JP2017131427A - X-ray image diagnostic apparatus and bone density measurement method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an X-ray image diagnostic apparatus and a bone density measurement method capable of more accurately measuring a bone density by accurately extracting a lateral protrusion of a lumbar vertebra.SOLUTION: An X-ray image diagnostic apparatus 1 generates a difference image from each of X-ray images captured by using an X-ray beam having two different, high and low, energy peaks, then detects a lumbar vertebra region from the difference image, and divides a lumbar vertebra peripheral part into local regions to detect a lateral protrusion region and a soft tissue region from pixel value distributions, etc. of each local region. The pixel values of the lumbar vertebra region are corrected using the detected pixel values of the soft tissue region as a reference, and a bone density is calculated based on the corrected lumbar vertebra region.SELECTED DRAWING: Figure 2

Description

  The present invention relates to an X-ray image diagnostic apparatus and a bone density measuring method, and more particularly to extraction of a measurement region in bone density measurement by the DXA method.

For example, DXA (Dual Energy X-Ray)
Absorptiometry) method. In the DXA method, the lumbar spine of a subject is imaged using an X-ray beam having two different energy peaks, and an X-ray image in which only bones are selectively drawn is obtained. When bone density is measured using the lumbar vertebrae, a total of four areas from the first lumbar vertebra to the fourth lumbar vertebra are determined as measurement areas, and it is necessary to accurately extract the lumbar vertebrae from the image. Patent Document 1 proposes a technique for extracting the contour line of the lumbar spine.

JP 2014-236912 A

  However, the method of Patent Document 1 is to extract the contour of the lumbar vertebrae, and does not consider the extraction of the transverse process or the separation and extraction of bone and soft tissue. In the measurement of bone density, it is necessary to accurately separate and extract the boundary between bone and soft tissue. This is because the bone mass may be overestimated or underestimated if bone and soft tissue are not accurately separated and extracted.

  The present invention has been made in view of the above problems, and by accurately extracting the transverse process of the lumbar spine, the bone region is accurately separated and extracted from the surrounding soft tissue, thereby accurately measuring the bone density. An object of the present invention is to provide an X-ray image diagnostic apparatus and a bone density measuring method that can be performed.

  A first invention for achieving the above-described object is an X-ray source that irradiates a subject with X-rays, and an X-ray detection that detects transmitted X-rays that are disposed opposite to the X-ray source and are transmitted through the subject. A differential image for generating a differential image from each X-ray image captured using an X-ray beam having two different energy peaks, an image processing unit for generating an X-ray image based on the transmitted X-ray A generation unit, a detection unit that detects a lumbar region from the difference image and detects a transverse process region and a soft tissue region from the periphery of the lumbar vertebra, and a pixel value of the lumbar region based on the pixel value of the detected soft tissue region An X-ray image diagnostic apparatus comprising: a correcting unit that corrects a bone density calculating unit that calculates a bone density based on the corrected lumbar spine region.

  The second invention is a bone density measuring method using a computer, the step of generating a difference image from each X-ray image photographed using X-ray beams having two kinds of high and low energy peaks, Detecting a lumbar region from the difference image, detecting a transverse process region and a soft tissue region from the periphery of the lumbar spine, correcting a pixel value of the lumbar region based on the detected pixel value of the soft tissue region, and correction And calculating a bone density based on the lumbar spine region.

  According to the present invention, an X-ray diagnostic imaging apparatus capable of accurately separating and extracting a bone region from surrounding soft tissue by accurately extracting a transverse process of a lumbar vertebra, and thereby accurately measuring a bone density, and a bone Providing a density measurement method can be provided.

1 is an overall configuration diagram of an X-ray diagnostic imaging apparatus 1 according to the present invention. Functional block diagram for bone density measurement Flow chart showing overall flow of bone density measurement process The flowchart which shows the flow of the horizontal protrusion detection process of step S103. (A) Example of lumbar vertebra image 50 obtained from difference image, (b) Diagram showing contours of lumbar vertebrae L1 to L4, (c) Example of local region set in peripheral regions R11 to R42 of each lumbar vertebra contour part The flowchart which shows the flow of the horizontal protrusion detection process of step S204. The flowchart which shows the flow of the separation point calculation process of step S306. Example of local region histogram 61 The flowchart which shows the flow of the soft tissue detection process of step S104 Distribution example of transverse process area (b) and soft tissue area (a) of contour part of lumbar vertebra L1 The figure which shows another example of the soft tissue detection process of step S104 Example of distribution of transverse process area (b) and soft tissue area (a) of contour portion of lumbar vertebra L1 according to the example of FIG. Correction example of pixel values D1 to D4 of the lumbar region based on the pixel values M1 to M4 of the soft tissue Functional configuration diagram related to X-ray diaphragm control Flowchart for explaining the flow of the entire process including the X-ray aperture control process Restricted X-ray field example

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[First Embodiment]
First, the overall configuration of the X-ray image diagnostic apparatus 1 will be described with reference to FIG.
As shown in FIG. 1, the X-ray diagnostic imaging apparatus 1 includes an imaging device 10, an operation device 20, and a bed 15 on which a subject 3 is placed. The imaging device 10 and the operation device 20 are connected by communication using a transmission path such as a communication cable.

  The imaging apparatus 10 includes an X-ray source 12, an X-ray diaphragm 13 provided in the X-ray source 12, an X-ray receiver 16 that is disposed so as to face the X-ray source 12 with the subject 3 interposed therebetween. Image data based on the transmitted X-ray data detected by the X-ray receiver 16 is transmitted to the controller device 20 via the transmission path. The operation device 20 includes a control device 21, an image processing device 22, a storage device 23, an input device 24, a display device 25, and the like.

  The X-ray source 12 includes an X-ray tube and a high voltage generator, and generates a predetermined dose of X-rays according to a control signal transmitted from the control device 21. The X-ray source 12 emits an X-ray beam having two different energy peaks. Hereinafter, X-rays having a high energy peak are referred to as high-energy X-rays, and X-rays having a low energy peak compared to high-energy X-rays are referred to as low-energy X-rays.

  The X-ray source 12 is provided with an X-ray diaphragm 13. The X-ray diaphragm 13 has a plurality of X-ray shielding plates (diaphragm blades), and opens and closes the diaphragm blades to a predetermined position in accordance with the opening information of the diaphragm blades notified from the control device 21. An irradiation area is formed.

  The X-ray receiver 16 is a flat panel detector (FPD) in which two-dimensionally arrayed X-ray detection elements constituted by a combination of a scintillator and a photodiode, for example. I. (Image intensifier) or the like, and is provided at a position facing the X-ray source 12 through the subject 3. For example, the X-ray receiver 16 is installed on the lower surface of the top plate of the bed 15.

  Each detection element of the X-ray receiver 16 detects transmitted X-rays, which are X-rays irradiated from the X-ray source 12 and transmitted through the subject 3, and converts them into electrical signals corresponding to the X-ray intensity. The X-ray receiver 16 creates an X-ray image based on the transmitted X-ray data that is the converted electric signal. The created X-ray image data is sent to the image processing device 22 of the controller device 20 and stored in the storage device 23.

  The image processing device 22 acquires the X-ray image data transferred from the X-ray receiver 16 and performs image processing for displaying on the display device 25. The image processing includes image processing such as processing for determining the display range by detecting the X-ray aperture position based on pixel value information of the image, black-and-white reverse display processing, unnecessary region deletion, and the like.

  The storage device 23 stores an X-ray image generated based on transmission X-ray data detected by the X-ray receiver 16. In addition, the storage device 23 stores programs related to imaging and fluoroscopic operations, various imaging conditions, programs and data necessary for bone density measurement processing described later, and the like.

  The control device 21 is a computer including a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. The control device 21 controls the operation of X-ray exposure in the X-ray source 12 based on the input signal input from the input device 24, and relates to the creation of an image in the X-ray receiver 16 and the correction of the X-ray aperture position. Control of processing and display operation in the display device 25 is performed. Moreover, the control apparatus 21 performs the bone density measurement process which measures bone density based on an X-ray image. Details of the bone density measurement process will be described later.

  The display device 25 is configured by a CRT, a liquid crystal panel, or the like, and displays an X-ray image captured by the imaging device 10, display data input from the control device 21, and the like.

  The input device 24 is an input device such as a keyboard and a mouse, for example, and inputs various instructions and information input by the operator to the control device 21. The operator performs operations interactively using external devices such as the display device 25 and the input device 24. Note that the input device 24 may be a touch panel configured integrally with the display screen of the display device 25.

  Next, the functional configuration of the X-ray image diagnostic apparatus 1 according to the present invention will be described with reference to FIG. As shown in FIG. 2, the control device 21 of the X-ray diagnostic imaging apparatus 1 includes an image acquisition unit 41, a difference image generation unit 42, a detection unit 43, a correction unit 44, a bone density calculation unit 45, and the like. The detection unit 43 includes a lumbar spine detection unit 431, a transverse process detection unit 432, and a soft tissue detection unit 433.

  The image acquisition unit 41 acquires the low energy image and the high energy image generated by the image processing device 22 and sends them to the difference image generation unit 42. The low energy image and the high energy image may be acquired from the storage device 23. The low energy image is an image of a subject photographed with low energy X-rays emitted from the X-ray source 12. A high energy image is an image of a subject photographed with high energy X-rays emitted from the X-ray source 12.

  The difference image generation unit 42 generates a difference image that is a difference between the low energy image and the high energy image acquired by the image acquisition unit 41. The difference image generation unit 42 sends the generated difference image to the detection unit 43.

  The detection unit 43 detects the lumbar spine from the difference image and separates and extracts the transverse process and the soft tissue from the peripheral part of the lumbar spine. In the process of separating and extracting the transverse process and the soft tissue, the detection unit 43 divides the peripheral region of the contour portion of the lumbar vertebra into a plurality of local regions, calculates a histogram for each local region, and analyzes the histogram to determine the transverse process. Whether the region is a soft tissue region or not is determined. Details of detection of the lumbar spine, transverse process, and soft tissue will be described later.

The correcting unit 44 corrects the density value of the lumbar region based on the soft tissue density value detected by the detecting unit 43. Specifically, the correction unit 44 calculates a correction coefficient for correcting the lumbar vertebra region for each lumbar vertebra, and corrects the concentration value of each lumbar vertebra using the correction coefficient. Details of the correction processing by the correction unit 44 will be described later.
The bone density calculation unit 45 calculates the bone density based on the lumbar spine region corrected by the correction unit 44.

Next, the bone density measurement process will be described with reference to FIGS.
FIG. 3 is a flowchart showing the overall flow of the bone density measurement process.
The control device 21 has a high energy image obtained by irradiating the subject 3 with high energy X-rays, and a low energy image obtained by irradiating the low energy X-ray subjects 3. Are obtained, and these difference images are generated (step S101). The control device 21 detects the lumbar spine from the edge image of the difference image acquired in step S101 (step S102). The control device 21 detects the transverse process in the peripheral region of the side surface of the lumbar vertebra from the side edge information of the lumbar vertebra obtained in step S102 (step S103). Further, the control device 21 detects a soft tissue other than the lumbar vertebrae and transverse processes in the peripheral region of the side surface of the lumbar vertebra (step S104), and corrects the pixel value of the lumbar region based on the pixel value of the soft tissue (step S105). . The control device 21 measures the bone density based on the corrected pixel value (step S106).

  With reference to FIG. 4 and FIG. 5, the lateral protrusion detection processing in step S103 will be described. FIG. 5A is a schematic diagram of the lumbar image 50 detected by the lumbar vertebra detection process in step S102.

  The control device 21 extracts the aperture region 51 from the lumbar spine image 50 as shown in FIG. 5A (step S201), and calculates the subject region 52 excluding the region where no subject exists (straight X-ray region). (Step S202). The subject area 52 is an area indicated by a broken line in FIG. The contour of the lumbar vertebra is extracted from the obtained object region 52 from the edge information of the contour and the pixel value information of the image (step S203). The contour of the lumbar spine can be extracted by, for example, performing direction-dependent filter processing and deleting the noise component of the edge information according to the pixel determination result. FIG. 5B is a diagram showing the contours of the lumbar vertebrae L1, L2, L3, and L4 extracted by the process of step S203.

  For each lumbar vertebra L1, L2, L3, L4 obtained by the process of step S203, the control device 21 has left and right regions (peripheral regions) R11, R12, R21 on the lateral side of the lumbar vertebra as shown in FIG. R22, R31, R32, R41, and R42 are each divided into local regions. Then, for each local region, the transverse process (and intervertebral disc) is extracted from the pixel value information and edge information (step S204).

  FIG. 5C is a diagram showing a state in which the peripheral regions R11, R12, R21, R22, R31, R32, R41, and R42 of the contour of each lumbar vertebra L1, L2, L3, and L4 are divided into local regions. Each area of the local area has a size of, for example, about 1 cm × 1 cm (7 pixels × 7 pixels, etc.), and the lateral protrusion extraction process of FIG. 6 (including the separation point calculation process of FIG. 7) is performed for each local area. Done.

The lateral protrusion extraction process in step S204 will be described with reference to FIGS.
First, as shown in the flowchart of FIG. 6, the control device 21 removes the noise component for each local region using a smoothing filter (step S301), and creates a histogram for each local region (step S302). The control device 21 compares the lowest pixel value of the histogram obtained in step S302 with the pixel value of the lumbar vertebra (step S303), and determines that the local region is the lumbar vertebra if the difference is within a preset threshold value. To do. When the lumbar vertebra is determined (step S303; lumbar vertebra), the distribution part including the lumbar pixel value is deleted from the histogram created in step S302 (step S304), and the histogram after the lumbar pixel removal is analyzed (step S305). . If the lumbar spine is not included, the obtained histogram is analyzed.

  In the histogram analysis, first, the inclination of the entire pixel value distribution (total distribution) is determined from the histogram after the lumbar pixel distribution is deleted (step S305). If the inclination is less than the predetermined threshold value, it is determined that the local region is only soft tissue or lateral projection (step S305; less than the inclination threshold value), and the “no density difference flag” is stored (step S306).

  On the other hand, if the magnitude of the slope of the histogram after deleting the lumbar pixel distribution (the slope of the total distribution) is greater than or equal to a predetermined threshold (step S305; greater than or equal to the tilt threshold), soft tissue and transverse processes are present in the local region. Determined to be mixed.

  Note that in the histogram analysis processing in step S305, it may be determined whether or not the lateral protrusion and the soft tissue are included from the width of the pixel value distribution of the histogram after smoothing. If the width of the pixel value distribution in the histogram is equal to or greater than a predetermined threshold, it is determined that the lateral protrusion and the soft tissue are included. If the width of the pixel value distribution of the histogram is less than a predetermined threshold value, it is determined as either a lateral projection or a soft tissue, and a no-density difference flag is stored.

  The control device 21 executes a process for obtaining a separation point between the soft tissue and the transverse process (separation point calculation process) for a region including the transverse process and the soft tissue (step S307).

  An example of the separation point calculation process is shown in FIG. In the example shown in FIG. 7, the control device 21 smoothes the histogram after removing the lumbar pixel distribution (the histogram obtained in step S302 or step S304), and then searches for the most frequent point that is the point with the highest frequency (step S401). Further, the absolute value of the inclination between the most frequent point and the end point (inclination end point) and the absolute value of the inclination between the most frequent point and the start point (inclination start point) are calculated (step S402), and the sizes of the inclination end point and the inclination start point are calculated. Compare (step S403).

In step S403, if the difference between the inclination start point and the inclination end point is equal to or greater than a predetermined value and the “inclination start point” is large, it is determined that the horizontal protrusion has the most frequent point.
When the “tilt end point” is large, it is determined that the most frequent point is in the soft tissue.
In accordance with the determination result of step S403, the control device 21 calculates a histogram separation point (step S404).

  If it is determined in step S403 that the “tilt start point” is large and the lateral protrusion has a mode point, in step S404, the control device 21 calculates the absolute value of the tilt at each point from the mode point to the end point in the histogram. A point having a small inclination (for example, a point having the minimum inclination) is defined as a separation point T between the lateral protrusion and the soft tissue. When the separation point T is calculated, the process returns to step S308 in FIG. 6, and the control device 21 calculates an average pixel value from the start point to the separation point (step S308), and stores the value in the RAM or the like as the pixel value of the lateral protrusion. (Step S309).

  On the other hand, when it is determined in step S403 that the “tilt end point” is large and the soft tissue has the most frequent point P3 (see FIG. 8), in step S404, the control device 21 moves from the histogram start point P1 to the most frequent point P3. The absolute value of the inclination at each point is calculated, and a point with a small inclination (for example, a point with the minimum inclination) is set as a separation point between the transverse process and the soft tissue. When the separation point T is calculated, the process returns to step S308 in FIG. 6, and the control device 21 calculates an average pixel value from the start point P1 to the separation point T (step S308), and stores the value in the RAM or the like as the pixel value of the lateral protrusion. Store (step S309).

  Next, the control device 21 executes a soft tissue detection process (step S104 in FIG. 3). Details of the soft tissue detection processing will be described with reference to the flowchart of FIG.

  The soft tissue detection process shown in FIG. 9 is performed on the peripheral regions R11 to R42 of the lumbar contour portion. When there is no “no density difference” flag in the target local region (step S501; no flag), the control device 21 ends the processing as it is. On the other hand, when the “no density difference” flag is stored in the target pixel (step S501; flag present), it is determined whether the local region is a transverse process or a soft tissue.

  The control device 21 compares the lateral protrusion average pixel value calculated in step S308 of FIG. 6 with the average pixel value in the local region (step S502). When the difference value between the horizontal projection average pixel value and the average pixel value in the local region is equal to or greater than the threshold (step S502; equal to or greater than the threshold), the soft tissue (a) is determined (step S503). When the difference value between the average value (average pixel value) of the lateral projection pixel values and the average pixel value in the local region is less than the threshold (step S503; less than the threshold), the lateral projection (b) is determined ( Step S504).

  As a result of the transverse process detection process of FIG. 6 and the soft tissue detection process of FIG. 9, as shown in FIG. 10, the lateral process (b) or the soft tissue (a ) Is stored. That is, the transverse process and the soft tissue are separated and extracted from the peripheral region R11 of the lumbar vertebra. As described above, each pixel in the peripheral region of the contour of the lumbar vertebra is discriminated as a lumbar vertebra, a transverse process, or a soft tissue, and an appropriate pixel value can be stored.

In the soft tissue detection process in step S104 of FIG. 3, as shown in the flowchart of FIG. 11, the lateral projection area may be determined by the area expansion process.
That is, it is determined whether the local area stores the “no density difference” flag (step S601; flag present) and is a transverse process or a soft tissue. The control device 21 compares the lateral protrusion average pixel value in the local region to which the pixel belongs calculated in step S308 of FIG. 6 with the average pixel value in the local region (step S602). When the difference value between the average pixel value of the lateral protrusion and the average pixel value in the local region is greater than or equal to the threshold (step S502; greater than or equal to the threshold), the soft tissue (a) is determined (step S603). When the difference value between the average pixel value of the lateral protrusion and the average pixel value in the local region is less than the threshold (step S603; less than the threshold), the lateral protrusion (b) is determined (step S604). Then, region expansion processing for searching for an approximate pixel (a pixel whose pixel value approximates) is performed from the region determined to be the lateral protrusion (b), and the lateral protrusion region is extracted (step S605).

As a result of the transverse process detection process of FIG. 6 and the soft tissue detection process of FIG. 11, as shown in FIG. 12, the lateral process (b) or the soft tissue (a ) Are stored, and the lateral projection region (b) is extracted by region expansion processing. By the region expansion process, the local region is further separated into a transverse process and a soft tissue.
As described above, each pixel around the lumbar contour portion can be identified as a lumbar vertebra, a transverse process, or a soft tissue, and an appropriate pixel value can be stored.

  The discrimination of lumbar vertebrae, transverse processes, or soft tissue is completed for each peripheral region R11, R12, R21, R22, R31, R32, R41, R42 of each lumbar vertebra L1-L4, and an appropriate pixel value is stored in each pixel. Then, the control device 201 performs correction processing (step S105 in FIG. 3).

  In the correction process, the pixel value of the lumbar vertebra region around the lumbar vertebra is corrected based on the pixel value of the soft tissue region obtained in step S104 (FIGS. 10 and 11). That is, as shown in FIG. 13, for the peripheral region (within the broken line frame) of each lumbar vertebra L1 to L4, the control device 21 determines that the pixel values of the soft tissue regions M1 to M4 other than the lumbar vertebra and the transverse process are “0”, for example. The correction coefficients k1 to k4 are calculated in each peripheral region so that As shown in the following formula (1), the control device 21 subtracts correction coefficients k1 to k4 from the average pixel values D1 to D4 of the lumbar vertebrae, and calculates bone average pixel values B1 to B4.

  Bx = Dx−kx (1)

  The control device 21 measures the bone density using the average pixel values D1 to D4 of the lumbar vertebra corrected by the equation (1) (step S106 in FIG. 3).

  As described above, according to the X-ray image diagnostic apparatus 1 of the first embodiment, it is possible to accurately discriminate the transverse process and the soft tissue in the peripheral region of the lumbar contour portion. If the pixel value of the lumbar region is corrected based on this result and the bone density is measured, the accurate bone density can be obtained.

[Second Embodiment]
Next, an X-ray diagnostic imaging apparatus 1 according to the second embodiment will be described with reference to FIGS.

  As shown in FIG. 14, the control device 21A of the X-ray diagnostic imaging apparatus 1 according to the second embodiment includes an image acquisition unit 41, a difference image generation unit 42, and a detection unit 43 (a lumbar spine detection unit 431, a transverse process detection unit). 432, a soft tissue detection unit 433), and an X-ray diaphragm control unit 47.

  The X-ray diaphragm control unit 47 feeds back information on the lumbar spine and transverse process areas detected by the same method as in the first embodiment to the X-ray diaphragm 13 and controls the X-ray irradiation field.

  Since the image acquisition unit 41, the difference image generation unit 42, and the detection unit 43 are the same as those in the first embodiment, a duplicate description will be omitted, and the same components will be described with the same reference numerals.

FIG. 15 is a flowchart for explaining the flow of the X-ray aperture control process according to the second embodiment.
The X-ray image diagnostic apparatus 1 first collects fluoroscopic images as positioning images (step S701). Then, the lumbar region and the transverse process region are detected by the same method as that in the first embodiment (step S702). The control device 21A converts the detection results of the lumbar vertebrae and the transverse processes into image coordinate axes (step S703).

  As shown in FIG. 16, the position is converted into the positions of the upper, lower, left and right aperture blades from the minimum and maximum points of the X and Y coordinates (step S704). The control device 21A controls the upper, lower, left and right diaphragm blades of the X-ray diaphragm 13 to the positions converted in step S704. Thereby, the X-ray irradiation field is narrowed down to a position including the lumbar vertebra and the transverse process. Thereafter, imaging for bone density measurement using high energy X-rays and low energy X-rays is performed (step S705), and bone density measurement processing is performed in the same manner as in the first embodiment.

  The X-ray diaphragm adjusting function of the second embodiment makes it possible to narrow the X-ray irradiation field to the minimum necessary area for bone density measurement. Thereby, it becomes possible to suppress the exposure dose of the patient.

  The preferred embodiments of the X-ray image diagnostic apparatus and the like according to the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to such examples. It will be apparent to those skilled in the art that various changes and modifications can be made within the scope of the technical idea disclosed in the present application, and these are naturally within the technical scope of the present invention. Is done.

1 X-ray diagnostic imaging device 10 Imaging device 12 X-ray source 13・ X-ray diaphragm 15... Bed 16... X-ray receiver 20. Control device 22 Image processing device 23 Storage device 24 Input device 25 Display device 41... Image acquisition unit 42... Difference image generation unit 43.・ Correction unit 45... Bone density calculation unit 47... X-ray diaphragm control unit 50. .... Histograms L1-L4 ... Lumbar vertebrae R11, R12 R21, R22, R31, R32, R41, R42 ... Peripheral region T of the lumbar contour ... Separation point P1 ... Histogram start point P2 ...・ ・ ・ ・ ・ ・ Histogram end point P3 ・ ・ ・ ・ ・ ・ ・ ・ ・ ・ ・ Histogram of histogram

Claims (11)

An X-ray source for irradiating the subject with X-rays;
An X-ray detector arranged to face the X-ray source and detecting transmitted X-rays transmitted through the subject;
An image processing unit that generates an X-ray image based on the transmitted X-ray;
A difference image generation unit that generates a difference image from each X-ray image captured using X-ray beams having two types of high and low energy peaks;
A detection unit for detecting a lumbar region from the difference image and detecting a transverse process region and a soft tissue region from a peripheral part of the lumbar vertebra,
A correction unit that corrects the pixel value of the lumbar spine region with reference to the pixel value of the detected soft tissue region;
A bone density calculator that calculates bone density based on the corrected lumbar spine region;
An X-ray diagnostic imaging apparatus comprising:   The X-ray image diagnosis according to claim 1, wherein the detection unit divides the peripheral part of the lumbar vertebra into a plurality of local regions, and determines whether the local region is a transverse process or a soft tissue for each local region. apparatus.   The detection unit according to claim 2, wherein the detection unit calculates a histogram for each local region, and determines whether the local region is the lateral protrusion or the soft tissue based on a distribution of the histogram. X-ray image diagnostic apparatus.   The X-ray image diagnostic apparatus according to claim 3, wherein the histogram is a histogram after lumbar vertebra removal obtained from pixels excluding the lumbar vertebra in the local region.   The detection unit searches for the mode point of the histogram and compares the slope between the start point of the histogram and the mode point, and the slope of the end point of the histogram and the mode point. The X-ray image diagnostic apparatus according to claim 3, wherein a separation point between the protrusion and the soft tissue is calculated.   The X-ray image diagnostic apparatus according to claim 5, wherein an average pixel value from a start point of the histogram to the separation point is set as a pixel value of the lateral protrusion.   The detection unit compares the average pixel value of the lateral protrusion with the average pixel value of each pixel in the local region for the local region determined that the density difference is smaller than a predetermined threshold based on the histogram calculated for the local region. The X-ray diagnostic imaging apparatus according to claim 2, wherein when the difference is equal to or greater than a predetermined threshold value, it is determined as a soft tissue, and when the difference is less than the threshold value, it is determined as a transverse process.   The X-ray image diagnosis apparatus according to claim 3, wherein the detection unit further performs a region expansion process from the region determined to be the lateral protrusion.   The correction unit calculates a correction coefficient for correcting the lumbar region for each lumbar vertebra based on the pixel value of the soft tissue detected by the detection unit, and corrects the pixel value of each lumbar vertebra using the correction coefficient. The X-ray image diagnostic apparatus according to claim 1.   The X-ray image diagnosis apparatus according to claim 1, further comprising an X-ray diaphragm control unit configured to control the X-ray irradiation field to be narrowed down to the lumbar spine region and the transverse process region detected by the detection unit. A bone density measuring method using a computer,
Generating a difference image from each X-ray image taken using X-ray beams having two different high and low energy peaks;
Detecting a lumbar region from the difference image and detecting a transverse process region and a soft tissue region from the periphery of the lumbar vertebrae;
Correcting the pixel value of the lumbar region with reference to the pixel value of the detected soft tissue region; and
Calculating bone density based on the corrected lumbar region;
A bone density measuring method comprising:

Images