CN104021582A - CT (Computed Tomography) iterative image reconstruction method - Google Patents

Abstract

The invention discloses a CT (Computed Tomography) iterative image reconstruction method, which can be applied to precise sectional image reconstruction in incomplete projection data situations such as low X-ray tube current, under-sampling or limited angle. The method comprises steps of projection-driven iterative calculation, adaptively-determined ordered subset number, adaptive weight, and distance-driven orthographic projection and back projection calculation. A set of all projection data processed by each time of iterative calculation at each projection angle is firstly divided into a plurality of ordered subsets and each subset undergoes the following steps of distance-driven orthographic projection, subtraction with real projection data and weighting, distance-driven back projection and to-be-reconstructed image correction. In the scanning modes of low X-ray tube current, under-sampling or limited angle, a reconstructed image which can fully meet imaging clinical diagnosis demands can be obtained, the method can be self-sufficiently applied to CT imaging aiming at reducing the X-ray radiation dose or can be used for front-end processing of other CT image reconstruction algorithm.

Description

A kind of CT iterative image reconstruction method

Technical field

The present invention relates to CT image reconstruction technique field, relate in particular to following CT iterative image reconstruction technology: by low X ray bulb electric current and owe sampling or the incomplete projections of finite angle is enough accurately rebuild faultage image, thereby can significantly reduce under the prerequisite of CT examination x-ray radiation dosage, a kind of CT iterative image reconstruction method of abundant suitable iconography clinical diagnosis demand is being provided.

Background technology

X-ray CT (Computed Tomography, computer tomography) scanning is that current clinical imageology checks one of indispensable basic means.But First CT machine was was formally installed and used in so far more than 40 years in the world since 1971, people are being perplexed by x-ray radiation dosage and the carcinogenic harmfulness problem of x-ray radiation all the time, because they are directly connected to patient's healthy and life security.Be accompanied by CT imaging examination and there is no so far the role in clinical that can shake and day by day highlight, the carcinogenic harmfulness problem of x-ray radiation dosage and x-ray radiation causes people's concern more, to such an extent as to becomes the generally acknowledged key technical problem of current Medical Imaging.

Clinically, adopting lower X-ray tube electric current to carry out CT scan is to reduce the most important measure of x-ray radiation dosage.Obviously, on the basis of low ball tube current, if can enough accurately rebuild faultage image from owing the data for projection of sampling or finite angle, can further reduce the radiation dose of X-ray.Yet, in this case, the incomplete projections that will inevitably obtain low signal-to-noise ratio and owe sampling or finite angle.Filtered back projection (Filtered Back Projection, FBP) class image reconstruction algorithm requires high to the signal to noise ratio (S/N ratio) of data for projection, completeness, be applied to above-mentioned data for projection and can obtain the reconstruction image with serious artifact, affects clinical diagnosis.By contrast, Class of Iterative image reconstruction algorithm can enough accurately be rebuild faultage image by above-mentioned data for projection, thereby the reconstruction image of abundant suitable iconography clinical diagnosis demand is provided.

In the image rebuilding method using in clinical practice in the past, algebraic reconstruction technique (Algebra Reconstruction Technique by propositions such as Gorden R., ART) (see " art_ Baidupedia " webpage), greatest hope (Expectation Maximization, EM) the classical Class of Iterative image reconstruction algorithm of algorithm one class, because being difficult to meet clinical permissible requirement computing time, their application in business CT machine have been limited.Along with the development of parallel computing and the reduction of computer hardware cost, above-mentioned clinical practice restriction is close to disappearance.But, classical Class of Iterative CT image reconstruction algorithm also exists following two drawbacks to affect its application in reducing CT examination x-ray radiation Dose Problem: (1) reconstructed image quality depends on the relaxation factor of selecting according to experience to a great extent, and selected rather loaded down with trivial details, the adaptivity of relaxation factor and engineering practicability are poor; (2) quality of reconstruction image still needs to improve, to meet iconography clinical diagnosis demand.

Summary of the invention

Poor based on CT image reconstruction algorithm anti-noise ability in the past, data security is required to high, self-adaptation and engineering practicability is not good enough, reconstructed image quality still can not abundant suitable iconography clinical diagnosis demand, to such an extent as to can not in reducing CT examination x-ray radiation Dose Problem, obtain the drawbacks such as applications well effect, the feature of the comprehensive classical Class of Iterative CT image reconstruction algorithm of the present invention, and Binding distance drive just/technological thoughts such as backprojection operation strategy and order subset, adaptivity, a kind of CT iterative image reconstruction method as described below has been proposed.

The technical solution adopted in the present invention is as follows:

A kind of CT iterative image reconstruction method, by CT machine, in the incomplete projections of owing to collect in sampling or finite angle scanning situation of low X-ray tube electric current, carry out image reconstruction, adopt the iterative algorithm of nested type, the projection traversal iteration that comprises outer field algorithm self iteration and internal layer, in algorithm self iteration, m is the loop control variable that records algorithm self iterations, and M is the maximum iteration time of algorithm self iteration; In projection traversal iteration, v is the loop control variable that records projection traversal iterations, the maximum iteration time of projection traversal iteration is V, travels through iteration, for the projection of internal layer each time under each projection angle, according to the acquisition order of data for projection, it is divided into O=180V mutually disjoint order subset fifty-fifty, use o=1,2, K, O indicates these order subsets, and not exclusively true data for projection is denoted as to P ^real, its projection angle adds up to V (identical with the maximum iteration time of projection traversal iteration), remembers that image to be reconstructed is designated as X, and this method for reconstructing step is as follows:

S1) initiation parameter

The value of given M, initialization m=1, v=1, X (m, v, o)=0, wherein symbol X (m, v, o) represents that image X to be reconstructed is the function of parameter m, v, o;

S2) projection traversal iteration

For this projection traversal iteration, the orthogonal projection that the processing of each order subset is driven through distance successively, make with true data for projection back projection, the image correction four processes to be reconstructed that poor and weighting, distance drive, concrete steps are as follows:

1. the orthogonal projection that, distance drives: the orthogonal projection computing strategy that adopts distance to drive carries out orthogonal projection computing to image X (m, v, o) and obtains data for projection P ^dd, and remember that the pixel that X (m, v, o) spatial location coordinate is (x, y) is c (x, y, d) to being numbered the contribution margin of the X-ray detector units of d; Meanwhile, calculate adaptivity weights omega, the formula that calculates ω is $\mathrm{\ω}={[v\×\underset{x,y,d}{\mathrm{\Σ}}{c}^2(x,y,d)]}^{-\frac{1}{2}};$

2., do poor and weighting with true data for projection: will corresponding true data for projection P ^realwith ω * P ^dddiffer from, obtain P ^diff, i.e. P ^diff=P ^real-ω * P ^dd;

3. the back projection that, distance drives: adopt the backprojection operation strategy of distance driving to P ^diffcarry out backprojection operation and obtain offset images, be designated as X ^diff;

4., image correction to be reconstructed: X (m, v, o) and offset images are done and and by result again assignment to X (m, v, o), i.e. X (m, v, o)=X (m, v, o)+X ^diff,

Make o=o+1, repeat step 1.～4. link, until then o=O, obtains the reconstruction image corresponding to v projection angle, i.e. the reconstruction image of v projection angle by the reconstruction image stack of the subset of the O corresponding to above-mentioned make v=v+1, o=1; The initial pictures of traversal iteration using X% as projection next time, after having traveled through all projection angles, i.e. v=V, projection traversal iteration completes;

S3) make m=m+1, v=1, o=1, repeats S2) process, until during m=M, algorithm finishes, and obtains finally rebuilding image X=X (M, V, O).

The orthogonal projection computing strategy principle that described distance drives is that the border of each pixel and each X-ray detector units is mapped in a public coordinate axis, and its lap is as the weight of orthogonal projection.

The backprojection operation strategy principle that described distance drives is identical with the orthogonal projection computing strategy principle that distance drives, the border that is about to each pixel and each X-ray detector units is mapped in a public coordinate axis, and its lap is as the weight of back projection.

The invention has the beneficial effects as follows, can and owe by low X-ray tube electric current can accurately rebuild faultage image in sampling or the incomplete projections situation of finite angle, thereby can significantly reduce under the prerequisite of CT examination x-ray radiation dosage, the reconstruction image of abundant suitable iconography clinical diagnosis demand is being provided.The present invention can also be applied in other CT image reconstruction algorithms as front-end processing link.

Accompanying drawing explanation

Fig. 1 is the inventive method FB(flow block).

Fig. 2 is step S2 in the inventive method) FB(flow block) of projection traversal iterative part.

Embodiment

Below in conjunction with drawings and Examples, the invention will be further described, but be not limited to this.

Embodiment:

The embodiment of the present invention as shown in Figure 1-2, a kind of CT iterative image reconstruction method, by CT machine, in the incomplete projections of owing to collect in sampling or finite angle scanning situation of low X-ray tube electric current, carry out image reconstruction, adopt the iterative algorithm of nested type, the projection traversal iteration that comprises outer field algorithm self iteration and internal layer, in algorithm self iteration, m is the loop control variable that records algorithm self iterations, and M is the maximum iteration time of algorithm self iteration; In projection traversal iteration, v is the loop control variable that records projection traversal iterations, the maximum iteration time of projection traversal iteration is V, travels through iteration, for the projection of internal layer each time under each projection angle, according to the acquisition order of data for projection, it is divided into O=180V mutually disjoint order subset fifty-fifty, use o=1,2, K, O indicates these order subsets, and not exclusively true data for projection is denoted as to P ^real, its projection angle adds up to V (identical with the maximum iteration time of projection traversal iteration), remembers that image to be reconstructed is designated as X, and this method for reconstructing step is as follows:

S1) initiation parameter

The value of given M, initialization m=1, v=1, X (m, v, o)=0, wherein symbol X (m, v, o) represents that image X to be reconstructed is the function of parameter m, v, o;

S2) projection traversal iteration

For this projection traversal iteration, the orthogonal projection that the processing of each order subset is driven through distance successively, make with true data for projection back projection, the image correction four processes to be reconstructed that poor and weighting, distance drive, concrete steps are as follows:

1. the orthogonal projection that, distance drives: the orthogonal projection computing strategy that adopts distance to drive carries out orthogonal projection computing to image X (m, v, o) and obtains data for projection P ^dd, and remember that the pixel that X (m, v, o) spatial location coordinate is (x, y) is c (x, y, d) to being numbered the contribution margin of the X-ray detector units of d; Meanwhile, calculate adaptivity weights omega, the formula that calculates ω is $\mathrm{\ω}={[v\×\underset{x,y,d}{\mathrm{\Σ}}{c}^2(x,y,d)]}^{-\frac{1}{2}};$

2., do poor and weighting with true data for projection: will corresponding true data for projection P ^realwith ω * P ^dddiffer from, obtain P ^diff, i.e. P ^diff=P ^real-ω * P ^dd;

3. the back projection that, distance drives: adopt the backprojection operation strategy of distance driving to P ^diffcarry out backprojection operation and obtain offset images, be designated as X ^diff;

4., image correction to be reconstructed: X (m, v, o) and offset images are done and and by result again assignment to X (m, v, o), i.e. X (m, v, o)=X (m, v, o)+X ^diff,

Make o=o+1, repeat step 1.～4. link, until then o=O, obtains the reconstruction image corresponding to v projection angle, i.e. the reconstruction image of v projection angle by the reconstruction image stack of the subset of the O corresponding to above-mentioned make v=v+1, o=1; The initial pictures of traversal iteration using X% as projection next time, after having traveled through all projection angles, i.e. v=V, projection traversal iteration completes;

S3) make m=m+1, v=1, o=1, repeats S2) process, until during m=M, algorithm finishes, and obtains finally rebuilding image X=X (M, V, O).

Claims (1)

1. a CT iterative image reconstruction method, by CT machine, in the incomplete projections of owing to collect in sampling or finite angle scanning situation of low X-ray tube electric current, carry out image reconstruction, adopt the iterative algorithm of nested type, the projection traversal iteration that comprises outer field algorithm self iteration and internal layer, in algorithm self iteration, m is the loop control variable that records algorithm self iterations, and M is the maximum iteration time of algorithm self iteration; In projection traversal iteration, v is the loop control variable that records projection traversal iterations, the maximum iteration time of projection traversal iteration is V, travels through iteration, for the projection of internal layer each time under each projection angle, according to the acquisition order of data for projection, it is divided into O=180V mutually disjoint order subset fifty-fifty, use o=1,2, K, O indicates these order subsets, and not exclusively true data for projection is denoted as to P ^real, its projection angle adds up to V, remembers that image to be reconstructed is designated as X, and this method for reconstructing step is as follows:

S1) initiation parameter

The value of given M, initialization m=1, v=1, X (m, v, o)=0, wherein symbol X (m, v, o) represents that image X to be reconstructed is the function of parameter m, v, o;

S2) projection traversal iteration

For this projection traversal iteration, the orthogonal projection that the processing of each order subset is driven through distance successively, make with true data for projection back projection, the image correction four processes to be reconstructed that poor and weighting, distance drive, concrete steps are as follows:

1. the orthogonal projection that, distance drives: the orthogonal projection computing strategy that adopts distance to drive carries out orthogonal projection computing to image X (m, v, o) and obtains data for projection P ^dd, and remember that the pixel that X (m, v, o) spatial location coordinate is (x, y) is c (x, y, d) to being numbered the contribution margin of the X-ray detector units of d; Meanwhile, calculate adaptivity weights omega, the formula that calculates ω is $\mathrm{\ω}={[v\×\underset{x,y,d}{\mathrm{\Σ}}{c}^2(x,y,d)]}^{-\frac{1}{2}};$

2., do poor and weighting with true data for projection: will corresponding true data for projection P ^realwith ω * P ^dddiffer from, obtain P ^diff, i.e. P ^diff=P ^real-ω * P ^dd;

3. the back projection that, distance drives: adopt the backprojection operation strategy of distance driving to P ^diffcarry out backprojection operation and obtain offset images, be designated as X ^diff;

4., image correction to be reconstructed: X (m, v, o) and offset images are done and and by result again assignment to X (m, v, o), i.e. X (m, v, o)=X (m, v, o)+X ^diff,

Make o=o+1, repeat step 1.～4. link, until then o=O, obtains the reconstruction image corresponding to v projection angle, i.e. the reconstruction image of v projection angle by the reconstruction image stack of the subset of the O corresponding to above-mentioned make v=v+1, o=1; The initial pictures of traversal iteration using X% as projection next time, after having traveled through all projection angles, i.e. v=V, projection traversal iteration completes;

S3) make m=m+1, v=1, o=1, repeats S2) process, until during m=M, algorithm finishes, and obtains finally rebuilding image X=X (M, V, O).