KR20160068647A - Apparatus and method for reconstructing skeletal image - Google Patents

Abstract

Disclosed are an apparatus and a method for reconstructing skeletal image in order to diagnose osteoporosis, which can reconstruct a high-resolution image based on a low-resolution image. The method for reconstructing skeletal image may comprise the steps of: separating a region of interest (ROI) corresponding to a skeletal system from an image of an examination target based on a finite element method (FEM); and reconstructing a high-resolution skeletal image by performing topology optimization on the ROI.

Description

[0001] APPARATUS AND METHOD FOR RECONSTRUCTING SKELETAL IMAGE [0002]

The present invention relates to a technique for reconstructing a skeletal system image to improve the resolution of a medical image.

As the age of the elderly welfare, medical care, etc. Are emerging, osteoporosis is recognized as a social problem to be solved urgently due to increase of prevalence and quality of life. For example, according to 2010 National Health Statistics, the prevalence of osteoporosis in women over 50 years old was 34.9% for women and 7.8% for men, and 1 in 3 women over 50 years old was found to have osteoporosis. Approximately 200 million women worldwide are suffering from osteoporosis.

The medical definition of osteoporosis is a condition in which the amount of bone is reduced and the quality of the bone is weakened due to qualitative changes, leading to a high possibility of fracture. Here, the strength of the bone means the strength of the bone against the fracture, and is determined by the bone mass and bone quality.

The BMD can be expressed as bone mineral density (BMD), and for clinical bone mineral density measurements, dual energy X-ray absorptiometry (DXA), quantitative computed tomography (QCT) Computed Tomography: CT) are used. According to the DXA-based diagnostic method proposed by the World Health Organization (WHO), it is difficult to diagnose osteoporosis early because the abnormality can be found after the bone mass is lost by 30 to 50% or more exist.

In recent years, it has been known that the strength of bones is determined not only by high density, but also by the structural properties of cancellous bone. Only 64% of the mechanical strength of cancellous bone can be predicted by measuring only bone density, but it can be predicted up to 94% of mechanical strength when considering bone structure. In particular, the network of cancellous bone can not be recovered by drug therapy once it has been broken, and a decrease in the connectivity of trabecular bone has a greater effect on bone strength than the decrease in bone thickness in the same loss of bone mass.

Therefore, it is necessary to consider both the bone mineral density and the bone structure of the cancellous bone in order to accurately diagnose it. However, in order to express the fine bone structure of the cancellous bone, a high resolution of 50 ~ Due to the limitations of resolution, it is difficult to provide information about bone quality, such as micro-bone structure. For example, in the case of CT based on X-ray, the inspection cost and the inspection time are shorter than MRI, but it is difficult to observe the fine bone tissue due to the low resolution of about 600 μm, and the radiation dose increases as the resolution is increased. In the case of micro CT, it is possible to visualize fine bone tissue with a resolution of 30 μm, but there is a difficulty in clinical use because of high radiation dose. In addition, magnetic resonance imaging (MRI) resonates with the body's hydrogen nuclei to image the difference in the signal from the tissue, but as the resolution of the image increases, the imaging time becomes longer and the noise becomes worse and the signal-to-noise ratio (SNR) drops.

Accordingly, there is a demand for an image technique that provides a high-resolution image of 300 mu m or less capable of grasping a fine bone structure without increasing radiation exposure and imaging time.

Embodiments of the present invention are intended to reconstruct a low-resolution image due to limited radiation exposure, shooting time, and the like into a high-resolution image capable of grasping a micro-bone structure using a finite element analysis and a phase optimization.

In addition, by providing a high-resolution image showing the bone density and the fine bone structure, it is intended to perform an early diagnosis of lesions related to bone density, such as osteoporosis, more accurately.

A method for reconstructing a skeletal image includes the steps of: determining a region of interest (ROI) to be hyperfilmed with respect to a skeletal system image obtained by photographing a subject; and performing a finite element modeling and topology optimization Optimization may be performed to reconstruct the skeleton image that has been highly resolved.

According to an aspect of the present invention, the step of reconstructing the skeletal system image includes: performing meshing on a skeletal system image including the ROI; transforming a bone density of the bone corresponding to the ROI into a modulus of elasticity Applying the elastic modulus to each finite element constituting the region of interest, applying a predetermined load condition to each finite element to which the elastic modulus is applied, varying each finite element applied with the load condition Calculating the strain energy of each finite element based on the strain energy of each finite element, and determining the density value of each finite element based on the strain energy of each finite element.

According to another aspect of the present invention, the step of performing the element partitioning includes dividing the skeletal system image into an image including a plurality of finite elements, and the bone density of the bone is included in a region of interest Can represent the bone density of the bone corresponding to the finite element.

According to another aspect of the present invention, there is provided a method of reconstructing a skeletal system image, the method comprising: performing phase optimization to determine a new bone density for the ROI; and converting a new bone density to an elasticity, Lt; / RTI >

According to a further aspect, there is provided a method of calculating bone density, comprising: calculating a bone density of each finite element contained in the region of interest based on an objective function and a predetermined constraint representing compliance of a region of interest; And estimating whether or not the subject has a lesion.

According to another aspect, the high resolution skeletal system image may include a trabecular structure of a bone corresponding to the region of interest.

According to another aspect, the highly resolved skeleton image can display an image having a resolution of 300 mu m or less.

The apparatus for reconstructing a skeleton image includes an interest region determining unit for determining a region of interest (ROI) to be hyperfilmed with respect to a skeletal system image obtained by photographing the subject, and a finite element modeling and phase And an image reconstruction unit reconstructing a skeleton image that has been highly resolved by performing optimization (topology optimization).

According to an aspect of the present invention, the image reconstruction unit performs element meshing on a skeletal system image including the ROI, converts the bone density of the bone corresponding to the ROI into the elasticity, Wherein the load factor is applied to each finite element constituting the region of interest and a predetermined load condition is applied to each finite element to which the modulus of elasticity is applied and the strain of each finite element Energy can be calculated and the density value of each finite element can be determined based on the strain energy of each finite element.

According to another aspect, the image reconstruction unit divides the skeletal system image into an image including a plurality of finite elements, and the bone density of the bone is divided into a finite element included in a region of interest to be high- Can represent the bone density of the corresponding bone.

According to another aspect of the present invention, the image reconstructing unit may perform a phase optimization to determine a new bone density for the ROI, and convert the new bone density to an elasticity, thereby reconstructing the skeleton image have.

A skeletal system image reconstruction apparatus includes a memory in which at least one program is loaded and at least one processor, and the at least one processor is configured to perform skeleton image reconstruction based on a skeleton system image, A process of determining a region of interest (ROI) to be transformed and a process of reconstructing a skeleton image of a high resolution by performing finite element modeling and topology optimization on the ROI .

According to an aspect of the present invention, there is provided a method of reconstructing a skeletal system image, the method comprising: performing meshing on a skeletal system image including the ROI; transforming a bone density of the bone corresponding to the ROI into an elasticity A step of applying the modulus of elasticity to each finite element constituting the region of interest, a step of applying a predetermined load condition to each finite element to which the modulus of elasticity is applied, a variation of each finite element to which the load condition is applied Calculating a strain energy of each finite element based on the strain energy of each finite element, and determining a density value of each finite element based on the strain energy of each finite element.

According to another aspect, there is provided a computer-readable storage medium containing instructions to control a computer system to provide a skeletal system image, the instructions comprising the steps of: obtaining a skeletal system image of a subject, Determining a region of interest (ROI) of the region of interest, and reconstructing a skeletal image that has been highly resolved by performing finite element modeling and topology optimization on the ROI, The system can be controlled.

According to one aspect, the instructions comprise calculating a bone density of each of the finite elements included in the region of interest based on an objective function representing a strain energy of the region of interest and predetermined constraints, And estimating whether or not the subject has a lesion based on the image.

According to the present invention, the imaging of the subject is performed in a short time considering the radiation dose and the like by using the finite element analysis and the phase optimization, so that the high-resolution imaging capable of grasping the fine bone structure without increasing the radiation dose or increasing the imaging time Can be reconstructed.

In addition, by applying the finite element analysis and the phase optimization, it is possible to reconstruct a high resolution image capable of grasping the fine bone structure without separately purchasing a medical imaging device capable of high resolution imaging.

In addition, by providing a high-resolution image showing the bone density and the fine bone structure, it can not only help early diagnose a lesion based on bone density such as osteoporosis, but also improve the accuracy of diagnosis.

1 is a view showing a CT photographing apparatus according to an embodiment of the present invention.
2 is a block diagram for explaining an internal configuration of a skeletal system image reconstruction apparatus according to an embodiment of the present invention.
3 is a flowchart illustrating a method of reconstructing a skeletal system image in an embodiment of the present invention.
4 is a view showing a finite element model generated based on a finite element analysis in an embodiment of the present invention.
5 is a diagram for explaining a phase optimization process in an embodiment of the present invention.
Figure 6 is a diagram illustrating a high-resolution area of interest in one embodiment of the present invention.
7 is a block diagram for explaining an example of the internal configuration of a computer system in an embodiment of the present invention.

Hereinafter, embodiments according to the present invention will be described in detail with reference to the accompanying drawings. However, the present invention is not limited to or limited by the embodiments. In addition, the same reference numerals shown in the drawings denote the same members.

FIG. 1 is a diagram showing a schematic configuration of a medical imaging device and a skeleton-based image reconstruction device in an embodiment of the present invention.

Referring to FIG. 1, the medical imaging apparatus 101 is an apparatus for photographing a subject for diagnosis of osteoporosis, measurement of bone density, confirmation of bone microstructure, and the like.

Herein, the medical imaging apparatus 101 may include all the medical devices that convert the intensity of the image of the subject into elasticity. For example, the medical device 101 may be any medical imaging device capable of converting image intensity to elasticity, such as a computed tomography (CT), a quantitative computed tomography (QCT), a magnetic resonance imaging . The subject can be a specific body part to be imaged for the measurement of bone density, for example, a thigh, a hip, a wrist, an ankle, a knee, a lumbar vertebra, and the like.

As described above, the medical imaging apparatus 101 can photograph the subject and the photographing information can be transmitted to the skeletal-system image reconstruction apparatus 100. [ For example, when a subject is photographed in a QCT photographing apparatus, the QCT photographing apparatus can detect X-rays transmitted through the subject and transmit the detected X-rays to the skeleton-based image reconstructing apparatus 100. Then, the skeletal system image reconstruction apparatus 100 can generate a low-resolution QCT image based on the received X-rays.

Then, the skeletal-system image reconstruction apparatus 100 can reconstruct a high-resolution skeletal system image of 300 μm or less by performing finite element method (FEM) and topology optimization based on a medical image. Here, the skeleton-based image reconstruction apparatus 100 may be connected to the medical imaging apparatus 101 in a wired or wireless manner, or may be coupled to the medical imaging apparatus 100 in an add-on type. For example, the apparatus 100 for reconstructing a skeletal system image may be a computer system, a workstation, or the like.

Hereinafter, an operation of reconstructing a high-resolution skeletal system image based on the finite element analysis and the phase optimization will be described with reference to FIGS. 2 and 3. FIG.

FIG. 2 is a block diagram illustrating an internal configuration of a skeleton-based image reconstruction apparatus according to an exemplary embodiment of the present invention. FIG. 3 is a flowchart illustrating a skeleton-based image reconstruction method according to an exemplary embodiment of the present invention.

The apparatus for reconstructing skeletal system image 200 according to the present embodiment may include a processor 210, a bus 220, a network interface 230, and a memory 240. The memory 240 may include an operating system 241 and a skeletal image reconstruction routine 242. The processor 210 may include a region of interest determiner 211 and an image reconstructor 212. In other embodiments, the skeletal system image reconstruction apparatus 200 may include more components than the components of FIG. However, there is no need to clearly illustrate most prior art components. For example, the skeletal system image reconstruction apparatus 200 may include other components such as a display or a transceiver.

The memory 240 may be a computer-readable recording medium and may include a permanent mass storage device such as a random access memory (RAM), a read only memory (ROM), and a disk drive. In addition, the memory 240 may store program codes for the operating system 241 and the skeleton image reconstruction routine 242. These software components may be loaded from a computer readable recording medium separate from the memory 240 using a drive mechanism (not shown). Such a computer-readable recording medium may include a computer-readable recording medium (not shown) such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, or a memory card. In other embodiments, the software components may be loaded into the memory 240 via the network interface 230 rather than from a computer readable recording medium.

The bus 220 may enable communication and data transfer between the components of the skeletal system image reconstruction apparatus 200. The bus 220 may be configured using a high-speed serial bus, a parallel bus, a Storage Area Network (SAN), and / or other suitable communication technology.

The network interface 230 may be a computer hardware component for connecting the skeletal system image reconstruction device 200 to a computer network. The network interface 230 may connect the skeletal system image reconstruction apparatus 200 to a computer network via a wireless or wired connection.

The processor 210 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations of the skeletal system image reconstruction apparatus 200. [ The instructions may be provided by the memory 240 or the network interface 230 and to the processor 210 via the bus 220. The processor 210 may be configured to execute program codes for the region of interest separator 211 and the image reconstructor 212. [ Such program code may be stored in a recording device such as memory 240. [

The ROI determining unit 211 and the image reconstructing unit 212 may be configured to perform the respective steps of FIG.

In step 310, the ROI determining unit 211 may determine a region of interest (ROI) to be high-resolution on the skeleton image captured by the subject.

For example, a skeletal system image may include a skeletal system image such as a spine, a femur, and the like among a plurality of images segmented through image segmentation in an image such as QCT, CT, MIR, etc. of the subject. Then, the area-of-interest determination unit 211 may determine a specific part of the skeletal system included in the skeletal system image to be high-resolution. E.g. Some regions, such as the head, upper, or lower portion of the femur, may be determined as regions of interest.

Here, the image of the subject can be displayed at a resolution lower than 300 mu m. For example, the resolution of the image of the subject can be about 600 mu m.

In step 320, the image reconstructing unit 212 reconstructs the skeletal system image by performing finite element modeling and topology optimization on the ROI.

The image reconstructing unit 212 may perform element meshing on the ROI to represent the ROI as a plurality of finite elements. Then, the image reconstructing unit 212 can obtain the structural behavior values of each finite element by performing the finite element analysis based on the bone density, the elastic modulus, and the load condition of the bone corresponding to the element-divided region. For example, the image reconstructing unit 212 can obtain the structural behavior value by converting the bone density to the elastic modulus, applying the converted elastic modulus and predetermined load condition to each finite element.

The concrete procedure of applying the finite element modeling to the skeletal system image of the subject is as follows. Referring to FIG. 4, the skeletal system image 402 may be extracted through image segmentation on the image 401 of the subject, and the skeletal system image reconstruction apparatus may include a skeleton system Finite element modeling can be applied.

Finite element modelling

(One) We perform element meshing for the region of interest that we want to enhance the skeletal system image.

(2) Allocate the properties of individual finite elements using the relation between bone density and elasticity.

(3) Give load conditions for the area of interest.

(4) Obtain the structural behavior values of each finite element through finite element analysis.

In detail, in step 321, the image reconstructing unit 212 may perform element meshing on a skeletal image including a region of interest. At this time, the image reconstructing unit 212 may perform element division for dividing the skeleton image into a plurality of images at a target resolution to be high-resolution. E.g. If the resolution of the skeleton system image is about 600 mu m and the target resolution is 50 mu m, the image reconstruction unit 212 can perform element division to divide the skeleton system image into 12x12.

In step 322, the image reconstructing unit 212 may convert the bone density of a bone corresponding to a finite element determined as a region of interest, into a modulus of elasticity. For example, the image reconstructing unit 212 can convert the bone density into the elasticity ratio using a predefined relationship between the bone density and the elastic modulus. At this time, the image reconstructing unit 212 may convert the bone density into the elastic modulus for each finite element included in the region of interest.

In step 323, the image reconstructing unit 212 may apply the converted elastic modulus and the predetermined load condition to each finite element. Here, the load condition is a daily load case, and may include one-legged stance, abduction, adduction, and the like. Thus, as the load condition is applied to each finite element, a change amount may occur in the bone. Then, the image reconstructing unit 212 may perform phase optimization to find a structure in which the strain energy of the bone corresponding to the region of interest is minimized based on the generated change amount.

For example, the image reconstruction unit 212 may extract a new bone density for a region of interest to find a structure that minimizes compliance. Then, the image reconstructing unit 212 can recalculate the strain energy by converting the new bone density back to the elastic modulus, and applying the converted elastic modulus to the finite element. At this time, if the difference between the new bone density and the previous bone density used for calculating the strain energy is equal to or less than the preset reference value, the image reconstructing unit 212 may determine that the phase optimization is completed and terminate the phase optimization. In other words, the image reconstruction unit 212 can determine that the calculated strain energy based on the previous bone density is the minimum strain energy.

On the other hand, if the difference value is larger than the reference value, the image reconstructing unit 212 determines that the minimum strain energy is not found, converts the new bone density into the elasticity, applies the converted elasticity and the load condition to the finite element, Can be repeatedly performed. That is, the image reconstructing unit 212 may repeatedly perform the finite element modeling and the phase optimization until the minimum strain energy is calculated.

As described above, the image reconstructing unit 212 can reconstruct a high-resolution image by performing topology optimization on a region of interest. For example, the image reconstructing unit 212 can reconstruct the resolution of the low-resolution region of interest at 625 mu m to a high-resolution image of 300 mu m or less (for example, 78 mu m). Accordingly, bone microstructure (for example, bone mineral structure) of the region of interest can be provided at a high resolution to help early diagnosis of lesions based on bone density, such as osteoporosis, and to improve the accuracy of diagnosis. Also, by applying finite element modeling and topology optimization using existing medical imaging devices, high resolution images can be reconstructed without purchasing additional high resolution imaging devices.

At this time, the phase optimization can represent a method of reconstructing a structure having the maximum rigidity by redistributing the density of the element for a predetermined load condition with a minimum mass. Accordingly, the image reconstructing unit 212 can rearrange the bone microstructure to obtain the maximum mechanical efficiency by rearrangement of the cancellous bone with respect to the mechanical stimulus predetermined to the minimum bone mass. Here, mechanical stimulation may indicate a force applied to the femur when standing, running, or the like. In other words, the image reconstructing unit 212 reconstructs the high-resolution bone microstructure image by performing the aggregate formation process through the phase optimization based on the low-resolution bone mass.

를 포함하며, 유한 요소 모델링 및 위상최적화를 통해 개별

를 복수 개로 분할하여 고해상도의 영상이 재구성될 수 있다. 다시 말해, 저해상도 영상의 밀도 분포는 그대로 이용하면서, 기계적 하중에 대한 구조 강성은 최대화할 수 있다.For example, the image reconstructing unit 212 may perform phase optimization until the compliance is minimized. Referring to FIG. 5, the process of performing the phase optimization is as follows. Here, compliance represents the reciprocal of stiffness, high compliance provides low compliance, and low rigidity, compliance can be high. According to Fig. 5, a low-resolution image has 625

, And finite element modeling and topology optimization

The high-resolution image can be reconstructed. In other words, the structural stiffness of the mechanical load can be maximized while using the density distribution of the low-resolution image as it is.

Topology Optimization

(One) Design variable setting

- The bone density 504 of individual finite elements (e.g., pixels)

(2) Set objective function

- Minimization of compliance in the area of interest 503

(3) Constraint setting

- The difference between the pixel value of the low resolution image 501 and the average value difference between the pixels constituting the high resolution image 502

(4) Through topology optimization, we calculate the optimal bone density in the individual finite element (eg, pixel) that satisfies the objective function and the constraint. In other words, in the following equation (1), it is desired to find the bone structure having the maximum rigidity through the phase optimization.

(5) reconstruct the high resolution skeletal image in the area of interest.

Referring to Equation (1), a high-resolution skeletal image can be reconstructed through the above phase optimization process.

[Equation 1]

는 컴플라이언스(compliance)를 나타내며, 설계변수인 밀도(

)는 컴플라이언스의 변수를 나타내고, 제약함수

는 저해상도 영상과 고해상화된 영상에서 각 픽셀들의 차이의 평균값을 나타낼 수 있다. 그리고, 상기 각 픽셀들의 차이는 기설정된 허용 오차(epsilon) 이내일 수 있다. 즉, 미세 골구조는 허용 오차 이내에서 저해상도 영상의 밀도 분포를 따르면서 생성될 수 있다. 그리고, u _i는 힘 F_i가 주어졌을 때 각 요소의 변위량, K는 강성 행렬을 나타낼 수 있다.In Equation (1), the objective function

Represents the compliance, and the design variable density (

) Represents a variable of compliance, and the constraint function

Can represent the average value of the difference between each pixel in the low resolution image and the high resolution image. The difference between the pixels may be within a predetermined tolerance epsilon. That is, the fine bone structure can be generated by following the density distribution of the low resolution image within the tolerance. And u _i is the displacement of each element when the force F _i is given, and K is the stiffness matrix.

As described above, according to Equation (1), the image reconstruction unit 211 can calculate the bone density of each of the finite elements included in the ROI based on the objective function and the constraint condition indicating the compliance of the ROI. In other words, the image reconstruction unit 211 can find the finite element corresponding to the density having the maximum stiffness with respect to the predetermined mechanical force F through the phase optimization based on the equation (1), and based on the found finite element, The bone microstructure can be reconstructed.

In step 330, the image reconstructing unit 212 may estimate the osteoporosis of the subject based on the bone density of each finite element included in the ROI and the skeleton image corresponding to the reconstructed ROI. For example, the image reconstructing unit 212 can estimate the skeletal system lesion of the subject based on the skeletal system image and the bone mineral density. Then, the image reconstruction unit 212 can provide the estimation result to the medical staff and the like.

In the above description, it is assumed that the osteoporosis is estimated based on the calculated bone density and bone structure. However, in the embodiment, the image reconstruction unit 212 displays the bone density and bone structure in a display Time. Then, the medical staff can check whether or not the connection is disconnected based on the bone structure, and diagnose osteoporosis by referring to the bone density.

Figure 6 is a diagram illustrating a high-resolution area of interest in one embodiment of the present invention.

6, since the original image 601 obtained by CT, QCT, MRI, or the like is low in resolution of 625 μm, the bone microstructure of the region of interest 602 corresponding to the femur in the original image 601 It is difficult to use it for the diagnosis of osteoporosis because the pixels are almost invisible and can not be seen.

At this time, it can be confirmed that the reconstructed skeleton image 603 has a resolution of 78 μm by applying the finite element analysis and the phase optimization to the region of interest 602 of the original image 601. As the resolution of the reconstructed skeletal system image is improved to 300 μm or less, the bone microstructure of the region of interest corresponding to the femur can be clearly identified. As a result, osteoporosis can be diagnosed early on the basis of bone microstructure, and diagnosis accuracy can be increased. In addition, it is possible to shorten the imaging time required for ensuring the same resolution, thereby reducing medical costs.

7 is a block diagram for explaining an example of the internal configuration of a computer system in an embodiment of the present invention. The computer system 700 includes at least one processor 710, a memory 720, a peripheral interface 730, an input / output subsystem 740, A power circuit 750, and a communication circuitry 760. [ At this time, the computer system 700 may correspond to a workstation.

The memory 720 may include, for example, a high-speed random access memory, a magnetic disk, a SRAM, a DRAM, a ROM, a flash memory, or a non-volatile memory. have. The memory 720 may include software modules, a set of instructions, or various other data required for operation of the computer system 700. At this point, accessing memory 720 from other components, such as processor 710 or peripheral device interface 730, may be controlled by processor 710.

Peripheral device interface 730 may couple the input and / or output peripheral devices of computer system 700 to processor 710 and memory 720. The processor 710 may execute a variety of functions and process data for the computer system 700 by executing a software module or set of instructions stored in the memory 720.

The input / output subsystem 740 may couple various input / output peripheral devices to the peripheral interface 730. For example, input / output subsystem 740 may include a controller for coupling a peripheral device such as a monitor, keyboard, mouse, printer, or a touch screen or sensor, as needed, to peripheral interface 730. According to another aspect, the input / output peripheral devices may be coupled to the peripheral device interface 730 without going through the input / output subsystem 740.

The power circuitry 750 may provide power to all or a portion of the components of the terminal. For example, the power circuitry 750 may include one or more power sources, such as a power management system, a battery or alternating current (AC), a charging system, a power failure detection circuit, a power converter or inverter, And may include any other components for creation, management, distribution.

The communication circuitry 760 may enable communication with other computer systems using at least one external port. Or as described above, the communication circuitry 760 may also communicate with other computer systems by sending and receiving RF signals, also known as electromagnetic signals, including RF circuits.

7 is merely an example of the computer system 1200, and the computer system 700 may have additional components that are omitted in FIG. 7, or that are not shown in FIG. 7, Lt; RTI ID = 0.0 > components. ≪ / RTI > For example, the computer system may further include a display, etc. in addition to the components shown in Fig. The components that may be included in computer system 700 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing or application specific integrated circuits.

The methods according to embodiments of the present invention may be implemented in the form of a program instruction that can be executed through various computer systems and recorded in a computer-readable medium.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA) , A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (12)

Determining a region of interest (ROI) of a skeletal system image of a subject to be high-resolution; And
Reconstructing a skeletal image that has been highly resolved by performing finite element modeling and topology optimization on the ROI
/ RTI > image reconstruction method. The method according to claim 1,
Wherein reconstructing the skeletal system image comprises:
Determining a new bone density for the region of interest by performing the phase optimization, and converting the new bone density to an elasticity, thereby reconstructing the skeleton image reconstructed with high resolution. The method according to claim 1,
Calculating a bone density of each of the finite elements included in the ROI based on an objective function representing strain energy of the ROI and a predetermined constraint; And
Estimating whether or not the subject has a lesion based on the calculated bone density and the skeletal system image
Wherein the image reconstruction method further comprises: The method according to claim 1,
Wherein the high resolution skeleton image includes:
And a trabecular structure of the bone corresponding to the region of interest. The method according to claim 1,
Wherein the high resolution skeleton image includes:
A method of reconstructing a skeletal system image showing an image having a resolution of 300 mu m or less. A region of interest (ROI) determining a region of interest (ROI) of a skeleton image captured by the subject; And
An image reconstruction unit for reconstructing a skeletal system image that has been highly resolved by performing finite element modeling and topology optimization on the ROI,
The reconstructing device comprising: The method according to claim 6,
Wherein the image reconstruction unit comprises:
Wherein the topology optimization is performed to determine a new bone density for the ROI, and a new bone density is converted into an elasticity to repeat the operation of reconstructing the skeleton image. The method according to claim 6,
Wherein the high resolution skeleton image includes:
And a trabecular structure of bone corresponding to the region of interest. The method according to claim 6,
Wherein the high resolution skeleton image includes:
An apparatus for reconstructing a skeletal system image showing an image having a resolution of 300 mu m or less. At least one program loaded memory; And
At least one processor
Lt; / RTI >
Wherein the at least one processor, under control of the program,
A process of determining a region of interest (ROI) to be high-resolution on a skeletal image captured by the subject; And
A process of reconstructing a skeleton image that has been highly resolved by performing finite element modeling and topology optimization on the ROI
A reconstruction apparatus for reconstructing a skeletal image. A computer readable storage medium comprising instructions for controlling a computer system to provide a skeletal image,
The command includes:
Determining a region of interest (ROI) of a skeletal system image of a subject to be high-resolution; And
Reconstructing a skeletal image that has been highly resolved by performing finite element modeling and topology optimization on the ROI
≪ / RTI > computer-readable storage medium having stored thereon a computer-readable storage medium having computer-executable instructions for performing the method. 12. The method of claim 11,
The command includes:
Calculating a bone density of each finite element included in the ROI based on an objective function indicating compliance of the ROI and a predetermined constraint; And
Estimating whether or not the subject has a lesion based on the calculated bone density and the skeletal system image
The computer system comprising: a computer readable medium;

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