CN111105385A - Method for processing human body joint data provided based on tomography technology - Google Patents
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Abstract
The invention provides a method for processing human body joint data provided based on a tomography technology, which comprises the following steps: 1) aiming at the same joint part of the same photographed person, obtaining human body joint data I corresponding to a scanning sequence of the first tomography and human body joint data II corresponding to a scanning sequence of the second tomography; 2) and registering the cartilage part in the human body joint data I and the cartilage part in the human body joint data II into the same spatial domain based on the osteogenic parts in the human body joint data I and the human body joint data II. By the scheme, the corresponding relation between corresponding detection positions of two tomography scans can be determined, so that the data can be used for qualitatively and quantitatively analyzing and comparing articular cartilage images of the same patient in different periods.
Description
Technical Field
The present invention relates to medical image processing technology, and is especially method of providing qualitative and quantitative analysis data for articular cartilage.
Background
Osteoarthritis (OA) is a degenerative joint disease, also known as degenerative arthritis or osteoarticular disease, and is a common disease in the middle-aged and elderly. Figure 1a shows a comparison of a normal knee joint and a typical knee joint with osteoarthritis, from which it can be seen that the patient has had articular cartilage damaged leaving part of the bone exposed, which is also accompanied by cartilage ulcers and meniscus damage, with the cartilage fragments being present in the joint in free form. In this case, various inflammations are generated due to immune reactions of the human body. The degeneration of cartilage tissue is the main cause of pain of osteoarthritis patients, and after the cartilage tissue is changed, the patients are easy to suffer from joint pain, stiffness and function loss, thus seriously affecting the quality of life and increasing the life burden. Studies have shown that OA is caused by many factors, including age, occupation, lifestyle habits, weakness of quadriceps, abnormal joint stress and decreased stability, obesity, genetics, trauma, endocrine disorders, growth factor and immune factor deficiencies. The traditional treatment method adopts conservative treatment in the early stage of the disease and joint replacement in the later stage of the disease.
With the development of regenerative medicine technology, cell engineering technology and tissue engineering technology have been examined for safety and effectiveness in the field of cartilage defect treatment for 10 years, some have been approved by FDA, and have been initially commercialized, such as matrix-induced autologous chondrocyte transplantation by the company of janus seif. Adipose-derived stem Cells (ADSCs) are convenient in material source, have pluripotency and are paid attention by a plurality of experts. In the laboratory, the ADSCs are identified by carrying out in-vitro separation culture on human abdominal fat, inducing adipogenesis, osteogenesis and chondrogenesis and cell surface markers, and the activity of the adipogenesis and the chondrogenesis is observed. Clinical observation and literature reports that abdominal adipose tissues of patients are clinically extracted, separated and purified in vitro, collected into a composite active cell component-Structural Vascular Fraction (SVF) Rich in ADSCs and vascular interstitial cells, single or composite sodium hyaluronate, and autologous Platelet Rich Plasma (PRP), and injected into joint cavities of patients with OA to treat osteoarthritis are of academic and clinical interest. The primary goal of regenerative medicine techniques such as ADSCs is to restore damaged articular cartilage to a patient to improve their condition.
Because there are individual differences among different patients, in order to verify whether the exogenous ADSCs have an effect on the regeneration and repair of cartilage defects of one patient, it is necessary to detect the articular cartilage condition of the patient, for example, to compare the articular cartilage condition of the patient before and after an operation. Because articular cartilage has a complex three-dimensional structure, a tomography technology such as Magnetic Resonance Imaging (MRI) is mostly adopted in the existing detection mode. Taking MRI as an example, it can reflect the structural condition of the detected object in three-dimensional space, and has the advantages of non-invasive, no ionizing radiation, high time and space resolution, good soft tissue contrast, etc., and is judged by most experts and scholars at present as the best examination method for evaluating articular cartilage damage repair.
However, most of the existing diagnosis based on MRI technology is to directly provide the MRI scanning sequence to doctors and patients, and the doctors observe and judge the damage and repair condition of the articular cartilage according to experience, which brings much pressure to the work of the doctors. This is because articular cartilage is a very thin layer of tissue attached to osteogenic bone, and its pre-and post-operative changes can be very subtle, and these changes can be difficult to discern by the naked eye. Further, due to the body position of the patient during imaging and the operation of the imaging device by the doctor, there may be some difference in spatial position between the sequences obtained by two MRI scans, and even a slight difference in the thickness of the very thin articular cartilage may affect the judgment of the doctor.
Therefore, a data processing method capable of performing qualitative and quantitative analysis of articular cartilage based on MR technology is required.
Disclosure of Invention
Accordingly, the present invention is directed to overcoming the above-mentioned disadvantages of the prior art and providing a method for providing joint data of a human body based on a tomography technique, comprising:
1) acquiring a scanning sequence of a human body joint part based on a tomography technology;
2) determining a contour curve of cartilage tissue in the slice map and a contour curve of osteogenic tissue in the slice map for one slice map in the scan sequence;
3) and determining cartilage tissue data in the human joint part according to the contour curve of the cartilage tissue and the contour curve of the osteogenic tissue in each layer diagram, wherein the cartilage tissue data corresponds to a space region, and the space region is a third space region formed by removing the intersection of a first space region determined by the contour curve of the cartilage tissue and a second space region determined by the contour curve of the osteogenic tissue.
Preferably, according to the method, wherein step 3) further comprises:
providing osteogenic tissue data in the joint region of the body, the osteogenic tissue data being used to determine a second spatial region defined by a contour curve of the osteogenic tissue.
Preferably, according to the method, wherein step 2) comprises:
2-1a) determining for the slice plane map an approximate contour of cartilage tissue and an approximate contour of osteogenic tissue therein;
2-2a) performing active contour model evolution on the basis of the approximate contour of the cartilage tissue to obtain a contour curve of the cartilage tissue in the layer map, and performing active contour model evolution on the basis of the approximate contour of the osteogenic tissue to obtain a contour curve of the osteogenic tissue in the layer map.
Preferably, according to the method, wherein step 2) comprises:
and respectively setting the rough contour of the cartilage tissue and the rough contour of the osteogenesis tissue in the current layer map to be based on the obtained contour curve of the cartilage tissue and the contour curve of the osteogenesis tissue in another layer map adjacent to the current layer map.
Preferably, according to the method, wherein step 2) comprises:
2-1b) aiming at the bedding plane, setting the rough outline of the cartilage tissue and the rough outline of the osteogenesis tissue in the current bedding plane to be obtained by screening based on a set gray threshold range;
2-2b) performing a region growing method on the approximate contour of the cartilage tissue to obtain a contour curve of the cartilage tissue in the slice plane map, and performing a region growing method on the approximate contour of the osteogenic tissue to obtain a contour curve of the osteogenic tissue in the slice plane map.
Preferably, according to the method, wherein step 1) comprises:
1-1) configuring parameters of a magnetic resonance scanner with high resolution to:
repetition time(TR):14.10ms；echo time(TE):5.0ms；field of view(FOV):171mm×171mm；data matrix:320×320；slice thickness:0.53mm；
1-2) acquiring a T23D steady state double echo pressure fat sequence of the human body joint part by adopting the configured magnetic resonance scanner.
Preferably, according to the method, wherein step 1) comprises:
and when the human joint part is shot by adopting the tomography imaging technology, fixing the body position of the shot person and the flexion and extension degree of the joint part.
A method of processing human body joint data provided based on tomographic imaging techniques, comprising:
1) aiming at the same joint part of the same photographed person, obtaining human body joint data I corresponding to a scanning sequence of the first tomography and human body joint data II corresponding to a scanning sequence of the second tomography;
2) and registering the cartilage part in the human body joint data I and the cartilage part in the human body joint data II into the same spatial domain based on the osteogenic parts in the human body joint data I and the human body joint data II.
Preferably, according to the method, any one of the above-mentioned methods for providing human body joint data based on tomography is adopted to provide the human body joint data I and the human body joint data II.
Preferably, according to the method, before the step 2), the method further comprises:
2-0) performing tomographic interpolation on at least one of the human body joint data I and the human body joint data II.
Preferably, according to the method, wherein step 2) comprises:
2-11) calculating mutual information between the image corresponding to the human body joint data I and the image corresponding to the human body joint data II;
2-12) determining a correspondence between each image corresponding to the human body joint data I and a corresponding image corresponding to the human body joint data II when the mutual information is maximized;
wherein the information entropy of an image is calculated based on the occurrence probability of each gray value i in the image to obtain the mutual information.
Preferably, according to the method, wherein step 2-0) comprises:
interpolating the image of the human body joint data I and the image of the human body joint data II to obtain an image sequence A and an image sequence B which contain equal number of images;
the step 2) comprises the following steps:
2-21) calculate normalized mutual information for image a in image sequence a and image B in image sequence B using:
wherein,
(a, ρ) is an image obtained by rotationally translating the image a, ρ is a parameter of the rotational translation, I (x, y) is mutual information for the image x and the image y, and H (x, y) is joint entropy for the image x and the image y; and
step 2-22):
based on the Powell algorithm, by adjusting the parameter ρ of the rotational translation, the corresponding relationship between each image in the image sequence A and the corresponding image in the image sequence B when NMI (B, A) is maximized is searched and determined.
Preferably, according to the method, wherein step 2) comprises:
2-31) determining a spatial region 2a and a spatial region 2b respectively corresponding to the bone formation tissue based on the human joint data I and the human joint data II;
2-32) the spatial zone 2a and the spatial zone 2b have substantially the same spatial position by registration;
2-33) registering the spatial region 1a corresponding to the cartilage tissue in the human joint data I and the spatial region 1b corresponding to the cartilage tissue in the human joint data II into the same spatial domain based on the correspondence between the osteogenic tissue and the cartilage tissue and the registration result in the step 2-32).
A computer-readable storage medium, in which a computer program is stored which, when executed, is adapted to carry out the method of any of the above.
A computer device comprising a memory and a processor, on which memory a computer program is stored which is executable on the processor, characterized in that the processor implements the steps of the method of any one of the above when executing the program.
Compared with the prior art, the embodiment of the invention has the following advantages:
the method comprises the steps of determining the contour of cartilage tissue and the contour of osteogenic tissue contained in each layer map respectively according to a scanning sequence of a human joint part obtained by one-time tomography, obtaining a first space region corresponding to the contour of the cartilage tissue and a second space region corresponding to the contour of the osteogenic tissue in a three-dimensional space according to the two contours on each layer, and removing the intersection of the first space region and the second space region to obtain the region of the cartilage tissue in the three-dimensional space. Thus, a very thin cartilage tissue layer closely adhered to the ends of the bone in an irregular bowl shape can be accurately separated.
By combining gray threshold screening with manual delineation (or a regional growth method) or using a contour curve of cartilage tissue (or osteogenic tissue) of another slice adjacent to the current slice as an approximate contour of the cartilage tissue (or osteogenic tissue) and performing a regional growth method on the approximate contour, non-communicating regions between cartilages on both sides of one joint and non-cartilage tissue such as ligaments can be eliminated, thereby further improving the accuracy of cartilage identification.
Performing active contour model evolution of the approximate contour of cartilage tissue (or osteogenic tissue) to obtain a contour curve for a connected tissue region can avoid identifying non-cartilage tissue as cartilage, and the active contour model is very beneficial for obtaining an accurate contour curve for cartilage (or osteogenic).
Configuring the scanner parameters to set values is very advantageous to obtain a large degree of discrimination between the types of soft tissue (e.g., articular cartilage, ligaments, joint capsule, synovium, etc.) and hard bone in the joint region.
And when the human joint part is shot by adopting the tomography imaging technology, fixing the body position of the shot person and the flexion and extension degree of the joint part. Therefore, the human joints with the same flexion and extension degrees can be shot at the same position and the same angle every time, so that the articular cartilage data corresponding to multiple times of shooting can be qualitatively and quantitatively analyzed and compared.
Performing interpolation on a fault aiming at least one result of two shooting results (corresponding to human body joint data I and II respectively) of the same joint part of the same shot person, and registering a cartilage part in the human body joint data I and a cartilage part in the human body joint data II into the same space domain based on the interpolation result. The operation granularity of aligning the two shooting results before and after the two shooting is reduced, and the influence caused by different space sections in the joint corresponding to the scanning sequence in the two shooting is reduced.
On the basis of the osteogenic parts in the human joint data I and II, the cartilage parts in the human joint data I and II are aligned, and the data amount with high confidence used for alignment is increased (the osteogenic parts do not change much in a short time, so that the information is used for alignment, and the confidence is higher). Alignment is performed by directly using the three-dimensional model data of the osteogenesis obtained in < embodiment 1>, the calculation amount is smaller, and the spatial correspondence relationship between cartilage photographed at two times before and after can be quickly determined.
Based on a mutual information calculation mode, the principle of information theory is utilized, and when the same information contained in one image corresponding to the human body joint data I and one image corresponding to the human body joint data II is the largest, the corresponding relation between the image of the human body joint data I and the image in the human body joint data II is calculated. A method is provided for facilitating automated implementation thereof by a computer. And when searching the image A of the human body joint data I and the image B in the human body joint data II when the mutual information is maximum, a Powell algorithm is adopted, and the solution can be quickly carried out.
In summary, embodiments of the present invention provide a method for providing human joint data for a scanning sequence acquired by a tomography technology, so that articular cartilage can be accurately separated, and a corresponding relationship between corresponding detection positions of two previous and subsequent tomography scans can be determined, so as to use the data for implementing qualitative and quantitative analysis and comparison of articular cartilage images of the same patient in different periods.
Drawings
Embodiments of the invention are further described below with reference to the accompanying drawings, in which:
FIG. 1a shows a comparison of a normal knee joint and a typical knee joint with osteoarthritis;
FIG. 1b shows a sagittal view of a knee joint;
FIG. 2 is a flow diagram of a method of processing a magnetic resonance scan sequence taken for a human knee joint to obtain data for building a three-dimensional model of articular cartilage in accordance with one embodiment of the present invention;
FIG. 3a is a cross-sectional view of a coronal plane of a 3D model of a human knee joint constructed in accordance with an embodiment of the present invention;
FIG. 3b is a cross-sectional view in one sagittal plane of the 3D model of the human knee corresponding to FIG. 3 a;
fig. 3c shows a three-dimensional view of cartilage tissue obtained after removing the intersection of a first spatial region determined by performing a contour curve of the cartilage tissue with a second spatial region determined by a contour curve of the osteogenic tissue for the 3D model of the human knee joint corresponding to fig. 3 a;
FIG. 3d shows a schematic view of the thickness measurement taken at a spatial location in the cartilage of the knee joint of FIG. 3 c;
fig. 3e is a schematic diagram of a three-dimensional model of cartilage with right knee attached to osteogenesis on both sides of a patient before, 12 weeks after, and 24 weeks after treatment, using the method of one embodiment of the present invention;
FIG. 4 is a flowchart of a method for registering knee cartilage images of the same patient at different stages of treatment according to an embodiment of the present invention;
FIG. 5a is a graph of the results of mapping two three-dimensional models of articular cartilage from two shots of the same knee of the same patient to the same spatial domain, according to one embodiment of the present invention;
fig. 5b shows a schematic diagram of a quantitative analysis for the three-dimensional model of the aligned knee cartilage in fig. 5 a.
Detailed Description
As described in the background art, MRI is performed by exposing a human body to a special magnetic field, transmitting radio frequency pulses, and receiving energy absorbed through the human body, and obtaining magnetic resonance images through computer processing. Since the amount of information generated by one scan is extremely large, the conventional MRI technique selects a plurality of slices to perform image restoration, and the restored image sequence is also referred to as an MRI scan sequence. The inventors believe that direct observation of the MRI scan sequences places much stress on the diagnosis of the physician, and that the MRI scan sequences detected at different times may have some differences in spatial location, which does not facilitate qualitative and quantitative analysis and alignment of the articular cartilage images of the same patient at different times.
As described above, since whether the shape of articular cartilage is abnormal is a key for OA detection, it is necessary to distinguish cartilage from other tissues from MRI scan results. The inventors have found, after studies, that a difficulty in constructing a three-dimensional model of articular cartilage using images obtained by a tomography technique is that the tissue of articular cartilage is very thin and closely attached to the ends of bone in a form similar to a bowl, so that defects such as tissue loss, inaccuracy, and the like easily occur when separating the cartilage tissue. FIG. 1b shows a sagittal view of a knee joint, where it can be seen that other body tissues such as the joint capsule and synovium, synovial cavity, ligaments, etc., are also included around the articular cartilage, and are of similar texture to the articular cartilage, making them susceptible to being mistaken for cartilage tissue.
In this regard, the applicant has found that different tissues in the human body have different densities and different absorption rates of radio frequency pulses, which are reflected in that different tissues have different gray scales in the tomographic imaging result, and the different tissues are not always completely communicated with each other, so that the cartilaginous part in the joint of the human body can be identified by using the two points. After the parts of the human body joint are distinguished, three-dimensional data can be restored by using a data processing means and visually displayed in a visual model manner.
In order to qualitatively and quantitatively analyze and compare the wear degrees of the articular cartilage of the same patient in different treatment periods, 3D images of the results of several MRI scans of the patient can be registered so as to ensure that the images obtained in different treatment periods are in the same coordinate system. Given the patient's progression and recovery, the length, area, and volume of cartilage may vary from MRI scan to MRI scan, which adds difficulty to registration. In this regard, the applicant proposes that mutual information of the two images can be calculated for the two images based on an information theory, and a registration result of the two images when the mutual information is maximum, that is, a registration result of the two images when the intersection between the information entropies of the two images is maximum, can be solved. For example, making the two images the highest correlation within the window corresponds to aligning or registering the images.
In view of the above, the present invention provides a method for providing human body joint data based on a tomographic imaging technique so as to restore a three-dimensional model of an individual articular cartilage, and a method for processing an image sequence obtained by tomographic scanning of a human body joint so as to compare human body cartilage corresponding to at least two shots, respectively. The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
< example 1>
Referring to fig. 2, according to an embodiment of the present invention, there is provided a method of processing a magnetic resonance scan sequence taken for a human knee joint to obtain data for building a three-dimensional articular cartilage model, including:
step 1, acquiring image data of the knee joint by using a nuclear magnetic resonance scanner.
When gathering the knee joint image, can indicate the patient to lie flat and adjust the position for the degree of flexion and extension of knee joint, locating place, compare in the rotation angle of scanner are fixed, so that all can shoot the same region of people knee joint under the same gesture with the same angle when shooing at every turn.
According to an embodiment of the present invention, in order to facilitate obtaining accurate articular cartilage data, image processing such as denoising may be performed on the obtained scan sequence using an image analysis program.
According to an embodiment of the invention, a scanner with high resolution may also be used for acquisition, or image processing may be combined with a scanner with high resolution. The scanner with high resolution used here may be, for example, SIEMENS PRISMA 3.0.0T magnetic resonance scanner, and performs parameter adjustment based on the joint scanning protocol of siemens prism magnetic resonance scanner itself and in combination with the characteristics of the articular cartilage to be scanned to obtain the corresponding scanning sequence.
By adjusting the pulse parameters of the scanner, the contour between various tissues (especially various soft tissues such as articular cartilage, ligaments, joint capsule and synovium) in the shot scanning sequence can be clear and distinct, and the extraction of data corresponding to the cartilage tissue area in the subsequent step is very facilitated. Taking the SIEMENS PRISMA 3.0.0T magnetic resonance scanner as an example, the specific parameters of the sequence can be set for the knee joint as: a reproducibility Time (TR) of 14.10 ms; echo Time (TE) 5.0 ms; field of view (FOV) 171mm × 171 mm; data matrix 320 × 320; slice thickness of 0.53 mm.
The manner in which a tomographic sequence for a knee joint is acquired according to one embodiment of the present invention is described below by way of an example.
First, the body position of the patient is adjusted to a predetermined state, a rough scan is performed once to locate a region of interest corresponding to the knee joint, and a rough image of the region is obtained as a location image. After the posture of the patient is kept unchanged, the patient is simply adjusted based on the region of interest and is subjected to formal and detailed scanning, so that an image sequence of the region of interest is obtained. Taking the example of scanning the knee joint with the siemens prism magnetic resonance scanner, a knee joint scout image and a sequence named as T2_ de3d _ we _ sag _ iso can be obtained by two scans, and the scanner can be configured by referring to the parameters during scanning. According to the physical imaging principle of the T2_ de3d _ we _ sag _ iso sequence, the T23D steady-state double echo pressure lipid sequence obtains the joint imaging of two echoes of fisp and psif (namely a T1 image and a T2 image) in one TR time to locate the spatial position of each voxel in the object, wherein the main difference of the fisp and the psif lies in the excitation sequence and the encoding sequence, the fisp is firstly subjected to slice encoding, then phase encoding and finally frequency encoding, and the order of the psif is reversed. This imaging method has a high signal-to-noise ratio and achieves high contrast of soft tissue in the obtained image, and the T2 image has a high ratio, and is therefore particularly suitable for imaging a region containing many different soft tissues such as a joint. During the imaging process, water appears as a high signal and articular cartilage as a moderate signal.
The configuration parameters can be adjusted correspondingly for different scanners, so that the obtained joint part has larger discrimination between soft tissue and hard bone. The principle of adjustment can be referred to the principle of generation of tissue contrast in a scan sequence obtained by a corresponding scanner: for example, short repetition Time (TR) short echo Time (TE) results in a bias towards T1; long TR and long TE are more important than T2; long TR and short TE favor proton weighted (PDW) images. Images composed of different weights reflect different tissue contrast differences. The specific adjustment process can be determined experimentally. Other parameters are mainly related to image signal-to-noise ratio and security. The empirical values are mainly used and can also be determined by experiments.
And 2, determining the contour of the osteogenic tissue and the contour of the cartilaginous tissue based on the acquired image data of the knee joint.
According to one embodiment of the invention, for each slice in the scan sequence obtained by the magnetic resonance scan, an approximate contour of cartilage tissue and an approximate contour of osteogenic tissue therein are obtained, where the approximate contours may be obtained directly by grayscale thresholding or in combination with manual delineation or region growing methods. For example, one layer in the scanning sequence can be imported into the mix software as a mask, and the threshold value of the screening can be set to be between 100 and 350 by self-defining, so as to obtain the approximate outline of the soft tissue. The threshold value screening can easily distinguish the osteogenic tissue from the soft tissue, however, the gray level difference of each type of soft tissue is relatively small, and the result based on the gray level threshold value screening probably also includes other soft tissues with the approximate gray level to the articular cartilage, such as ligaments and the like, so that in one embodiment of the invention, further processing needs to be performed on the screening result, such as manually delineating the articular cartilage therein or performing a regional growth method on the screening result. The zonal growth method herein can determine the connected regions in the screening results, and since articular cartilage is not connected to other soft tissues, non-articular cartilage tissue and non-connected regions between cartilage forming bones on both sides of the joint can be removed based on this method.
In yet another embodiment of the present invention, to obtain a more accurate contour of the osteogenic tissue and contour of the cartilaginous tissue, an active contour model evolution is applied to the approximate contour. The active contour model may be, for example, an existing model such as a variational level set model, a snake active contour model, or the like. Contours corresponding to a connected tissue can be derived based on the active contour model, and thus for schemes such as those employing grey-scale threshold based screening to derive the approximate contours, the use of region growing or active contour model evolution can alternatively be selected.
Taking the snake active contour model as an example, a point on the rough contour of the cartilage tissue (or the osteogenic tissue) can be taken as a control point v(s) ═ x(s), y(s) ], s ∈ [0,1], and the coordinate position of the control point v(s) in the image is assumed to be determined by x(s) and y(s), and s is an independent variable describing the boundary in the form of fourier transform. S is understood here as a complex number of contour points made up of k x, y coordinates, i.e. s (k) x (k) + jy (k), k being the total number of points on the contour line, and if x (k) and y (k) are represented as trigonometric functions, s is the boundary of the fourier transform, which ranges from 0 to 1. In the snake active contour model, adjacent control points are connected by straight lines to form the rough contour. Defining an energy function at the snake's control points, expressed as:
wherein, of the three terms connected by the plus sign, the first term is the modulus of the first derivative of v(s), called the elastic energy, the second term is the modulus of the second derivative of v(s), called the bending energy, and the third term is the image energy, α and β are parameters set for a control point, and the values of α and β at a certain point determine the degree to which the curve can stretch and bend at this point.
When the approximate contour of the cartilage tissue (or the osteogenic tissue) is evolved by adopting a snake active contour model, the solution enables EtotalThe positions of the points on the minimum contour line v(s), i.e., the values of x (k) and y (k) above, are used as the contour curve of the cartilage tissue (or the bone-forming tissue).
In some embodiments of the present invention, the active contour model may be combined with the previous way of performing region growing (or manual delineation) further on the results of the grayscale threshold screening. For example, a contour curve obtained by a region growing method (or manually sketching) may be used as a starting contour point of a snake active contour model of the current layer, or the region growing method (or manually sketching) may be performed on a contour obtained by evolution of the snake active contour model. Preferably, the region growing method (or manual delineation) is performed before the active contour model evolution is performed to improve the accuracy of the evolution and reduce the amount of computation required for the evolution.
According to an embodiment of the present invention, the approximate contour of the cartilage tissue and the approximate contour of the bone formation tissue in the current layer diagram are set to a contour curve of the cartilage tissue and a contour curve of the bone formation tissue, respectively, in another layer diagram adjacent to the current layer diagram. The reason for this is that the difference between adjacent slice maps of the tomography is relatively small, and the operation such as the gray threshold screening, the manual delineation, the region growing, etc. in the foregoing embodiment can be omitted by directly using the contour curve calculated for another adjacent slice map as the approximate contour of the current slice, so that the approximate contours of the cartilage tissue and the bone formation tissue can be quickly determined. Preferably, the operation based on the gray threshold screening and the region growing (or manually drawing) is performed on the first slice to obtain the rough contour of the cartilage tissue and the bone tissue, and the contour curve of the cartilage tissue and the bone tissue in the slice adjacent to the remaining slices is used as the rough contour of the cartilage tissue and the bone tissue in the slice.
And 3, according to the contour curves of the cartilage tissue and the osteogenesis tissue in each layer diagram, determining a cartilage tissue area in the knee joint, wherein the cartilage tissue area is a spatial area, and the cartilage tissue area is a third spatial area formed by removing the intersection of the first spatial area determined by the contour curve of the cartilage tissue and a second spatial area determined by the contour curve of the osteogenesis tissue.
After obtaining the contour curves of the cartilage tissue and the osteogenic tissue in each slice in the tomographic scanning sequence, a spatial model of the cartilage tissue and a spatial model of the osteogenic tissue can be obtained based on the contour curves, where the spatial model can be built based on the contour curves by any suitable existing technique, such as interpolation of the scanning sequence to obtain a three-dimensional model. Fig. 3a shows a cross-sectional view of a coronal plane of a 3D model of a human knee joint constructed in accordance with an embodiment of the present invention, in which the intercondylar notch of the knee joint is observable, with the highlighted portion being the knee cartilage. Fig. 3b shows a cross-sectional view of a sagittal plane of the 3D model of the human knee corresponding to fig. 3a, in which the junction of the patella and femoral head is observable, with the highlighted portion being the knee cartilage.
Referring to fig. 3a and 3b, it can be seen that the knee joint has two lateral osteogenesis, each of which ends is covered with a very thin layer of cartilage tissue. Since cartilage tissue is unlikely to grow inside the bone, subtracting the intersection of cartilage tissue with the respective bone tissue on both sides from that determined in the earlier step helps to determine a more accurate cartilage tissue area.
In one embodiment of the invention, the intersection of the first spatial region determined by the contour curve of the cartilage tissue and the second spatial region determined by the contour curve of the osteogenic tissue is removed from the first spatial region, and the result is the cartilage tissue region. Fig. 3c shows a perspective view in three-dimensional space of cartilage tissue obtained after removing the intersection of the first spatial region with the second spatial region from the first spatial region is performed for the human knee joint 3D model corresponding to fig. 3 a.
As can be seen from fig. 3c, the three-dimensional model of the cartilage tissue region calculated by the method of the present invention can clearly reflect the worn part and the worn condition of the articular cartilage, which can assist the diagnosis of articular cartilage lesions, and the data based on the three-dimensional model enables the doctor to perform qualitative and quantitative analysis on a very thin layer of cartilage tissue closely attached to the osteogenesis in the joint in three-dimensional space. For example, referring to what is shown in fig. 3d, the thickness at a spatial location in the knee cartilage is measured to monitor changes in the cartilage at that location over a period of time. The data which can be used for qualitative and quantitative analysis can be implemented by adopting the existing tomography instrument under the completely non-invasive condition, patients do not need to suffer from the pain caused by the operation, and hospitals do not need to purchase new large-scale medical equipment.
In some embodiments of the invention, the articular cartilage data corresponding to the three-dimensional model may also be provided in other forms of data. For example, the length, area, volume of cartilage, the shape of cartilage (circularity, length of each axis of an ellipse, length of each side of a rectangle, etc.). As another example, the image texture features for cartilage based on gray level co-occurrence matrix include: an energy value corresponding to a level map of the articular cartilage portion obtained based on the tomography, contrast, correlation, variance, inverse difference moment, sum mean, sum variance, sum entropy, difference variance, difference entropy, correlation Information Measure (IMC), another correlation information measure (AIMC), maximum correlation coefficient, and the like.
In still further embodiments of the present invention, correlated data corresponding to a three-dimensional model of bone formation is provided in addition to the articular cartilage data.
According to one embodiment of the invention, a three-dimensional model for a joint is provided in the form of a user graphical interactive interface. Different visual rendering effects (colors, illumination and the like) are added to the segmented osteogenesis and soft tissues based on the three-dimensional visualization technology, and all parts of the knee joint can be visually displayed. Various visualization tools such as rotation/translation, magnification/reduction, window width and window level adjustment and the like are provided, and a basis is provided for qualitative evaluation of treatment effects and result display; furthermore, various measurement tools are provided, such as length, area, volume and mean/variance quantification analysis tools. The abrasion part and degree of the articular surface cartilage can be qualitatively and quantitatively analyzed through direct observation and measurement of the 3D image of the knee joint.
< example 2>
The inventors have found that during medical practice, in order to follow the progression of a patient to determine the appropriate treatment, or to study whether articular cartilage is repaired at a certain time after adipose-derived mesenchymal stem cell therapy has been administered to a patient, it is necessary to compare the articular cartilage status of the patient at two times.
Fig. 3e is a schematic view showing a three-dimensional model of cartilage with right knee joint attached to bilateral osteogenesis of a patient before, 12 weeks after and 24 weeks after treatment, obtained by the method of < example 1 >. It can be seen that the qualitative analysis of the cartilage wear condition photographed several times can be conveniently performed based on this method, however, in order to quantitatively compare the cartilage wear condition, it is also necessary to spatially align the cartilage for the same joint photographed twice before and after to compare the change condition of the cartilage as a whole and the cartilage thickness at the same position. However, the scan sequences obtained by the two tomographic scans are likely to correspond to different spatial sections in the joint, and there may be a difference in the rotation angle between the scanner and the joint and the degree of flexion and extension of the joint of the patient at the time of the two tomographic scans, which makes it difficult to align the scan sequence of the first photographing and the scan sequence of the second photographing into the same spatial domain.
In this regard, with reference to fig. 4, a method of registering knee cartilage images of the same patient at different stages of treatment according to one embodiment of the present invention includes:
step 1, aiming at the same joint part of the same photographed person, obtaining human body joint data I corresponding to a scanning sequence of first tomography and human body joint data II corresponding to a scanning sequence of second tomography.
Here, the human joint data I and II may be a scan sequence itself obtained by tomography, data corresponding to the scan sequence, or data corresponding to a three-dimensional model of articular cartilage and bone obtained by < embodiment 1> of the present invention.
And 2, carrying out on-fault interpolation on at least one of the human body joint data I and the human body joint data II. Interpolation can be performed in any suitable manner in the present invention, for example by image processing to predict a slice between two adjacent slices of a scan sequence. In this way, the granularity on the fault can be reduced, so that the joint fault corresponding to the layer diagram obtained by interpolation of one scanning sequence is close to the joint fault corresponding to the layer diagram of the other scanning sequence in space, thereby reducing the influence caused by the fact that the scanning sequences correspond to different space sections in the joint when the two times of shooting are carried out.
And 3, registering the cartilage part in the human body joint data I and the cartilage part in the human body joint data II to the same spatial domain based on the osteogenic parts in the human body joint data I and the human body joint data II according to the interpolation result.
The inventors have found that the osteogenic changes in human joints are very small compared to cartilage over a period of several months, especially for adults who are relatively more prone to developing osteoarthritis. Therefore, the alignment result of the articular cartilage can be obtained by aligning the scanning sequence of the first shooting and the scanning sequence of the second shooting to the same spatial domain based on the articular bone shot twice before and after by utilizing the characteristic.
According to an embodiment of the present invention, when data corresponding to a three-dimensional model of articular cartilage and bone formation obtained by < embodiment 1> of the present invention is used as the human joint data I and II in step 1, the spatial region 2a corresponding to the bone formation in the human joint data I and the spatial region 2b corresponding to the bone formation in the human joint data II can be directly determined. The spatial regions 2a and 2b have substantially the same spatial position by registration. According to the registration result of the osteogenic region and the correspondence between the osteogenic tissue and the cartilage tissue, a result of registering the spatial region 1a corresponding to the cartilage tissue in the human joint data I and the spatial region 1b corresponding to the cartilage tissue in the human joint data II into the same spatial region can be obtained.
According to yet another embodiment of the present invention, the human joint data I and II may also be aligned based on the principles of information theory. By calculating mutual information between the image corresponding to the human body joint data I and the image corresponding to the human body joint data II, a correspondence between each image corresponding to the human body joint data I and the corresponding image corresponding to the human body joint data II when the mutual information is maximized is found. When the mutual information is calculated, the information entropy of an image can be calculated according to the occurrence probability of each gray value i in the image, so that the mutual information is obtained. Preferably, the mutual information is calculated such that the image includes a bone-forming part of the joint. In some embodiments of the invention, it is also possible to calculate mutual information for only the part of the cartilage in the image for the alignment operation.
According to an embodiment of the present invention, mutual information between the image a corresponding to the human body joint data I and the image B corresponding to the human body joint data II is first calculated based on the following pair:
I(A,B)＝H(A)+H(B)-H(A,B)，
wherein P is calculated based on the probability of occurrence of a gray i level in an imageA(i)、PB(i)、PA(i, j). The gray value of a pixel in an image can be regarded as a random variable, and the gray value of each point can be regarded as an event corresponding to the random variable, so that the occurrence probability of the gray i can be defined as:
where N is the total number of pixels in the entire image, hiIs the total number of pixels with gray scale value i in the image. Based on this equation, P can be calculatedA(i)、PB(i)、PA(I, j), thereby obtaining mutual information I (A, B).
And performing mutual information calculation on the images of the human body joint data I and II, and finding out the corresponding relation between each image corresponding to the human body joint data I and the corresponding image corresponding to the human body joint data II when the mutual information is maximum, thereby finishing the alignment operation of the images obtained by the two times of tomography.
For another example, normalized mutual information between the image a corresponding to the human joint data I and the image B corresponding to the human joint data II may be calculated. Firstly, an image of human body joint data I and an image of human body joint data II are interpolated to obtain an image sequence A and an image sequence B, so that the image sequence A and the image sequence B contain an equal number of images. Then, normalized mutual information is calculated for image a in image sequence a and image B in image sequence B using the following equation:
wherein,
(a, ρ) is an image obtained by rotationally translating the image a, ρ is a parameter of the rotational translation, I (x, y) is mutual information for the image x and the image y, and H (x, y) is joint entropy for the image x and the image y.
Subsequently, based on the Powell algorithm, by adjusting the parameter ρ of the rotational translation, the correspondence between each image in the image sequence a and the corresponding image in the image sequence B at which NMI (B, a) is maximized is search-determined.
The Powell algorithm is a greedy algorithm for solving the maximum mutual information by selecting a plurality of linearly independent search directions in a multi-dimensional search space and adjusting the directions during each iteration to speed up obtaining the image a and the image B when the mutual information is maximized. It is understood, however, that other search algorithms, such as a traversal search, may also be employed in the present invention.
Taking the Powell algorithm as an example, the searching process comprises the following steps:
step 3-1, selecting an initial parameter x0＝ρ0(△x0,△y0,△z0；θ0,
ω0) With each parameter representing a translation value along the three x, y, z axes and a rotation angle around the three x, y, z axes, respectively, 6 linearly independent initial search directions d are set in a six-dimensional search space0,d1，...,d5Setting an iteration allowance error epsilon>0, making k equal to 0;
step 3-2, performing basic search: let y0＝xkIn sequence along d0,d1，...,d5And (3) performing one-dimensional search:
f(yi-1+λi-1di-1)＝maxλf(yi-1+λdi-1)
yi＝yi-1+λi-1di-1
wherein λiFor the step length of one-dimensional search in each search direction, i is 1, 2.
Step 3-3. check whether the termination criterion is met: taking the direction of acceleration dn＝yn-y0If d | | |n||<E, the iteration is terminated to obtain ynIf not, turning to step 4;
step 3-4, determining the searching direction: according to the principle that the conjugation degree of the search direction in each iteration is not reduced, the condition can be met
M of (d), and then f (y) is determined0)-2f(yn)+f(2fn-y0)<2[f(ym)-f(ym+1)]If yes, turning to step 3-5, otherwise, turning to step 3-6;
step 3-5, adjusting the search direction: from point ynStarting in the direction dnOne-dimensional search is performed to find lambdanSo that f (y)n+λndn)＝maxλf(yn+λdn) And let xk+1＝yn+λdn,di＝di+1, i ═ m, m +1,.., n-1, k ═ k +1, return to step 3-2;
and 3-6, not adjusting the searching direction: let xk+1＝ynAnd k is k +1, and the step 3-2 is returned.
Thereby, the alignment operation for the images obtained for the two preceding and succeeding tomographic scans can be completed.
In one embodiment of the present invention, after the alignment operation for the images obtained for the two preceding and succeeding tomographic scans is performed, the determination of the contour curve of the cartilage tissue in the slice plane map and the contour curve of the osteogenic tissue in the slice plane map for one slice plane map in the scanning sequence as described in < embodiment 1> above, respectively, continues for the image acquired for each tomography, and providing cartilage tissue data in the human joint region according to the contour curve of the cartilage tissue and the contour curve of the osteogenic tissue in the respective layer maps, wherein the cartilage tissue data is used to determine a spatial region, which is an operation of removing a third spatial region formed by intersection of a first spatial region determined by the contour curve of the cartilage tissue and a second spatial region determined by the contour curve of the osteogenic tissue from the first spatial region. Thereby providing data for qualitative and quantitative analysis of the aligned cartilage tissue.
Fig. 5a shows the result of mapping two three-dimensional models of articular cartilage obtained from two shots of the same knee joint of the same patient to the same spatial domain, wherein the lighter gray areas correspond to the three-dimensional model of articular cartilage obtained from the earlier shot and the darker gray areas correspond to the three-dimensional model of articular cartilage obtained from the later shot, according to an embodiment of the present invention. It can be seen that the articular cartilage from these two shots is mapped onto the same spatial domain and clearly reflects in which spatial regions the articular cartilage from the later shot is thicker than the articular cartilage from the earlier shot.
Fig. 5b shows a schematic diagram of a quantitative analysis for the three-dimensional model of aligned knee cartilage in fig. 5a, wherein the grid regions correspond to articular cartilage taken earlier and the non-grid regions correspond to articular cartilage taken later. It can be seen that at the same location of the articular cartilage, the later shot thickness of cartilage was 7.09mm, which is improved compared to the earlier shot thickness of cartilage of 5.81 mm.
It should be noted that, all the steps described in the above embodiments are not necessary, and those skilled in the art may make appropriate substitutions, replacements, modifications, and the like according to actual needs. For example, the MRI techniques that provide imaging in the present invention may also be replaced with other tomographic imaging techniques, such as Computed Tomography (CT). For example, the images of the joint portions of the same patient at different stages of treatment may be registered, for example, the images of the joint portions of different patients may be registered, and for example, the healthy joint of a human body may be registered with the joint of the patient.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A method of processing human body joint data provided based on tomographic imaging techniques, comprising:
1) aiming at the same joint part of the same photographed person, obtaining human body joint data I corresponding to a scanning sequence of the first tomography and human body joint data II corresponding to a scanning sequence of the second tomography;
2) and registering the cartilage part in the human body joint data I and the cartilage part in the human body joint data II into the same spatial domain based on the osteogenic parts in the human body joint data I and the human body joint data II.
2. The method of claim 1, further comprising, prior to step 2):
2-0) performing tomographic interpolation on at least one of the human body joint data I and the human body joint data II.
3. The method according to claim 1 or 2, wherein step 2) comprises:
2-11) calculating mutual information between the image corresponding to the human body joint data I and the image corresponding to the human body joint data II;
2-12) determining a correspondence between each image corresponding to the human body joint data I and a corresponding image corresponding to the human body joint data II when the mutual information is maximized;
wherein the information entropy of an image is calculated based on the occurrence probability of each gray value i in the image to obtain the mutual information.
4. The method of claim 3, wherein step 2-0) comprises:
interpolating the image of the human body joint data I and the image of the human body joint data II to obtain an image sequence A and an image sequence B which contain equal number of images;
the step 2) comprises the following steps:
2-21) calculate normalized mutual information for image a in image sequence a and image B in image sequence B using:
wherein,
is an image obtained by performing a rotational translation on the image a, ρ is a parameter of the rotational translation, I (x, y) is mutual information for the image x and the image y, and H (x, y) is a joint entropy for the image x and the image y; and
step 2-22):
based on the Powell algorithm, by adjusting the parameter ρ of the rotational translation, the corresponding relationship between each image in the image sequence A and the corresponding image in the image sequence B when NMI (B, A) is maximized is searched and determined.
5. The method according to claim 1, wherein providing the human joint data I and the human joint data II comprises:
i) acquiring a scanning sequence of a human body joint part based on a tomography technology;
ii) determining contour curves of cartilage tissue in the slice map and contour curves of osteogenic tissue in the slice map for one slice map in the scan sequence;
iii) determining cartilage tissue data in the human joint region according to the contour curve of the cartilage tissue and the contour curve of the osteogenic tissue in the respective level maps, wherein the cartilage tissue data corresponds to a space region which is a third space region formed by removing an intersection between a first space region determined by the contour curve of the cartilage tissue and a second space region determined by the contour curve of the osteogenic tissue;
step iii) further comprises:
providing osteogenic tissue data in the joint region of the body, the osteogenic tissue data being used to determine a second spatial region defined by a contour curve of the osteogenic tissue.
6. The method of claim 5, wherein step ii) comprises:
ii-1a) determining for the slice plane map an approximate contour of cartilage tissue and an approximate contour of osteogenic tissue therein;
ii-2a) evolving by using an active contour model based on the approximate contour of the cartilage tissue to obtain a contour curve of the cartilage tissue in the slice plane map, and evolving by using the active contour model based on the approximate contour of the osteogenic tissue to obtain a contour curve of the osteogenic tissue in the slice plane map.
7. The method of claim 5, wherein step ii) comprises:
ii-1b) aiming at the bedding plane, setting the rough outline of the cartilage tissue and the rough outline of the osteogenesis tissue in the current bedding plane to be obtained by screening based on a set gray threshold range;
ii-2b) performing a zone growth method on the approximate contour of the cartilage tissue to obtain a contour curve of the cartilage tissue in the slice plane map, and performing a zone growth method on the approximate contour of the osteogenic tissue to obtain a contour curve of the osteogenic tissue in the slice plane map.
8. The method of claim 5, wherein step ii) comprises:
and respectively setting the rough contour of the cartilage tissue and the rough contour of the osteogenesis tissue in the current layer map to be based on the obtained contour curve of the cartilage tissue and the contour curve of the osteogenesis tissue in another layer map adjacent to the current layer map.
9. The method according to any one of claims 5-8, wherein step 2) comprises:
2-31) determining a spatial region 2a and a spatial region 2b respectively corresponding to the bone formation tissue based on the human joint data I and the human joint data II;
2-32) the spatial zone 2a and the spatial zone 2b have substantially the same spatial position by registration;
2-33) registering the spatial region 1a corresponding to the cartilage tissue in the human joint data I and the spatial region 1b corresponding to the cartilage tissue in the human joint data II into the same spatial domain based on the correspondence between the osteogenic tissue and the cartilage tissue and the registration result in the step 2-32).
10. A computer-readable storage medium, in which a computer program is stored which, when being executed, is adapted to carry out the method of any one of claims 1-9.
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| CN111797557A (en) * | 2020-07-03 | 2020-10-20 | 西安交通大学 | Sand dust geometric feature extraction and three-dimensional reconstruction method based on image processing |
| CN116309751A (en) * | 2023-03-15 | 2023-06-23 | 北京医准智能科技有限公司 | Image processing method, device, electronic equipment and medium |
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| CN112155732B (en) * | 2020-09-29 | 2022-05-17 | 苏州微创畅行机器人有限公司 | Readable storage medium, bone modeling and registering system and bone surgery system |
| CN112258478A (en) * | 2020-10-22 | 2021-01-22 | 北京柏惠维康科技有限公司 | Data processing method and pose precision verification system |
| CN113253295B (en) * | 2021-06-22 | 2021-09-24 | 中国人民解放军国防科技大学 | Laser reflection tomography multi-angle laser echo registration method |
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| CN111797557A (en) * | 2020-07-03 | 2020-10-20 | 西安交通大学 | Sand dust geometric feature extraction and three-dimensional reconstruction method based on image processing |
| CN111797557B (en) * | 2020-07-03 | 2023-06-27 | 西安交通大学 | Sand dust geometric feature extraction and three-dimensional reconstruction method based on image processing |
| CN116309751A (en) * | 2023-03-15 | 2023-06-23 | 北京医准智能科技有限公司 | Image processing method, device, electronic equipment and medium |
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