CN103295264A - Spine 3D modeling method based on common C arm machine - Google Patents

Abstract

A spine 3D modeling method based on a common C arm machine includes the following steps that a cone beam projection image of the common C arm machine is pretreated to obtain a cone beam projection image after treatment to serve as an actual cone beam of the C arm machine; virtual translation is carried out on the actual cone beam obtained through the step one, and a corresponding virtual translation cone beam projection image is calculated according to the mapping relation between a virtual translation cone beam and the actual cone beam; after relevant parameters in the 3D modeling method are regulated, a spine 3D voxel model is rebuilt based on the virtual translation cone beam projection image. According to the spine 3D modeling method based on the common C arm machine and relaying on common C arm machines owned by most hospitals at present, the spine 3D model having significance in the aspects of clinical diagnosis, operation planning and operation guiding operation can be rebuilt.

Description

Backbone 3D modeling method based on common C arm machine

Technical field

The present invention relates to the computer generated image technology, particularly a kind of backbone 3D modeling method based on common C arm machine.

Background technology

In the hospital surgery clinical practice, field of spinal surgery especially, C arm machine is a kind of equipment commonly used.By the image that C arm machine obtains, the doctor can judge that operation techniques such as planning and implantation awl nut screw also can undergo surgery to the patient to the disease damage situation of patient's backbone.But, only can obtain patient's projected image by common C arm machine, and can not directly obtain by C arm machine judgement and the guiding operation technique far reaching backbone 3D model of the state of an illness clinically.

Find through the literature search to prior art, FDK algorithm (the Practical cone-beam reconstruction that people such as Feldkamp propose, J.Opt.Soc.Am.A 1,612-619,1984) the human body 3D voxel model that is suitable for based on cone beam projection makes up, but its requirement possesses full scan path data for projection in the 360o scope, and common C arm rotation sweep angular range generally is no more than 180o, can not directly adopt the FDK algorithm.The ultrashort scanning pattern 3D voxel model that people such as Noo and Kudo proposes makes up algorithm (Feldkamp-type VOI reconstruction from super-short-scan cone-beam data, J.Medical Physics.31 (6), 1357-1362,2004; New Super-Short-Scan Algorithms for Fan-Beam and Cone-Beam Reconstruction, IEEE Nuclear Science and Medical Imaging Symposium, Record, Norfolk, USA, 2003,2:902-906) allow sweep limit to be lower than 180o, the imaging plane perpendicular bisector must always pass through rotation center in the whole rotation angle range but it requires, and common C arm imaging plane perpendicular bisector can not directly adopt these algorithms generally not by C arm rotation center.

Summary of the invention

Purpose of the present invention just is to overcome above-mentioned the deficiencies in the prior art, and a kind of backbone 3D modeling method based on common C arm machine is provided, so that the common C arm machine that relies on present domestic most of hospitals all to possess is reconstituted in far reaching backbone 3D model clinically.

For achieving the above object, the present invention adopts following design proposal: a kind of backbone 3D modeling method based on common C arm machine may further comprise the steps:

Step 1, the cone beam projection image of common C arm machine is carried out pre-service, the cone beam projection image after obtaining handling is as the actual cone-beam of C arm machine;

Step 2, the actual cone-beam that step 1 is obtained carry out virtual translation, and according to the mapping relations between virtual translation cone-beam and the actual cone-beam, calculate corresponding virtual translation cone beam projection image;

Behind the correlation parameter in step 3, the adjustment 3D modeling method, based on virtual translation cone beam projection image reconstruction backbone 3D voxel model.

Pre-service described in the step 1 refers to 2D cone beam projection image g that common C arm machine is obtained _m(λ, u v) carry out medium filtering except making an uproar the cone beam projection image g after obtaining handling _c(v), wherein λ is the C arm anglec of rotation for λ, u, and u and v are respectively the row, column coordinate of image pixel, and the cone angle of establishing cone-beam is γ _m

Virtual translation direction described in the step 2 and the intrafascicular axis normal of actual cone and sensing C arm rotation center, the mapping relations of described virtual translation cone-beam and actual cone-beam are: virtual translation cone-beam light source point equals the actual cone light source beam to the distance between its imaging plane and puts distance B between its imaging plane, virtual translation cone-beam light source point passes through C arm rotation center to the vertical line of its imaging plane, and be parallel to the vertical line that the actual cone light source beam is put its imaging plane, virtual translation cone beam projection imaging plane and actual cone-beam projection imaging plane are positioned at same plane; If R puts distance between the C arm rotation center for the actual cone light source beam, angle between the line of actual cone light source beam point and C arm rotation center and the intrafascicular axis of actual cone is θ, virtual translation cone beam projection and actual cone beam projection are set up cartesian coordinate system respectively, make the initial point of two coordinate systems lay respectively at the intersection point of light source point on imaging plane separately, make the row of two coordinate systems all identical perpendicular to C arm Plane of rotation and forward to axle, the row that make two coordinate systems to axle all be positioned at C arm Plane of rotation and forward identical, then the projected pixel coordinate under two coordinate systems and the corresponding relation of gray-scale value are:

u＝u _v-R.sin(θ)，v＝v _v，

$g_v(\mathrm{\&lambda;},u_v,v_v)=g_c(\mathrm{\&lambda;},u,v)\&CenterDot;\left(\frac{\sqrt{{\mathrm{<figure-callout\; id="2"\; label="D"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">D</figure-callout>}}^2+u_v^2}}{\sqrt{{\mathrm{<figure-callout\; id="2"\; label="D"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">D</figure-callout>}}^2+u^2+v^2}}\right)$

In the following formula, u and v are respectively row-coordinate and the row coordinate of actual cone-beam projected pixel, u _vAnd v _vBe respectively row-coordinate and the row coordinate of virtual translation cone beam projection pixel, g _c(λ, u v) are the grey scale pixel value that actual cone-beam is projected in the respective coordinates place, g _v(λ, u _v, v _v) be the virtual translation cone beam projection at the grey scale pixel value at respective coordinates place, λ is the C arm anglec of rotation.

Correlation parameter in the adjustment 3D modeling method described in the step 3 specifically comprises: the radius that virtual translation cone-beam light source point is moved along circular arc path is adjusted into R.cos (θ), with the interval lower limit u of virtual translation cone beam projection data line filtering processing _lBe adjusted into R.sin (θ)-D.tan (γ _m), upper limit u _uBe adjusted into R.sin (θ)+D.tan (γ _m), wherein, γ _mCone angle for cone-beam, R puts distance between the C arm rotation center for the actual cone light source beam, to be virtual translation cone-beam light source point put distance between its imaging plane to the distance between its imaging plane or actual cone light source beam to D, and θ is the line of actual cone light source beam point and C arm rotation center and the angle between the intrafascicular axis of actual cone.Adopt formula:

$f\left(\stackrel{\&RightArrow;}{x}\right)=\frac{1}{2\mathrm{\&pi;}}{\&Integral;}_0^{{\mathrm{\&lambda;}}_u}\mathrm{d\&lambda;}\frac{1}{R.\cos \left(\mathrm{\&theta;}\right)+\stackrel{\&RightArrow;}{x}.{\stackrel{\&RightArrow;}{e}}_1}{\left[w(\mathrm{\&lambda;},\tilde{u})g_f(\mathrm{\&lambda;},\tilde{u},\tilde{v})\right]}_{\tilde{u}=\tilde{u}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x}),\tilde{v}=\tilde{v}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x})},$ Based on virtual translation cone beam projection image reconstruction backbone 3D model.

With

Represent 3D model voxel coordinate and gray-scale value respectively.Other intermediate computations formula are specific as follows:

$g_f(\mathrm{\&lambda;},\tilde{u},\tilde{v})={\&Integral;}_{u_l}^{u_u}\mathrm{du}{h}_H(\tilde{u}-u_v)\frac{\mathrm{<figure-callout\; id="2"\; label="D\; D"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">D</figure-callout>}}{\sqrt{D^{}}}(\frac{\&PartialD;}{\&PartialD;\mathrm{\&lambda;}}+\frac{\&PartialD;u_v}{\&PartialD;\mathrm{\&lambda;}}\frac{\&PartialD;}{\&PartialD;u_v}+\frac{\&PartialD;v_v}{\&PartialD;\mathrm{\&lambda;}}\frac{\&PartialD;}{\&PartialD;v_v})g_v(\mathrm{\&lambda;},u_v,v_v),$

$h_H(\tilde{u}-u)=\frac{1}{\mathrm{\&pi;}(\tilde{u}-u)},$

$\frac{\&PartialD;u_v}{\&PartialD;\mathrm{\&lambda;}}=D+\frac{u_v^2}{D},$

$\frac{\&PartialD;v_v}{\&PartialD;\mathrm{\&lambda;}}=\frac{u_v{v}_v}{D},$

$f\left(\stackrel{\&RightArrow;}{x}\right)=\frac{1}{2\mathrm{\&pi;}}{\&Integral;}_0^{{\mathrm{\&lambda;}}_u}\mathrm{d\&lambda;}\frac{1}{R.\cos \left(\mathrm{\&theta;}\right)+\stackrel{\&RightArrow;}{x}.{\stackrel{\&RightArrow;}{e}}_1}{\left[w(\mathrm{\&lambda;},\tilde{u})g_f(\mathrm{\&lambda;},\tilde{u},\tilde{v})\right]}_{\tilde{u}=\tilde{u}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x}),\tilde{v}=\tilde{v}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x})},$

$\tilde{u}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x})=\frac{D[\stackrel{\&RightArrow;}{x}-\stackrel{\&RightArrow;}{\mathrm{\&alpha;}}\left(\mathrm{\&lambda;}\right)].{\stackrel{\&RightArrow;}{e}}_2}{[\stackrel{\&RightArrow;}{x}-\stackrel{\&RightArrow;}{\mathrm{\&alpha;}}\left(\mathrm{\&lambda;}\right)].{\stackrel{\&RightArrow;}{e}}_1},$

$\tilde{v}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x})=\frac{D[\stackrel{\&RightArrow;}{x}-\stackrel{\&RightArrow;}{\mathrm{\&alpha;}}\left(\mathrm{\&lambda;}\right)].{\stackrel{\&RightArrow;}{e}}_3}{[\stackrel{\&RightArrow;}{x}-\stackrel{\&RightArrow;}{\mathrm{\&alpha;}}\left(\mathrm{\&lambda;}\right)].{\stackrel{\&RightArrow;}{e}}_1},$

$\stackrel{\&RightArrow;}{\mathrm{\&alpha;}}\left(\mathrm{\&lambda;}\right)=(R.\cos \left(\mathrm{\&theta;}\right).\cos \left(\mathrm{\&lambda;}\right),R.\cos \left(\mathrm{\&theta;}\right).\sin \left(\mathrm{\&lambda;}\right),0),$

${\stackrel{\&RightArrow;}{e}}_1=(-\cos \mathrm{\&lambda;},-\sin \mathrm{\&lambda;},0),$

${\stackrel{\&RightArrow;}{e}}_2=(-\sin \mathrm{\&lambda;},\cos \mathrm{\&lambda;},0),$

${\stackrel{\&RightArrow;}{e}}_3={\stackrel{\&RightArrow;}{e}}_2\&times;{\stackrel{\&RightArrow;}{e}}_1=\left(\mathrm{0,0,1}\right),$

$w(\mathrm{\&lambda;},\tilde{u})=\frac{c\left(\mathrm{\&lambda;}\right)}{c\left(\mathrm{\&lambda;}\right)+c(\mathrm{\&lambda;}+\mathrm{\&pi;}-2\arctan \left(\widetilde{u/D}\right))},$

$c\left(\mathrm{\&lambda;}\right)=\left\{\begin{array}{ccc}{\mathrm{<figure-callout\; id="2"\; label="cos"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">cos</figure-callout>}}^2\frac{\mathrm{\&pi;}(\mathrm{\&lambda;}-d)}{2d}& \mathrm{if}& 0\&le;\mathrm{\&lambda;}\&le;\mathrm{<figure-callout\; id="1"\; label="d"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">d</figure-callout>}\\ 1& \mathrm{if}& d\&le;\mathrm{\&lambda;}\&le;{\mathrm{\&lambda;}}_u-\mathrm{<figure-callout\; id="2"\; label="d\; cos"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">d</figure-callout>}& \\ {\cos }^{}{}^2\frac{\mathrm{\&pi;}(\mathrm{\&lambda;}-{\mathrm{\&lambda;}}_u+d)}{2d}& \mathrm{if}& {\mathrm{\&lambda;}}_u-d\&le;\mathrm{\&lambda;}\&le;{\mathrm{\&lambda;}}_u\\ 0& & \mathrm{others}\end{array}\right.,$

d＝10°

Compared with prior art, the invention has the beneficial effects as follows:

1) adopt the backbone 3D modeling method the present invention is based on common C arm machine, the common C arm machine that relies on domestic most of hospitals at present all to possess can be reconstituted in all significant backbone 3D models of clinical diagnosis, surgery planning, guiding operation technique aspect.

2) method principle of the present invention is simple, and it is convenient to carry out, and need not to purchase or customize other extra means except C arm machine itself.

Description of drawings

Fig. 1 is the backbone 3D modeling method process flow diagram that the present invention is based on common C arm machine;

Fig. 2 concerns synoptic diagram for actual cone-beam in the step 2 of the present invention and virtual translation cone-beam;

Fig. 3 is actual cone beam projection and virtual translation cone beam projection pixel coordinate and gray-scale value derivation graph of a relation in the step 2 of the present invention.

Embodiment

The present invention is described in detail below in conjunction with the drawings and specific embodiments.

Referring to Fig. 1, the backbone 3D modeling method that the present invention is based on common C arm machine comprises three steps:

The 1st step, the 2D cone beam projection image g that common C arm machine is obtained _m(λ, u v) carry out medium filtering except making an uproar pre-service, obtain handling back cone beam projection image g _c(v), wherein λ is the C arm anglec of rotation for λ, u, and u and v are respectively the row, column coordinate of image pixel.If the cone angle of cone-beam is γ _m

In the 2nd step (cooperating referring to Fig. 2), the actual cone-beam 1 of common C arm machine is carried out virtual translation, and according to the mapping relations between virtual translation cone-beam 2 and the actual cone-beam 1, calculate corresponding virtual translation cone beam projection image.The intrafascicular axis normal of virtual translation direction and actual cone and sensing C arm rotation center 31.Virtual translation cone-beam light source point 24 equals actual cone light source beam point 14 to the distance between its imaging plane 11 to the distance between its imaging plane 21, if it is D, virtual translation cone-beam light source point passes through C arm rotation center 31 to the vertical line 22 of its imaging plane, and be parallel to the vertical line 12 that the actual cone light source beam is put its imaging plane, virtual translation cone beam projection imaging plane 21 is positioned at same plane with actual cone-beam projection imaging plane 11.If the distance that R arrives between the C arm rotation center 31 for actual cone light source beam point 14, the line of actual cone light source beam point 14 and C arm rotation center 31 and the angle between the intrafascicular axis of actual cone are θ.If virtual translation cone beam projection and actual cone beam projection are set up cartesian coordinate system respectively, make the initial point of two coordinate systems lay respectively at the intersection point 13,23 of light source point on imaging plane separately, make the row of two coordinate systems all identical perpendicular to C arm Plane of rotation 32 and forward to axle 15,25, the row that make two coordinate systems to axle 16,26 all be positioned at C arm Plane of rotation 32 and forward identical, then projected pixel coordinate and the gray-scale value corresponding relation under two coordinate systems is:

u＝u _v-R.sin(θ) _v＝v _v

$g_v(\mathrm{\&lambda;},u_v,v_v)=g_c(\mathrm{\&lambda;},u,v)\&CenterDot;\left(\frac{\sqrt{{\mathrm{<figure-callout\; id="2"\; label="D"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">D</figure-callout>}}^2+u_v^2}}{\sqrt{{\mathrm{<figure-callout\; id="2"\; label="D"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">D</figure-callout>}}^2+u^2+v^2}}\right)$

In the following formula, u and v are respectively row-coordinate and the row coordinate of actual cone-beam projected pixel, u _vAnd v _vBe respectively row-coordinate and the row coordinate of virtual translation cone beam projection pixel, g _c(λ, u, v) and g _v(λ, u _v, v _v) be respectively actual cone beam projection and virtual translation cone beam projection at the grey scale pixel value at respective coordinates place.Actual cone beam projection and virtual translation cone beam projection pixel coordinate and gray-scale value are derived relation referring to Fig. 3.

The 3rd step, adjust the correlation parameter in the 3D modeling method, specifically comprise: the radius that virtual translation cone-beam light source point 24 moves along circular arc path is adjusted into R.cos (θ), the interval lower limit u that the filtering of virtual translation cone beam projection data line is handled _lBe adjusted into R.sin (θ)-D.tan (γ _m), upper limit u _uBe adjusted into R.sin (θ)+D.tan (γ _m).γ _mImplication see the 1st the step, R, the implication of D and θ see the 2nd the step.Adopt formula $f\left(\stackrel{\&RightArrow;}{x}\right)=\frac{1}{2\mathrm{\&pi;}}{\&Integral;}_0^{{\mathrm{\&lambda;}}_u}\mathrm{d\&lambda;}\frac{1}{R.\cos \left(\mathrm{\&theta;}\right)+\stackrel{\&RightArrow;}{x}.{\stackrel{\&RightArrow;}{e}}_1}{\left[w(\mathrm{\&lambda;},\tilde{u})g_f(\mathrm{\&lambda;},\tilde{u},\tilde{v})\right]}_{\tilde{u}=\tilde{u}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x}),\tilde{v}=\tilde{v}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x})},$ Based on virtual translation cone beam projection image reconstruction backbone 3D model.

With

Represent 3D model voxel coordinate and gray-scale value respectively.Other intermediate computations formula are specific as follows:

$g_f(\mathrm{\&lambda;},\tilde{u},\tilde{v})={\&Integral;}_{u_l}^{u_u}\mathrm{du}{h}_H(\tilde{u}-u_v)\frac{\mathrm{<figure-callout\; id="2"\; label="D\; D"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">D</figure-callout>}}{\sqrt{D^{}}}(\frac{\&PartialD;}{\&PartialD;\mathrm{\&lambda;}}+\frac{\&PartialD;u_v}{\&PartialD;\mathrm{\&lambda;}}\frac{\&PartialD;}{\&PartialD;u_v}+\frac{\&PartialD;v_v}{\&PartialD;\mathrm{\&lambda;}}\frac{\&PartialD;}{\&PartialD;v_v})g_v(\mathrm{\&lambda;},u_v,v_v),$

$h_H(\tilde{u}-u)=\frac{1}{\mathrm{\&pi;}(\tilde{u}-u)},$

$\frac{\&PartialD;u_v}{\&PartialD;\mathrm{\&lambda;}}=D+\frac{u_v^2}{D},$

$\frac{\&PartialD;v_v}{\&PartialD;\mathrm{\&lambda;}}=\frac{u_v{v}_v}{D},$

$f\left(\stackrel{\&RightArrow;}{x}\right)=\frac{1}{2\mathrm{\&pi;}}{\&Integral;}_0^{{\mathrm{\&lambda;}}_u}\mathrm{d\&lambda;}\frac{1}{R.\cos \left(\mathrm{\&theta;}\right)+\stackrel{\&RightArrow;}{x}.{\stackrel{\&RightArrow;}{e}}_1}{\left[w(\mathrm{\&lambda;},\tilde{u})g_f(\mathrm{\&lambda;},\tilde{u},\tilde{v})\right]}_{\tilde{u}=\tilde{u}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x}),\tilde{v}=\tilde{v}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x})},$

$\tilde{u}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x})=\frac{D[\stackrel{\&RightArrow;}{x}-\stackrel{\&RightArrow;}{\mathrm{\&alpha;}}\left(\mathrm{\&lambda;}\right)].{\stackrel{\&RightArrow;}{e}}_2}{[\stackrel{\&RightArrow;}{x}-\stackrel{\&RightArrow;}{\mathrm{\&alpha;}}\left(\mathrm{\&lambda;}\right)].{\stackrel{\&RightArrow;}{e}}_1},$

$\tilde{v}*(\mathrm{\&lambda;},\stackrel{\&RightArrow;}{x})=\frac{D[\stackrel{\&RightArrow;}{x}-\stackrel{\&RightArrow;}{\mathrm{\&alpha;}}\left(\mathrm{\&lambda;}\right)].{\stackrel{\&RightArrow;}{e}}_3}{[\stackrel{\&RightArrow;}{x}-\stackrel{\&RightArrow;}{\mathrm{\&alpha;}}\left(\mathrm{\&lambda;}\right)].{\stackrel{\&RightArrow;}{e}}_1},$

$\stackrel{\&RightArrow;}{\mathrm{\&alpha;}}\left(\mathrm{\&lambda;}\right)=(R.\cos \left(\mathrm{\&theta;}\right).\cos \left(\mathrm{\&lambda;}\right),R.\cos \left(\mathrm{\&theta;}\right).\sin \left(\mathrm{\&lambda;}\right),0),$

${\stackrel{\&RightArrow;}{e}}_1=(-\cos \mathrm{\&lambda;},-\sin \mathrm{\&lambda;},0),$

${\stackrel{\&RightArrow;}{e}}_2=(-\sin \mathrm{\&lambda;},\cos \mathrm{\&lambda;},0),$

${\stackrel{\&RightArrow;}{e}}_3={\stackrel{\&RightArrow;}{e}}_2\&times;{\stackrel{\&RightArrow;}{e}}_1=\left(\mathrm{0,0,1}\right),$

$w(\mathrm{\&lambda;},\tilde{u})=\frac{c\left(\mathrm{\&lambda;}\right)}{c\left(\mathrm{\&lambda;}\right)+c(\mathrm{\&lambda;}+\mathrm{\&pi;}-2\arctan \left(\widetilde{u/D}\right))},$

$c\left(\mathrm{\&lambda;}\right)=\left\{\begin{array}{ccc}{\mathrm{<figure-callout\; id="2"\; label="cos"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">cos</figure-callout>}}^2\frac{\mathrm{\&pi;}(\mathrm{\&lambda;}-d)}{2d}& \mathrm{if}& 0\&le;\mathrm{\&lambda;}\&le;\mathrm{<figure-callout\; id="1"\; label="d"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">d</figure-callout>}\\ 1& \mathrm{if}& d\&le;\mathrm{\&lambda;}\&le;{\mathrm{\&lambda;}}_u-\mathrm{<figure-callout\; id="2"\; label="d\; cos"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">d</figure-callout>}& \\ {\cos }^{}{}^2\frac{\mathrm{\&pi;}(\mathrm{\&lambda;}-{\mathrm{\&lambda;}}_u+d)}{2d}& \mathrm{if}& {\mathrm{\&lambda;}}_u-d\&le;\mathrm{\&lambda;}\&le;{\mathrm{\&lambda;}}_{\mathrm{<figure-callout\; id="0"\; label="u"\; filenames="HDA0000139901670000011.png"\; state="\{\{state\}\}">u</figure-callout>}}\\ 0& & \mathrm{others}\end{array}\right.,$

d＝10°。

Claims (4)

1. backbone 3D modeling method based on common C arm machine is characterized in that: may further comprise the steps:

Step 1, the cone beam projection image of common C arm machine is carried out pre-service, the cone beam projection image after obtaining handling is as the actual cone-beam of C arm machine;

Step 2, the actual cone-beam that step 1 is obtained carry out virtual translation, and according to the mapping relations between virtual translation cone-beam and the actual cone-beam, calculate corresponding virtual translation cone beam projection image;

Behind the correlation parameter in step 3, the adjustment 3D modeling method, based on virtual translation cone beam projection image reconstruction backbone 3D voxel model.

2. the backbone 3D modeling method based on common C arm machine as claimed in claim 1 is characterized in that: the pre-service described in the step 1 refers to 2D cone beam projection image g that common C arm machine is obtained _m(λ, u v) carry out medium filtering except making an uproar the cone beam projection image g after obtaining handling _c(v), wherein λ is the C arm anglec of rotation for λ, u, and u and v are respectively the row, column coordinate of image pixel, and the cone angle of establishing cone-beam is γ _m

3. the backbone 3D modeling method based on common C arm machine as claimed in claim 1, it is characterized in that: the virtual translation direction described in the step 2 and the intrafascicular axis normal of actual cone and sensing C arm rotation center, the mapping relations of described virtual translation cone-beam and actual cone-beam are: virtual translation cone-beam light source point equals the actual cone light source beam to the distance between its imaging plane and puts distance B between its imaging plane, virtual translation cone-beam light source point passes through C arm rotation center to the vertical line of its imaging plane, and be parallel to the vertical line that the actual cone light source beam is put its imaging plane, virtual translation cone beam projection imaging plane and actual cone-beam projection imaging plane are positioned at same plane; If R puts distance between the C arm rotation center for the actual cone light source beam, angle between the line of actual cone light source beam point and C arm rotation center and the intrafascicular axis of actual cone is θ, virtual translation cone beam projection and actual cone beam projection are set up cartesian coordinate system respectively, make the initial point of two coordinate systems lay respectively at the intersection point of light source point on imaging plane separately, make the row of two coordinate systems all identical perpendicular to C arm Plane of rotation and forward to axle, the row that make two coordinate systems to axle all be positioned at C arm Plane of rotation and forward identical, then the projected pixel coordinate under two coordinate systems and the corresponding relation of gray-scale value are:

u＝u _v-R.sin(θ)，v＝v _v，

$g_v(\mathrm{\&lambda;},u_v,v_v)=g_c(\mathrm{\&lambda;},u,v)\&CenterDot;\left(\frac{\sqrt{D^2+u_v^2}}{\sqrt{D^2+u^2+v^2}}\right)$

In the following formula, u and v are respectively row-coordinate and the row coordinate of actual cone-beam projected pixel, u _vAnd v _vBe respectively row-coordinate and the row coordinate of virtual translation cone beam projection pixel, g _c(λ, u v) are the grey scale pixel value that actual cone-beam is projected in the respective coordinates place, g _v(λ, u _v, v _v) be the virtual translation cone beam projection at the grey scale pixel value at respective coordinates place, λ is the C arm anglec of rotation.

4. the backbone 3D modeling method based on common C arm machine as claimed in claim 1, it is characterized in that: the correlation parameter in the adjustment 3D modeling method described in the step 3 specifically comprises: the radius that virtual translation cone-beam light source point is moved along circular arc path is adjusted into R.cos (θ), with the interval lower limit u of virtual translation cone beam projection data line filtering processing _lBe adjusted into R.sin (θ)-D.tan (γ _m), upper limit u _uBe adjusted into R.sin (θ)+D.tan (γ _m), wherein, γ _mCone angle for cone-beam, R puts distance between the C arm rotation center for the actual cone light source beam, to be virtual translation cone-beam light source point put distance between its imaging plane to the distance between its imaging plane or actual cone light source beam to D, and θ is the line of actual cone light source beam point and C arm rotation center and the angle between the intrafascicular axis of actual cone.

Images