KR101575620B1 - Detection method and system for region of interest using normalized signal intensity of medical image - Google Patents
Detection method and system for region of interest using normalized signal intensity of medical image Download PDFInfo
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- KR101575620B1 KR101575620B1 KR1020140020449A KR20140020449A KR101575620B1 KR 101575620 B1 KR101575620 B1 KR 101575620B1 KR 1020140020449 A KR1020140020449 A KR 1020140020449A KR 20140020449 A KR20140020449 A KR 20140020449A KR 101575620 B1 KR101575620 B1 KR 101575620B1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
- A61B5/0037—Performing a preliminary scan, e.g. a prescan for identifying a region of interest
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
- G01R33/48—NMR imaging systems
- G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
- G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
- G01R33/5608—Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels
Abstract
A method and system for detecting a region of interest through medical image signal intensity normalization are provided. A method for detecting a region of interest according to an embodiment of the present invention includes calculating signal strengths of pixels constituting a medical image, normalizing signal intensities using signal intensity statistics of a medical image, To detect the region of interest. This makes it possible to quantitatively analyze and access lesions and vascular structures, etc. objectively.
Description
The present invention relates to medical image analysis, and more particularly, to a method and system for detecting a region of interest in medical images such as MRI (Magnetic Resonance Imaging) and MRA (Magnetic Resonance Angiography).
MRI is a widely used diagnostic tool for all parts of the body including the brain. T2-weighted images, proton density images, as well as tissue-specific or lesion-specific contrasts using a variety of sequences.
For example, DWI (Diffusion Weighted Imaging) can be used to accurately diagnose ischemia and ischemic areas in the primitive period of cerebral infarction. FLAIR (FLUID Attenuated Inversion Recovery) is used to detect white matter disease (Leukoaraiosis, Small vessel disease) can be accurately diagnosed for its extent and severity. In addition, TOF MRA (Time-Of-Flight Magnetic Resonance Angiography) can detect the blood vessel status in the deep brain without using contrast agents.
Since the brain tissue is surrounded by the skull, MRI with good soft tissue resolution is widely used for diagnosis of brain diseases and its clinical application is getting wider. This is because it overcomes limitations due to the inherent characteristics of MRI and develops in accordance with disease-specific or tissue characteristics.
For example, in the case of Diffusion images, artifacts due to motion or Eddy Current are many because images are formed by diffusion gradient. Nevertheless, in the case of DWI, the fat suppression can be sufficiently utilized to selectively select the diffusion effect of water molecules, so that a good image can be obtained for clinical use.
MRI In particular, techniques such as DWI, FLAIR, TOF-MRA, and CEMRA are very useful in various clinical settings, but they are used only for qualitative diagnosis of lesions or vascular structures, and proper quantitative analysis and approaches are difficult. This is because the factor that determines the intensity of MRI signal is the proton density of the water molecule, but there are many factors that affect the signal strength when making the image.
For example, Bo values, T1 / T2 effects, and various variables that affect these factors all affect the MRI signal strength. Therefore, the same signal strength can not be repeatedly obtained even if the same person is subjected to the same test.
SUMMARY OF THE INVENTION The present invention has been made in order to solve the above problems, and it is an object of the present invention to provide a method for easily and objectively performing quantitative analysis and access to a lesion or a blood vessel structure, And to provide a method and system for detecting a region of interest by normalizing signal intensities using the region of interest.
According to an aspect of the present invention, there is provided a method for detecting a region of interest, the method including: calculating signal intensities of pixels constituting a medical image; Normalizing the signal intensities using signal strength statistics of the medical image; And detecting the region of interest in the medical image, using the normalized signal intensities.
The normalizing step may normalize the signal intensities using an average of the signal intensities.
In addition, the normalizing step may normalize the signal intensities using the standard deviation of the signal intensities.
In the normalization step,
Normalized signal strength = [(signal strength) - (average of signal intensities)] / (standard deviation of signal intensities)
Using the above equations, the signal strengths can be normalized.
In addition, the detecting step may detect pixels having a signal intensity exceeding a cut-off determined with reference to the normalized signal intensities as a region of interest in the medical image.
The cut-off may be different depending on diseases to be detected, lesions and vascular structures.
In addition, the method for detecting a region of interest according to an embodiment of the present invention may further include pseudocoloring the detected region of interest.
The method of detecting a region of interest according to an embodiment of the present invention may further include extracting data on the detected region of interest.
According to another aspect of the present invention, there is provided a computer-readable recording medium having the steps of: calculating signal intensities of pixels constituting a medical image; Normalizing the signal intensities using signal strength statistics of the medical image; And detecting a region of interest in the medical image, using the normalized signal intensities. A program for performing a method of detecting a region of interest is recorded.
In the normalization step,
Normalized signal strength = [(signal strength) - (average of signal intensities)] / (standard deviation of signal intensities)
Using the above equations, the signal strengths can be normalized.
In addition, the detecting step may detect pixels having a signal intensity exceeding a cut-off determined with reference to the normalized signal intensities as a region of interest in the medical image.
And, according to another embodiment of the present invention, a method for detecting a region of interest performed by a program recorded on a computer-readable recording medium may further comprise pseudocoloring the detected region of interest .
As described above, according to the embodiments of the present invention, the signal intensity is normalized by using the signal intensity statistic of the medical image to detect the region of interest, so that the quantitative analysis and the access to the lesion and the vascular structure can be easily and objectively It becomes.
In particular, because of the sequence-centered normalization, which applies the statistical properties of the data itself, regardless of the MR imaging sequence, it is possible to obtain a specific disease (eg, DWI or FLAIR) (Eg, cerebral infarction or small vessel disease) can find, use, and utilize critical points that can be universally used regardless of internal or external factors such as devices, imaging techniques, or individuals.
In addition, since the MRI signal intensity characteristics differ not only in the median value but also in the distribution of the individual devices and individuals, considering the fact that the MRI has high disease-specific or specific tissue specificity and good contrast with surrounding tissues, , It becomes easy to quantitatively distinguish lesion tissues due to normalization.
FIG. 1 is a flowchart illustrating a method of detecting a region of interest by normalizing a medical image signal intensity according to an exemplary embodiment of the present invention.
2 is a graph showing a comparison between the DWI signal intensity and the AUC of the normalized DWI signal intensity,
FIG. 3 is a graph showing a result of pseudocolorization of a normal DWI image and a normalized DWI signal intensity,
4 is a graph showing the comparison of the FLAIR signal intensity and the normalized FLAIR signal intensity AUC,
FIG. 5 is a graph showing the result of pseudo-coloring of the white matter denaturation region found by the general FLAIR image and the normalized FLAIR signal intensity,
6 is a graph showing a comparison between the TOF-MRA signal intensity and the AUC of the normalized TOF-MRA signal intensity,
7 is a block diagram of a computing system in which a method of detecting a region of interest in accordance with another embodiment of the present invention can be performed.
Hereinafter, the present invention will be described in detail with reference to the drawings.
In the embodiment of the present invention, brain MRI (Magnetic Resonance Imaging) and MRA (Magnetic Resonance Angiography) signal intensities are normalized to detect a region of interest (e.g., cerebral infarction, small vessel disease, blood vessel, etc.) I suggest a way to do it.
In the embodiment of the present invention, signal intensity is normalized using the head and neck total volume data statistics under the general assumption that the statistical characteristics of the head and the neck including the brain, which is the MRI region, have a similar distribution regardless of the absolute value of the signal intensity.
That is, the individual signal intensity differences can be modeled as two variables: the dynamic range difference (scale value) of the signal and the signal center value position (offset), and the similarity of the entire statistical distribution curve is treated as being maintained.
Through this, we normalize the data distribution of each individual, and use them to provide a basis for easy qualitative / quantitative analysis of specific diseases or tissues (or structures) in various MRIs.
BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a flowchart provided in the description of a method for detecting a region of interest according to an embodiment of the present invention. Fig. A method for detecting a region of interest according to an embodiment of the present invention detects a region of interest through signal intensity normalization in brain images such as MRI and MRA.
Specifically, as shown in FIG. 1, a brain image such as an MRI or an MRA is acquired (S110), and signal intensities of the pixels constituting the acquired brain image are calculated (S120).
Next, in order to normalize the signal strength, the mean and standard deviation (SD) of the signal intensities calculated in step S120 are calculated (S130 and S140), and the signal intensities are normalized using the following equation (S150).
(Normalized MR signal intensity) sequence
= [(original MR signal intensity) - (mean of original MR signal intensity)] sequence /
Here, 'sequence' means a brain imaging method, for example, MRI (DWI, FLAIR), TOF-MRA, and the like.
The 'normalized MR signal intensity' is the signal intensity normalized by S150, the 'original signal intensity' is the signal intensity calculated in step S120, and the 'mean of original MR signal intensity' is the average of the signal intensities calculated in step S130 , 'SD of original MR signal intensity' is the standard deviation of the signal intensities calculated in step S140.
The above formula is specific for the site (eg, head and neck), and is applied to both MR sequence (eg, DWI) specific (both region specific and MR sequence specific). That is, this regularization method is limited to the case where both the site and the MR sequence match. For example, even in the same sequence (eg T2WI), it should not be applied to head and neck MR and abdominal MR, and should not be compared if the sequences are different (eg, T2WI and DWI). As a result, MR DWI (sequence) of the head and neck or MR T2WI of the abdomen should be compared.
Thereafter, a cut-off for extracting a disease, a lesion, and a blood vessel structure is applied (S160), and pixels having a signal intensity exceeding the cut-off are subjected to ROI (S170).
Here, the cut-off is determined with reference to the signal intensities normalized in step S150, and is different according to diseases, lesions, and blood vessel structures to be detected.
Next, the ROI detected in step S170 is pseudo-colorized, data on the region of interest (ROI) is extracted (S180), and the region of interest (ROI) .
Up to now, a method of detecting a region of interest through normalization of a medical image signal intensity has been described in detail with a preferred embodiment.
Hereinafter, clinical significance and threshold utilization of the normalized signal intensity in the brain image will be described in detail. To do this, the receiver operating characteristic curve (AOC) and area under ROC curve (AUC) for a specific disease (or structure) were calculated using signal strength and normalized signal intensity obtained from various medical imaging devices in each sequence , Respectively.
In DWI images,
1.1. AUC and critical point analysis for cerebral infarction in DWI images
DWI signal intensity and normalized signal intensity for cerebral infarction were compared in DWI images of 33 patients with cerebral infarction. A comparative analysis was performed on the ROC curve and the AUC for the two signal strengths. DWI images were acquired using the Achieva (3.0T, Philips), Symphony (1.5T, Siemens), Magnetom Avanto (1.5T, Siemens), Verio (3.0T, Siemens), Intera System) were obtained through various MRI imaging devices.
As shown in FIG. 2, the original DWI signal intensity is AUC 0.6699, whereas the normalized DWI signal intensity is 0.9899, indicating a meaningful difference (chi square = 21.93, p < 0.001 ). This means that the normalized DWI signal intensity compared to the DWI signal intensity can be more easily utilized because of the high sensitivity and specificity for the cerebral infarction.
The original DWI signal intensity showed a sensitivity of 48.5%, a specificity of 81.8%, a positive likelihood ratio of 2.7, and a negative likelihood ratio of 0.6 for the 807 value for cerebral infarction. On the other hand, the normalized DWI signal intensity showed a sensitivity of 93.9%, a specificity of 100%, a positive likelihood ratio of 31.0, and a negative likelihood ratio of 0.1 for a cut-off of 2.13 for cut-offs.
1.2. Clinical significance of signal intensity normalization for cerebral infarction in DWI images
Cerebral infarction consists of a severe ischemic core, an irreversible lesion in the center of the lesion, and an ischemic penumbra, which maintains some degree of cerebral blood flow around the lesion and is likely to recover to normal tissue. These two organizations are not distinguishable from conventional DWI images.
However, when the above normalized DWI signal intensity is used, it is easy to stratify the signal intensity from the peripheral portion to the deep portion of the cerebral infarction, so that the peripheral portion of the cerebral ischemia and the deep portion can be divided as shown in FIG.
The left image in FIG. 3 is a general DWI image (b1000), and the white region of the basal ganglia is a cerebral infarction area. The right image of FIG. 3 is obtained by pseudo-coloring the signal intensity (lowest-highest: 2.1-5.7) of the cerebral infarction by searching the cerebral infarct with the lowest value 2.13 after DWI signal strength normalization. The normalized signal intensity at the periphery and medial part of the lesion is lower than that at the center of the cerebral infarction.
Thus, it is possible to selectively diagnose and follow the peripheral ischemic region, which is the target region for treatment of cerebral infarction. It can also be used to distinguish between lesions of subacute and subacute stages, and can be applied to differentiation of ischemic lesions and brain cancer tissues.
2. Observation of lesions in FLAIR images
2.1 AUC for cerebral white matter degeneration in FLAIR images
96 FLAIR images were analyzed. The FLAIR images were acquired using the Achieva (3.0T, Philips), Verio (3.0T, Siemens), Signa excite (1.5T, GE), Symphony (1.5T, Siemens), Magnetom Avanto Siemens), Intera (1.5T Philips), AIRIS Elite (3.0T, Hitachi), Genesis (1.5T, Genesis HealthCare System) and Signa HDxt (1.5T, GE).
As shown in FIG. 4, the original FLAIR signal intensity is AUC 0.63 for brain white matter degeneration, while the normalized FLAIR signal intensity is 0.97, which is also meaningful (chi square = 79.9, p <0.001).
The original FLAIR signal intensity showed a sensitivity of 49.5%, a specificity of 62.5%, a positive likelihood ratio of 1.3, and a negative likelihood ratio of 0.8 for 680 values for brain white matter degeneration. On the other hand, the normalized FLAIR signal intensity showed a sensitivity of 96.1%, a specificity of 85.6%, a positive likelihood ratio of 6.7, and a negative likelihood ratio of 0.04 at a cut-off of 0.7 for cutoff of denervation.
2.2 Clinical significance of signal intensity normalization for cerebral white matter degeneration in FLAIR images
In FLAIR images, brain white matter degeneration has been reported to be associated with cognitive impairment or dementia, and ischemic cerebral infarction refers to subacute or chronic phase lesions. However, there has been no report on the clinical significance of each region of different signal intensity.
In this regard, it can be seen that the signal intensity of the brain white matter denatured part is different as shown in FIG. The clinical significance of each FLAIR signal intensity is expected to be different.
In the left image of FIG. 5, a normal FLAIR image shows bilateral white matter degeneration around both ventricles. The right image of FIG. 5 is the result of pseudo-coloring of the white matter denatured signal intensity (lowest-highest: 0.7-2.2) using the lowest value 0.7 through FLAIR signal intensity normalization. Differences in signal intensity are observed within white matter degeneration.
In the TOF-MRA image,
3.1. AUC analysis of brain blood vessels in TOF-MRA images
47 TOF-MRA images were analyzed. TOF-MRA images were obtained from machines such as Achieva (3.0T, Philips), Verio (3.0T, Siemens), Magnetom Avanto (1.5T, Siemens) and Symphony (1.5T, Siemens).
As shown in FIG. 6, the TOF-MRA signal intensity is AUC 0.87 for the carotid artery while the normalized TOF-MRA signal intensity is 0.99, indicating a significant difference and a high AUC (chi square = 11.8, p ≪ 0.001).
The TOF-MRA signal intensity showed a sensitivity of 89.1%, specificity of 73.9%, a positive likelihood ratio of 3.4, and a negative likelihood ratio of 0.1 at a signal intensity of 60 for the carotid artery. On the other hand, the normalized TOF-MRA signal intensity showed a sensitivity of 100.0%, a specificity of 93.5%, a positive likelihood ratio of 15.3, and a negative likelihood ratio of 0.00 for a cut-off of 1.0 for the carotid artery.
3.2. Significance of Diagnosis of Artery (Carotid Artery, Cerebral Artery) in TOF-MRA Image
It is difficult to find the value corresponding to the artery in the section obtained from the TOF-MRA image. Particularly, in order to reconstruct three-dimensional images, the method proceeds based on the signal value of the arterial region image, and the method using the embodiment of the present invention helps to find the blood vessel even if the MRA is constituted by any machine or characteristic. In this way, it is easy to grasp the angiographic characteristics of the arteries of an individual, and can be used to predict the production and growth characteristics of arteriosclerotic plaques closely related to vascular diseases. In addition, this process is very useful when drawing hemodynamic data using intravascular signal intensity.
7 is a block diagram of a computing system in which a method of detecting a region of interest in accordance with another embodiment of the present invention can be performed. 7, the
The
The
The
The
Up to now, preferred embodiments have been described in detail for a method of detecting a region of interest and a computing system capable of performing the method. It is needless to say that the embodiment of the method of detecting a region of interest according to the above embodiments is also included in the technical scope of the present invention.
While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention.
200: computing system 210: communication interface
220: Monitor 230: Processor
240: user interface 250: storage medium
Claims (12)
Normalizing the signal intensities using signal strength statistics of the medical image; And
Detecting a region of interest in the medical image using normalized signal intensities,
Wherein the normalizing step comprises:
Normalized signal strength = [(signal strength) - (average of signal intensities)] / (standard deviation of signal intensities)
Using the above equations, the signal strengths are normalized,
Wherein the equation is site-specific and is applied in a sequence-specific manner.
Wherein the detecting step comprises:
And detecting pixels having a signal intensity exceeding a cut-off determined with reference to the normalized signal intensities as a region of interest in the medical image.
The cut-
Wherein the lesion is different according to disease, lesion and vascular structure to be detected.
And pseudocoloring the detected region of interest. ≪ RTI ID = 0.0 > 11. < / RTI >
And extracting data on the detected region of interest.
Normalizing the signal intensities using signal strength statistics of the medical image; And
Detecting a region of interest in the medical image using normalized signal intensities,
Wherein the normalizing step comprises:
Normalized signal strength = [(signal strength) - (average of signal intensities)] / (standard deviation of signal intensities)
Using the above equations, the signal strengths are normalized,
The computer-readable recording medium having recorded thereon a program for performing the method of detecting a region of interest, wherein the expression is region-specific and sequence-specific.
Wherein the detecting step comprises:
And detecting pixels having a signal intensity exceeding a cut-off determined with reference to the normalized signal intensities as a region of interest in the medical image. A computer-readable medium having recorded thereon a program capable of performing a method of detecting a region of interest Lt; / RTI >
Further comprising the step of pseudo-coloring the detected region of interest. ≪ RTI ID = 0.0 > [0002] < / RTI >
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