WO2019066051A1 - Interior ct phase imaging x-ray microscope apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve the contrast of an image and to be excellent in operability even in the case of using a non-stained biological tissue mainly composed of light elements of the same kind, a structure in which resin and thin films of similar composition are laminated An interior CT phase imaging X-ray microscope apparatus capable of measuring a high spatial resolution three-dimensional tomogram of a desired region of a part of a sample by a more practical interior CT image reconstruction method. SOLUTION: A phase difference XRM apparatus provided with a phase difference image data measuring optical system capable of easily exchanging an optical system having a plurality of image recording ranges, firstly, the phase difference of the whole sample at a low magnification with a large image recording range. An X-ray microscopic image is taken, and a region of interest (ROI) of a sample to be imaged with high magnification and high spatial resolution is determined based on the low magnification phase difference X-ray microscopic image, and changed to a high magnification X-ray optical system It was made to take high magnification and high spatial resolution data. By doing this, high spatial resolution interior CT image reconstruction can be performed by using the reconstruction data of the measurement point inside the desired high magnification measurement ROI among the phase difference X-ray microscopic reconstruction images at the low magnification measurement. Provided is an interior CT phase imaging XRM device that can be configured.

Description

Interior CT phase imaging X-ray microscope

The present invention relates to an X-ray microscope (XRM), and more particularly to a phase difference X-ray microscope which converts the phase of X-rays into contrast to measure the structure of a sample.

XRM is widely used to observe the submicron level structure of a sample. This is a method of irradiating the sample with X-rays, projecting the X-rays transmitted through the sample on an enlarged scale on the detector, watermarking the structure inside the sample, and reflecting the degree of absorption of the X-ray inside the sample object A projected image is obtained, which is known, for example, from the following non-patent document 1.

Furthermore, Non-Patent Document 2 already discloses a method for acquiring a phase image obtained by imaging the phase of X-rays that is changed by passing a substance rather than the absorption of X-rays. Further, a Talbot interferometer-XRM for obtaining an enlarged image of a sample using a phase image acquisition method using a phase grating and a magnifying optical system with a Fresnel zone plate (FZP) is already known from Non-Patent Document 3.

Next, a method of constructing a three-dimensional image is to irradiate X-rays completely covering the cross section of the sample, measure projection data on all straight lines passing through the sample cross section, and use the data to reconstruct an image. It is supposed to Such an approach is known from [4]. However, even if a tomographic image of only a small region of interest (ROI: Region of Interest) in the object (sample) is desired, the projection data on all the straight lines passing through the sample cross section is generally not only the ROI. It was necessary.

Generally, when imaging at high resolution and spatial resolution, the projected image often measures the ROI portion of the sample at high magnification, and conventional CT methods can not always produce high-precision three-dimensional image reconstruction due to lack of surrounding data The This is because, in the Filtered Back Projection (FBP) method, which is a calculation procedure used for CT image reconstruction, projection data on a straight line that does not pass through the ROI is also required to generate the ROI image. . However, projection data on a straight line not passing through the ROI does not contain any information of the ROI. Therefore, in the field of X-ray CT, a method of CT imaging in which only the ROI is irradiated with X-rays and only projection data on a (all) straight line passing through the ROI is measured to generate only an image of the ROI It has been developed as a CT.

In this interior CT, (1) significant reduction of exposure dose (sample damage) outside the ROI, (2) detector size and X compared with conventional X-ray CT which also measures unnecessary projection data Reduction of line beam width, (3) enabling imaging of large objects that do not fit within the field of view, and (4) enabling high resolution CT that irradiates X-rays only to the small field of the object for magnified imaging, etc. Can be mentioned.

In the interior CT, projection data on a straight line which does not pass through the ROI is not measured, so a method for performing image reconstruction from incomplete projection data in which a part is missing is required. In parallel beam projection data collection, projection data p (r, θ) (r is the radius, θ) where a straight line passes the object f (x, y) and the ROI to be imaged (see Figure 1) (Angle) can only be measured. In this case, since p (r, θ) where the straight line does not pass the ROI is not measured, the projection data of each angle θ is truncated due to truncation on the left and right. It is necessary to correctly reconstruct the image f (x, y) in the ROI from such truncated projection data.

In this reconstruction of the interior CT, many studies have been conducted for many years, and in Non-Patent Document 5, the reconstruction of the image of the interior CT is that the solution is uniquely determined from the projection data and is not determined as the mathematically correct image reconstruction. Is mathematically proven. Because of this non-uniqueness, many approximate image reconstruction methods have been studied.

As a representative method, (1) a method of extrapolating the missing part on the right and left of each direction projection data with a smooth function and then reconstructing the image, and (2) image reconstruction by the successive approximation method with the incomplete projection data remaining Although the method etc. of doing were researched, the artifact by the approximation error generate | occur | produced and it did not reach practical use (nonpatent literature 6, 7 and 8). An example of a typical artifact generated in this interior CT is a shading artifact (see FIG. 2A) in which distortion occurs in the low frequency component of the image (see FIG. 2A) and a cupping effect in which the value increases around the ROI (FIG. 2). It is known that (b)) occurs and the value of the image is not stable.

For these previous studies, a rigorous image reconstruction method for interior CT was devised (8, 9), and any small area B inside the ROI (ie, in Figure 3 (a) It is proved that the solution for image reconstruction of the interior CT is uniquely determined if there is a priori knowledge that the value of the image f (x, y) is known in advance in the circle region inside). If there are a plurality of regions B for guaranteeing the uniqueness of the solution, they may be small or anywhere in the ROIS. This result is known as Patent Document 1.

On the other hand, a method has been devised that enables accurate image reconstruction using another a priori knowledge (Non-Patent Document 7), and performs signal reconstruction with high accuracy from insufficient measurement data called compressed sensing. Based on the method, it has been shown that if the image f (x, y) is piecewise uniform throughout the ROI, then the solution for image reconstruction of the interior CT is uniquely determined. Here, piecewise uniform means that the image is composed of a finite number of regions having a completely constant value, as in a numerical phantom (see FIG. 3 (b)). This result is known as Patent Document 2.

Y. Yoneda: New Emission X-ray Microscope, Review of Scientific Instruments, Vol. 33, (1962), 529-532 U. Bonse and M. Hart: An X-ray Interferometer, Applied Physics Letter, 6, (1965) 155-156 Y. Takeda, W. Yashiro, T. Hattori, A. Takeuchi, Y. Suzuki and A. Momose: Differential Phase X-ray Imaging Microscopy with X-ray Talbot Interferometer, Applied Physics Express, Vol. ) 117002 Natterer F: The Mathematics of Computerized Tomography. Wiley, 1986 International Journal of Biomedical Imaging 2007: Article ID 63634, 2007 Ye Y, Yu H, Wei Y, Wang G: A general local reconstruction approach based on a truncated Hilbert transform. Kudo H, Courdurier M, Noo F, Defrise M: Tiny a priori knowledge solves the interior problem in computed tomography. Physics in Medicine and Biology 53: 2207-2231, 2008 Yu H, Wang G: Compressed sensing based interior tomography. Physics in Medicine and Biology 54: 2791-2805, 2009 Yang J, Yu H, Jiang M, Wang G: High order total variation minimization for interior tomography. Inverse Problems, 26: Article ID 35013, 2010 Courdurier M, Noo F, Defrise M, Kudo H: Solving the interior problems of computed tomography using a priori knowledge. Inverse Problems 24: Paper No. 065001, 2008 Wataru Yashiro, Yoshihiro Takeda, and Atsushi Momose: Efficiency of capturing a phase image using cone-beam x-ray Talbot interferometry, Journal of the Optical Society of America A, 8 (2008) 2025-2039 Akira Hyakusei: X-ray phase imaging using Talbot effect, synchrotron radiation, 23 (2010) 382-392

U.S. Patent No. 7,697,658 U.S. Patent No. 8,811,700

In XRM, when the target is an unstained biological soft tissue with low X-ray absorption and low contrast and a laminated structure of resin and thin film of similar composition, the phase of the electron beam transmitted through the sample object is A so-called phase difference XRM which is converted to contrast and observed is used. Since the present invention can construct high-resolution images even when X-rays are irradiated around the ROI of the sample among the phase differences XRM described above, the interior CT phase is particularly advantageous for reducing the X-ray exposure of the sample. It relates to imaging XRM.

Even in the phase difference XRM to which the present invention relates, the principle of absorption contrast is that the heavier the element is present at a higher density in the sample object, the larger the scattering of X-rays and the clear contrast can be obtained. Image contrast occurs. However, in the case of a non-stained biological tissue mainly composed of the same light element, or a structure in which a thin film of resin or similar composition is laminated, it is difficult to obtain contrast in the image. As the X-ray grating, phase imaging XRM is used, in which the phase of X-rays transmitted through a sample object is converted into contrast and observed.

However, when attempting to obtain an image with high spatial resolution with the conventional phase difference XRM, the image recording range becomes narrow due to the limit of the size of the optical system and the image detector. Even if phase difference X-ray image data for using the CT method can not be acquired in a state where such phase difference X-ray CT data of the whole sample can not be acquired, the image reconstruction of the desired system can not be performed. There is a drawback that high spatial resolution CT data can not be acquired.

Although the interior CT image analysis method is used to overcome the above-mentioned drawbacks, in order to apply the exact solution method known by the above-mentioned Non-Patent Document 7 and Non-Patent Document 8, prior to imaging, it is a priori for the object. Knowledge (the value of the image in a small region B inside the ROI in FIG. 3a) needs to be known, but the situation where the value of the image is known before imaging is very rare. Further, in the exact solution of Non Patent Literature 7 described above, since it is assumed that part of the ROI of the image is piecewise uniform, smooth density change is lost in this method.

Therefore, the present invention improves the image contrast and improves operability even in the case where a non-stained biological tissue mainly composed of the same light element, and a structure in which a thin film of resin or similar composition is laminated are targeted. Another object of the present invention is to provide an interior CT phase imaging XRM capable of measuring a high spatial resolution three-dimensional tomogram of a desired region of a sample by the superior and more practical interior CT image reconstruction method.

In order to achieve the above-mentioned object, a phase imaging XRM provided with an optical system for measuring phase difference image data capable of easily exchanging two or more kinds of image recording ranges was devised. According to the present invention, first, the phase difference XRM image of the entire sample is photographed at a low magnification with a large image recording range, and the interest of the sample photographed at high magnification and high spatial resolution based on the low magnification phase difference XRM image The region ROI was determined and changed to a high magnification X-ray optical system to obtain high magnification and high spatial resolution data. By doing this, it is possible to perform interior CT image reconstruction by using, among the phase difference XRM reconstruction images at the time of low magnification measurement, reconstruction data of measurement points inside the desired high magnification measurement ROI. It is possible to construct a capable interior CT phase imaging XRM.

Further, in the present invention, in the interior CT phase imaging XRM described above, optical elements such as X-ray gratings of X-ray optical systems having different magnifications and Fresnel zone plate (FZP) are optimum for each optical system having different magnifications. It is preferable from the viewpoint of usability of the interior CT phase imaging XRM that a mechanism is provided which is installed in a jig capable of adjusting the position and inclination so as to be able to be used and which can easily switch these optical systems by operations such as translation and rotation. Some optical systems do not have an FZP attached for maximum field of view, and they also include non-magnifying 1 × optical systems.

Furthermore, in the interior CT phase imaging XRM described above, the position change of the optical element is slightly changed, and a plurality of images are acquired to detect phase change, amplitude change, or scattering by the sample as an image. Is preferred.

For low-magnification image data captured, projection data is acquired by X-rays passing through the sample under conditions that all projection images of the cross-section perpendicular to one axis at least for sample rotation can be imaged for cross-sectional imaging. The first stage approximate reconstruction is performed by the CT image reconstruction method using the projection data. Next, the projection data is measured at the maximum magnification at which the projection image of all the ROIs can be measured, and the image numerical value representing the physical quantity in the ROI is at least piecewise uniform or based on the reconstructed CT image. It is possible to specify a region which is piecewise represented by a polynomial, and by representing the physical quantity in a piecewise uniform or piecewise polynomial form in the position of the specified region and the inside thereof, the reconstruction of the first step It is an image reconstruction method for interior CT that performs high-precision second-stage reconstruction.

In the present invention, in the image reconstruction method for interior CT described above, the numerical value of the projection data representing the physical quantity may include the absorption of the X-ray by the imaging target, or the value by the imaging target It may include a phase shift of x-rays, or may include diffraction of the x-rays by the subject, or may include scattering of the electron beam by the subject.

In the image reconstruction method of the interior CT, the at least piecewise uniform or piecewise polynomial region identified in the ROI is obtained using a CT image obtained by the first-step reconstruction. It may be set manually by a human or may be set by image processing using a CT image obtained by the first stage reconstruction. Alternatively, it may be preset by at least one of the specification from the previously acquired CT image of the imaging target, the model representing the structure of the imaging target, and the specification from the a priori information in the ROI. Note that the at least piecewise uniformly or piecewise polynomial region identified in the ROI using a CT image obtained by the approximate reconstruction in the first step is the boundary of the ROI. It may be formed including a part.

In the image reconstruction method for the interior CT, the CT image reconstruction method according to the first step includes a filtered back projection (FBP) method, a successive approximation method, and a statistical reconstruction method. The second stage of reconstruction may be performed by CT including the differential backprojection Hilbert transform, constrained successive approximation, and constrained statistical reconstruction. It may be performed by at least one of the image reconstruction methods.

According to the present invention described above, the phase difference of X-rays passing through the sample object is imaged, the distribution of the phase difference of X-rays is quantitatively measured, and strictness is achieved with much less a priori knowledge than the prior art. It is possible to provide an interior CT phase imaging XRM equipped with a more practical interior CT image reconstruction method capable of image reconstruction.

It is a figure explaining interior CT in which this invention relates by comparison with normal CT. It is a figure which shows the case where conventional exact reconfiguration | reconstruction becomes possible, and the example where conventional exact reconfiguration | reconstruction becomes possible. It is a figure which shows the example of the typical artifact which generate | occur | produces in interior CT. It is a figure explaining the X-ray optical system used by this invention. It is a figure explaining arrangement | positioning of each optical element by the X-ray optical system used by this invention. It is a figure explaining adjustment and switching of each optical element by two types of X-ray optical systems used by this invention. It is a figure explaining the principal part composition of the interior CT phase imaging XRM device of the present invention. It is a figure explaining the whole structure of the interior CT phase imaging XRM apparatus of this invention. It is a figure explaining the measurement method of interior CT phase imaging XRM device of the present invention. It is a flowchart figure which shows the detail of the two-step image reconstruction method of this invention. It is a figure which shows an example of the imperfect image and exact image in the two-step image reconstruction method of this invention. It is a figure which shows the case where conventional exact reconfiguration | reconstruction becomes possible, and the example where conventional exact reconfiguration | reconstruction becomes possible. It is a figure explaining the piecewise uniform and piecewise polynomial said by this invention. It is a figure which shows the specific example which sets a priori information area | region B to a part of ROIS in a priori knowledge non-identification type | mold image reconstruction method (Example 3). It is a figure which shows the concrete simulation experiment example of an a priori knowledge identification-type image reconstruction method and an a priori knowledge non-identification type image reconstruction method. It is a figure explaining the method which implement | achieves mathematically exact image reconstruction of interior CT, without utilizing the a priori information which is the image reconstruction method of this invention. It is a figure explaining the view in the case of generalizing the image reconstruction method of this invention. It is an explanatory view at the time of applying the above-mentioned image reconstruction method to 180 degrees parallel beam, fan beam short scan, and polygon orbital fan beam scan.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Example 1

First, as a basic idea of the present invention, an apparatus was configured by adding an optical system switching mechanism to a commercially available XRM apparatus. Among the devices, the X-ray source, the sample mounting mechanism, and the X-ray detector were fixed in terms of size and weight. It is also possible for these devices to be position changeable optical systems. In this embodiment, an optical system with two magnifications is incorporated, with the distance between the X-ray source and the sample center being 500 mm and the distance between the sample center and the X-ray detector and element surface being 760 mm.

The arrangement of each optical element of the Talbot-Lau interferometer having a magnifying optical system to which the FZP adopted in the present invention is added is described in FIG. 4 (a). Here, the condensing mirror 2 is formed on a spheroid surface in which one of the two focal points is the X-ray source 1 and the other is the sample 4 so that X-rays can be totally reflected on the inner surface of the hollow glass tube There is. Inside the hollow glass tube, a single layer film of a metal film or a multilayer film of two or more types of films different in density may be formed. The X-rays transmitted through the G0 grating 3 and the sample 4 are transmitted through the Fresnel zone plate (FZP) 5 and the G1 grating 6 to be incident on the X-ray detector 7 and recorded as an image. In the present invention, the FZP 5 is prepared to obtain 10 × and 40 × magnifications, and the entire optical system is switched.

In FIG. 4B, the G0 grating 3 has a virtual X-ray source in the X-ray transmitting part of the grating, and the Talbot-Lau interferometer has an optical system in which many thin X-ray sources are superimposed. As a result, the X-ray source 1 is not a microfocus X-ray source, and functions as a high spatial resolution microscope even if there is a certain area. Since X-ray source with high X-ray intensity has a finite area, G0 grating 3 is required to obtain high spatial resolution, but even when X-ray source size is 10 μm or less, G0 grating is not used is there. In the present embodiment, the X-ray source having a diameter of 70 μm was used.

The distances from sample positions of G0 gratings 3-1 and 3-2, FZPs 5-1 and 5-2, and G1 gratings 6-1 and 6-2 used in this embodiment are shown in FIG. The instructor pitch of the G0 grid 3 was 3.0 μm for both low magnification and high magnification. The low magnification FZP5-1 is × 10, the high magnification FZP5-2 is × 40, the pitch of the low magnification G1 grating 6-1 is 2.9 μm, and the pitch of the high magnification G1 grating 6-2 is 2.4 μm. Table 1 collectively shows the installation positions of the optical elements of the present embodiment. The method of determining the position of these optical elements is described in detail in Non-Patent Document 10.

In this embodiment, a 1024-ch × 1024-ch two-dimensional detector with a pixel size of 0.65 μm × 0.65 μm is used as the X-ray detector. The imaging area size and spatial resolution of this embodiment are determined by the detection area and pixel size of this detector, and at a low magnification (× 10 times), the detection area of 0.65 μm × 1024 × 1/10 = 66 μm (angle) and 0 It becomes a detection pixel size of .65 μm / 10 = 65 nm. Similarly, at high magnification (× 40), the detection area is 16.4 μm (corner) and the detection pixel size is 16.3 nm. The actual spatial resolution was affected by the accuracy of completion of each optical element, etc., and it was about 150 nm at low magnification and about 50 nm at high magnification from the measurement of the standard sample.

The configuration of the magnification changing mechanism of the optical system in this embodiment is shown in FIG. In FIG. 6, the X-rays passing through the G0 grating 3 pass through the sample 4 and then pass through the FZP 5-1 and the G1 grating 6-1, or pass through the FZP 5-2 and the G1 grating 6-2. As shown in Table 1 above, the magnification as a microscope is determined by FZP, and a G1 lattice suitable for that is used in combination. Therefore, in the present invention, the FZP 5-1 and the G1 grating 6-1, and the FZP 5-2 and the G1 grating 6-2 are used in their respective combinations, and the magnification of the microscope is changed by moving the optical element mounting table 8 There is.

In FIG. 6, the G0 grating 3 is fixed to the mounting jig 31 and the position of the swivel 32, the rotatable mechanism 33, and the G0 grating can be adjusted in the X vertical plane around the X-ray optical path X near the center of the G0 grating. The X-ray beam path X and its vertically movable movement mechanism 34 can adjust its position and mounting state. The movement in the X-ray optical path X direction is provided with a movement mechanism that can be externally controlled. The sample 4 is fixed to a sample mounting jig 41 and fixed to a sample XY moving table 42. The sample XY moving table 42 is fixed to a sample rotating table 43. The sample rotation table 43 can be rotated about an axis perpendicular to the X-ray optical path X at an externally controllable rotation angle θ. Further, the sample rotation table 43 is structured to be capable of performing Z parallel movement in the direction of the rotation axis.

After imaging the entire image of the sample at low magnification, the sample XY movement table 42 and the Z translation function of the sample rotation table move the position of the sample to view the desired specific region (Region of Interest, ROI) of the sample. It is possible to adjust to the center.

The FZPs 5-1 and 5-2 are fixed to the mounting jig 51 capable of adjusting the height Z, fixed to the FZP XY moving base 52, and then mounted on the optical system switching base 8. As a result, FZP 5-1 and FZP 5-2 can be independently adjusted on the optical system switching base with respect to the X-ray optical path X direction and two axes YZ perpendicular thereto.

The G1 grids 6-1 and 6-2 are respectively fixed to a mounting jig 61 capable of height adjustment Z, and can be swivel-adjustable in an X vertical plane around an X-ray optical path X near the center of the G1 grid, After being fixed to the rotatable rotation mechanism 63 and the G1 grating XY moving base 64, it is mounted on the X-ray optical system switching base 8. Thereby, the G1 grating 6-1 and the G1 grating 6-2 independently of each other on the X-ray optical system switching table 8, the X-ray optical path X direction and the perpendicular plane to the X-ray grating of the two axes YZ and G1 grating perpendicular thereto. It is adjustable for internal rotation and rotation about an axis perpendicular to the x-ray path X.

The adjustment items of the FZPs 5-1 and 5-2 and the G1 gratings 6-1 and 6-2 are adjusted respectively with respect to the X-ray optical paths of two kinds of microscope magnifications. Although some of these adjustments can be performed manually, it is desirable to be able to adjust by external control. The vertical Y movement of the G1 gratings 6-1 and 6-2 with respect to the X-ray optical path must be able to be moved by external control for measurement to obtain phase image information.

Although the FZPs 5-1 and 5-2, and the G1 gratings 6-1 and 6-2 configured on the X-ray optical system switching stand 8 as described above constitute X-ray optical paths of two different magnifications, By moving the entire optical system switching base 8 relative to the X-ray optical path X, it is possible to easily switch the XRM magnification. In this embodiment, two types of XRM magnifications are switched. However, two or more types of optical systems can be switched. When the magnification is 1 ×, no FZP is installed in the X-ray optical path X.

FIG. 7 shows a configuration diagram including the device control system of this embodiment. The X-ray source 1 uses the X-ray source controller 93 to control the G0 lattice adjustment mechanism, the sample position adjustment mechanism, the FZP position adjustment mechanism, the G1 position adjustment mechanism, and the X-ray optical system switching stand 8 as illustrated. Further, the X-ray detector 9 is connected to the XRM controller 91 via the X-ray detector controller 95 via the device 94. These adjustment parameters and X-ray detector measurement data are stored in the data storage device 92.

The block diagram of the whole microscope apparatus of a present Example is shown in FIG. The devices shown in FIG. 7 are installed on an optical bench 103 installed on a task 104 waiting for an isolation function to prevent vibration from the floor, and constitute an XRM. The XRM is installed in an X-ray shielding and apparatus outer panel 101 provided with a temperature control apparatus 102 for controlling the temperature strictly. The internal temperature control can be controlled to 0.1 ° C.
(How to acquire phase CT image data)

A transmission X-ray image of the sample 4 is obtained on the X-ray detector 8 by the phase difference XRM apparatus of the present embodiment described above. In order to capture a phase image, a direction perpendicular to the X-ray optical path X and perpendicular to the stripe of the grating at a moving pitch of 1/3 or less of the grating pitch of the G1 grating 6-1 or 6-2 used for imaging A transmission image is obtained by imaging every time when the minute amount ΔY is moved (in this case, the Y direction). Due to the structure of the sample, slightly refracted X-rays pass through the G1 grating, distort the image of the grating, measure it as image data with the X-ray detector 8, and record it in the data storage device 92. When imaging a low magnification (X10) X-ray microscopic image of this example, 0.725 μm which is a quarter of the pitch (2.9 μm) of the G1 grating is ΔY, 0, 0.725 μm, 1.45 μm, 2 4 transmission images are taken with a movement of 175 μm. From these four images, a phase image is acquired using the method described in Non-Patent Document 11. At this time, not only a phase image but also an absorption image and a scattering image can be acquired.

In order to acquire a CT image, G1 is moved by ΔY every time the rotation axis of the sample rotation table 43 shown in FIG. 6 is rotated by Δθ using the imaging method of the transmission X-ray image described above. Are imaged, and CT data of a phase image for each sample rotation angle is recorded in the data storage device 92. At this time, it is possible to simultaneously record as CT data for each sample rotation angle of absorption and scattering images. This measurement method makes ΔY constant and moves angularly every Δθ to record a transmission image, measures transmission image data every Δθ every time ΔY is moved, records it in a data storage device, and measures all data It is also possible to carry out data processing after completion to obtain CT data of a phase image, an absorption image, and a scatter image.
(How to acquire phase interior CT image data)

Some of the samples 4 may need to perform particularly high spatial resolution data measurements. In the case of the apparatus of this embodiment, a detection area of 66 μm (corners) and a detection pixel size of 65 nm at low magnification (× 10 ×) and a detection area of 16.4 μm (corners) at high magnification (× 40 ×) and 16.3 nm The detection pixel size of For example, the case where the sample size is 50 μm in diameter and it is desired to measure the 10 μm portion of the sample with the highest spatial resolution CT corresponds to this example. If the sample size is 50 μm in diameter, it will be measured at a low magnification setting of low magnification XRM, and CT images will be acquired at a 65 nm detection pixel size. At high magnification settings, strict CT image reconstruction can not be performed.

Even in such a case, the interior CT image reconstruction method of the present invention is capable of image reconstruction with high accuracy, and a data acquisition method for performing phase interior CT image reconstruction will be described.

When measuring at low magnification, as shown in FIG. 9A, the entire sample 4 is irradiated with X-rays, and the X-rays pass through the FZP 5-1 and G1 grid 6-1 and enter the X-ray detector 8 . Under this condition, it is possible to acquire CT data and perform strict CT image reconstruction. On the other hand, when the same sample is imaged by XRM with high magnification setting, the state as shown in FIG. 9B is obtained from the relationship between the magnification of FZP5-2 and the size of the X-ray detector 8, and only 41 parts of the sample The condition is such that X-ray transmission in all directions necessary for CT image reconstruction is performed, but the other part is insufficient measurement data, and strict CT image reconstruction including the sample 41 part can not be performed. . Therefore, in the present invention, a method of performing accurate CT image reconstruction of the sample 41 portion of FIG. 9 (b) using the strict CT image reconstructable data obtained by the low magnification measurement shown in FIG. 9 (a) Developed. Measurement data obtained by low magnification measurement or a reconstructed CT image can be used as a priori information of the sample 4 of unknown structure.

Furthermore, according to this method, after acquiring the entire CT image data in FIG. 9A, measurement is performed with the setting shown in FIG. 9B. In FIGS. 9A and 9B, the X-ray shutter 11 attached to the X-ray source 1 moves the X-ray shutter 11 from the X-ray optical path X only when it is necessary to acquire data, The sample 4 is to be irradiated with X-rays. Furthermore, when acquiring data of only a part of the sample at a high magnification setting, the X-ray will be irradiated only to the required part using the 4-quadrant X-ray slit 12 or the X-ray pinhole. There is an advantage that it is possible to significantly reduce the overall X-ray exposure.
(Image reconstruction method for interior CT)

In the present invention, a method of identifying a priori knowledge from measured projection data (a priori knowledge identification type image reconstruction method) and a method of identifying a priori knowledge on the periphery of an image without identification (approximately applies to any image). We developed a method to fix (a priori knowledge non-identification type image reconstruction method).

FIG. 10 shows the process flow of the two-stage image reconstruction method of the present invention. In the first step (S61), an incomplete image including an artifact is generated using a conventional FBP method, successive approximation image reconstruction method, statistical image reconstruction method or the like without a priori knowledge. Although this incomplete image includes artifacts, the artifacts generated in the interior CT are low frequency components, and therefore, information on boundaries such as tissues and structures is generated accurately in most cases. From the above-described incomplete image shown also in FIG. 11A, the region B which is an arbitrary small region in the ROI that can be used as a priori knowledge is specified by image analysis (processing) by the user manually or using software. (S62).

In the second step, the region B obtained in the first step is used for a priori knowledge, as shown in FIG. 11 (b), by the strict image reconstruction method (ie, the first stage reconstruction Image reconstruction (with higher accuracy) is performed (S63). It is determined according to what kind of prior information area B could be extracted by the first step.

Here, the results obtained from the above incomplete image are summarized in the following [Result 1] to [Result 4]. For example, if B is a constant value, [Result 1], if it is not a constant value but B is close to the density change of the polynomial, [Result 2], if it is a piecewise uniform B [Result 3 ] And, if it is B close to the density change of the piecewise polynomial, select and use [Result 4].

[Result 1 (constant value a priori knowledge)]
As shown in FIG. 12 (a), it is known that an arbitrary small a priori information region B exists inside the ROI, and the image f (x, y) in B has a constant value C (constant). If there is, the solution of the image reconstruction of the interior CT is uniquely determined. As a result, 1 reduces the a priori knowledge of the exact solution in Non-Patent Documents 5 and 6.

[Result 2 (polynomial a priori knowledge)]
As shown in FIG. 12 (a), it is known that an arbitrary small a priori information region B exists inside the ROI, and in B, the image f (x, y) is an M-order polynomial. If there is, the solution of the image reconstruction of the interior CT is uniquely determined. Here, the polynomial means that the density change of the image f (x, y) has the following form.

However, the degree M of the polynomial needs to be known, and the coefficients a _mn of the polynomial may be unknown. [Result 1] strengthens the restriction of the form of the function in which the degree of the polynomial is set to M = 0 in [Result 2], that is, [Result 2] gives a priori knowledge of [Result 1] It has been reduced.

[Result 3 (piecewise uniform a priori knowledge)]
As shown in FIG. 12 (a), if an arbitrary small region B exists inside the ROI, and it is known that the image f (x, y) is piecewise constant in B. , The solution of image reconstruction of interior CT is decided uniquely. Here, as shown in FIG. 13, the piecewise uniform means that B is composed of a finite number (L) of areas D ₁ , D ₂ ,..., D _L , and constant values C ₁ and C _{2 are used} in each area. , ..., it is that it is C _L. However, the number of regions L and the values of the fixed values C ₁ , C ₂ ,..., C _L may be unknown in advance, and [Result 3] is a reduction of a priori knowledge of [Result 1].

[Result 4 (piecewise polynomial a priori knowledge)]
As shown in FIG. 12 (a), it is known that an arbitrary small region B exists inside the ROI, and in B, the image f (x, y) is an M-order piecewise polynomial. If there is, the solution of the image reconstruction of the interior CT is uniquely determined. Here, the piecewise polynomial, region D _1, D ₂ of a finite number in the B as shown in FIG. 13 (L number), ..., is composed of D _L l th region image f _l (x in, y The concentration change of) is in the following form.

However, the degree M of the polynomial needs to be known, the number of regions L and the polynomial coefficients a _mn ^(l) may be unknown, and [Result 4] is a priori knowledge of [Result 2] and [Result 3] Has been reduced.

Thus, theoretically considering the a priori knowledge of the object necessary for performing the exact image reconstruction, the destination described in the above-mentioned prior art (non-patent documents 5 and 6, patent document 1) It enables strict image reconstruction with much less a priori knowledge than empirical knowledge.

Although the above non-patent documents 5 and 6 use a priori knowledge of an arbitrary small area B in the same ROI as [Result 1] to [Result 4], the value itself of the image f (x, y) is necessary In contrast to the above, in the present invention, in [Result 1] to [Result 4], the value of the image f (x, y) is much less a priori such as (piecewise) uniform or (piecewise) polynomial It differs greatly in the good point only by knowledge. Further, although Non-Patent Document 5 uses piecewise uniform type a priori knowledge, it is necessary to make an impossible assumption that piecewise uniformity is uniform throughout the ROI, not arbitrary small region B in the ROI. It is different.

Further, FIGS. 14 (a) to (c) show an example of actual reconstruction when using the a priori knowledge of the above [Result 1] to [Result 3]. In any case, it can be seen that a little prior knowledge has achieved a large artifact reduction.
Example 2

According to the invention described in the above embodiment, the a priori knowledge required for the exact image reconstruction of the interior CT can be much less. However, in actual CT imaging, there are relatively few cases where prior knowledge of an object of interest is known before imaging. Therefore, in the second embodiment, in the process flow shown in FIG. 13 described above, the region B which is an arbitrary small region in the ROI that can be used as a priori knowledge in step S62 is automatically identified. The automatic identification of the a priori information area can be realized, for example, by using an image analysis (processing) technique.
Example 3

The success or failure of the a priori knowledge automatic estimation type image reconstruction method of the above-mentioned first embodiment depends on whether or not the identification of the a priori knowledge (area B) in the first step is successful. Therefore, in the third embodiment, the “prior knowledge non-identifying image reconstruction method” in which the image reconstruction is performed without identifying the region B is described. Although the assumption that the portion of the edge around the ROI is piecewise uniform or piecewise polynomial is not strictly correct, the method remains an approximate image reconstruction method, but in the context of many CT imaging As [Finding 3] holds, by fixing and arranging at the part of the edge that is the periphery of the ROI, as shown in FIGS. 14 (a) to (c), another approximation that does not use a priori knowledge Image reconstruction can be performed with much higher accuracy compared to conventional image reconstruction methods.

15 (a) to 15 (f) show specific simulation experiments of the a priori knowledge identification type image reconstruction method (Example 1) and the a priori knowledge non-identification type image reconstruction method (Example 3). An example is shown by comparison with the prior art. Chest CT images were used for the experiment, and image reconstruction was performed with the heart portion located at the center as ROIS (see FIG. 15 (a)).

In the first embodiment, the user looks at the reconstructed image by the FBP method in the first step, and the a priori information region B is specified in the ROI S, and the strict image reconstruction in the second step is performed (note that The a priori knowledge that we have is piecewise uniform at B corresponding to [Result 3]). The result is shown in FIG. Further, in the third embodiment, the a priori information area B is fixed to the peripheral portion of the ROI S (the frame type in FIG. 14C) to perform image reconstruction (note that the a priori knowledge used is [Result 3) piecewise uniform at B). The result is shown in FIG.

Further, as a comparative example, a result of the local FBP method of applying the FBP method by extrapolating the lost portion of the projection data in a smooth function in FIG. 15 (b), the image f (x in the same priori information area B, y The result by the method (nonpatent literature 5, 6) which used the true value of) for a priori knowledge is shown in FIG.15 (c) not only small a priori information area B but piecewise uniform over the whole ROI S. The result by the compression sensing method (nonpatent literature 5) which applies the total variation (TV: Total variation) which is restraint of is each shown in FIG.15 (d).

As apparent from these results, in the local FBP method, a strong cupping effect occurs and image degradation is remarkable, and in the compressed sensing method, details and smooth density changes are considerably lost due to the influence of TV. On the other hand, it can be seen that in the methods of Embodiments 1 and 3 of the present invention, the image is reconstructed quite well by reducing the artifacts.

[Image reconstruction method]
An image reconstruction method for generating an image from projection data based on the uniqueness of the solutions of [Result 1] to [Result 4] described above will be described. First, vectors obtained by discretizing the image f (x, y) and the projection data p (r, θ) are represented by f and b, respectively, and a projection operation matrix that associates projection data with the image is represented by A. However, the image f includes not only the pixels in the ROI S but all the pixels belonging to the object existing area in the cross section (note that it is necessary), and the projection data vector b is created by arranging all the measured values in a line. Also, an evaluation function for evaluating whether a priori knowledge is satisfied in the a priori information area B is represented by F (x). At this time, image reconstruction can be formulated as any of the following three optimization problems.

[Formulation 1]

[Formulation 2]

[Formulation 3]

However, x∈C represents a constraint known in advance with respect to an image, and the following are often used.
(A) (Support constraint) The image f becomes zero outside the support area Ω _OBJ where it is known in advance.
(B) (non-negative condition) The components of the image f do not take negative values.
(C) (Projected data value on Hilbert straight line) In the image reconstruction method using Hilbert transform described later, the Hilbert straight line L (u) described later is used to compensate for the loss of information when Af = b is rewritten as Hf = c. ) Projection data values are used.

If F (x) is a function that does not have a local minimum called a convex function, an iterative solution or non-iterative solution that solves the above-mentioned problems is numerous in the field of mathematical optimization and image reconstruction. It is known and all these approaches are available. For example, a constrained statistical image reconstruction method or a constrained conditional successive approximation method can be used. In addition, as another class of image reconstruction methods, it is possible to construct an image reconstruction method based on a later-described framework called differential back projection (DBP). Although the details of the DBP method will be described later, in the DBP method, the relational expression between the image f and the projection data b represented by Af = b is not used as it is, but the image f and Hilbert image (Hilbert image) are temporarily obtained by a method called DBP. The image reconstruction is performed by converting the incomplete image c called H) into the relational expression Hf = c, and formulating it as follows. Methods of this class are referred to by the names such as differential backprojection + truncation Hilbert transform (Non-Patent Documents 5 and 6, Patent Document 1).

[Formulation 4]

[Formulation 5]

[Formulation 6]

Of course, the problem formulated as described above is solved using an iterative or non-iterative solution.

Next, an evaluation function F (x) for evaluating a priori knowledge in the a priori information area B, which is a key for performing exact or accurate image reconstruction in the present invention, will be described.

First, unlike the methods of Non-Patent Document 5 and Non-Patent Document 6 in which constraints based on a priori knowledge are applied to the entire ROI S in designing F (x), the image reconstruction method of the present invention A constraint condition is imposed only on the prior information area B which is an arbitrary small area in the above. In the case of [Result 1], since the first derivative of f (x, y) is a priori knowledge that becomes zero in B , the norm of the first derivative of f (x, y) in B is minimized Or dispersion of the concentration change in B may be minimized. In the case of [Result 2], since the M + 1th derivative of f (x, y) is a priori knowledge that becomes zero in B, minimize the norm of the M + 1th derivative, It is sufficient to minimize an error between a function obtained by applying an Mth-order polynomial to f (x, y) and f (x, y) ((x, y) ∈B). For Results 3], f (x, y ) is from a priori knowledge to be piecewise uniform within B, L ₀ norm or L ₁ total variation based on the norm in the B (TV: Total Variation) You can minimize the norm. In the case of [Result 4], since the Mth derivative of f (x, y) is a priori knowledge that becomes piecewise uniform in B, a TV defined based on M + 1 derivatives Minimize the norm.

Examples of typical F (x) are shown in Table 2. However, in Table 2, the parameter p is the order of the norm, and its value may be 0 ≦ p ≦ 2 in [Result 1] and [Result 2], but it is uniform or M in [Result 3] and [Result 4]. It is necessary to use 0 ≦ p ≦ 1 in order to avoid that the influence of the boundary of the finite number (L) of partial regions having the density change of the next polynomial is evaluated too large. However, in the case of 0 ≦ p <1, since F (x) does not become a convex function, it is necessary to devise an iterative solution method and a non-iterative solution method, and it can be said to use p = 1. In addition, although the thing by which there were a plurality of candidates with F (x) shown in Table 2 was tested by numerical experiment, all worked well generally and no big difference was seen.

Furthermore, as described above, a method for realizing mathematically exact image reconstruction of interior CT using low-magnification overall image data without using prior information on objects in the prior art as described above will be described in detail below. Describe.
Example 4

The uniqueness of the solution (in which the solution of the image reconstruction problem is settled into one) is established in the interior CT, and in order to enable the exact image reconstruction, some additional information in addition to the interior CT projection data is necessary. In the fourth embodiment, as an alternative, the interior CT measures projection data which is not usually measured, that is, extra projection data which does not pass through the region ROI Ω where the image is desired to be obtained, and utilizes this supplementary data. That is, an important feature of the exact solution of the fourth embodiment of the present invention is to measure the minimum necessary extra projection data not passing through the ROI Ω.

In particular, by having an imaging optical system capable of changing the magnification of sample imaging, it is effective to use supplementary data as extra projection data, a part of which captures an entire image of the sample and does not pass through the ROI Ω. Become.

The above method has generality that can be applied to various optical systems of microscopic CT such as electron beam, X-ray, neutron beam etc. First, the electron beam source is relatively in circular orbit by rotating the sample The principle of the 360-degree moving fan beam CT will be described. As shown in FIG. 16A, consider the situation of interior CT where projection data is measured by irradiating only the electron beam passing through the ROI Ω in the 360-degree circular orbit fan beam CT. Of course, it is impossible to reconstruct mathematically exact ROI Ω image only by the interior CT projection data, and as shown in FIG. 16 (b), the arc segment E of a part of the circular orbit is used as additional information. The entire projection data is measured by irradiating an electron beam covering the entire object. When this partial whole projection data (hereinafter, also simply referred to as “whole projection data”) is added to the interior CT projection data to enable strict ROI Ω reconstruction, arbitrary arc segment E, It has been mathematically proved that accurate ROI Ω image reconstruction is possible if the whole projection data is small arc segments which are not one point.

In addition, for the above problems, the differential back projection (DBP) method used as a tool to show the uniqueness of the solution in the study of the interior CT exact solution method using the a priori information on the past objects Although it can not be proved by the image reconstruction method (nonpatent literature [5]-[9]) which combined with the pruning Hilbert transform, it proves by using the new image reconstruction method which introduced the truncation Hilbert transform for filtering processing in FBP method did. Here, the uniqueness of the solution of the interior CT image reconstruction problem in the 360-degree circular orbit fan beam CT revealed by the present invention is summarized as follows.

[Uniqueness of solution]
If there is a full scan (without left and right truncation) projection data of an arbitrary arc segment E (which may be small) in addition to the interior CT projection data, the solution for the image reconstruction of the interior CT is unique.

<Uniqueness of Solution in Obtained Interior CT>
Here, the results of the uniqueness of the solution in the interior CT image reconstruction, which were successfully proved based on the new image reconstruction method in which the truncation Hilbert transform is introduced into the filtering processing of the FBP method described later, are described collectively. In addition, as a definition of a symbol, an object image (object) is represented by f (x, y), and ROI is represented by Ω.

First, based on FIG. 17, the way of thinking upon generalization will be described. When an arbitrary geometric system is used, it is common to measure a set of line integral values on a straight line of the object, and a coordinate conversion relationship holds between the two projection data. Therefore, as shown in FIG. 17A, the projection data measured by an arbitrary geometric system is temporarily transformed into projection data of a virtual 360 degree circular orbit fan beam CT, and the data is 360 degrees. Investigate whether the condition of uniqueness in the case of circular orbit fan beam CT is satisfied and derive the uniqueness of the solution applicable to projection data measured in any geometric system, the following conclusion is finally obtained Be

[Uniqueness of solution (arbitrary geometric system)]
If the projection data is measured such that both of the following two conditions are satisfied, the image f (x, y) is uniquely determined in the ROI Ω, and an exact reconstruction of Ω is possible.

(Condition 1) All projection data passing through the ROI Ω have been measured (measurement conditions of interior CT).

(Condition 2) Projection data (partial whole projection data) on all straight lines passing an arbitrary small arc segment E (corresponding to a certain circle including the object) existing outside the object is measured (partial whole projection data) Conditions of projection data to be measured extra to define the solution uniquely).

However, the length of the small arc segment E is a segment corresponding to a certain circle including the object inside, and may be anywhere as short as possible, if it is not one point. The meaning of the two geometric conditions described above is shown in FIG.

In addition, three cases in which the uniqueness of the above generalized solution is valid are described below.

(A) In the case of 180 degree parallel beam scan In the data acquisition method of CT, consider the case where the interior CT is carried out with the 180 degree parallel beam scan which is the most basic in electron beam imaging. The projection data is represented by p (r, θ) (projection angle range −π / 2 ≦ θ <π / 2), where r is a radius and θ is an angle. Now, it is assumed that overall projection data (without truncation) is measured in an angular range of −ε ≦ θ ≦ ε (ε is a small angle), and only interior CT projection data is measured otherwise. At this time, if the small circular arc segment E is as shown in FIG. 18A, it is understood that the condition of uniqueness of the above solution is satisfied, and the solution of the ROI reconstruction is unique.

[Uniqueness of solution (180 degree parallel beam)]
If the total projection data is measured in an arbitrary small angle range E (how small it may be) in addition to the interior CT projection data in the angle range of −π / 2 ≦ θ <π / 2, the image f (x, y in ROI Ω ) Is uniquely determined, and strict reconstruction of Ω is possible.

This uniqueness can be interpreted as the limit in which the radius of the circular orbit is infinite in the uniqueness of the solution in the case of the 360-degree circular orbit fan beam CT, and the 360-degree circular orbit fan also in the 180-degree parallel beam scan The uniqueness of the solution similar to the beam CT is established. This result can be proved for the first time by using uniqueness generalized to arbitrary geometric systems.

(B) In the case of fan beam short scan Consider the case of fan beam short scan shown in FIG. The position of the X-ray source on the circular orbit is β∈ [−π / 2−α _max , π / 2 + α _max ) (α _max is an overscan angle determined from the condition of the short scan, Non Patent Literature [7]), The fan beam projection data is represented by g (u, β), where u is the coordinates on the straight line detector. Now, it is assumed that overall projection data (without truncation) is measured in an angular range of −ε ≦ β ≦ ε (ε is a small angle), and only interior CT projection data is measured otherwise. At this time, if the small circular arc segment E is as shown in FIG. 21 (b), it is understood that the condition of uniqueness of the above-mentioned solution is satisfied, and the solution of the ROI reconstruction is unique.

[Uniqueness of solution (fan beam short scan)]
In addition to the interior CT projection data of the angle range of -π / 2-α _max β β <π / 2 + α _max , the ROI can be obtained by measuring the total projection data in an arbitrary small angle range E (which may be small). The image f (x, y) is uniquely determined by Ω, and strict reconstruction of Ω is possible.

(C) Fan beam scan using polygonal trajectory:
Consider a fan beam scan using a regular pentagonal trajectory shown in FIG. 18 (c). The position of the X-ray source on the regular pentagonal orbit is β∈ [-π, π) (β is the azimuth angle seen from the center of the regular pentagon to the point on the orbit), fan beam projection with u on the linear detector Data are expressed as g (u, β). Now, the entire projection data (without truncation) is measured in the angle range of -ε ≦ β ≦ ε (ε is determined so that one side of the regular pentagonal trajectory is E with a small angle), otherwise the interior CT projection data Can only be measured.

At this time, if the small circular arc segment E is as shown in FIG. 18C, it can be understood that the condition of uniqueness of the above solution is satisfied, and the solution of the ROI reconstruction is unique.

[Uniqueness of solution (fan beam scan using polygonal trajectory)]
If the total projection data is measured in an arbitrary small angle range E (how small it may be) in addition to the interior CT projection data in the angle range of −π ≦ β <π, the image f (x, y) is ROI Ω It is uniquely determined, and strict reconstruction of Ω is possible.

(B) Use Scout-View scan projection data for alignment as whole projection data For the purpose of positioning the ROI Ω that you want to see before shooting well within the field of view, such a low magnification that all objects are within the field of view Scout-View scan is performed. The projection data with low magnification can be used as total projection data of the partial trajectory E by using the function of this Scout-View scan.

Next, a second measurement is performed on the ROI Ω specified by the Scout-View scan, and only the electron beam passing through the ROI is irradiated by rotation of the Ry axis to measure all interior CT projection data, and image information data It is stored in the storage device 61. Thereby, data acquisition for reconstructing the interior CT image can be performed.

Using the measurement data of all of the internal CT projection data of the second time performed on partial data of the whole sample projection of the first measurement stored in the image information data storage device 61 and the ROI Ω using the image reconstruction method of the description, Reconstruct the interior CT image.

DESCRIPTION OF SYMBOLS 1 ... X-ray source, 2 ... X-ray condensing mirror, 3 ... G0 grating | lattice, 4 ... sample, 5-1, 5-2 ... FZP, 6-1, 6-2 ... G1 grating | lattice, 7 ... X-ray detector 8 ... optical element mounting table, 11 ... X-ray shutter, 12 ... 4 quadrant X-ray slit, 31 ... G0 lattice attachment jig, 32 ... G0 lattice swivel, 33 ... G0 lattice rotating mechanism, 34 ... G0 lattice moving mechanism, 41 ... Specimen mounting jig, 42 ... Specimen XY moving table, 43 ... Specimen rotating table, 51 ... FZP mounting jig, 52 ... FZP XY moving table, 61 ... G1 lattice mounting jig, 62 ... G1 lattice swivel, 63 ... G1 lattice rotating mechanism 64: G1 lattice XY moving table 91: XRM controller 92: data storage device 93: X-ray source controller 94: controller for position adjustment 95: X-ray detector controller 101: X-proof Wire shield and device external panel, 102 ... temperature control device, 103 ... optical bench, 104 Stand.

Claims (9)

An x-ray source that emits x-rays;
A first grid disposed between the x-ray source and the sample for shaping the x-ray source into a grid;
A sample stage for holding the sample in rotation;
An X-ray imaging optical system disposed behind the sample;
A second grid disposed behind the sample;
Means for detecting the X-ray image of the sample as an intensity distribution, the X-ray microscope apparatus comprising:
An interior CT phase imaging X-ray microscope apparatus comprising a mechanism capable of simultaneously exchanging the X-ray imaging optical system and the second grating in a combination of two or more sets. The interior CT phase imaging X-ray microscope apparatus according to claim 1, further comprising a condenser lens for condensing X-rays emitted from the X-ray source between the X-ray source and the first grating. A mechanism capable of changing the position of the first grating installed between the condenser lens and the sample, the mechanism capable of simultaneously exchanging the X-ray imaging optical system and the second grating in a plurality of combinations An interior CT phase imaging X-ray microscope apparatus comprising: Projection data is acquired by the X-ray passing through a region desired to be measured inside the sample, and tomographic imaging (CT) is performed using a plurality of projection data obtained by imaging one rotation-controllable axis at each rotation step. The first stage of approximate reconstruction is performed by the image reconstruction method, and at least the image numerical value of the projection data representing the physical quantity is divided within the area desired to be measured inside the sample based on the reconstructed CT image. Identify the region A represented uniformly and piecewise in a polynomial manner, and use the property that the physical quantity is piecewise uniformly or piecewise represented in a polynomial manner in the position of the region A and the inside thereof It is characterized in that it is possible to three-dimensionally measure the structure in the area desired to be measured inside the sample by the image reconstruction method of the interior CT which performs the second stage reconstruction with higher accuracy than the reconstruction at the first stage. The Interior CT phase imaging X-ray microscope apparatus. As a preparatory step of acquiring projection data by the X-rays passing through a region desired to be measured inside the sample, a part of the CT data image is measured at a low magnification at which CT data of the entire sample can be acquired; It is possible to three-dimensionally measure the structure in the area desired to be measured inside the sample by the image reconstruction method of the interior CT using the data of the first preparation stage together with the data for performing the second stage reconstruction with high accuracy. Interior CT phase imaging X-ray microscope device characterized by. 5. The method of reconstructing an image of interior CT according to claim 3, wherein the numerical value of the image of the projection data is a method of reconstructing the image of interior CT including the phase shift of the electron beam by the imaging target. Features an interior CT phase imaging X-ray microscope device. The image reconstruction method for interior CT according to claim 3 or 4, wherein the image numerical value of the projection data is the image reconstruction method for interior CT including the diffraction of the electron beam by the imaging target. To interior CT phase imaging X-ray microscope device. The method according to claim 3 or 4, wherein the region A specified in a region desired to be measured in the sample is obtained by the approximate reconstruction of the first step. An interior CT phase imaging X-ray microscope apparatus characterized in that it can be selected from among a plurality of regions A specified from a CT image in a piecewise uniform or piecewise polynomial expression. 5. The method for reconstructing an interior CT image according to claim 3, wherein the region A specified within a region desired to be measured inside the sample is obtained by the approximate reconstruction of the first step. An interior CT phase imaging X-ray microscope apparatus characterized by being manually settable by human using CT images. In the method of reconstructing an interior CT image according to claim 3 or 4, the CT image obtained by the approximate reconstruction in the first step is used to specify within a region where measurement inside the sample is desired. The region A to be measured is a specification from a previously acquired CT image of the same or similar sample within a region where measurement inside the sample is desired, a specification from a model representing the structure of the object to be photographed, or a priori information An interior CT phase imaging X-ray microscope apparatus characterized by being preset by at least one.

Images