of the vertical distance between the upper point and the lower point as a reference value d, wherein the calculation formula is as follows:
upper left dot coordinates: lt (x) 1 , y 1 ) = (x 0 - 0.5*d, y 0 - 0.8*d)；
Lower right dot coordinates: rb (x) 2 , y 2 ) = (x 0 + r, y 0 + 0.8*d)；
And step 2.7.3, drawing the target area.
5. The method for quantifying cardiac magnetic resonance according to claim 1, wherein step 3 specifically comprises:
step 3.1, preprocessing through threshold analysis;
step 3.2, removing the connected domain with small area through connected domain analysis, and leaving at least 3 connected domains as candidate regions;
step 3.3, positioning the left ventricle through contour detection;
step 3.4, positioning the front insertion part of the right ventricle by adopting a convex hull algorithm;
step 3.5, performing image inversion;
step 3.6, filtering background noise by at least one method of threshold analysis, median filtering and corrosion expansion;
and 3.7, obtaining a connected domain with the largest area as the accurate target area through connected domain analysis.
6. A cardiac magnetic resonance mapping quantification system, comprising:
the input module is used for inputting the original data of the heart magnetic resonance mapping image;
a calculation module for processing the raw data according to the cardiac magnetic resonance mapping quantification method of any of claims 1-5;
and the output module is used for outputting the processing result of the calculation module.
7. A computer-readable storage medium having stored thereon: computer program for implementing a cardiac magnetic resonance mapping quantification method according to any of the claims 1-5.
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