JP2015123302A - Medical image diagnostic apparatus and image processing apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a medical image diagnostic apparatus where noise suppressing effect at an appropriate level can be obtained in volume data.SOLUTION: An X-ray CT apparatus 1 as a medical image diagnostic apparatus includes: a scanner 11; a volume data generation part 64 for generating volume data; a neighborhood matrix setting part 71 for setting a neighborhood matrix to the volume data; a target image identification part 72 for identifying the target image in the neighborhood matrix; a noise amount calculation part 73 for calculating noise levels while comparing the neighborhood matrix image with the target image; a histogram generation part 74 for generating a first histogram intended for all noise levels of the volume data and generating a second histogram intended for the noise level for each tomographic layer; and a noise level calculation part 77 for calculating the noise level for each tomographic layer on the basis of the first and second histogram.

Description

  Embodiments as one aspect of the present invention relate to a medical image diagnostic apparatus and an image processing apparatus that generate and display an image.

  Currently, image processing techniques are used in various fields.

  Image processing is performed to deal with degradation or modification of images acquired by video tape recorders, digital cameras, etc., or to check whether structures are manufactured as designed. It is performed for the purpose of clearly grasping patterns and structures themselves.

  In the medical field, various types of image processing are performed in various medical image diagnostic apparatuses such as an X-ray CT (computed tomography) apparatus, a SPECT (single photo emission computed tomography) apparatus, and an MRI (magnetic resonance imaging) apparatus. The utility of drawing blood flow or contrast medium flow, extracting lesions, extracting contours of organs, and the like is widely recognized.

  Various applications can be considered for measuring the noise level included in an image. For example, it is used for a noise reduction filter or used for successive approximation reconstruction. The noise reduction filter compares the standard deviation of an arbitrary area with the noise level included in the image. If the standard deviation of the area is higher than the noise level, it is determined that the signal component is likely to be high, and noise reduction processing is almost performed. On the contrary, if the standard deviation of the region is lower than the noise level, it is determined that the possibility of being a signal component is low, and noise reduction processing is performed. In this way, the signal component in the image can keep the signal and reduce noise in other portions.

  On the other hand, in successive approximation reconstruction, the process of projecting the estimated reconstructed image, calculating the error between the measured projection data and the projection data obtained by the projection process, back projecting the error, and reflecting the error in the estimated reconstruction data Repeat many times. It is quite difficult to determine the conditions that determine when to stop this series of reconstruction processes. Conventionally, the determination is made based on the number of series of processes and the size of the error reflected in the reconstruction data. However, the optimal stop timing differs depending on how much high-frequency components are included in the target human body structure, the number of projection directions included in the projection data, the noise level, and the like. Therefore, if the processing is stopped when the signal level of the noise level is larger than the signal level related to the human body structure in the error image reflected in the reconstructed data, it is possible to determine the stop regardless of the collection condition and the target.

  As a method for measuring the noise level of an X-ray image, there are a conventional method for determining noise based on an incident dose, a method for determining noise using a histogram of standard deviation values, and the like. The former cannot be applied to the application example. Although the latter can be applied, for example, when the head and neck are imaged with CT under the same X-ray condition (multi-row CT with a single-rotation scanning method), the noise level contained in the CT image is large at the head and neck. Is different. Further, a low-contrast signal is in soft tissue, but bone tissue is dominant over soft tissue in the region from the base of the skull to the jaw, and measuring noise in a region other than the region of interest leads to an error.

JP 2008-161693 A

  According to the prior art, noise to be measured varies greatly according to the following parameters in CT images and X-ray 3D imaging.

(1) Body thickness: When subjects having different body thicknesses are photographed under the same X-ray conditions, noise is larger in a subject having a thick body thickness. For example, considering the case where the chest and abdomen are scanned at the same time under the same X-ray condition, the noise is small in the chest region and the noise is large in the abdominal region, although it is one volume data.

(2) X-ray condition [mAs]: When the same subject is imaged under different X-ray conditions, noise is larger in an image having a lower X-ray condition. The X-ray condition [mAs] is a value obtained by multiplying the tube current [mA] and the pulse width (sec.). If the tube voltage is constant, the X-ray dose is approximately proportional to the value of mAs.

(3) X-ray condition [kV]: If the same subject is imaged with two different tube voltages, and the noise on the projected image is substantially equal, the signal intensity of the image captured on the high voltage side will be relatively low. In addition, S / N decreases.

(4) Reconstruction filter: If the same projected image is reconstructed with two different reconstruction filters, the noise of the image increases as the high-frequency enhancement degree of the reconstruction filter increases.

(5) Voxel of interest: Noise in the image changes depending on what is of interest. For example, even within the same cross-sectional image in the same 3D data, the noise of the bone portion, the noise of the soft tissue portion, and the noise of the portion emphasized by the contrast agent are all different.

(6) Artifact: When a metal artifact or the like has occurred, the artifact may become more dominant than the noise level.

  However, according to the conventional technique, since the noise level is determined based on the incident dose, a large error occurs in the noise level due to the influence of the tube voltage, the influence of the reconstruction filter, and the like.

  On the other hand, when the noise level is measured based on the standard deviation value, data other than the region of interest enters, and the artifact may become dominant.

  In order to solve the above-described problem, the medical image diagnostic apparatus according to the present embodiment includes, as described in claim 1, an imaging apparatus that images a subject, and volume data based on data collected by the imaging apparatus. Volume data generation means for generating a neighborhood matrix setting means for setting a neighborhood matrix in the volume data, target image identification means for identifying a target image of the neighborhood matrix, and comparing the neighborhood matrix image and the target image First noise level calculation means for calculating the noise level and a first histogram for all the noise levels of the volume data, and a second histogram for the noise level for each tomography And generating the histogram based on the first and second histograms. Having a second noise level calculating means for calculating a noise level for each.

  In order to solve the above-described problem, the medical image diagnostic apparatus according to the present embodiment includes, as described in claim 5, an imaging apparatus for imaging a subject, and volume data based on data collected by the imaging apparatus. Volume data generation means for generating a neighborhood matrix setting means for setting a neighborhood matrix in the volume data, target image identification means for identifying a target image of the neighborhood matrix, and comparing the neighborhood matrix image and the target image First noise level calculating means for calculating the noise level, histogram generating means for generating a histogram for all the noise levels of the volume data, and second for calculating the noise level based on the histogram. Noise level calculation means.

  In order to solve the above-described problem, the medical image diagnostic apparatus according to the present embodiment includes, as described in claim 11, an imaging apparatus for imaging a subject, and volume data based on data collected by the imaging apparatus. Volume data generation means for generating a cross-sectional area calculation means for calculating a cross-sectional area of a human tissue of a tomogram perpendicular to the body axis direction of the subject based on the volume data, and a neighborhood matrix is set for the volume data A neighboring matrix setting means, a target image identifying means for identifying a target image of the neighboring matrix, a first noise level calculating means for calculating a noise level by comparing the neighboring matrix image and the target image, Histogram generating means for generating a histogram for the noise level for each cross-sectional area, and a cross-sectional area for each tomography Based on, a second noise level calculating means for calculating a noise level from the corresponding histogram, the.

  In order to solve the above-described problem, the medical image diagnostic apparatus according to the present embodiment includes, as described in claim 12, an imaging apparatus that images a subject, and volume data based on data collected by the imaging apparatus. Volume data generation means for generating a total absorption coefficient calculation means for calculating a total absorption coefficient of a human tissue of a tomogram perpendicular to the body axis direction of the subject based on the volume data, and a neighborhood matrix for the volume data Neighborhood matrix setting means for setting a target image, target image identification means for identifying a target image of the neighborhood matrix, first noise level calculation means for comparing the neighborhood matrix image with the target image, and calculating a noise level Histogram generating means for generating a histogram for the noise level for each total absorption coefficient; Based on the total absorption coefficient for each, it has a second noise level calculating means for calculating a noise level from the corresponding histogram, the.

  In order to solve the above-described problem, the image processing apparatus according to the present embodiment sets a neighborhood matrix for volume data generated based on data collected by the imaging device, as described in claim 13. Setting means; target image identification means for identifying a target image of the neighborhood matrix; first noise level calculation means for comparing the neighborhood matrix image and the target image to calculate a noise level; Based on the first and second histograms, generating a first histogram for all noise levels and generating a second histogram for the noise level for each tomography, Second noise level calculation means for calculating a noise level for each tomography.

  In order to solve the above-described problem, the image processing apparatus according to the present embodiment sets a neighborhood matrix for volume data generated based on data collected by the imaging device, as described in claim 14. Setting means; target image identification means for identifying a target image of the neighborhood matrix; first noise level calculation means for comparing the neighborhood matrix image and the target image to calculate a noise level; Histogram generation means for generating a histogram for all noise levels, and second noise level calculation means for calculating the noise level based on the histogram.

  In order to solve the above-described problem, the image processing apparatus according to the present embodiment provides a body of a subject based on volume data generated based on data collected by an imaging apparatus. A cross-sectional area calculating means for calculating a cross-sectional area of a human tissue of a fault perpendicular to the axial direction; a neighborhood matrix setting means for setting a neighborhood matrix in the volume data; and a target image identifying means for identifying a target image of the neighborhood matrix; First noise level calculation means for calculating a noise level by comparing the neighborhood matrix image with the target image; and a histogram generation means for generating a histogram for the noise level for each cross-sectional area; Second noise level calculation means for calculating a noise level from a corresponding histogram based on the cross-sectional area for each tomography; A.

  In order to solve the above-described problem, the image processing apparatus according to the present embodiment provides a body of a subject based on volume data generated based on data collected by an imaging apparatus. Total absorption coefficient calculating means for calculating a total absorption coefficient of human tissue of a fault perpendicular to the axial direction, neighborhood matrix setting means for setting a neighborhood matrix in the volume data, and target image identification for identifying a target image of the neighborhood matrix A first noise level calculating unit that calculates a noise level by comparing the neighborhood matrix image and the target image; and generating a histogram for the noise level for each total absorption coefficient And a second noise level for calculating a noise level from a corresponding histogram based on the total absorption coefficient for each fault. Has a Le calculating means.

1 is a configuration diagram showing an X-ray CT apparatus of a first embodiment. The block diagram which shows the function of X-ray CT cutting of 1st Embodiment. The figure for demonstrating calculation of frequency Fs [k] corresponding to noise level Ns [k]. The figure which shows the relationship between Fs [k] / Fm and noise by the setting of "a" in Formula (3). The block diagram which shows the function of X-ray CT cutting of 2nd Embodiment. The block diagram which shows the image processing apparatus of 3rd Embodiment. The block diagram which shows the function of the image processing apparatus of 3rd Embodiment. (A), (b) is sectional drawing containing the center part which shows the example of the attention area | region centering on the center part of volume data. The block diagram which shows the function of X-ray CT cutting of 4th Embodiment.

  The medical image diagnostic apparatus and image processing apparatus of this embodiment will be described with reference to the accompanying drawings.

(First embodiment)
The medical image diagnostic apparatus according to the first embodiment will be described as an X-ray CT apparatus. In the X-ray CT apparatus, a rotation / rotation (rotate / rotate) type in which an X-ray tube and an X-ray detector are rotated as one body, and a large number of detection elements are arrayed in a ring shape. There are various types such as a fixed / rotation (STATIONION / ROTATE) type in which only the tube is rotated around the subject, and the present invention can be applied to any type. Here, the rotation / rotation type that currently occupies the mainstream will be described.

  In addition, the mechanism for converting incident X-rays into electric charges is based on an indirect conversion type in which X-rays are converted into light by a phosphor such as a scintillator and the light is further converted into electric charges by a photoelectric conversion element such as a photodiode. The generation of electron-hole pairs in semiconductors and their transfer to the electrode, that is, the direct conversion type utilizing a photoconductive phenomenon, is the mainstream.

  In addition, in recent years, a so-called multi-tube type X-ray CT apparatus in which a plurality of pairs of an X-ray tube and an X-ray detector are mounted on a rotating ring has been commercialized, and development of peripheral technologies has been advanced. . The X-ray CT apparatus of the present embodiment can be applied to both a conventional single-tube type X-ray CT apparatus and a multi-tube type X-ray CT apparatus. Here, a single tube X-ray CT apparatus will be described.

  FIG. 1 is a configuration diagram showing the X-ray CT apparatus of the first embodiment.

  FIG. 1 shows an X-ray CT apparatus 1 according to the first embodiment. The X-ray CT apparatus 1 is mainly composed of a scanner device 11 and an image processing device (console) 12. The scanner device 11 of the X-ray CT apparatus 1 is usually installed in an examination room and configured to generate X-ray transmission data regarding the patient O. On the other hand, the image processing apparatus 12 is usually installed in a control room adjacent to the examination room, and is configured to generate projection data based on transmission data and generate / display a reconstructed image.

  The scanner device 11 of the X-ray CT apparatus 1 includes an X-ray tube (X-ray source) 21, a diaphragm 22, an X-ray detector 23, a DAS (data acquisition system) 24, a rotating unit 25, a high voltage power supply 26, and a diaphragm driving device. 27, a rotation drive device 28, a top plate 30, a top plate drive device 31, and a controller 32 are provided.

  The X-ray tube 21 generates X-rays by causing an electron beam to collide with a metal target according to the tube voltage supplied from the high-voltage power supply 26 and irradiates the X-ray detector 23 toward the X-ray detector 23. Fan beam X-rays and cone beam X-rays are formed by X-rays emitted from the X-ray tube 21. The X-ray tube 21 is supplied with electric power necessary for X-ray irradiation under the control of the controller 32 via the high voltage power supply 26.

  The diaphragm 22 adjusts the irradiation range of the X-rays irradiated from the X-ray tube 21 in the slice direction and the direction perpendicular thereto by the diaphragm driving device 27. That is, by adjusting the aperture of the diaphragm 22 by the diaphragm driving device 27, the X-ray irradiation range in the slice direction and the direction perpendicular thereto can be changed.

  The X-ray detector 23 is a one-dimensional array type detector having a plurality of detection elements in the channel direction and a single detection element in the column (slice) direction. Alternatively, the X-ray detector 23 is a two-dimensional array detector (also referred to as a multi-slice detector) having a matrix, that is, a plurality of detection elements in the channel direction and a plurality of detection elements in the column direction. The X-ray detector 23 detects X-rays irradiated from the X-ray tube 21 and transmitted through the patient O.

  The DAS 24 amplifies the transmission data signal detected by each X-ray detection element of the X-ray detector 23 and converts it into a digital signal. Output data from the DAS 24 is supplied to the image processing apparatus 12 via the controller 32 of the scanner apparatus 11.

  The rotating unit 25 integrally holds the X-ray tube 21, the diaphragm 22, the X-ray detector 23, and the DAS 24. The rotating unit 25 can rotate around the patient O together with the X-ray tube 21, the diaphragm 22, the X-ray detector 23, and the DAS 24 with the X-ray tube 21 and the X-ray detector 23 facing each other. It is configured. A direction parallel to the rotation center axis of the rotating unit 25 is defined as a Z-axis direction, and a plane orthogonal to the Z-axis direction is defined as an X-axis direction and a Y-axis direction.

  The high voltage power supply 26 supplies power necessary for X-ray irradiation to the X-ray tube 21 under the control of the controller 32.

  The aperture driving device 27 has a mechanism for adjusting the X-ray slice direction in the aperture 22 and the irradiation range in the direction perpendicular thereto under the control of the controller 32.

  The rotation driving device 28 has a mechanism for rotating the rotating unit 25 so that the rotating unit 25 rotates around the hollow portion with the positional relationship maintained by the control of the controller 32.

  The top plate 30 can place the patient O thereon.

  The top plate driving device 31 has a mechanism for moving the top plate 30 up and down along the Y-axis direction and entering / withdrawing along the z-axis direction under the control of the controller 32. The central portion of the rotating unit 25 has an opening, and the patient O placed on the top 30 of the opening is inserted.

  The controller 32 includes a CPU (central processing unit) and a memory. The controller 32 controls the X-ray detector 23, the DAS 24, the high voltage power supply 26, the aperture driving device 27, the rotation driving device 28, the top plate driving device 31, and the like to execute scanning.

  The image processing apparatus 12 of the X-ray CT apparatus 1 is configured based on a computer, and can communicate with a network (local area network) N. The image processing apparatus 12 is mainly composed of basic hardware such as a CPU 41, a memory 42, a hard disk drive (HDD) 43, an input device 44, and a display device 45. The CPU 41 is interconnected to each hardware component constituting the image processing device 12 via a bus as a common signal transmission path. Note that the image processing apparatus 12 may include a storage medium drive 46.

  The CPU 41 is a control device having a configuration of an integrated circuit (LSI) in which an electronic circuit made of a semiconductor is enclosed in a package having a plurality of terminals. When an instruction is input by operating the input device 44 by an operator such as a doctor or a laboratory technician, the CPU 41 executes a program stored in the memory 42. Alternatively, the CPU 41 reads a program stored in the HDD 43, a program transferred from the network N and installed in the HDD 43, or a program read from the storage medium installed in the storage medium drive 46 and installed in the HDD 43. It is loaded into the memory 42 and executed.

  The memory 42 is a storage device having a configuration that combines elements such as a ROM (read only memory) and a RAM (random access memory). The memory 42 stores IPL (initial program loading), BIOS (basic input / output system), and data, and is used for work memory of the CPU 41 and temporary storage of data.

  The HDD 43 is a storage device having a configuration in which a metal disk coated or vapor-deposited with a magnetic material is incorporated in a non-detachable manner. The HDD 43 is a storage device that stores programs installed in the image processing apparatus 12 (including an OS (operating system) in addition to application programs), projection data, and image data. In addition, the OS can be provided with a graphical user interface (GUI) that can use the graphics for displaying information to the operator and perform basic operations with the input device 44.

  The input device 44 is a pointing device that can be operated by an operator, and an input signal according to the operation is sent to the CPU 41.

  The display device 45 includes an image composition circuit (not shown), a video random access memory (VRAM), a display, and the like. The image synthesizing circuit generates synthesized data obtained by synthesizing character data of various parameters with image data. The VRAM develops the composite data as display image data to be displayed on the display. The display is configured by a liquid crystal display, a cathode ray tube (CRT), or the like, and sequentially displays display image data as a display image.

  The storage medium drive 46 is detachable from the storage medium, reads out data (including a program) stored in the storage medium, outputs it on the bus, and stores data supplied via the bus. Write to media. Such a storage medium can be provided as so-called package software.

  The image processing apparatus 12 performs logarithmic conversion processing and correction processing (preprocessing) such as sensitivity correction on the raw data input from the DAS 24 of the scanner device 11 to generate projection data, and stores it in a storage device such as the HDD 43. Remember. Further, the image processing device 12 performs scattered radiation removal processing on the preprocessed projection data. The image processing device 12 removes scattered radiation based on the value of the projection data within the X-ray exposure range, and based on the projection data to be subjected to scattered radiation correction or the value of the adjacent projection data. The estimated scattered radiation is subtracted from the target projection data to perform scattered radiation correction. The image processing device 12 generates image data by segment reconstruction based on the corrected projection data, and stores the image data in a storage device such as the HDD 43.

  FIG. 2 is a block diagram illustrating functions of the X-ray CT incision 1 according to the first embodiment.

  When the CPU 41 shown in FIG. 1 executes the program, the X-ray CT apparatus 1 has an interface unit 61, a scan condition setting unit 62, a scan control unit 63, a volume data generation unit 64, a noise level, as shown in FIG. It functions as a measurement unit 65, a noise reduction unit 66, and a 3D processing unit 67. In addition, although each component 61 thru | or 67 which comprises the X-ray CT apparatus 1 shall function when CPU41 runs a program, it is not limited to that case. There may be a case where all or part of the constituent elements 61 to 67 constituting the X-ray CT apparatus 1 are provided as hardware in the X-ray CT disconnection 1.

  The interface unit 61 is an interface such as a GUI (Graphical User Interface) that mediates the components 62 to 67 and the input device 44 and the display device 45. The interface unit 61 performs display for allowing the operator to select shooting conditions, shooting programs, reconstruction conditions, and the like.

  The scan condition setting unit 62 displays a scan condition (imaging condition) setting screen on the display device 45 via the interface unit 61, and is input via the interface unit 61 when the operator operates the input device 44. It has a function of setting scan conditions according to an input signal. On the scan condition setting screen, the volume data voxel value range based on the region, target organ, and target region, tube voltage, tube current, X-ray beam thickness, slice thickness, helical pitch, etc. The various parameters are set one by one. The scan condition setting unit 62 sets a rough X-ray condition for each part. However, since there is a difference in the size of the site depending on the patient O, the scan condition setting unit 62 may set an X-ray condition for each parameter representing the size of the patient O, or on the scan condition setting screen. You may enable it to change the magnitude | size of the site | part of the patient O. FIG.

  The scan control unit 63 has a function of controlling the controller 32 of the scanner device 11 and scanning an area including the target portion of the patient O on the top board 30 according to the scan condition set by the scan condition setting unit 62. When a contrast medium injector device (not shown) is provided outside, the scan control unit 63 controls the controller 32 of the scanner device 11 to control the contrast medium injector device.

  The volume data generation unit 64 has a function of generating volume data by performing preprocessing and reconstruction processing using a Feldkamp method or the like based on the projection data acquired by the scan of the scan control unit 63.

  The noise level measurement unit 65 has a function of measuring the noise level for voxels in the volume data.

  The noise level measurement unit 65 includes a neighborhood matrix setting unit 71, a target image identification unit 72, a noise amount calculation unit 73, a histogram generation unit 74, a mode value calculation unit 75, a frequency application unit 76, and a noise level calculation unit 77. .

  Here, the fault described below is perpendicular to the Z-axis direction and forms a voxel group having one or a plurality of voxels in the Z-axis direction (thickness direction). In the following description, the voxel group includes one or more voxels.

  The neighborhood matrix setting unit 71 has a function of setting all voxels in the volume data as a target voxel (a target voxel group) and dividing the target voxel into a neighborhood matrix. Here, the neighborhood matrix may be an N × N (N is an integer of 2 or more) two-dimensional neighborhood matrix or an N × N × N three-dimensional neighborhood matrix. Here, the neighborhood matrix will be described as a two-dimensional neighborhood matrix.

  The target image identification unit 72 has a function of identifying a target image with respect to the neighborhood matrix set by the neighborhood matrix setting unit 71. Here, if the average is calculated and all of the neighborhood matrices have a constant value, and the noise follows a Gaussian distribution, the constant value can be approximated as an average value in the neighborhood matrix.

  The target image identification unit 72 uses the following equation (1) to calculate a neighborhood matrix (center coordinates are X coordinate i, Y coordinate j) in the k-th slice among the first to K-th slices. The average voxel value ave [i, j, k] is calculated. However, “2D + 1” indicates one side of the neighborhood matrix size.

  Here, an image having an average value for all voxels is generated as a target image. However, the present invention is not limited to this, and a neighborhood matrix image may be approximated by a polynomial or a spline function, and an image created by approximation processing may be used as a target image. Alternatively, an image created by adding and averaging a certain range of images in the tomographic direction (Z direction) may be used as the target image. The target image may be set by applying a noise reduction filter such as a median filter.

  The noise amount calculation unit 73 has a function of comparing the target image identified by the target image identification unit 72 with the neighborhood matrix image and calculating the noise amount (noise level) of the neighborhood matrix. Here, when the noise amount is a standard deviation value and the target image is an image having an average value, the noise amount is calculated as follows.

  The noise amount calculation unit 73 calculates the standard deviation value std [i, j, k] of the specific target voxel (X coordinate i, Y coordinate j) in the k-th layer fault using the following equation (2). calculate.

  The histogram generation unit 74 has a function of generating a histogram H (shown in the upper part of FIG. 3) for all the noise amounts calculated by the noise amount calculation unit 73. In addition, the histogram generation unit 74 has a function of generating a histogram H [k] (shown in the lower part of FIG. 3) for the noise amount in the k-th layer.

  The mode value calculation unit 75, based on the histogram H generated by the histogram generation unit 74, the noise level Nm corresponding to the mode value (shown in the upper part of FIG. 3) and the frequency Fm in that case (in FIG. 3). (Shown in the upper row). Further, the mode value calculation unit 75, based on the histogram H [k] generated by the histogram generation unit 74, the noise level Ns [k] of the kth layer corresponding to the mode value (shown in the lower part of FIG. 3). ). Here, the mode value calculation unit 75 happens to remove the abnormal value that happens to be abnormally high by the frequency representing a specific amount of noise, so that the histogram H and the histogram H [k] are identified before identifying the mode value. On the other hand, the frequency representing the noise amount on both sides and the frequency of the center may be added together at a constant rate.

  In this case where the noise amount is calculated as the standard deviation value, there is a problem that the standard deviation value increases depending on the structure at the boundary of the organ. However, the region occupied by the organ boundary is only a part of the region occupied by the substance other than the organ boundary. Therefore, the above problem can be almost ignored by obtaining the mode value by the histogram.

  The frequency application unit 76 calculates the frequency Fs [k] of the kth layer corresponding to the noise level Ns [k] calculated by the mode calculation unit 75 based on the histogram H generated by the histogram generation unit 74. It has the function to do.

  FIG. 3 is a diagram for explaining the calculation of the frequency Fs [k] corresponding to the noise level Ns [k].

  The upper part of FIG. 3 shows an example of the histogram H. The lower part of FIG. 3 shows an example of a histogram H [k] in the k-th layer where standard deviation values exist. As shown in FIG. 3, the frequency Fs [k] of the kth layer corresponding to the noise level Ns [k] based on the histogram H [k] is calculated on the histogram H.

  Returning to the description of FIG. 2, the noise level calculation unit 77 uses the noise level Nm calculated by the mode calculation unit 75, the frequency Fm in that case, the noise level Ns [k], and the frequency application unit 76. Based on the calculated frequency Fs [k], the noise level N [k] of the kth layer is calculated according to the following equation (3).

  Here, “a” in the above formula (3) is a> 0, and typically, in the k-th layer including a target with less artifacts and stable noise measurement, a = ½, On the other hand, a = 2 is set for the k-th layer including a target whose artifact is large and noise measurement is not stable. FIG. 4 shows the relationship between Fs [k] / Fm and noise depending on the setting of “a”.

  On the other hand, when the ratio of the frequency Fm to the total frequency F of the histogram H (the peak of the histogram H shown in the upper part of FIG. 3) is too low, or when the frequency Fm is close to the average value F0 of the frequency, the reliability of the frequency Fm is also achieved. Low. Therefore, the following formulas (4) and (5) may be used.

  Returning to the description of FIG. 2, the noise reduction unit 66 is based on the noise level N [k] calculated by the noise level calculation unit 77 of the noise level measurement unit 65, and the tomographic image of the kth layer in the volume data. It is determined whether or not the standard deviation value of the minute area is smaller than (or less than) the noise level N [k]. If the standard deviation value is smaller than the noise level N [k], the average value in the minute image is higher than the average value. If it is large, it is not synthesized because there is a structure, or synthesized at a very low rate.

  The 3D processing unit 67 performs rendering processing such as MPR (Multi Planar Reconstruction) processing, surface rendering processing, and volume rendering processing on the volume data partially reduced by the noise reduction unit 66 to generate a three-dimensional image. It has a function. The three-dimensional image is displayed on the display device 45 via the interface unit 61 or stored in a storage device such as the HDD 43.

  According to the X-ray CT apparatus 1 of the first embodiment, an appropriate level of noise suppression effect can be obtained in volume data by accurately identifying different noise for each tomography.

  In addition, although the example which used the noise level measurement result for the noise suppression process was demonstrated here, this invention is not limited to it. It may be used to determine whether to stop the successive approximation reconstruction process described above, or the CT image noise level is analyzed during imaging, and the X-ray condition is increased if the noise level is greater than a preset noise level On the other hand, if it is small, it can be used for X-ray condition control in which the X-ray condition is lowered.

(Second Embodiment)
The medical image diagnostic apparatus according to the second embodiment will be described as an X-ray CT apparatus. The configuration of the X-ray CT apparatus 1A of the second embodiment is the same as that of the X-ray CT apparatus 1 of the first embodiment shown in FIG.

  FIG. 5 is a block diagram showing the function of the X-ray CT cut 1A of the second embodiment.

  When the CPU 41 shown in FIG. 1 executes the program, the X-ray CT apparatus 1A has an interface unit 61, a scan condition setting unit 62A, a scan control unit 63, a volume data generation unit 64, a noise level, as shown in FIG. It functions as a measurement unit 65A, a noise reduction unit 66A, and a 3D processing unit 67. The constituent elements 61, 62A, 63, 64, 65A, 66A, and 67 constituting the X-ray CT apparatus 1A function when the CPU 41 executes a program. However, the present invention is limited to this case. is not. There may be a case where all or part of the constituent elements 61, 62A, 63, 64, 65A, 66A, and 67 constituting the X-ray CT apparatus 1A are provided in the X-ray CT disconnection 1A as hardware.

  In the X-ray CT cut 1A of the second embodiment shown in FIG. 5, the same functions as those of the X-ray CT cut 1 of the first embodiment shown in FIG.

  The scan condition setting unit 62A has a function of setting a voxel value range based on the region of interest in addition to the function of the scan condition setting unit 62 shown in FIG. In general, a signal having such a low contrast as to be affected by noise exists in soft tissues such as a tumor in liver tissue and a relationship between gray matter and white matter in brain parenchyma. Therefore, −100 [HU] to 300 [HU] are set as the voxel value range. Alternatively, when normal liver tissue is darkly stained with a contrast agent and a difference from a poorly stained tumor is diagnosed, 300 [HU] to 800 [HU] are set as the voxel value range.

  The noise level measurement unit 65A has a function of measuring the noise level for voxels in the volume data.

  The noise level measurement unit 65A includes a neighborhood matrix setting unit 71, a target image identification unit 72, a noise amount calculation unit 73A, a histogram generation unit 74A, and a mode value calculation unit 75A.

  Here, the fault described below is perpendicular to the Z-axis direction and forms a voxel group having one or a plurality of voxels in the Z-axis direction (thickness direction). In the following description, the voxel group includes one or more voxels.

  First, the noise amount calculation unit 73A has a function of determining whether or not the target image calculated by the target image identification unit 72 is within the voxel value range set by the scan condition setting unit 62A. Specifically, the noise amount calculation unit 73A determines whether or not all the voxel values of the target image are within the voxel value range, and if all are within the range, calculates the noise amount. If it is outside, the noise amount is not calculated. Since the target image in the second embodiment is an image having the average value of the neighborhood matrix in all voxels, the noise amount calculation unit 73A determines the criterion −100 [HU to determine whether or not the average value is in the voxel value range. ] To 300 [HU].

  Next, the noise amount calculation unit 73A has a function of comparing the target image calculated by the target image identification unit 72 with the neighborhood matrix image if within the voxel value range, and calculating the noise amount of the neighborhood matrix. Here, assuming that the noise amount is a standard deviation value and the target image is an image having an average value, the noise amount calculation unit 73A uses the equation (2) to specify a specific target voxel (X coordinate) in the k-th slice. The standard deviation value std [i, j, k] of i, Y coordinate j) is calculated as the noise amount.

  The histogram generation unit 74A has a function of generating a histogram H (shown in the upper part of FIG. 3) for all noise amounts calculated by the noise amount calculation unit 73A.

  The mode value calculation unit 75A generates a noise level Nm (shown in the upper part of FIG. 3) corresponding to the mode value Fm (shown in the upper part of FIG. 3) based on the histogram H generated by the histogram generation unit 74A. It has a function to calculate as a level.

  Based on the noise level Nm calculated by the mode value calculation unit 75A of the noise level measurement unit 65A, the noise reduction unit 66A determines that the standard deviation value of the minute region in the tomographic image of the kth layer of the volume data is the noise level. It is determined whether or not it is smaller than (or less than) Nm, and if the standard deviation value is smaller than the noise level Nm, it is synthesized at a high rate with the average value in the minute image. Synthesize at a low rate.

  According to the X-ray CT apparatus 1A of the second embodiment, it is possible to obtain an appropriate level of noise suppression effect in volume data by accurately identifying the noise level of the tissue of interest.

(Third embodiment)
FIG. 6 is a configuration diagram illustrating an image processing apparatus according to the third embodiment.

  FIG. 6 shows an image processing apparatus 12B of the third embodiment. Similar to the image processing apparatus 12 shown in FIG. 1, the image processing apparatus 12 </ b> B is configured based on a computer and can communicate with the network N. The image processing device 12B is mainly composed of basic hardware such as a CPU 41, a memory 42, an HDD 43, an input device 44, and a display device 45. The CPU 41 is interconnected to each hardware component constituting the image processing device 12B via a bus as a common signal transmission path. Note that the image processing apparatus 12B may include a storage medium drive 46.

  In the image processing apparatus 12B shown in FIG. 6, the same members as those in the image processing apparatus 12 shown in FIG.

  FIG. 7 is a block diagram illustrating functions of the image processing apparatus 12B according to the third embodiment.

  When the CPU 41 illustrated in FIG. 6 executes the program, the image processing apparatus 12B functions as an interface unit 61, a noise level measurement unit 65B, a noise reduction unit 66, and a 3D processing unit 67 as illustrated in FIG. In addition, although each component 61, 65B, 66, 67 which comprises the image processing apparatus 12B shall function when CPU41 runs a program, it is not limited to that case. The image processing apparatus 12B may be provided with all or part of the constituent elements 61, 65B, 66, and 67 constituting the image processing apparatus 12B as hardware.

  In the image processing apparatus 12B shown in FIG. 7, the same functions as those of the image processing apparatus 12 shown in FIG.

  The noise level measurement unit 65B has a function of measuring the noise level of voxels in the volume data based on volume data acquired from the HDD 43 or an image server (not shown) external to the image processing device 12B.

  The noise level measurement unit 65B includes a neighborhood matrix setting unit 71, a standard image identification unit 72, a noise amount calculation unit 73B, a histogram generation unit 74, a mode value calculation unit 75, a frequency application unit 76, a noise level calculation unit 77, and a region of interest. A setting unit 78 and a voxel value range setting unit 79 are included.

  The attention area setting section 78 has a function of setting an attention area centered on the center of the volume data.

  FIGS. 8A and 8B are cross-sectional views including the central portion, showing an example of a region of interest centered on the central portion of the volume data.

  8A and 8B show a region of interest M centering on the central portion C of the volume data V. FIG. In the example shown in FIG. 8A, the attention area M forms a cube centered on the central portion C. In the example shown in FIG. 8B, the attention area M forms a sphere centered on the central portion C.

  Returning to the description of FIG. 7, the voxel value range setting unit 79 sets all the voxels in the attention region set by the attention region setting unit 78 as the attention voxels (target voxel group), and smoothes (moves) the attention voxels. It has a function of calculating an average voxel value of the target voxel by performing (average) filtering. Further, the voxel value range setting unit 79 generates a histogram of the calculated average voxel values, and sets a voxel value range having a constant width around the average voxel value corresponding to the mode value in the histogram. Have

  According to the attention area setting section 78 and the voxel value range setting section 79, it is not necessary to register the voxel value range based on the attention area in advance. For example, the voxel value range and the liver substance in the scan for imaging the liver substance are contrasted. There is an advantage that it is not necessary to separately register the voxel value ranges in the scan to be observed without dividing them.

  The noise amount calculation unit 73B extracts a neighborhood matrix corresponding to the voxel value range set by the voxel value range setting unit 79 from the voxel values of the target image calculated by the target image identification unit 72. It has a function of calculating the noise amount of the extracted neighborhood matrix.

  According to the image processing apparatus 12B of the third embodiment, an appropriate level of noise suppression effect is obtained in volume data by accurately identifying noise in a region of interest without presetting a voxel value range based on the region of interest. be able to.

(Fourth embodiment)
The medical image diagnostic apparatus according to the fourth embodiment will be described as an X-ray CT apparatus. The configuration of the X-ray CT apparatus 1C of the fourth embodiment is the same as that of the X-ray CT apparatus 1 of the first embodiment shown in FIG.

  FIG. 9 is a block diagram illustrating functions of the X-ray CT cut 1C according to the fourth embodiment.

  When the CPU 41 shown in FIG. 1 executes the program, as shown in FIG. 9, the X-ray CT incision 1C includes an interface unit 61, a scan condition setting unit 62A, a scan control unit 63, a volume data generation unit 64, noise, and the like. It functions as a level measurement unit 65C, a noise reduction unit 66C, and a 3D processing unit 67. In addition, although each component 61, 62A, 63, 64, 65C, 66C, 67 which comprises X-ray CT disconnection 1C functions by CPU41 executing a program, it is limited to that case. It is not a thing. There may be a case where all or part of the constituent elements 61, 62A, 63, 64, 65C, 66C, and 67 constituting the X-ray CT cut 1C are provided as hardware in the X-ray CT cut 1C.

  In the X-ray CT incision 1C of the fourth embodiment shown in FIG. 9, the X-ray CT incision 1 of the first embodiment shown in FIG. 2 and the X-ray CT incision of the second embodiment shown in FIG. The same functions as those of 1A are denoted by the same reference numerals and description thereof is omitted.

  The noise level measurement unit 65C has a function of measuring the noise level for voxels in the volume data.

  The noise level measurement unit 65C includes a neighborhood matrix setting unit 71, a target image identification unit 72, a noise amount calculation unit 73A, a histogram generation unit 74C, a mode value calculation unit 75C, and a cross-sectional area calculation unit 80.

  Here, the fault described below is perpendicular to the Z-axis direction and forms a voxel group having one or a plurality of voxels in the Z-axis direction (thickness direction). In the following description, the voxel group includes one or more voxels.

The cross-sectional area calculation unit 80 has a function of calculating the cross-sectional area of the human tissue for each tomographic plane. The cross-sectional area calculation unit 80 can calculate the cross-sectional area by calculating the number of voxels having a CT value equal to or higher than the soft tissue level in the tomographic plane and multiplying the number of voxels by the size of the voxel cross-section. Specifically, the soft tissue has a CT value of 0 [HU] or more, but calculates the number of voxels having a CT value of −50 [HU] or more in consideration of noise. Is 0.05 [cm], the cross-sectional area is 0.0025 [cm ² ], so the number of voxels X0.0025 [cm ² ] is the cross-sectional area value. The sectional area value of each fault plane is stored for each fault.

The histogram generation unit 74C has a function of registering the noise amount calculated by the noise amount calculation unit 73A in the histogram H (a) corresponding to the cross-sectional area of the k-th layer calculated by the cross-sectional area calculation unit 80. Here, “a” indicates a specific cross-sectional area range, for example, the cross-sectional area of the k-th layer is 377.5 [cm ² ], and “a” is 370 [cm ² ] to 400 [cm ² ]. The range of the cross-sectional area is shown.

  The mode value calculation unit 75C registers all noise amounts of the volume data in the histogram, and then, for each histogram H (a) generated by the histogram generation unit 74C, the noise level Nm corresponding to the mode value Fm (a). It has a function to calculate (a) as a noise level.

  The noise reduction unit 66C uses the noise level Nm [a] calculated from the histogram H (a) corresponding to the cross-sectional area of the k-th layer calculated by the noise level measurement unit 65 to obtain the standard deviation value of the minute region. It is determined whether or not the noise level is less than (or less than) the noise level Nm [a]. When the standard deviation value is smaller than the noise level Nm [a], the average value in the minute image is synthesized at a high rate. Do not synthesize if there is, or synthesize at a very low rate.

  The cross-sectional area calculation unit 80 may have a function of calculating the total absorption coefficient of the human tissue of the tomogram perpendicular to the body axis direction of the subject as a substitute for the cross-sectional area as a total absorption coefficient calculation unit. In that case, the histogram generation unit 74C registers the noise amount calculated by the noise amount calculation unit 73A in the histogram H (a) corresponding to the total absorption coefficient of the k-th layer calculated by the cross-sectional area calculation unit 80. Then, the mode value calculation unit 75C registers all the noise amounts of the volume data in the histogram, and then the noise corresponding to the mode value Fm (a) for each histogram H (a) generated by the histogram generation unit 74C. The level Nm (a) is calculated as the noise level.

  According to the X-ray CT apparatus 1C of the fourth embodiment, an appropriate level of noise suppression effect can be obtained in volume data by accurately identifying the noise level for each human body cross-sectional size. In the fourth embodiment, only the cross-sectional size is considered, but more precisely, there is a method of creating a histogram for each specific range based on the total absorption amount in the fault instead of the cross-sectional area.

  Although several embodiments of the present invention have been described above, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

1, 1A, 1C X-ray CT apparatus 11 Scanner apparatus 12, 12B Image processing apparatus 61 Interface unit 62, 62A Scan condition setting unit 63 Scan control unit 64 Volume data generation unit 65, 65A, 65B, 65C Noise level measurement unit 66 Noise Reduction unit 67 3D processing unit 71 Neighborhood matrix setting unit 72 Target image identification unit 73, 73A, 73B Noise amount calculation unit 74, 74A, 74C Histogram generation unit 75, 75A, 75C Mode value calculation unit 76 Frequency application unit 77 Noise level Calculation unit 78 Area of interest setting unit 79 Voxel value range setting unit 80 Cross-sectional area calculation unit

Claims (16)

An imaging device for imaging a subject;
Volume data generation means for generating volume data based on data collected by the imaging device;
A neighborhood matrix setting means for setting a neighborhood matrix in the volume data;
Target image identification means for identifying a target image of the neighborhood matrix;
First noise level calculation means for calculating a noise level by comparing the neighborhood matrix image and the target image;
Histogram generating means for generating a first histogram for all noise levels of the volume data and generating a second histogram for the noise level for each tomography;
Second noise level calculation means for calculating a noise level for each tomography based on the first and second histograms;
A medical image diagnostic apparatus. The second noise level calculation means includes:
Based on the first histogram, the first noise level corresponding to the mode value and the first frequency in that case are calculated, and the mode is determined for each fault based on the second histogram. Mode value calculating means for calculating a second noise level corresponding to the value;
Frequency application means for calculating a second frequency corresponding to the second noise level for each tomography based on the first histogram;
The noise level calculation means is configured to determine the fault level based on the first noise level, the first frequency, the second noise level for each fault, and the second frequency for each fault. The medical image diagnostic apparatus according to claim 1, wherein the noise level is calculated every time. The noise level calculation means includes the first noise level Nm, the first frequency Fm, the second noise level Ns [k] in the k (k = 1, 2,...) Layer, k Based on the second frequency Fs [k] of the layer, “a” is a constant larger than 0,

The medical image diagnostic apparatus according to claim 2, wherein the noise level N [k] of the kth layer is calculated according to: The noise level calculation means includes the first noise level Nm, the first frequency Fm, the second noise level Ns [k] in the kth layer, and the second frequency Fs in the kth layer. Based on [k] and the sum F of all frequencies in the first histogram or the average value F ₀ of all frequencies in the first histogram, “a” is a constant larger than 0. ,

The medical image diagnostic apparatus according to claim 2, wherein the noise level N [k] of the kth layer is calculated according to: An imaging device for imaging a subject;
Volume data generation means for generating volume data based on data collected by the imaging device;
A neighborhood matrix setting means for setting a neighborhood matrix in the volume data;
Target image identification means for identifying a target image of the neighborhood matrix;
First noise level calculation means for calculating a noise level by comparing the neighborhood matrix image and the target image;
A histogram generating means for generating a histogram for all noise levels of the volume data;
Second noise level calculation means for calculating a noise level based on the histogram;
A medical image diagnostic apparatus.   The medical image diagnosis apparatus according to claim 5, wherein the first noise level calculation unit calculates the noise level when a voxel value of the target image is within a voxel value range based on a region of interest.   The medical image diagnostic apparatus according to claim 5, further comprising a voxel value range setting unit that sets the voxel value range based on the selection of the region of interest.   The medical image diagnostic apparatus according to claim 7, wherein the voxel value range setting unit selects a central part of the volume data or a central part of a tomographic image as the attention area.   The medical image diagnostic apparatus according to claim 7, wherein the voxel value range setting unit selects the region of interest based on a voxel value of the volume data.   The medical image diagnostic apparatus according to claim 9, wherein the voxel value range setting unit selects the region of interest based on an average value of the neighborhood matrix image or the target image. An imaging device for imaging a subject;
Volume data generation means for generating volume data based on data collected by the imaging device;
Based on the volume data, a cross-sectional area calculating means for calculating a cross-sectional area of a human tissue of a tomogram perpendicular to the body axis direction of the subject;
A neighborhood matrix setting means for setting a neighborhood matrix in the volume data;
Target image identification means for identifying a target image of the neighborhood matrix;
First noise level calculation means for calculating a noise level by comparing the neighborhood matrix image and the target image;
Histogram generating means for generating a histogram for the noise level for each cross-sectional area;
Second noise level calculation means for calculating a noise level from a corresponding histogram based on the cross-sectional area for each tomography;
A medical image diagnostic apparatus. An imaging device for imaging a subject;
Volume data generation means for generating volume data based on data collected by the imaging device;
Based on the volume data, a total absorption coefficient calculating means for calculating a total absorption coefficient of a human tissue of a tomogram perpendicular to the body axis direction of the subject;
A neighborhood matrix setting means for setting a neighborhood matrix in the volume data;
Target image identification means for identifying a target image of the neighborhood matrix;
First noise level calculation means for calculating a noise level by comparing the neighborhood matrix image and the target image;
Histogram generating means for generating a histogram for the noise level for each total absorption coefficient;
Second noise level calculation means for calculating a noise level from a corresponding histogram based on the total absorption coefficient for each fault;
A medical image diagnostic apparatus. Neighborhood matrix setting means for setting a neighborhood matrix to volume data generated based on data collected by the imaging device;
Target image identification means for identifying a target image of the neighborhood matrix;
First noise level calculation means for calculating a noise level by comparing the neighborhood matrix image and the target image;
Histogram generating means for generating a first histogram for all noise levels of the volume data and generating a second histogram for the noise level for each tomography;
Second noise level calculation means for calculating a noise level for each tomography based on the first and second histograms;
An image processing apparatus. Neighborhood matrix setting means for setting a neighborhood matrix to volume data generated based on data collected by the imaging device;
Target image identification means for identifying a target image of the neighborhood matrix;
First noise level calculation means for calculating a noise level by comparing the neighborhood matrix image and the target image;
A histogram generating means for generating a histogram for all noise levels of the volume data;
Second noise level calculation means for calculating a noise level based on the histogram;
An image processing apparatus. A cross-sectional area calculating means for calculating a cross-sectional area of a human tissue of a tomogram perpendicular to the body axis direction of the subject based on the volume data generated based on the data collected by the imaging apparatus;
A neighborhood matrix setting means for setting a neighborhood matrix in the volume data;
Target image identification means for identifying a target image of the neighborhood matrix;
First noise level calculation means for calculating a noise level by comparing the neighborhood matrix image and the target image;
Histogram generating means for generating a histogram for the noise level for each cross-sectional area;
Second noise level calculation means for calculating a noise level from a corresponding histogram based on the cross-sectional area for each tomography;
An image processing apparatus. Based on the volume data generated based on the data collected by the imaging device, a total absorption coefficient calculating means for calculating the total absorption coefficient of the human tissue of the tomogram perpendicular to the body axis direction of the subject;
A neighborhood matrix setting means for setting a neighborhood matrix in the volume data;
Target image identification means for identifying a target image of the neighborhood matrix;
First noise level calculation means for calculating a noise level by comparing the neighborhood matrix image and the target image;
Histogram generating means for generating a histogram for the noise level for each total absorption coefficient;
Second noise level calculation means for calculating a noise level from a corresponding histogram based on the total absorption coefficient for each fault;
An image processing apparatus.

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