An algorithm for efficient metal artifact ... - Wiley Online Library

An algorithm for efficient metal artifact reductions in permanent seed implants Chen Xu Département de Radio-Oncologie et Centre de Recherche en Cancérologie, Université Laval, Centre Hospitalier Universitaire de Québec, 11 Côte du Palais, Québec, Québec G1R 2J6, Canada and Département de Génie Électrique et Génie Informatique, Laboratoire de Vision et Systèmes Numériques, Université Laval, Québec, Québec G1K 7P4, Canada

Frank Verhaegen Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht 6201 BN, The Netherlands and Oncology Department, Montreal General Hospital, McGill University, 1650 Cedar Avenue, Montreal, Quebec H3G 1A4, Canada

Denis Laurendeau Département de Génie Électrique et Génie Informatique, Laboratoire de Vision et Systèmes Numériques, Université Laval, Québec, Québec G1K 7P4, Canada

Shirin A. Enger Département de Radio-Oncologie et Centre de Recherche en Cancérologie, Université Laval, Centre Hospitalier Universitaire de Québec, 11 Côte du Palais, Québec, Québec G1R 2J6, Canada

Luc Beaulieua兲 Département de Radio-Oncologie et Centre de Recherche en Cancérologie, Université Laval, Centre Hospitalier Universitaire de Québec, 11 Côte du Palais, Québec, Québec G1R 2J6, Canada and Département de Physique, de Génie Physique et d’Optique, Université Laval, Québec, Québec G1K 7P4, Canada

共Received 17 May 2010; revised 28 October 2010; accepted for publication 4 November 2010; published 14 December 2010兲 Purpose: In permanent seed implants, 60 to more than 100 small metal capsules are inserted in the prostate, creating artifacts in x-ray computed tomography 共CT兲 imaging. The goal of this work is to develop an automatic method for metal artifact reduction 共MAR兲 from small objects such as brachytherapy seeds for clinical applications. Methods: The approach for MAR is based on the interpolation of missing projections by directly using raw helical CT data 共sinogram兲. First, an initial image is reconstructed from the raw CT data. Then, the metal objects segmented from the reconstructed image are reprojected back into the sinogram space to produce a metal-only sinogram. The Steger method is used to determine precisely the position and edges of the seed traces in the raw CT data. By combining the use of Steger detection and reprojections, the missing projections are detected and replaced by interpolation of non-missing neighboring projections. Results: In both phantom experiments and patient studies, the missing projections have been detected successfully and the artifacts caused by metallic objects have been substantially reduced. The performance of the algorithm has been quantified by comparing the uniformity between the uncorrected and the corrected phantom images. The results of the artifact reduction algorithm are indistinguishable from the true background value. Conclusions: An efficient algorithm for MAR in seed brachytherapy was developed. The test results obtained using raw helical CT data for both phantom and clinical cases have demonstrated that the proposed MAR method is capable of accurately detecting and correcting artifacts caused by a large number of very small metal objects 共seeds兲 in sinogram space. This should enable a more accurate use of advanced brachytherapy dose calculations, such as Monte Carlo simulations. © 2011 American Association of Physicists in Medicine. 关DOI: 10.1118/1.3519988兴 Key words: fan-beam CT, sinogram, prostate, brachytherapy seeds, metal artifacts, reconstruction, projection, interpolation

I. INTRODUCTION Permanent prostate brachytherapy 共BT兲 is a procedure widely used for treating early stage prostate cancers.1,2 The 47

Med. Phys. 38 „1…, January 2011

procedure involves the insertion of small radioactive metal capsules 共seeds兲 into the prostate to maximize the radiation dose to the target tumors, while minimizing the damage to

0094-2405/2011/38„1…/47/10/$30.00

© 2011 Am. Assoc. Phys. Med.

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Xu et al.: Metal artifact reductions in seed implants

the nearby healthy tissue. After the seeds have been implanted, postimplant dosimetry evaluation should be performed to determine the actual dose distribution in the target and other organs. This is usually achieved by using computed tomography 共CT兲 images owing to their widespread availability and seed detection capability. However, implanted metal seeds generate artifacts in x-ray CT imaging because the attenuation coefficient of a metal in the range of diagnostic x rays is much higher than that of soft tissues and bone. Thus, little or no x-ray radiation can pass through these highly attenuating objects, producing gaps in the acquired raw CT data 共sinogram兲. The reconstruction of the incomplete sinogram using the filtered backprojection 共FBP兲 method, a standard CT reconstruction algorithm used in commercial CT scanners, leads to artifacts that significantly degrade CT image quality and constitute a major obstacle for accurate heterogeneous dose calculations in low-energy brachytherapy.3–9 Therefore, developing an effective metal artifact reduction 共MAR兲 method for metallic seed implants is critical for radiation therapy planning. A number of MAR techniques have been developed based on the assumption that the projections associated with the metal objects are completely missing or corrupted and useless for CT image reconstruction. These MAR methods can be classified into two categories according to their strategy: Projection completion methods9–26 and iterative reconstruction methods.27–31 Using these methods, the missing data are either replaced by synthetic data 共in FBP reconstruction兲 or ignored 共in iterative reconstruction兲. Some hybrid MAR strategies32 that combine the two methods have also been proposed. Iterative reconstruction techniques produce better image quality than projection completion methods and might have the potential of generating artifact-free CT images. However, this category of algorithms is much more computationally expensive than standard FBP methods and is difficult to implement in commercial CT scanners. To combine with the FBP reconstruction methods for clinical application, another category of algorithms, i.e., projection completion, has been proposed. In these methods, the missing projections are first detected in the sinogram and then the data are corrected within the detected areas using various techniques including subtraction,9 linear or polynomial interpolation,10–19 wavelet interpolation,20,21 linear prediction,22 and adaptive filtering.23–26 Finally, the artifactreduced CT images are reconstructed from the corrected sinogram by using FBP. A key step for this category of algorithms is the detection of the projections corrupted by metal objects. Using projection completion methods for MAR has produced promising results for hip prostheses,12,18 surgical clips,11 and dental fillings.19 However, to our knowledge, until now, few MAR applications for BT seeds have been published. Takahashi et al.9 proposed a subtraction-based reprojection method for reducing the artifacts due to I-125 seeds. Although their method is interesting and the artifacts were reduced to some extent, significant streaking artifacts still remain after MAR since the missing projections were Medical Physics, Vol. 38, No. 1, January 2011

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FIG. 1. Flow chart of the proposed MAR procedure.

not accurately detected and the corrupted projection data were not removed completely from the original sinogram. The goal of this work is to develop an automatic projection completion method for correcting the metal artifacts caused by brachytherapy seeds for clinical applications. The effort is focused on developing an effective method for detecting the missing projections generated by a large number of closely packed, small objects such as BT seeds. The performance of the algorithm has been tested for both phantom experiments and patient case studies. II. METHODS AND MATERIALS II.A. Overview of MAR procedure

The proposed automatic method for MAR is based on the interpolation of missing projections by using raw helical CT data directly. One major challenge when using interpolationbased method for BT seeds compared to the previous work dealing with other metal objects is to detect precisely the projected traces 共namely, the position and edges兲 that are associated with the very small size and relatively large number of the metal objects 共seeds兲. The flow chart of our proposed MAR procedure is shown in Fig. 1. First, an initial image was reconstructed by using inverse fan-beam transform from a block of 180-degree raw helical projection data. Then, the binary metal objects segmented from this reconstructed image were reprojected back into the sinogram space by using a fan-beam transform. To determine precisely the position and edges of the seed traces 共missing projections associated with the seed implants兲, a method used for the detection of curvilinear structures in grayscale images, the Steger method,33 was applied to the raw CT data 共for determining the position兲 and its gradient matrix 共for determining the edges兲. Finally, the seed traces were detected by combining the use of Steger curve detection and reprojected traces. In the proposed detection procedure, the Steger method is

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FIG. 2. Example subsinogram containing the raw helical projection data acquired during a single 180° rotation.

used to detect all curvilinear structures in the raw CT data. The reprojected traces are then used to separate real seed traces from the noise detected by the Steger method. Once these missing projections have been detected, it is then straightforward to apply a linear interpolation algorithm for replacing them by the interpolated values. II.B. Example of the phantom sinogram

A phantom was made of agar gel and consists of six slices of 5 mm in which 75 seeds 共activity at background level兲 were implanted for imaging purposes. The seed configuration is based on a distribution used in an actual clinical case. The phantom was scanned on a helical CT scanner 共Somatom Emotion, Siemens Medical, Germany兲 with 672 detectors for each view angle using the following postimplant protocol: 130 kV tube voltage, 150 mA tube current, field of view of 190 mm, helical pitch of 1.5, 2 mm slice thickness, and 2 mm reconstruction increment. The acquired sinogram is represented as a two-dimensional matrix, where the rows and columns represent the detector channel and projection view, respectively. Our MAR procedure was performed on a sliceby-slice basis. First, the entire sinogram matrix was divided into a set of subsinograms 共blocks of data兲 each of which contains the raw helical projection data acquired during a single 180° rotation. Each subsinogram was then corrected separately. Figure 2 shows a subsinogram containing six white lines 共traces兲 corresponding to the missing projections caused by six seeds. To show the traces better, only the image region that contains the seed traces is shown in the figure. Our MAR procedure will be described in detail in subsections II C–II E using this example subsinogram 共Fig. 2兲. II.C. Detection of missing projections using the reprojection method

A common procedure for detecting the corrupted data in sinograms using a reprojection method consists of several steps: Reconstruction of an initial image, segmentation of the metal objects, and reprojection of the segmented metal objects back into the sinogram space. In our MAR procedure, an initial image was first reconstructed using inverse fanbeam transform adapted to the CT scanner geometry from Medical Physics, Vol. 38, No. 1, January 2011

FIG. 3. Detection of missing projections with reprojected seed traces. 共a兲 Initial image reconstructed from the sinogram 共Fig. 2兲. 共b兲 Binary metalonly image made by object positions in 共a兲. 共c兲 Metal-only sinogram computed from 共b兲.

the subsinogram shown in Fig. 2. The corresponding initial transverse image, as reconstructed by the scanner software, is shown in Fig. 3共a兲. A key step when using a reprojection-based method is to segment accurately the metal objects from the reconstructed initial image. It is usually accomplished with a simple threshold-based segmentation method that separates background pixels 共soft tissue and bone兲 from the pixels belonging to metal objects. In the previously published MAR methods, the threshold value was either predefined or determined by Otsu’s method.34 To achieve more accurate reprojection results, a set of morphologic operations 共e.g., closing and opening兲 is usually performed after the thresholding operation to refine the detected metal objects and to smooth their boundaries. In this study, a thresholding technique was used for segmenting metal objects from the reconstructed initial image

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with a predefined initial gray-level threshold value. However, there are some differences between our method and that used in previously published MAR procedures: – Only the position of the metal objects needs to be detected and used for making a binary metal-only image for reprojection since the seed traces in the sinogram are very fine and the distance between the traces is sometimes very small. – In our segmentation procedure, a multithresholding process 共described below兲 is used to decompose each merged object into individual single objects after the initial gray-level thresholding step. The position of the segmented object in the image was determined by finding the pixel having the highest gray-level among other pixels located inside the object region. If more than one merged pixel has the same gray-level value, the centroid of these pixels was used as the object position. The detected object positions are further used to generate a binary metal-only image shown in Fig. 3共b兲. Finally, a metal-only sinogram 关used as a mask shown in Fig. 3共c兲兴 was obtained by computing the sinogram from the binary metal-only image using a fan-beam transform with the same set of geometric parameters that were used for reconstructing the initial image. Although the BT seeds can be separated from the low gray-level tissues using gray-level thresholding, when several seeds are clustered together in the image, such a simple method sometimes fails to resolve them and thus misclassifies them as one 共merged兲 object that should then be divided into individual seeds. Figure 4共b兲 shows the initial result obtained by thresholding the image shown in Fig. 4共a兲 with a predefined initial value of 40. It can be seen that all the objects were separated from the background pixels, but three closely spaced seeds 共included in a rectangle兲 were not resolved and were thus misclassified as a single object. To better show the gray-level distribution inside the merged object region, the corresponding region of the initial image is zoomed and shown in Fig. 4共c兲, in which the merged object region is surrounded by the polygon. In our segmentation procedure, a decomposition process followed the initial gray-level thresholding operation to extract further the individual objects from merged regions. Our decomposition method is based on a multilevel 共iterative兲 thresholding process. First, a threshold range was determined by finding the lowest 共Min兲 and highest 共Max兲 gray-level values inside the merged object region 关polygon in Fig. 4共c兲兴 on the initial image. Then, the merged region was thresholded iteratively using a set of different grayscale values ranging from Min to 共Max− 1兲 with iteration increment by 1. At each iteration 共after thresholding the region with a given threshold兲, the number of the binary objects in the region was checked and compared to the one obtained with the previous threshold. If some new objects were detected, their positions were recorded. Using this iterative thresholding method, all the three single objects finally were decomposed from the merged object. The final decomposition results of three resolved seed objects are shown in Fig. 4共d兲. Using our Medical Physics, Vol. 38, No. 1, January 2011

FIG. 4. Decomposition of the merged object into single objects. 共a兲 Initial image reconstructed from a given subsinogram. 共b兲 Segmented objects from the image 共a兲 using a predefined initial threshold. 共c兲 Zoomed image region of 共a兲 specified by rectangle in 共b兲. 共d兲 Decomposed single objects from the merged object in 共c兲.

thresholding method, the single and merged seed objects in the reconstructed initial image have been well detected and decomposed for both phantom and patient cases, and no false positive errors were found in the decomposition process. However, this will need to be further evaluated over a larger number of image sets.

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In the algorithm, the backward-projection 共reconstruction兲 and forward-projection 共reprojection兲 were computed using the MATLAB 共The MathWorks, Inc., Natick, MA兲 functions fan2para, iradon, radon, and para2fan according to the CT scanner geometry.

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H=

冉

rxx rxy ryx ryy

冊

.

The partial derivatives rxx, rxy, ryx, and ryy contained in the above Hessian matrix can be computed numerically by convolving the image z共x , y兲 with a set of Gaussian filters,

II.D. Accurate detection of missing projections using Steger method

rx共x,y兲 = z共x,y兲 ⴱ gx,␴共x,y兲,

In most reported reprojection-based methods, the reprojected metal traces contained in the metal-only sinogram are directly used for determining the missing projections based on the criterion that the pixels in the original sinogram are considered as the missing data if their corresponding pixels in the metal-only sinogram are within the reprojected metal trace region. However, it is very difficult to produce reprojected metal object traces that overlap perfectly with the real missing reprojections in the original sinogram positions due to imperfection of the forward-projection of the metal objects. As all the reprojection-based methods, in the forward-projection calculation, the object position at each rotation 共view兲 angle during the scan is assumed to be same. However, in a helical scan, the object position at different views can change if the orientation of the object is not parallel to the scanning axis or there is motion during the scan. In these cases, the reprojected trace 共forward-projection兲 might not map precisely the real metal object trace but it still close to it. By using a shorter block of data of 180° 共instead of 360°兲, the effect of this imperfection of the forwardprojection will be of lesser importance. Thus, the original sinogram would be either over- or undercorrected. To detect precisely the missing projections in helical raw CT data, a correction procedure for the reprojected metal regions was added in our previous MAR work for hip prostheses.18 This method achieved good results for hip prostheses and can possibly be adopted to process other metal objects, but it is unsuitable for BT seeds 共with a large number of closely spaced small metal objects兲. Thus, another more effective method for precisely detecting the seed traces, namely, the position and edges, in the sinogram is needed. It can be seen from Fig. 2 that the projection of a single seed is a bright sinusoidal curve in the sinogram. This curvilinear property suggests that it is natural to use a line detection method for precisely determining the position of the seed traces. In this work, the Steger method33 is used for the detection of curvilinear structures in sinogram images.

ry共x,y兲 = z共x,y兲 ⴱ gy,␴共x,y兲,

II.D.1. A brief overview of Steger method The Steger method is used for the detection of curvilinear structures in grayscale images with subpixel accuracy. In this method, a point lying on a curve in an image can be detected based on the following criteria: 共1兲 The first derivative in the direction of the normal vanishes and 共2兲 the absolute value of the second directional derivative should be greater than a specified threshold value. According to differential geometry theory,35 the direction of the normal at a curve point can be determined by finding the eigenvector that corresponds to the largest absolute eigenvalue of the following Hessian matrix: Medical Physics, Vol. 38, No. 1, January 2011

rxx共x,y兲 = z共x,y兲 ⴱ gxx,␴共x,y兲, rxy共x,y兲 = z共x,y兲 ⴱ gxy,␴共x,y兲, ryy共x,y兲 = z共x,y兲 ⴱ gyy,␴共x,y兲, where gx,␴共x,y兲 = g␴共y兲g␴⬘ 共x兲, gy,␴共x,y兲 = g␴⬘ 共y兲g␴共x兲, gxx,␴共x,y兲 = g␴共y兲g␴⬙ 共x兲, gxy,␴共x,y兲 = g␴⬘ 共y兲g␴⬘ 共x兲, gyy,␴共x,y兲 = g␴⬙ 共y兲g␴共x兲, 1

−t2/2␴2

g␴共t兲 =

冑2␲␴ e

g␴⬘ 共t兲 =

冑2␲␴3 e

g␴⬙ 共t兲 =

−t

t2 − ␴2

冑2␲␴5 e

,

−t2/2␴2

−t2/2␴2

,

,

The parameter ␴ used in the Gaussian filters is the standard deviation and is determined based on the width of the line to be detected. A detailed description of the Steger method can be found in Ref. 33.

II.D.2. Detection of seed traces with Steger method The position of seed traces can be detected by directly applying the Steger method to the sinogram with a predefined negative threshold.36 In the detection procedure, the algorithm scans each pixel in the complete sinogram to determine if it is a curve point based on the curve detection criteria described in the above section. This is illustrated by applying the Steger method on the sinogram of Fig. 2 with a predefined negative threshold 共⫺50兲. It can be seen from Fig. 5共a兲 that the seed traces were well detected but with a considerable amount of noise in the lateral views for which the CT scanner amplifies the acquired raw data 共including signal and noise兲 to compensate for large attenuation of pelvic bones. Thus, an identification process should be performed in

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value 共50兲. It can be seen from Fig. 5共c兲 that the detection results still contain noise corresponding to pixels that have the curve features but are not seeds. II.E. Noise free trace detection by combining the reprojection and the Steger results

FIG. 5. Detection of missing projections by combining the use of reprojections and Steger detection. 共a兲 Detected seed trace position by applying Steger method to the sinogram 共Fig. 2兲 with a negative threshold. 共b兲 Gradient matrix of the sinogram 共Fig. 2兲. 共c兲 Combination of reprojections 共cyan points兲 and edge positions detected by applying Steger method to the gradient matrix 共b兲 with a negative threshold for the top edges 共red points兲 and with a positive threshold for the bottom edges 共blue points兲. 共d兲 Final seed detection results 共noise free seed traces兲. 共e兲 Cropped image from 共c兲. 共f兲 Cropped image from 共d兲.

order to determine whether a detected curve point belongs to a real seed trace or is just noise. Section II E describes this process in detail. The Steger method can also be used to detect the edges of the seed traces. In our detection procedure, the gradient matrix of the sinogram was computed to produce another data set. Figure 5共b兲 shows the gradient matrix of the sinogram image of Fig. 2. This matrix was obtained by computing the gray-level differences in the Y 共vertical兲 direction, i.e., ⌬S / ⌬y, where S represents the pixel value of the sinogram. The spacing between points in the Y direction 共⌬y兲 was taken to be 1 共pixel兲. It should be noted that the origin of the images 共or matrices兲 used in this paper is at the top left corner of the images, ordered from top to bottom and left to right. In the resulting gradient image 关Fig. 5共b兲兴, the top edge pixels of the seed traces group to form the red lines with positive gradient values, while the bottom ones form the blue lines with negative gradient values. These properties allow the top and bottom trace edge positions to be determined separately by using the Steger method on the gradient matrix; the top edges 关red points in Fig. 5共c兲兴 are detected with a negative threshold value 共⫺50兲, and the bottom edges 关blue points in Fig. 5共c兲兴 are detected with a positive threshold Medical Physics, Vol. 38, No. 1, January 2011

As demonstrated in the previous section, the edges of the missing projections can be detected accurately but with a considerable amount of noise. However, using the reprojection method, the edges of the corrupted data regions cannot be detected accurately, but a binary metal-only sinogram 共no noise兲 can be obtained. Thus, the missing projections can be accurately detected by combining both results. This is illustrated in Fig. 5共c兲, superimposing the results of the Steger detection and reprojection 关Fig. 3共c兲兴. It can be seen from the figure that each seed trace 共missing reprojection兲 can be easily determined by finding two corresponding 共top and bottom兲 edge curves 共red and blue curves兲 surrounding and being adjacent to the reprojected trace position 共cyan curve兲. The final detected missing projection regions are shown in Fig. 5共d兲. For visualization purposes, not all the detected points are shown in the figure 共the angular increment is about 3.6° between points兲. The details of this combining process for determining edge positions can be illustrated by Figs. 5共e兲 and 5共f兲 that are cropped, respectively, from Figs. 5共c兲 and 5共d兲 with the same size and image position. For each reprojection point 共cyan兲 in Fig. 5共e兲, the algorithm searches a pair of corresponding top 共red兲 and bottom 共blue兲 edge points in the same view angle 共column兲. The top edge point was determined by finding the closest red point that is above this reprojection point or overlapping with it. The bottom edge point can also be determined by using the same method but under the reprojection point. Figure 5共f兲 shows the determined top and bottom edge points from Fig. 5共e兲. II.F. Reduction in artifacts by completion of detected missing projections

Once the missing projections have been detected, it is then straightforward to apply an interpolation algorithm for replacing them by interpolation of non-missing neighboring projections. In this work, a linear interpolation method was used for its simplicity and computational efficiency.12 To remove the corrupted data completely from the sinogram, the detected top and bottom edge positions were shifted by 2 pixels toward top and bottom, respectively, for determining the interpolation points. Finally, the missing projections in the sinogram of Fig. 2 were replaced by the interpolated values. The corrected sinogram is shown in Fig. 6共a兲. It can be seen from the figure that the seed traces 共corresponding to the missing projections兲 were accurately detected and completely corrected from the raw CT data. II.G. Reduction in artifacts for the entire sinogram acquired by CT scanner

In the above subsections, the proposed MAR method for a given block of 180-degree data has been described. In our

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procedure, the entire original sinogram was processed on a slice-by-slice basis. Each slice containing the raw helical projection data acquired during a single 180° rotation was corrected. Finally, the corrected sinogram was transferred back to the CT scanner for reconstructing the artifactreduced CT slices using the standard FBP method implemented as a built-in function of the commercial CT scanner. II.H. Monte Carlo dose calculations

To illustrate the impact of artifact correction, Monte Carlo 共MC兲 dose calculations were performed using the uncorrected and the MAR corrected images for one clinical case. The absorbed dose calculations were performed with the 37 GEANT4 code version 9.2 patch 02. The low-energy electromagnetic package was used. Both sets of images were imported via DICOM RT in GEANT4,38 together with the contours and seed positions. The clinical CT calibration curve was applied to extract voxel densities. Prostate, rectum, bladder, and interorgan tissues were considered as per Carrier et al.38 In both cases, the complete seed 3D geometry was simulated 共selectSeed by Isotron, Germany兲 and therefore interseed attenuation was taken into account. This specific case had 87 seeds of 0.78 U. Only the effect of the artifacts on the absorbed dose between the two calculations remains. Changes in density within the prostate were taken into account by varying the chemical composition with the density, a high density being associated with higher calcium content. Thus, in the case of uncorrected images, the overall effect of the artifacts is to produce a prostate with higher calcium content than what would normally be the case. This approach should correspond to the maximum potential effect of artifacts on dose.39 III. RESULTS

FIG. 6. Artifact reduction results for the phantom image. 共a兲 Correction result for the sinogram 共Fig. 2兲. 共b兲 Image reconstructed using the corrected sinogram 共a兲. 共c兲 Comparison of the intensity uniformity between the uncorrected 关Fig. 3共a兲兴 and corrected 共b兲 images: 共⫺兲 intensity profile along the solid line in Fig. 3共a兲, 共- -兲 intensity profile along the dashed line in Fig. 3共a兲, 共⫹兲 intensity profile along the dashed-plus sign line in 共b兲, and 共·兲 intensity profile along the dashed-dotted line in 共b兲. Medical Physics, Vol. 38, No. 1, January 2011

In the previous section, each step of the correction process was illustrated. A small portion of the sinogram 共a block of 180-degree raw CT data兲 for a phantom was corrected using our MAR algorithm. The corrected sinogram is shown in Fig. 6共a兲. In order to test the performance of the algorithm, an artifact-reduced CT image shown in Fig. 6共b兲 was reconstructed from the corrected sinogram to compare with the initial image shown in Fig. 3共a兲. The circles on the corrected image 关Fig. 6共b兲兴 indicate the area affected by the artifacts in initial image 关Fig. 3共a兲兴 and are used for quantitative evaluation of the performance of the algorithm described below. It can be seen easily by comparing the images before and after the MAR operation that the artifacts caused by metallic objects have been significantly reduced and that all the artificially low-density 共black兲 regions have been corrected. For a further quantitative evaluation of the performance of the algorithm, the intensity uniformity was compared between the uncorrected and the corrected phantom images by computing the intensity profile along two selected lines on each image. This performance evaluation is based on the fact that the original phantom image without inserting metallic objects should have a relatively uniform intensity distribution. The dashed 关Fig. 3共a兲兴 and dashed-plus sign 关Fig. 6共b兲兴

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FIG. 7. Artifact reduction results for three different clinical cases. The original CT images are shown in the left panels and their corresponding corrected ones are shown in the right panels. Each row corresponds to the same case before and after MAR. Window level is 1000 HU and the width is 1000 HU for all images in the figure.

lines, used as a reference, are in the nonartifact region, while the solid 关Fig. 3共a兲兴 and dashed-dotted 关Fig. 6共b兲兴 lines are in the artifact region. The computed intensity profiles shown in Fig. 6共c兲 demonstrate that the projection near the metal objects is affected by the metallic artifacts and is significantly reduced by the MAR method. The objective of this work is to develop an automatic MAR method for clinical applications. To validate our MAR algorithm for clinical cases, 20 sinograms that were obtained at the time of postimplant using the same CT scanner settings as the above phantom experiment were randomly selected from our research database. The experimental test results show that the proposed MAR method is effective for automatic reduction in metal artifacts caused by BT seeds. Figure 7 shows the test results obtained using our MAR algorithm for three clinical cases by visually comparing the corrected images 共right panels兲 with the original ones 共left panels兲. All CT images were reconstructed using the standard FBP method implemented as a built-in function of the CT scanner. A window level of 1000 HU and a window width of 1000 HU were selected to allow clear visualization of the MAR results. It can be seen from the test results that the streak artifacts in the original images generated by missing projections have been eliminated in their corresponding corrected images 共Fig. 7, right hand column兲. The impact of removing artifacts in the clinical case is depicted in Fig. 8. For both calculations, the full 3D seed geometry, materials, and associated densities were used, overriding the image information within the seed volumes. The seeds were all assumed parallel to the implant axis. However, in the noncorrected images, artifacts extend beyond the normal seed dimensions. This leads to important effects on the isodose distributions. For this particular case, the clinical D90 共as Dm,m, i.e., transport and scoring performed in the medium兲 was found to increase by 12% in the artifact-corrected images relative to the non-corrected one. Medical Physics, Vol. 38, No. 1, January 2011

FIG. 8. Monte Carlo dose calculation before 共dashed lines兲 and after 共solid lines兲 using the MAR algorithm. The isodoses are displayed on an uncorrected image to better visualize where the seeds and artifacts are located.

IV. DISCUSSION This paper presents an automated interpolation-based method for MAR from a large number of small metal objects 共seeds兲. The performance of the proposed method has been tested for both phantom and clinical cases. The MAR procedure was performed using an in-house program written in MATLAB. The computational time for processing a subsinogram is about 24 s, on average, when running on Windows Vista with an Intel CPU 2.66 GHz processor. It should be indicated that the MATLAB code was not optimized and used only to prototype the algorithm. The most time-consuming parts of the method are the reconstruction of the initial image and reprojection of the segmented metal objects, two common processes in the reprojection-based MAR procedure. This means that the computational time of our algorithm should not be very different from that of other published MAR methods. To accelerate the method, the most processing-intensive parts of the code should be rewritten in C⫹⫹. As mentioned previously, the Steger method is able to detect curvilinear structures in grayscale images with subpixel accuracy. In the presented MAR procedure, the edge positions of the seed traces were also detected with subpixel accuracy. However, for an interpolation-based method, the subpixel accuracy is not necessary. In this study, the spatial accuracy used to determine the interpolation positions is at the level of 1 pixel. Using projection completion-based methods for MAR involves two tasks: Identification of missing projections and correction of the corrupted data within the detected areas in the sinogram. The major challenge when using these meth-

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ods for BT seeds compared to other metal objects is to detect precisely the projected traces in the sinogram space. This work focused on developing an effective seed trace detection method for overcoming this challenge. As demonstrated by the test results, the missing projections associated with BT seeds have been detected accurately. As for the second task, a simple and fast linear interpolation algorithm was used in this work. Although most of the metal artifacts have been removed, some noise and additional artifacts were created in the corrected image by the interpolation process. Thus, further research could be needed to obtain a better correction method for BT seeds by adapting one of the published methods to improve the quality of the corrected image. When using interpolation-based methods to correct missing projections, the corrupted CT data in the original sinogram are totally removed and replaced by the interpolated values. Thus, not only the artifacts caused by BT seeds, but also the seed objects themselves, are removed from the CT images reconstructed from such corrected sinogram. In this case, the position and shape information of the BT seeds in the artifact-reduced images are lost after a MAR operation. Therefore, a possible improvement of our MAR procedure would be to preserve the information of the seed objects in the corrected CT images that are needed by clinical applications. A possible limitation of the current implementation resides in the fact that the method presented in this paper assumed that each brachytherapy seeds will appear as a single curve in the sinogram space. This would be true for any seed models having continuous radio-opaque markers for visualization. We have not tested this approach for seed models using multiple, widely spaced fiducial 共spherical兲 beads; these might leave more than one trace per seed in the sinogram space. Although our seed trace detection method was designed for correcting the metal artifacts caused by a large number of small metal objects 共BT seeds兲, it can also be used for other applications. Future work to develop a method to process the detected seed traces for 3D localization, namely, the position and orientation, of each seed with high precision36 is in process. Finally, it is important to underline that metal artifacts constitute a major obstacle for accurate heterogeneous dose calculations in low-energy brachytherapy, such as Monte Carlo simulations. Preliminary MC calculations were presented. For that specific image sets, the impact on D90 was on the order of 12%. A much more elaborated set of calculations, with various composition assignment schemes and a larger patient cohort, is needed to fully characterize the effect of artifacts on dose. Such a study is currently ongoing and will be presented in a future manuscript. V. CONCLUSION A robust and automated algorithm for metal artifact reduction in seed brachytherapy was developed for clinical application. The challenge of accurately detecting and correcting artifacts of many tens of tiny metal objects in the Medical Physics, Vol. 38, No. 1, January 2011

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sinogram space has been successfully demonstrated. It is claimed that with further improvements to the algorithms, the detected seed traces could be processed to extract, from a large sample of points, the position and orientation of each seed with high precision. Our algorithms should enable more accurate brachytherapy dose calculations, such as Monte Carlo simulations.

ACKNOWLEDGMENTS This work was supported, in part, by the National Cancer Institute of Canada 共NCIC兲 with funds from the Canadian Cancer Society 共Grant No. 017133兲 and by a Discovery Grant 共No. 262105兲 from the National Science and Engineering Research Council of Canada 共NSERC兲. a兲

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