2011 IEEE Nuclear Science Symposium Conference Record MICI5.S-209

Reducing Metal Artifacts by Pre-Processing

Projection Data in Dental CBCT

with a Half-size Detector

Qingli Wang, Liang Li, Li Zhang, Zhiqiang Chen, Yuxiang Xing, Kejun Kang

Abstract-In dental Cone-Beam CT reconstruction images, the

metallic implants will cause apparent streak artifacts, which may

seriously increase the difficulty of diagnosis. Here we present a

new metal artifacts reducing method designed for our dental

CBCT with a half-size detector. In our method, we first locate the

positions of all the metallic implants in the three-dimensional

space, and then the projection points of the metal positions will

greatly simplify the segmentation and modification of the metal

area in each projection image. This method is proved to be

efficient and accurate by experimental results.

I. INTRODUCTION

METAL artifacts remain a challenge for CT reconstruction. In medical CT especially, where the contrast of the soft­

tissues is far lower than that of metals, the streak artifacts caused by metal will seriously degrade the image quality, which may seriously increase the difficulty of diagnosis. Since 1987 when W.A. Kalender et al created a precedent for reducing metal artifacts by modifying the projection data of the metal areas[1l, the methods of reducing metal artifacts for 2D CT have been developed enormously. In 3D CT system, modifying the projection data line by line is cumbersome and prone to mistakes.

Carrying on the idea of Kalender, researchers have proposed lots of methods which modify the projection data to reduce the metal artifacts for the CBCT system. For example, Meilinger M. et al from University of Regensburg introduced a metal artifacts reducing method for the CBCT system[2], where they first segmented the metal area from the pre­reconstructed 3D image, and then replaced the metal area with the CT number of soft tissue or water. The projection data could be modified by the projection of the replacement items. This method proves efficacious when the number of metallic implants in the RO! is limited, but when there are more than one metallic implants in the same slice of the RO!, the pre­reconstruction 3D image would be seriously damaged by the

Manuscript received November 15,2011. This work was supported in part by the grants from NNSFC 10905030 and Beijing Natural Science Foundation (Research on key techniques of medical cone-beam CT reconstruction from little data based on compressed sensing theory).

Qingli Wang, Liang Li, Li Zhang, Zhiqiang Chen, Yuxiang Xing, Kejun Kang are all with Department of Engineering Physics, Tsinghua University and Key Laboratory of Particles & Radiation Imaging (Tsinghua University), Ministry of Education, Bejing, 100084, China.

Liang Li, e-mail: liliang02@mails.tsinghua.edu.cn

artifacts, hence immensely affecting the accuracy of the segmentation algorithm. Moreover, the pre-reconstruction of the 3D image would greatly reduce the computing speed, lowering the practicality of the whole system.

Yongbin Zhang et al from the University of Texas proposed a new method to reduce the metal artifacts in the CBCT system[3]. They first calculated the position of the metal in 3D space by marking the locations in two projection images from two selected angles, and then segmented and modified the data of the metal in every projection image by calculating the location using the 3D positions of the metal and the system geometry. In their method, to get the positions of the metals, we only need to do some simple geometry calculation, instead of several slices of pre-reconstruction and segmentation work. Such methods, using the correlation of images from different angles to mark and modify the metal area, have been proved to be more efficient and accurate.

We mainly work on reducing metal artifacts in half-size detector CBCT used in dental imaging. To improve the efficiency and the accuracy of the algorithm, we first calculate the three-dimensional coordinates of some points, which we named "metal source points" in the subsequent steps. Every metal source point is chosen from a corresponding metallic implant to mark its position in the three-dimensional space. Then in every angle, we can get the projections of the metal source points and name them "seed points". The seed points, surely belonging to the metal areas in each projection image, can be used to simplify the segmentation and modification of the projection data of the metal. The design of the half-size detector can reduce radiation dose for patients and costs for manufacturers, but it brings more difficulty in algorithm research. In this paper, we use six projection images (three pairs, each consists of two images that are 180 degrees apart) to calculate the three-dimensional coordinates of metals.

II. MATERIALS AND METHOD

In a CBCT system with half-size detector, the center of the X-ray shoots vertically through one side of the detector. Previous researches show that the half-detector CBCT system can accurately reconstruct examined objects using data of 360-degree projections. [4]

Fig. 1 shows the geometry of the half-size detector CBCT

system. Oxyz represents a Cartesian coordinate system. S(P)

978-1-4673-0120-6/11/$26.00 ©20 11 IEEE 3434

is the focal spot of the source, where p is the angular

parameter. There are two important concepts in our research,

one of which is the metal source point, representing the three­

dimensional coordinates of a point that belongs to the metal

(shown as M (x,y,z) in Fig. I), while the other is the seed

point (shown as p(p,a,b) in Fig.I), representing the

projection of the metal source point at every angle, used to

simplify the segmentation of the metal areas. -size detecdor

z ... b

./

S(Il) . . . . ... .. ... . . . . .. . . . . . ..... . . .

Fig. I. A schematic diagram showing the half-size detector CBCT system.

A. Algorithmic Process

In our half-size detector CBCT system, the whole

projection data contain 360 projection images. To reduce the

metal artifacts in the reconstruction images, we have to locate

all the metal projection areas and modify the corresponding

projection data.

Our method includes 6 steps, as shown in Fig. 2.

1. Segment the

three pairs of pre-

selected projection 2. Calculate the 3D 3.Calculate the seed

images. Locate the � positions of the � points in every

metal areas and the metal source points. angle.

mass center of each

piece.

6. Reconstruct the

modified projection 5. Modify the 4. Segment all the

data. Fill the metal � projection data of � projection images.

parts back into the the metal areas. Mark the metal

CT images. areas.

Fig. 2. The flow chat of the whole method.

We first segment the three pairs of projection images, which

are selected from the 6 chosen angles. Metal areas of these 6

images should be located. Then we find out the three­

dimensional coordinates of the metal source points through

geometry calculation. After all the source points are located,

metal seed points can be acquired by calculating the

projections of metal source points in every angle. Then we

segment all the projection images with the help of the seed

points, and the metal areas would be modified by a linear

interpolation algorithm. The modified data is reconstructed by

the FDK algorithm, and the metal parts would be filled back to

the CT images.

B. Locate the Metal Source Points and the Seed Points

Three pairs of segmented projection images are used to

calculate the three-dimensional coordinates of all the metal

source points (as shown in Fig. 3).

In the first two pairs of projection images, we mark the

mass center of each metal area as point P (1) , P2 ' P3 and P4 in

Fig. 3 (a)), then we draw rays between the mass centers and

the corresponding X-ray sources S(P) . In the Oxyz coordinate

system, we calculate the least square solutions M ( M1 ' M2 ' M3 and M4 in Fig. 3(a)) of every pair of rays

intersecting in the vertical view (Fig.3 (a)). Then we check the M points with the third pair of segmented images (Fig.3 (b)). The M points are projected onto the detector and if the

projection point � belongs to the metal area in the image, the

corresponding M point is marked as the metal source point.

S(B 5) a b

Fig. 3. A schematic diagram showing the calculation of the 3D positions of the metals. Fig.3 (a) calculate the M points yet to be checked; (b) check the M points with the third pair of segmented image.

Using the metal source points and the system geometry, we can locate the seed points in every projection angle, which helps the segmentation algorithm locate the metal areas rapidly and accurately.

C. Segmentation Algorithm

We designed a simple segmentation algorithm to meet the requirements. FigA (a) shows the projection image that we use as an example to introduce the segmentation algorithm. We first process the projection image with Sobel operator (as shown in figA (b)), which is usually used to pick up the verges from the images. Generally in medical projection images, the verges of metal areas are far sharper than those of the other tissues due to the great physical difference. Therefore we could choose a threshold to obtain the information we need from the verge image. When there is not any seed point in the image (such as in the three pairs of projection images used to calculate the metal source points), a high threshold is used to find out some single points in the metal areas. We set a threshold equaling 80% of the maximum gray value of the verge image and get figA (c), from which we can choose one

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point from every connected area to be the seed point of the segmentation. A low threshold (40% of the maximum gray value) is used to pick up the intact verge (figA (d)). We can sift the verge of metal (fig A (e)) out with the seed points we get from the high threshold image, or the projections of the metal source points. Then using the dilation and erosion method, we can obtain the i

d e f Fig. 4. The segmentation algorithm used to mark the metal areas. Fig.(a)

the projection image; (b) the verge image; (c) the verge image with a high threshold; (d) the verge image with a low threshold; (e) the verge of the metal sifted out; (f) the integrated metal area obtained.

D. Interpolation Algorithm

We used the linear interpolation method to modify the

projection data of the metal areas. Fig. 5 is an example to

explain the steps. Through the previous procedures we can

mark all the metal areas (as shown in Fig. 5 (b)) for each of

the projection images (as shown in Fig. 5 (a)). We first erase

the metal areas in the projection image, and then modify the

gray value point by point. As it is signed, the distances from

the point whose value is wanted to the four edges are du ' d d'

dl and dr' and the values of the edge points are I u' I d' II

and I r . The interpolated value is:

(duxld+ddxlu) (d d) (dlxlr+drxll) (d d ) -'---'"-;---=---,,,-:-.=..c.:x I + r + x u + d 1= (du+dd) (dl+dr) du+dd+d[+dr

c

(1)

Fig. 5. Using the linear interpolation method to modifY the projection data of the metal areas. Fig.(a) projection image; (b) metal area; (c) enlarged image showing the interpolation method; (d) result of modification.

III. RESULTS

The experimental data was collected from our half-size

detector dental CBCT system. The flat panel detector is a

100mm x 100mm square with 512 x 512 pixels. The straight line

connecting the X-ray source and the center of rotation passes

through the pixel at Row 256, Column 499 of the detector

vertically. The distance between the X-ray source and the

rotation axis R = 700mm, while the distance between the X­

ray source and the detector D = 1000mm . The voltage and the

current of the X-ray tube are 100keV and3mA respectively. As

the system rotates, the detector collects data at intervals of one

degree, so the whole data to be processed include 360

projection images.

We choose a simple model to verify and explain our method.

Five screws, used to produce metal artifacts, are fixed in a

plastic container. The background is all filled with water.

We used the FDK algorithm to reconstruct the data of the

half-size detector CT. Fig. 6 shows two slices of the results.

e

Fig. 6. The experimental results with the data collected from the actual system. The display window of the reconstruction results is [0, 4]. Fig. (a) one of the projection images; (b) the FDK result at Row 110 with the original data; (c) the FDK result at Row 110 with the modified data; (d) the FDK result at Row 200 with the original data; (e) the FDK result at Row 200 with the modified data.

Fig. (a) is one of the projection images, in which all of the

five screws are in the field of vision. We choose the 11 oth and

the 200th slice to introduce the effect of the method. Fig. (b)

and (d) are the images reconstructed from the original

projection data. The metal artifacts in these two images are

very acute, especially in the areas between each two metals,

where even the verges of the metals are damaged. Fig. (c) and

(e) are corresponding images reconstructed from the modified

projection data. The artifacts are considerably reduced.

IV. DISCUSSION AND CONCLUSION

In this paper, we propose an effective method to reduce the

metal artifacts for the half-size detector CBCT system. In our

method the segmentation is processed in the projection images

without artifacts and blurs, and this is more accurate than the

original metal artifacts reducing method which need to

segment the pre-reconstruction images. Moreover, instead of

pre-reconstruction, we only do some geometry calculation,

and this will increase computation speed a lot. In the dental

CBCT application, sometimes the accumulate projection

values of several teeth are even higher than the value of metal

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areas, and this may mislead the segmentation. We use the seed

points to conduct the segmentation step, which is proved to be

more efficient and accurate.

Some further researches still need to be carried out. We

need to find new segmentation algorithms to conform the

dental application, especially those suitable for the three pairs

of selected projection images, in which the segmentation is

without the help of the seed points. In the images with seed

points, we also need better segmentation algorithms to

increase computation speed.

Furthermore, in our experiment when there are more than 6

metallic implants, our method may obtain some fallacious

results for the metal source points. This mistake is mainly

caused by the ill-conditioned geometry calculation algorithm,

so in further researches we also need to focus on the algorithm

of source point coordinate calculation.

REFERENCES

[1] W. A. Kalender et aI, Reduction of CT artifacts caused by metallic implants, Radiology, 1987, 164: 576-577

[2] Meilinger M. et aI, Metal artifact reduction in cone beam computed tomography using forward projected reconstruction information, Z Med Phys. 2011 Sep;21(3):174-82.

[3] Yongbin Zhang et aI, Reducing metal artifacts in cone-beam CT images by preprocessing projection data, Radiation Oncology BioI. Phys., 2007 Vol. 67, No. 3: 924-932

[4] Li L, Chen Z Q, Zhang L, et al. A cone-beam tomography system with a reduced size planar detector: A backprojection-filtration reconstruction algorithm as well as numerical and practical experiments. Appl. Radiat. Isotopes, 65:1041-1047,2007.

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