- Open Access
An improved method for the removal of ring artifacts in high resolution CT imaging
© Rashid et al; licensee Springer. 2012
Received: 10 September 2011
Accepted: 27 April 2012
Published: 27 April 2012
In high resolution computed tomography (CT) using flat panel detectors, imperfect or defected detector elements cause stripe artifacts in sinogram which results in concentric ring artifacts in the image. Such ring artifacts obscure image details in the regions of interest of the image. In this article, novel techniques are proposed for the detection, classification, and correction of ring artifacts in the sinogram domain. The proposed method is suitable for multislice CT with parallel or fan beam geometry. It can also be employed for ring artifact removal in 3D cone beam volume CT by adopting a sinogram by sinogram processing technique. The detection algorithm is based on applying data driven thresholds on the mean curve and difference curve of the sinogram. The ring artifacts are classified into three types and a separate correction algorithm is used for each class. The performance of the proposed techniques is evaluated on a number of real micro-CT images. Experimental results corroborate that the proposed algorithm can remove ring artifacts from micro-CT images more effectively as compared to other recently reported techniques in the literature.
Flat panel detectors (FPDs) are used to obtain high resolution computed tomography (CT) image. But due to technical faults of these FPDs, ring artifacts are often generated in the CT image. These artifacts may be caused by damaged detector pixels, mis-calibrated detector pixels, impurities in scintillator crystal or dust on scintillator screens. All these phenomena attribute to the generation of a number of concentric superimposed rings in the reconstructed image which correspond to stripe artifacts in sinogram domain. These rings can be of different types and of different intensities. As for example, completely damaged detector pixels cause strong isolated or band rings. Similar artifacts also arise from dusty or damaged scintillator screens . On the other hand, mis-calibrated detector elements lead to less strong ring artifacts in the tomographic image . These artifacts are also sensitive to tube voltage. Changes in the tube voltage alter the intensity of the ring. As these artifacts severely degrades the image quality by obscuring significant image details, it is necessary to remove them, otherwise, post processing, such as noise reduction or segmentation of image information, becomes quite difficult.
There are a number of different methods to reduce these ring artifacts, e.g., hardware modification , flat-field correction , and signal processing in sinogram domain [5–7]. In hardware based approach, the detector array is moved during data acquisition to reduce the non-uniform sensitivity of different detector elements. Then an average response of all the detector pixels is calculated to suppress the ring artifacts [8, 9]. But in this approach special hardware arrangement is needed. In flat-field correction, image acquisition is done twice. At first image is obtained without placing the object in the X-ray beam and then with the object placed in the X-ray beam. The first image has the response of faulty detector elements, damaged scintillator and also inhomogeneities in the incident X-ray beam. But this approach fails to remove the rings completely if the response function of different detector element is different . In , two post-processing techniques both using mean and median filtering but working in different geometric planes (i.e., polar and cartesian) were proposed for the correction of ring artifacts. In the reported work, it was shown that the algorithm in polar coordinate (RCP) outperforms the algorithm in cartesian coordinate in removing ring artifacts in the images obtained from C-Arm CT system. In addition to that, the RCP method was also applied for removing ring artifacts from micro-CT images . But when there is a strong ring having high frequency content is present in the CT image, the RCP algorithm fails to remove it effectively. On the other hand, sinogram processing is used before reconstructing the image. As ring artifacts appear as stripes in the sinogram domain, it is more convenient to remove the stripes in sinogram [5, 6]. A sinogram correction technique using its original mean curve and smoothed version has been proposed in . But this approach is not effective for removing all types of artifacts. Strong rings with varying intensities appearing from damaged detector pixels cannot be removed by this method . Different filtering techniques like combined wavelet-Fourier filtering  and iterative morphological filtering  were also proposed as ring correction methods recently. These methods present a general analysis for eliminating ring artifacts from the tomographic slice. But all these reported works do not provide any classification of the different types of artifacts present in the CT image. These different types of rings that are present in a CT image was first mentioned in . But no separate detection or correction algorithm for these different types of rings was proposed in that work. Therefore, a single removal algorithm was applied for all types of rings which often does not give good result. But in a very recent work in this field, a two-class classification scheme was proposed for the ring artifacts present in a CT image . It classified strong artifacts due to completely dead pixels and weak artifacts due to mis-calibrated pixels. But X-ray beam intensities in the mis-calibrated pixels can be of time independent or time dependent nature. These two types were not separately addressed in . Normalization technique was proposed for correcting the artifacts due to mis-calibrated pixels. This technique works satisfactorily on artifacts that occur due to pixels having time independent X-ray beam intensity but it is not suitable in case of time dependent one. Hence, the performance of the reported work will not be satisfactory on CT images that are corrupted with this type of artifacts.
In this article, a novel ring removal scheme is proposed based on the detection, classification, and correction of rings in the sinogram domain. A rigorous three level detection algorithm is proposed here for detecting band rings and isolated rings. To check for any residual ring artifacts after the correction algorithm is applied, a feedback detection algorithm is proposed in the third level of detection. This novel algorithm is specially suitable for detecting minor residual rings. The artifacts detected in a corrupted image are classified into three types, as they are generated due to different kinds of technical faults and they show different characteristics in both sinogram and reconstructed image domain. The threshold levels used in detection and classification algorithms are data driven and automatic. Customized correction technique is also proposed here for correcting the three types of artifacts separately. The proposed algorithm can be used with parallel, fan or cone beam geometry.
This article is organized as follows. Materials and methods of the complete ring removal scheme is presented in Section 2 under three subsections, i.e., detection, classification, and correction of ring artifacts. In Section 3, experimental results and comparative analysis of performances of the proposed algorithm and two other recently reported ring removal algorithms are presented [11, 15]. Finally, Section 4 presents some concluding remarks.
2 Materials and methods
Steps of ring removal algorithm
1. Read corrupted sinogram image.
2. Detect isolated and band rings from first and second level of detection.
3. Classify detected artifacts into Type 1, Type 2, and Type 3.
4. Correct Type 3 artifacts by interpolation.
5. Correct Type 2 artifacts by DC shifting.
6. Correct Type 1 artifacts by exemplar based inpainting.
7. Check for any residual rings in third level detection. If residual ring is detected then go back to step 3. Otherwise exit.
2.1 Detection of ring artifacts
In sinogram domain, there are stripe artifacts of varying properties. Different types of fault in detector element as described above cause different types of isolated and contagious stripe artifacts. To detect all these artifacts accurately, a three step detection scheme is developed. In the first step, only the most prominent rings are detected around which the band rings occur. In the second step, the minor rings and the band rings are detected. The third step of detection algorithm works as a feedback step. It checks whether there remains any ring artifact after applying the correction algorithm. If such artifacts are identified, the correction algorithm is applied again on those artifacts. This process continues until all the artifacts are corrected. The three step detection algorithm is described in the following subsections.
2.1.1 First level detection of ring artifacts
where i and t are the position of the detector element and the view number of the sinogram, respectively. Throughout the entire article, the definitions of i and t are kept same.
2.1.2 Second level detection of ring artifacts
The same peak detection and threshold calculation technique, as used in the first level of detection, are applied on r(i). Here the value of the parameter δ is set to 2, a lower value than that was set at the preceding level of detection, to enable detection of the minor artifacts in this phase. But lowering the threshold level leads to detection of some false artifacts. Therefore to avoid the detection of such false artifacts, r(i) is smoothed. This reduces the number of trivial peaks in r(i) which are responsible for false artifact detection. To smooth r(i), minimax estimation via wavelet shrinkage technique is used . This technique is particularly suitable in peak preserving noise reduction. The value of the parameter δ at different levels of detection are validated by experimenting on a number of different types of images and it does not need to be varied from one image to another. The test images that are provided in the results section are all corrected beautifully using these parameter values, giving a proof of the experimental validation.
2.1.3 Third level detection of ring artifacts
This residual ring artifact detection technique is a new feature. In no other reported work in this field has proposed any technique for detecting such residual rings. This detection step enhances the performance of the total ring removal algorithm significantly. In addition to that, the iterative band ring detection method proposed here is also a novel concept compared to other reported band ring detection schemes, e.g., polyphase decomposition of sinogram .
2.2 Classification of ring artifacts
Different types of artifacts
Origin of artifact
completely damaged detector pixel and/or damaged scintillator
mis-calibrated detector pixel having time independent beam intensity
mis-calibrated detector pixel having time dependent beam intensity
Resemblance with response of adjacent non-defective pixel
repetitive occurrences of a specific value
constant shift from non-defective response
varying shift from non-defective response
2.2.1 Type 1 artifact
where N f is the total number of faulty detector cells present in the sinogram, and the value for the parameter δ is chosen to be the same as that in the second level of detection.
The classification criterion proposed here is a new one. This type of artifact was also classified in . But the classification technique was different. For classification, at first the non causal first derivative of the sinogram was computed. Then the difference array was calculated by taking the sum of derivative value along each detector pixel. The criteria for separating the artifacts due to the dead pixels from the artifacts due to the mis-calibrated pixels was based on this array. Generally, Type 1 artifacts show higher value in the array than the artifacts due to the mis-calibrated pixels. But if the mis-calibrated pixel has a high amplitude shift from the adjacent good pixels, then it will also have a high value in the difference array and hence can be classified as Type 1 artifact. Therefore, the classification technique proposed in  may not be robust in such cases. But in the proposed technique here, the classification criterion is derived from the statistical property of the response of faulty pixels. The most significant statistical properties which are exclusive for each type of fault are proposed here as classification criteria. Therefore, it can be inferred that the proposed classification scheme will be more robust in categorizing faults in a corrupted sinogram.
2.2.2 Type 2 artifact
2.2.3 Type 3 artifact
Type 3 fault may occur due to mis-calibrated detector element having time dependent X-ray beam intensity. It also does not have repetitive occurrence of a particular value in the responses of the detector elements. It follows the pattern of the response of adjacent non-defective cells but the amount of shift from the response of non-defective cells varies greatly from one point to another (see Figure 7b).
Here denotes the normalized value of λ i (t) and μ is the mean of . Thus σ for all the faulty detector cells except Type 1 are calculated and plotted against the detector cell position. Since the response of Type 3 artifact has varying shift from the adjacent non-defective cell's response, σ of this type of fault will definitely have higher value than that of Type 2. This is illustrated in Figure 7c. The threshold level, T σ to separate Type 3 artifacts from Type 2 is calculated in the same way as in classification of Type 1. If c m (i) is replaced by σ(i) and N f is replaced by then T σ can be calculated using (5). Here = total number of Type 2 and Type 3 artifacts in the sinogram. The detected pixels that have their σ higher than T σ are classified as Type 3 artifacts and the rest are grouped as Type 2 artifacts.
2.3 Correction of ring artifacts
While correcting the artifacts from the corrupted sinogram, Type 2 and Type 3 artifacts are required to be corrected first. The inpainting algorithm employed to correct Type 1 artifact divides the sinogram into two regions. The Type 1 artifacts are defined as the target region and the rest of the image is defined as the source region. This algorithm takes information from the source region to fill in the target region. For effective implementation of the inpainting algorithm, the source region is needed to be artifact free. For this purpose, Type 3 and Type 2 artifacts are corrected first to provide an artifact free source region. Detailed description of the correction techniques is presented in the following.
2.3.1 Correction algorithm for Type 3 artifact
To correct this type of artifact interpolation technique is used. In this type of artifact, defective response is quite similar to that of a non-defective one. As it contains some of the image information, complete reconstruction by computationally cumbersome techniques like inpainting is not desired here. DC shifting is not appropriate either because a deviation from the response by different value at different points is not unlikely. Therefore, a constant shifting will not remove artifacts of this type rather there is a probability that it will introduce new ring. Considering all these facts, interpolation technique is proposed here for correcting artifacts of this type. At first the positions of near most preceding and succeeding non-defective detector cells are found. Then using the responses of these two non-defective detector cells, the response of the Type 3 artifact in between them is interpolated. Spline interpolation technique is used for this purpose.
2.3.2 Correction algorithm for Type 2 artifact
2.3.3 Correction algorithm for Type 1 artifact
where the distance d(Ψ p , Ψ q ) between the two patches Ψ p and Ψ q is defined as the sum of the squared differences of the filled pixels in the two patches. Having found the source exemplar Ψ q , the value of each pixel to be filled p ∈ (Ψ p ∩ Φ) is copied from its corresponding position inside Ψ q .
1. Identify the target region Ω from the ring location of Type 1.
2. Set the iteration index k = 0.
3. Identify the fill front δ Ω k , i.e., contour of the target region Ω k .
4. Compute priorities P(p) for Ψ p ∈ δ Ω k .
5. Find the patch Ψ p with maximum priority, i.e., for
6. Find the exemplar Ψ q ∈ Φ that minimizes d(Ψ p , Ψ q ).
7. Copy the image data from Ψ q to Ψ p .
8. Update C(p).
9. Increase k by 1 for next iteration.
10. Check whether Φ k = ϕ. If yes then exit. Otherwise go back to step 3.
3 Result and analysis
The test images were acquired with a home made micro-CT which consists of a CMOS FPD and a micro focus X-ray tube (L8121 - 01, Hamamatsu, Japan). The micro-focus X-ray source is a sealed tube with a fixed tungsten anode having an angle of 25° against the electron beam and with a 200 μ m-thick beryllium exit window. The emitted X-ray beam span angle is about 43°. The source has a variable focal spot size from 5 μ m to 50 μ m depending on the applied tube power (Watt or kVp mA). The maximum tube voltage and tube current are 150 kVp and 0.5 mA, respectively. The micro-focus X-ray source has been operated in a continuous mode with an Al filter with a thickness of 1 mm. The FPD (C7943CA-02, Hamamatsu, Japan) used in this experiment consists of 1216 × 1220 effective matrix of transistors, photodiodes with a pixel pitch of 100 μ m and a CsI:Tl scintillator. The CsI:Tl has a columnar structure with a typical diameter of about 10 μ m and a thickness of 200 μ m. A computer-controlled rotating system was adopted in the object holder to achieve a cone-beam mode scan in the micro-CT. The precision of the rotational motion is 0.083° which allows the number of views larger than 4,000. The system has the built-in white and dark image correction schemes. Since our micro-CT system does not provide the CT images in Hounsfield unit (HU), we have normalized all the original (uncorrected) reconstructed images so that the maximum pixel intensity is 1.0 with arbitrary unit. The corrected images are scaled using the corresponding normalization factor of the uncorrected images.
The performance of the proposed algorithm is compared with two recently reported ring removal algorithms [11, 15]. The reported algorithm in  classifies strong artifacts due to dead pixels and weak artifacts due to mis-calibrated pixels and proposes 2D variable window moving average (2D VWMA) and 2D weighted moving average (2D WMA) filter for correcting the strong rings and normalization technique for correcting the mis-calibrated artifacts. The mis-calibrated artifacts due to time dependency of beam intensity were not separately classified and hence no algorithm was proposed for correcting this type of artifact. There are four adjusting parameters in that algorithm (rmax, rmin, l m , a) which are required to be set manually. rmax and rmin are suitably defined upper and lower threshold for detection and classification of ring artifacts. l m is the number of levels of polyphase decomposition of the sinogram image. Polyphase decomposition is used in the reported work to detect the band ring. a is a constant used in the equation of detection algorithm of the ring removal technique . These parameter values are required to be changed from one image to another for obtaining the best result. But in the ring removal algorithm proposed in this article, the data driven thresholding technique is automatic and performs satisfactorily on all the test CT images. The rigorous classification scheme presented here discovers a new class of artifact in CT image (Type 3). Since three separate correction techniques are proposed here for the three types of artifacts, the overall performance of the correction algorithm is certainly improved. Our proposed algorithm and the algorithm presented in , both works in the sinogram domain.
To compare the performance of our sinogram processing technique with post processing techniques of ring removal, a reported algorithm , which works in the reconstructed image domain is also considered. This algorithm applies mean and median filtering technique to remove ring artifacts but it works in a different geometrical plane (in polar coordinate). The filter width of the ring correction in polar coordinate (RCP) method  is selected as suggested in the original work, e.g., radial median filter width in polar coordinates, ; azimuthal filtering in polar coordinates, . On the other hand, the distance between the support points in the azimuthal direction for the polar coordinate is needed to be adjusted for our test CT images. We set equal to 0.7°, instead of 0.8°. In the original work , the distance between the support points in the radial direction (dRA) for both the cartesian and the polar coordinates is determined from the scanner geometry. In our case, this parameter is set to 1.0 for the polar coordinate . The RCP method uses three thresholds (Tmin, Tmax, and TRA) for image segmentation and bone structure elimination. These three thresholds are considered in HU unit in the original work. As in this work, the CT images are not calibrated in HU unit, therefore, these thresholds should be selected in such a way that the purpose of these thresholds is served. The lower (Tmin) and upper (Tmax) thresholds are set to the minimum and maximum values of the ring intensities found in the CT image, respectively. As a result, the image elements having intensities above or below the ring artifact intensities are not affected by the correction algorithm. Similarly, the third threshold (TRA) is set to the maximum value of ring intensities in the difference image obtained after median filtering. The main draw back of the RCP method is its failure to remove strong rings with varying intensities. Because, they generally contain significant high frequency information but the mean (low-pass) filtering in the RCP method is not appropriate to retain the correct varying intensity ring structures in the difference image and thereby may result in poor performance of the algorithm. In addition to that, this algorithm does not provide any classification scheme for the artifacts present in a CT image.
where X| PQ and Y| PQ are the reference and corrected CT images, respectively and, M I is the dynamic range of the reference image.
where, SSIM j is the SSIM calculated at the j th local window and M is the number of local windows in the image.
Quantitative performance analysis of the ring removal algorithms on simulated phantoms
This article has dealt with an improved ring artifact suppression method for FPD based CT images. A rigorous three step detection algorithm has been proposed here. The detection technique exploits both the mean curve and the difference curve to improve accuracy in ring artifact detection. Introduction of a feedback loop for detection is a new feature of the proposed ring removal algorithm. The iterative band ring detection method is also a new approach in detecting contagious artifacts, which is a common phenomena in CT images. A new class of artifacts has been included in the proposed classification scheme. All the threshold levels used in detection and clarification algorithm are data driven and automatic. Separate correction algorithms have been proposed here to correct each type of faults. Therefore, customized correction algorithms ensures the best result in removing ring artifacts from the CT images. The performance of the proposed algorithm has been tested and compared with other reported algorithms using both fan beam and cone beam geometry based CT images. To evaluate the performance of the proposed algorithm, some special cases has also been considered, e.g., images with highly structural and physiological details and images with high contrast cylindrical object at the iso-center. The comparative results have revealed that the proposed technique removes the ring more effectively compared to the other two reported techniques in this article.
This study was supported in part by the National Research Foundation (NRF) of Korea funded by the Korean government (MEST) (No: 2009-0078310).
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