WO2010013300A1 - 脳神経疾患検出技術 - Google Patents
脳神経疾患検出技術 Download PDFInfo
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- WO2010013300A1 WO2010013300A1 PCT/JP2008/063502 JP2008063502W WO2010013300A1 WO 2010013300 A1 WO2010013300 A1 WO 2010013300A1 JP 2008063502 W JP2008063502 W JP 2008063502W WO 2010013300 A1 WO2010013300 A1 WO 2010013300A1
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Definitions
- the present invention relates to a detection technique for a cranial nerve disease such as Alzheimer's disease and dementia with Lewy bodies.
- a preferred embodiment thereof is a detection program for an image containing a cranial nerve disease, and an image containing the cranial nerve disease is detected using a computer.
- a device for detecting an image including a cranial nerve disease is provided.
- Alzheimer's disease As the elderly population increases, the number of patients with cranial nerve diseases with dementia such as Alzheimer's disease is expected to increase. Since these diseases progress with aging and cause changes in the patient and the surrounding living environment, early diagnosis is important.
- Diagnosis of cranial nerve disease with such dementia is based on the results of neuropsychological tests, interviews, clinical findings such as Mini ⁇ Mental Status Examination (hereinafter referred to as MMSE), DSM-III-R Or it is performed using a method such as applying to a diagnostic standard such as ICD-10.
- MMSE Mini ⁇ Mental Status Examination
- ICD-10 a diagnostic standard
- image diagnosis such as CT, MRI, and SPECT is combined in the diagnosis in order to improve the correct diagnosis rate.
- the diagnostic accuracy of diagnostic imaging such as CT, MRI, and SPECT depends on the proficiency level and subjectivity of the reader, and the results vary depending on the facility and the examiner. There was a problem. From such a background, it is desired to use a method that can detect a cranial nerve disease more objectively.
- Non-patent Document 1 a positron emission tomographic imaging (hereinafter referred to as PET) image by administration of 2- [18F] fluoro-2-deoxy-D-glucose (hereinafter referred to as FDG), which is a glucose metabolism tracer, A method for comparing the normal group and calculating the t value of the pixel value in units of pixels and distinguishing Alzheimer's disease patients from healthy individuals is disclosed (Non-Patent Document 2).
- PET positron emission tomographic imaging
- FDG 2- [18F] fluoro-2-deoxy-D-glucose
- a method capable of objectively detecting early lesions is desired.
- cranial nerve diseases such as Alzheimer's disease by measuring a local decrease in brain function such as sugar metabolism.
- a cranial nerve disease such as Alzheimer's disease using a diagnostic image
- this method is based on comparison with healthy person data, it is necessary to prepare a healthy person database for implementation.
- the present invention has been made in view of the above circumstances, and a technique for detecting a cranial nerve disease such as Alzheimer's disease or Lewy body dementia from only the head function image of the subject himself / herself without using a healthy subject database.
- the purpose was to provide.
- the sensitivity, specificity, and correct diagnosis of the method according to the present invention are used using an image derived from an Alzheimer's disease patient and an image derived from a healthy subject. The rate was determined.
- the region of interest was set using 123 I-IMP-administered head SPECT images of 20 Alzheimer's disease patients (age 73.6 ⁇ 4.6 years) and 15 healthy subjects (age 60.5 ⁇ 7.1 years). Disease group and healthy group).
- Each image was anatomically standardized using an iNEUROSTAT program (manufactured by Nippon Physics Co., Ltd.). Next, the pixel value of each image was divided by using the average value of all pixel values in each image to normalize the pixel values (hereinafter collectively referred to as a normalized image).
- a comparison between groups was performed between the disease group and the healthy group, and a z score representing a decrease in pixel value was obtained for each pixel.
- the obtained z-score was placed in each pixel, and a threshold value of 3 was used to extract a cluster representing a pixel value decrease region.
- the largest one was selected and designated as a function-decreasing region 1.
- a z-score representing an increase was obtained, and a cluster representing an increased region of pixel values was extracted with a threshold value of 3.
- the largest cluster was selected and designated as function-preserving region 1.
- the brain template (FIG. 5) stored in the iNEUROSTAT program was compared with the normalized image, and a segment indicating a function-reduced area and a segment indicating a function-preserving area were selected on the template. Each selected segment is compared with the function-decreasing region 1 and the function-preserving region 1, and a region that is substantially common is extracted, and the region-of-interest data corresponding to the function-decreasing region and the function-preserving region (FIG. 9).
- An image in which the average pixel value of the function-preserving site is determined to be significantly larger than the average pixel value of the function-decreasing site by t-test is an image detected as an Alzheimer's disease patient image, and the other images are detected as healthy subject images The image was made. Based on this result, sensitivity, specificity, and correct diagnosis rate were determined by a known method.
- the sensitivity, specificity, and correct diagnosis rate were 82.4%, 88.2%, and 85.3%, all showing high values. From the above results, it was confirmed that an image of a patient derived from Alzheimer's disease can be detected objectively and with high accuracy by the method according to the present invention.
- Each image was anatomically standardized using the iNEUROSTAT program (manufactured by Nippon Physics Co., Ltd.).
- the standardized image was compared with a brain template (FIG. 5) stored in the iNEUROSTAT program, and the occipital lobe was selected as a segment indicating a function-decreasing site, and used as region-of-interest data.
- a sensorimotor area was selected as a segment indicating a function-preserving site and used as region-of-interest data.
- Sensitivity, specificity, and accuracy rate were assessed using 123 I-IMP heads of 15 patients with dementia with Lewy bodies (age 79.0 ⁇ 6.6 years) and 15 healthy subjects (age 60.5 ⁇ 7.1 years).
- SPECT images were used (hereinafter referred to as the DLB disease group and the healthy subject group, respectively).
- anatomical standardization was performed using the iNEUROSTAT program, and the region of interest was set for the function-reduced region and the function-preserving region by applying the region-of-interest data obtained above.
- a pixel value t-test was performed between the reduced function part and the function-preserving part with a risk rate of 5%.
- An image in which the average pixel value of the function-preserving site is determined to be significantly larger than the average pixel value of the function-decreasing site by t-test is an image detected as a Lewy body dementia image, and the other images are healthy person images As an image detected. Based on this result, sensitivity, specificity, and correct diagnosis rate were determined by a known method.
- the sensitivity, specificity, and correct diagnosis rate were 73.3%, 86.7%, and 80.0%, all showing high values. From the above results, it was confirmed that Lewy body dementia can be detected objectively and with high accuracy by the method according to the present invention.
- the function-specific function declines in the head function image, and the function may be preserved even in the disease example.
- the disease can be detected by comparing the head function image between these regions.
- the present invention is applied to various cranial nerve diseases having a function-reduced part and a function-preserving part on a head function image. It will be clear that this is possible. Examples of diseases with very high applicability include Alzheimer's disease, Lewy body dementia, frontotemporal dementia, and progressive supranuclear palsy.
- the present invention makes the work necessary for detecting cranial nerve diseases much easier than before, and brings great benefits to the fields of medical and image analysis programs.
- region-of-interest data data on a disease-specific functionally reduced site or a function-preserving site
- the region-of-interest data is used to extract a region for comparison between regions.
- the site of functional decline and the site of functional preservation can be the parietal lobe and sensorimotor area, respectively.
- the function-lowering site and the function-preserving site can be the occipital lobe and sensorimotor area, respectively.
- Interest area data can be obtained by various methods. For example, the results of comparison between groups of head function images derived from a plurality of subjects suffering from cranial nerve disease (hereinafter referred to as disease group) and head function images derived from a plurality of healthy persons (hereinafter referred to as healthy group). Can be obtained on the basis. By using this method, it is possible to set a region of interest in a site where the function is statistically lowered and a site where the function is statistically preserved in the target disease.
- a method for comparison between groups a known method, for example, a method described in a document (International Publication No. 2007/063656 pamphlet) can be used.
- each image included in the disease group and the healthy subject group is normalized using an average value of all pixel values for each image.
- the pixel value of the function-preserving site in the disease group becomes relatively high, and extraction based on comparison between groups becomes easier.
- the region-of-interest data can be obtained using only the head function image derived from the patient suffering from the cranial nerve disease. Specifically, by setting a threshold value for the pixel value on the head function image, it is possible to use a method of extracting a function-reduced part and a function-preserving part as region-of-interest data.
- region-of-interest data using a template preset on the standard brain. For example, a segment corresponding to a disease-specific function-reduced part and a function-preserving part is selected from each area data set in advance in Tarailach's brain chart and other brain atlases, etc., and is used as region-of-interest data. The method can be used.
- region extraction can be performed using region-of-interest data set by two different methods, and a region extracted in common can be used as region-of-interest data.
- region-of-interest data obtained by combining two or more methods, it can be expected that the accuracy of disease detection is further improved.
- the region-of-interest data preferably has a function-reduced part or a function-preserving part for an anatomically standardized brain image (standard brain) in order to provide versatility. Therefore, in a preferred embodiment, a head function image of a subject who performs disease detection is also analyzed after anatomical standardization. Or, conversely, the region-of-interest data may be transformed to match the head function image of the subject and used for analysis.
- Anatomical standardization can be performed using known methods such as literature (Minoshima S. et al., J. Nucl. Med., 1994, 35, p. 1528-37, or Friston K. Brain Mapping, 1995, 2, p.189-210) can be used.
- region of interest An area where image data is actually compared for disease detection may be referred to as a region of interest in this specification.
- the region of interest may be a region automatically extracted using the region of interest data described above, but further adjustments may be made automatically or manually.
- comparison between regions of interest can be performed by comparing pixel values of image data included in each region of interest.
- image data such as SPECT and PET. Therefore, the presence of a disease can be estimated if the pixel value of the region of interest of the function-reduced part is smaller than a certain value than the pixel value of the region of interest of the function-preserving part.
- a significance test method such as t test.
- the average pixel value of the region of interest in the function-preserving region is significantly higher than the average pixel value of the region of interest in the function-reduced region, rather than simply determining whether there is a significant difference. It is preferable to adopt a configuration in which it is determined whether or not it is large. By adopting such a configuration, the error rate of judgment is roughly halved.
- the embodiment of the present invention includes the following cranial nerve disease image detection apparatus.
- This device for the functional image of the head, a function-reduced site where the function can be specifically reduced in the cranial nerve disease as a detection target disease, and a function-preserving site where the function can be preserved also in the cranial nerve disease, respectively
- a region-of-interest setting unit that sets a region of interest, and a significant difference test using pixel values in the region of interest set in each of the reduced function region and the function-preserving region, and the interest of the function-preserving region
- a disease image detection unit that determines that the detection target disease is present when an average pixel value of the region is significantly larger than an average pixel value of the region of interest for the functionally degraded site.
- This program is a computer program that can use image data constituting a head function image and operates a computer including a storage means and a CPU, and is executed by the CPU, First memory means for storing image data corresponding to the first area of the head function image; image data corresponding to a second area different from the first area in the same head function image; A second memory means for storing cranial nerve disease detecting means for detecting a cranial nerve disease based on a comparison between image data stored in the first memory means and image data stored in the second memory means; To act as.
- the first and second memory means may be a memory logically formed on a physical medium by a program. Either one of the first memory means and the second memory means stores image data of a portion whose function can be specifically reduced in a cranial nerve disease as a detection target disease, and the other preserves the function also in the cranial nerve disease. It can be programmed to store image data of possible parts. That is, in a preferred embodiment, the first and second regions are regions of interest set in the aforementioned function-reduced part and function-preserving part, respectively.
- Still another embodiment of the present invention includes the following method for detecting a cranial nerve disease image.
- a function-reducing site where the function can be specifically reduced in a cranial nerve disease as a detection target disease, and a function-preserving site where the function can be preserved in the cranial nerve disease, respectively A region-of-interest setting step for setting a region of interest; and a significant difference test using pixel values in the region of interest set for each of the reduced function portion and the function-preserving portion, and the interest for the function-preserving portion
- Embodiments of the present invention include those that perform anatomical standardization of head function images. Then, the region of interest can be set for the standardized head function image. Conversely, the region-of-interest data set on the standard brain is converted into the shape of the subject's head function image using the inverse transformation method, and the converted region-of-interest data is superimposed on the subject's head function image. Thus, a method of setting a region of interest on the subject's head function image may be used.
- the region-of-interest data is specific to a disease, it is preferable that the region-of-interest data is configured to be automatically called and applied according to information such as a disease name.
- FIG. 1A is a diagram showing a configuration in the most preferred mode of the cranial nerve disease image detection device 20 according to the present invention
- FIG. 2 is a diagram showing an operation in the most preferred mode in the cranial nerve disease image detection device 20 according to the present invention. is there.
- the cranial nerve disease image detection apparatus 20 according to the present invention can be configured as a computer loaded with a cranial nerve disease image detection program 100 described later.
- the cranial nerve disease image detection device 20 according to the present invention functionally acquires an image acquisition unit 22 that acquires a head function image from the head function image imaging device 10 such as a SPECT device.
- FIG. 1B is a diagram for explaining a hardware configuration of the image detection apparatus 20.
- the image detection device 20 includes a CPU 40, a main storage device 42, an auxiliary storage device 44, and preferably a communication device 46. That is, the image detection device 20 is hardware-related. It can have the same configuration as a general-purpose computer.
- the auxiliary storage device 44 such as a hard disk stores a program for operating the device 20 as a detection device for a cranial nerve disease image. When this program is executed by the CPU 40, a function necessary for detecting a cranial nerve disease is provided. Is done. That is, all or part of the functions of the image acquisition unit 22, the image standardization unit 24, the ROI setting unit 26, the disease image detection unit 28, and the like are realized using software processing.
- the image detection device 20 is connected to an auxiliary storage device 50, a display 52, user interfaces 54 to 58, and the like via external interfaces 48a to 48e.
- the user interfaces 54 to 58 can be, for example, a touch panel 54, a keyboard 56, a mouse 58, and the like.
- the auxiliary storage device 50 can be an optical disk drive such as a DVD-ROM drive.
- the touch panel 54 is configured to be integrated with the display 52.
- the head function image capturing device 10 is connected to the image detection device 20 via the communication device 46, and an image captured by the head function image capturing device 10 is stored in an auxiliary storage device via a network. 44 can be captured.
- the head function image capturing device 10 various devices capable of acquiring a head function image can be used. Specifically, a SPECT apparatus, a PET apparatus, an MRI apparatus, or a CT apparatus is exemplified.
- the head function image capturing apparatus 10 includes an image capturing unit 12 and an image reconstruction unit 14.
- the imaging unit 12 acquires head function image data of the subject.
- the image reconstruction unit 14 performs an image reconstruction process on the acquired head function image data to generate a head function image.
- the imaging unit 12 acquires projection data from a subject to whom a radiopharmaceutical such as 99m Tc HMPAO and 123 I IMP has been administered.
- This projection data corresponds to the head function image data in the present embodiment.
- the image reconstruction unit 14 performs necessary reconstruction processing on the acquired projection data, and generates a series of tomographic images. This series of tomographic images corresponds to the head function image in the present embodiment. Note that image reconstruction can be performed using a known method.
- the image acquisition unit 22 acquires the head function image generated by the image reconstruction unit 14 (step S1).
- the head function image is stored in a computer-readable data format such as DICOM format.
- the head function image imaging device 10 stores the data in a storage medium such as a DVD, and reads the disk from a reading device (auxiliary storage device). 50). Desirably, it may be moved directly to the auxiliary storage device 44 via the communication device 46 as a computer data signal superimposed on a carrier wave.
- the auxiliary storage device 44 can be a hard disk, a flash memory, or the like.
- the image acquisition unit 22 reads head function image data stored in the auxiliary storage device 44 or the auxiliary storage device 50, and forms the software on the main storage device 42 or the auxiliary storage device 44. Store in logical memory area. The stored data is subjected to processing in the next processing block (image standardization unit 24).
- the image standardization unit 24 performs anatomical standardization processing on the head function image acquired by the image acquisition unit 22, and converts the head function image into a standard brain (step S2).
- This anatomical standardization process can be performed by a known method such as literature (Minoshima S. et al., J. Nucl. Med., 1994, 35, p.1528-37, or Friston K. J. et al., Human Brain Mapping, 1995, 2, p.189-210).
- the converted brain image data is stored in a logical memory area formed in software on the main storage device 42 and the auxiliary storage device 44. Depending on the embodiment, the converted brain image data may be displayed on the display 52.
- the region-of-interest setting unit 26 on the head function image converted into the standard brain, a part where the function can be lowered in the detection target disease (function-decreasing part) and a part where the function is likely to be preserved (function A region of interest is set for each (preservation site) (step S5).
- the region-of-interest setting unit 26 is coupled to a disease information input unit 32 and a region-of-interest database (described as ROI data in FIG. 1A) 34 stored in the auxiliary storage device 44, 50 or the like.
- the disease information input unit 32 can accept input from at least one of the user interfaces 54 to 58, and accepts input of detection target disease information represented by a disease name (step S3).
- the disease information is not particularly limited as long as it is information that can be used to select region-of-interest data from the database. Typically, it can be a common name for cranial nerve disease, but abbreviations, typical symptoms, and the like may be used. In inputting disease information, it is possible to combine known methods related to menu selection, such as displaying disease information in a pull-down menu to enable selection of a target disease.
- region-of-interest database 34 data on a site to be investigated for each cranial nerve disease, that is, a site where a decrease in function may occur in the disease (function-decreasing site), and the function may also be preserved in the disease
- Data namely, region-of-interest data
- the region-of-interest setting unit 26 reads out region-of-interest data corresponding to the detection target disease based on the input disease information (step S4), and corresponds to the region-of-interest data on the head function image converted into the standard brain.
- a region to be set is set as a region of interest (step S5).
- the region-of-interest data can be generated by various methods. A method of forming the region of interest data will be described later.
- the region of interest can be set manually without using automatic setting based on the region of interest data.
- the operator may automatically or manually adjust the automatically set region of interest.
- the region of interest can be determined by the operator selecting a desired region on the brain image displayed on the display 52 using the touch panel 54, the mouse 56, or the like.
- a brain atlas may be displayed on the display 52 so as to be superimposed on the head function image of the subject converted into the standard brain, and a desired region may be selected using the user interfaces 54 to 58 or the like. .
- the region-of-interest setting unit 26 stores the region-of-interest data set in each of the function-reduced part and the function-preserving part in different logical memories (logical memory areas formed in software on the main storage device 42 and the auxiliary storage device 44). ). The stored image data is used for the next processing.
- the disease image detection unit 28 performs an image detection process including a cranial nerve disease (step S6).
- FIG. 3 is a diagram illustrating a process flow in the disease image detection process.
- the disease image detection unit 28 reads out the data stored in the logical memory in step S5, and performs a significant difference test of the pixel value between the pixel belonging to the function-reduced part and the pixel belonging to the function-preserving part (step S5). S11).
- the significant difference test can be performed by a known method. In the most preferred embodiment, the t-test can be used as the significance test.
- the cranial nerve disease whose detection target is the image is It is determined as an image that may exist (step S13).
- the image is not determined as an image including a cranial nerve disease as a detection target (step S14).
- the disease image detection unit 28 stores necessary data such as a determination result in the logical memory, and the disease image detection process (step S6) ends.
- the output unit 30 outputs the result of the detection process performed by the disease image detection unit 28 to the display 52 via the display interface 48b (step S7). You may output to other output devices, such as a printer and a sound production
- the output format is not particularly limited. For example, the t value and / or the detection result (or both) are displayed on the image, and the average pixel value of the function-preserving part is more significant than the average pixel value of the function-decreasing part. If it is determined that the image is large, it is possible to adopt a format in which the image is provided with a color distinguishable from the others.
- the region-of-interest data stored in the region-of-interest database can use data set by various methods.
- a method based on a disease image a method using a template set on a standard brain, and a method based on comparison between a disease group and a healthy group will be described. .
- region-of-interest data is set based on a disease image.
- an image derived from a patient suffering from a cranial nerve disease (for example, Alzheimer's disease) to be detected is acquired. It is preferable to use a disease image previously converted into a standard brain.
- the disease image may be obtained by averaging pixel values for each pixel of a plurality of patient-derived images converted into the standard brain, but a representative example showing a typical image pattern for each disease may be used. good.
- part are each extracted using a threshold method, and it is set as region-of-interest data.
- FIGS. 4 (a) and 4 (b) An example of extracting region-of-interest data by this method is shown in FIGS. 4 (a) and 4 (b).
- FIG. 4 (a) and FIG. 4 (b) show a function-preserving site and a function-decreasing site when Alzheimer's disease is a detection target disease.
- part may be set using the disease image of the same example, you may set using a separate image, respectively.
- a method of using a brain template set on the standard brain will be described.
- a brain template set anatomically on a standard brain is compared with a disease image, and a region (segment) corresponding to a function-reduced part and a function-preserving part is selected.
- FIGS. 5A to 5G show examples of brain templates.
- This brain template can be compared with the detection target image, and a segment corresponding to a function-reduced site and a function-preserving site in the disease to be detected can be selected and used as region-of-interest data corresponding to the disease.
- a method based on comparison between groups between the disease group and the healthy group will be described.
- a plurality of disease images and a plurality of healthy person images are acquired.
- comparison between groups is performed for each pixel, and a numerical value (hereinafter referred to as an index value), such as t value or z score, which is an index of change in pixel value Get.
- an index value such as t value or z score, which is an index of change in pixel value Get.
- a corresponding index value is displayed on each pixel on the standard brain, and a function-reduced part and a function-preserving part are extracted using the threshold method, and are used as region-of-interest data.
- the methods exemplified above may be used alone or in combination.
- a region obtained by overlapping and displaying the regions of interest extracted by the above methods and extracting the common part may be used as the region of interest data.
- FIG. 6 is a diagram showing the configuration of the most preferred embodiment of the cranial nerve disease image detection program 100 according to the present invention together with the storage medium 200.
- the neurological disease image detection program 100 includes a main module 110 that supervises processing, an image data acquisition module 120, an image standardization module 130, a disease information input module 140, and a region of interest setting module 150. (In FIG. 6, described as an ROI setting module), a disease image detection module 160, and an output module 170.
- the neurological disease image detection program 100 is provided by being stored in the storage medium 200.
- the recording medium 200 include a flexible disk, a hard disk, a CD-ROM, a DVD, and a semiconductor memory.
- the recording medium 200 By inserting the recording medium 200 storing the cranial nerve disease image detection program 100 into a reading device (for example, the auxiliary storage device 50 in FIG. 1B) provided in the computer, the computer can access the cranial nerve disease image detection program 100.
- the program 100 can operate as the above-described cranial nerve disease detection apparatus 20.
- the program 100 may be installed and used in a high-speed storage device such as a hard disk (for example, the auxiliary storage device 44 in FIG. 1B).
- the neurological disease image detection program 100 according to the present invention may be provided via a network as a computer data signal superimposed on a carrier wave.
- the image data acquisition module 120 causes the computer to function as the image acquisition unit 22.
- the image standardization module 130 causes the computer to function as the image standardization unit 24.
- the disease information input module 140 causes the computer to function as the disease information input unit 32.
- the region-of-interest setting module 150 causes the computer to function as the region-of-interest setting unit 26.
- the disease image detection module 160 causes the computer to function as the disease image detection unit 28.
- the output module 170 causes the computer to function as the output unit 30.
- module configurations simply represent one of the methods for programming the program 100, and programming methods having functions equivalent to the program 100 are limited to such module configurations. It must be noted that this is not the case.
- FIG. 7 and 8 are diagrams showing the flow of processing in a preferred embodiment of the method for detecting cranial nerve disease according to the present invention.
- the cranial nerve disease image detection method according to the present invention can be performed by executing the above-described cranial nerve disease image detection program. However, it is not always necessary to program it, and it may be implemented by giving a command relating to each step directly to the computer.
- the region of interest used for the significant difference test is set on the head function image subjected to anatomical standardization, but is set on the head function image derived from the subject. There is no particular limitation as long as it can be obtained. For example, for each acquired head function image, a method in which an operator visually sets a function-reduced part and a function-preserving part can be taken.
- a method of setting a region of interest on the head function image of the subject may be used.
- the method according to the present invention is a method based on a significant difference test between a disease-specific function-reducing site and a function-preserving site, a function-reducing site and a function-preserving site specific to various diseases, By applying it to the head function image of the subject, it can be applied to other cranial nerve diseases.
- the figure which shows an example of a function structure of the cranial nerve disease image detection apparatus which concerns on this invention The figure which shows an example of the hardware constitutions of the cranial nerve disease image detection apparatus which concerns on this invention
- the figure which shows the example of extraction of the region of interest data by this method (a) Function preservation part, (b) Function fall part Figures showing examples of templates: (a) parietal lobe, (b) temporal lobe, (c) sensorimotor area, (d) frontal lobe, (e) occipital lobe, (f) posterior body gyrus, (g) anterior Part-like gyrus
- the figure which shows an example of a structure of the cranial nerve disease image detection program which concerns on this invention The figure which shows the flow of a process in an example of the cranial nerve disease image detection method which concerns on this invention.
- Region of interest set in Demonstration Example 1 (a) function-reduced part, (b) function-preserving part
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Abstract
Description
Kazunari Ishii, "Clinical application of positron emission tomography for diagnosis of dementia", Annals. of Nuclear Medicine, 2002, 16(8), p.515-525 K. Herholz et al., "Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET", NeuroImage, 2002, 17, p.302-316
上記の方法で脳神経疾患の検出が可能であることを証明するための一例として、アルツハイマー病患者由来の画像と健常者由来の画像を用い、本発明に係る方法の感度、特異度、および正診率を求めた。
関心領域の設定には、20例のアルツハイマー病患者(年齢73.6±4.6歳)および15例の健常者(年齢60.5±7.1歳)の123I-IMP投与頭部SPECT画像を用いた(以下、それぞれ疾患群および健常者群という)。
感度、特異度、および正診率の評価には、17例のアルツハイマー病患者(年齢60.1±8.2歳)および17例の健常者(年齢61.1±7.3歳)の123I-IMP投与頭部SPECT画像を用いた。各画像につき、iNEUROSTATプログラムを用いて解剖学的標準化を行い、上記で求めた関心領域データを適用して機能低下部位と機能温存部位とにそれぞれ関心領域を設定した。各画像につき、機能低下部位と機能温存部位との間で、危険率を5%とした画素値のt検定を行った。t検定によって機能温存部位の平均画素値が機能低下部位の平均画素値より有意に大きいと判定された画像を、アルツハイマー病患者画像として検出された画像とし、それ以外の画像を健常者画像として検出された画像とした。この結果を基に、公知の方法にて、感度、特異度、および正診率を求めた。
本発明に基づく方法で脳神経疾患の検出が可能であることを証明するための更に別の一例として、レビー小体型痴呆症患者由来の画像と健常者由来の画像を用い、本発明を用いた当該疾患の検出における感度、特異度、および正診率を求めた。
関心領域の設定には、15例のレビー小体型痴呆症患者(年齢79.0±6.6歳)の123I-IMP投与頭部SPECT画像を用いた。
感度、特異度、および正診率の評価には、15例のレビー小体型痴呆症患者(年齢79.0±6.6歳)および15例の健常者(年齢60.5±7.1歳)の123I-IMP投与頭部SPECT画像を用いた(以下、それぞれDLB疾患群および健常者群という)。各画像につき、iNEUROSTATプログラムを用いて解剖学的標準化を行い、上記で求めた関心領域データを適用して機能低下部位と機能温存部位とにそれぞれ関心領域を設定した。各画像につき、機能低下部位と機能温存部位との間で、危険率を5%とした画素値のt検定を行った。t検定によって機能温存部位の平均画素値が機能低下部位の平均画素値より有意に大きいと判定された画像を、レビー小体型痴呆症画像として検出された画像とし、それ以外の画像を健常者画像として検出された画像とした。この結果を基に、公知の方法にて、感度、特異度、および正診率を求めた。
なお、機能低下部位と機能温存部位に対応する各関心領域は、同一例の疾患画像を用いて設定しても良いが、それぞれ別々の画像を用いて設定しても良い。
12 撮像部
14 画像再構成部
20 脳神経疾患画像検出装置
22 画像取得部
24 画像標準化部
26 関心領域設定部
28 疾患画像検出部
30 出力部
32 疾患情報入力部
34 関心領域データ
100 脳神経疾患画像検出プログラム
110 メインモジュール
120 画像データ取得モジュール
130 画像標準化モジュール
140 疾患情報入力モジュール
150 関心領域設定モジュール
160 疾患画像検出モジュール
170 出力モジュール
200 記憶媒体
Claims (17)
- 頭部機能画像を利用可能な脳神経疾患検出装置であって、
前記頭部機能画像に対し、検出対象疾患とした脳神経疾患において特異的に機能が低下しうる機能低下部位と、当該脳神経疾患においても機能が温存されうる機能温存部位とに、それぞれ関心領域を設定する関心領域設定部と、
前記機能低下部位及び前記機能温存部位のそれぞれに設定された前記関心領域内の画素値を用いて有意差検定を行い、前記機能温存部位についての前記関心領域の平均画素値が前記機能低下部位についての前記関心領域の平均画素値より有意に大きい場合に前記検出対象疾患が存在すると判定する疾患画像検出部と、
を備える、脳神経疾患検出装置。 - 前記関心領域データを疾患情報と関連付けて格納した関心領域データベースをさらに備え、
前記関心領域設定部は、前記検出対象疾患とした疾患の疾患情報に基づいて前記関心領域データベースから前記関心領域データを読み出し、該読み出した関心領域データに基づいて、前記機能低下部位及び前記機能温存部位にそれぞれ関心領域を設定するように構成される、請求項1に記載の脳神経疾患検出装置。 - 前記検出対象疾患がアルツハイマー病である場合、前記機能低下部位として頭頂葉を設定し、前記機能温存部位として感覚運動野を設定する、請求項1または2に記載の脳神経疾患検出装置。
- 前記検出対象疾患がレビー小体型痴呆症である場合、前記機能低下部位として後頭葉を設定し、前記機能温存部位として感覚運動野を設定する、請求項1から3のいずれかに記載の脳神経疾患患者の画像の検出装置。
- 前記頭部機能画像につき解剖学的標準化を行う画像標準化部をさらに備え、
前記関心領域設定部は、前記画像標準化部によって解剖学的標準化を行った前記頭部機能画像上で、前記検出対象疾患とした脳神経疾患における前記機能低下部位及び前記機能温存部位にそれぞれ関心領域を設定するように構成される、請求項1から4のいずれかに記載の脳神経疾患検出装置。 - 前記頭部機能画像につき解剖学的標準化を行い、該解剖学的標準化における変換パラメータを取得する画像標準化部をさらに備え、
前記関心領域設定部が、前記変換パラメータを用いて前記関心領域データを変換することにより前記関心領域データの形状を前記頭部機能画像の脳形状に合わせ、該変換した前記関心領域データを、前記解剖学的標準化を行わない前記頭部機能画像に適用することにより、前記機能低下部位及び前記機能温存部位にそれぞれ関心領域を設定するように構成される、請求項1から4のいずれかに記載の脳神経疾患検出装置。 - 頭部機能画像を構成する画像データを利用可能であり、記憶手段とCPUとを具備するコンピュータを動作させるためのコンピュータ・プログラムであって、前記CPUで実行されることにより、該コンピュータを、
・ 頭部機能画像の第1の領域に対応する画像データを格納する第1のメモリ手段;
・ 同じ前記頭部機能画像において前記第1の領域とは異なる第2の領域に対応する画像データを格納する第2のメモリ手段;
・ 前記第1のメモリ手段に格納される画像データと前記第2のメモリ手段に格納される画像データとの比較に基づき脳神経疾患の検出を行う脳神経疾患検出手段;
として動作させる、コンピュータ・プログラム。 - 前記第1のメモリ手段に格納される画像データは、検出対象疾患とする脳神経疾患において特異的に機能が低下しうる機能低下部位に対応した画像データであり、
前記第2のメモリ手段に格納される画像データは、前記検出対象疾患とする脳神経疾患においても機能が温存されうる前記機能温存部位に対応した画像データであって、
前記第1のメモリ手段に格納される画像データの平均画素値と、前記第2のメモリ手段に格納される画像データの平均画素値との差が有意であるか否かの検定を行い、前記第2のメモリ手段に格納される画像データの平均画素値が、前記第1のメモリ手段に格納される画像データの平均画素値よりも優位に大きい場合に前記検出対象疾患とする脳神経疾患が存在すると判定するように前記コンピュータを動作させる、請求項7に記載のコンピュータ・プログラム。 - 前記コンピュータが具備するユーザインタフェースを通じたオペレータの入力に応じて、前記第1及び前記第2の領域を設定するように前記コンピュータを動作させる、請求項7または8に記載のコンピュータ・プログラム。
- 前記プログラムは、前記第1及び前記第2の領域の設定するための情報である関心領域データを利用可能であり、
前記プログラムは、前記関心領域データを用いて、前記頭部機能画像に対する前記第1及び前記第2の領域の設定を行うように前記コンピュータを動作させる、請求項7から9のいずれかに記載のコンピュータ・プログラム。 - 前記第1及び前記第2の領域を、検出対象とする脳神経疾患の種類に応じて設定するように前記コンピュータを動作させる、請求項10に記載のコンピュータ・プログラム。
- 前記検出対象とする脳神経疾患がアルツハイマー病である場合、前記第1の領域を頭頂葉に、前記第2の領域は感覚運動野を設定するように前記コンピュータを動作させる、請求項10または11のいずれかに記載のコンピュータ・プログラム。
- 前記検出対象とする脳神経疾患がレビー小体型痴呆症である場合、前記第1の領域を後頭葉に、前記第2の領域を感覚運動野に設定するように前記コンピュータを動作させる、請求項10から11のいずれかに記載のコンピュータ・プログラム。
- 前記関心領域データが解剖学的に標準化された脳に対して作成されたデータである場合、前記頭部機能画像に対して解剖学的標準化を行い、標準化された前記頭部機能画像に対して前記第1及び前記第2の領域の設定を行うように、前記コンピュータを動作させる、請求項10から13のいずれかに記載のコンピュータ・プログラム。
- 前記関心領域データが解剖学的に標準化された脳に対して作成されたデータである場合、前記頭部機能画像に対して解剖学的標準化を行うことにより、該解剖学的標準化のための変換パラメータを取得すると共に、該変換パラメータを用いて前記関心領域データを変換して前記頭部機能画像の脳形状に合わせ、該変換後の前記関心領域データを用いて、解剖学的標準化を行っていない前記頭部機能画像に対して前記第1及び前記第2の領域の設定を行うように、前記コンピュータを動作させる、請求項10から13のいずれかに記載のコンピュータ・プログラム。
- 請求項7から14のいずれかに記載のコンピュータ・プログラムを内臓の記憶手段に格納する、脳神経疾患検出のためのコンピュータ装置。
- 頭部機能画像に対し、検出対象疾患とした脳神経疾患において特異的に機能が低下しうる機能低下部位と、当該脳神経疾患においても機能が温存されうる機能温存部位とに、それぞれ関心領域を設定することと、
前記機能低下部位及び前記機能温存部位のそれぞれに設定された前記関心領域内の画素値を用いて有意差検定を行い、前記機能温存部位についての前記関心領域の平均画素値が前記機能低下部位についての前記関心領域の平均画素値より有意に大きい場合に、前記検出対象疾患が存在すると判定することと、
を含む、脳神経疾患画像の検出方法。
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JP (1) | JP4302183B1 (ja) |
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JP2012034988A (ja) * | 2010-08-11 | 2012-02-23 | Fujifilm Corp | 画像診断支援装置、方法及びプログラム |
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JP2012191599A (ja) * | 2011-02-23 | 2012-10-04 | Konica Minolta Medical & Graphic Inc | 放射線画像撮影システム |
JP2014006130A (ja) * | 2012-06-22 | 2014-01-16 | Fujifilm Ri Pharma Co Ltd | 画像処理プログラム、記録媒体、画像処理装置、及び画像処理方法 |
JP2015054218A (ja) * | 2013-09-13 | 2015-03-23 | 株式会社東芝 | 磁気共鳴イメージング装置及び画像処理装置 |
JP2017058287A (ja) * | 2015-09-17 | 2017-03-23 | 公益財団法人先端医療振興財団 | 生体の画像検査のためのroiの設定技術 |
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KR20190041411A (ko) | 2017-10-12 | 2019-04-22 | 니혼 메디피직스 가부시키가이샤 | 화상 처리 장치, 화상 처리 방법 및 프로그램 |
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Also Published As
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JPWO2010013300A1 (ja) | 2012-01-05 |
AU2008360162A1 (en) | 2010-02-04 |
US20110188719A1 (en) | 2011-08-04 |
CA2731657A1 (en) | 2010-02-04 |
EP2312337A1 (en) | 2011-04-20 |
EP2312337A4 (en) | 2012-03-28 |
US8693746B2 (en) | 2014-04-08 |
JP4302183B1 (ja) | 2009-07-22 |
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