A fully 3D approach for metal artifact reduction in computed tomography

Neural network guided sinogram-domain iterative algorithm for artifact reduction

BACKGROUND

Artifact reduction or removal is a challenging task when the artifact creation physics are not well modeled mathematically. One of such situations is metal artifacts in x-ray CT when the metallic material is unknown, and the x-ray spectrum is wide.

PURPOSE

A neural network is used to act as the objective function for iterative artifact reduction when the artifact model is unknown.

METHODS

A hypothetical unpredictable projection data distortion model is used to illustrate the proposed approach. The model is unpredictable, because it is controlled by a random variable. A convolutional neural network is trained to recognize the artifacts. The trained network is then used to compute the objective function for an iterative algorithm, which tries to reduce the artifacts in a computed tomography (CT) task. The objective function is evaluated in the image domain. The iterative algorithm for artifact reduction is in the projection domain. A gradient descent algorithm is used for the objective function optimization. The associated gradient is calculated with the chain rule.

RESULTS

The learning curves illustrate the decreasing treads of the objective function as the number of iterations increases. The images after the iterative treatment show the reduction of artifacts. A quantitative metric, the Sum Square Difference (SSD), also indicates the effectiveness of the proposed method.

CONCLUSION

The methodology of using a neural network as an objective function has potential value for situations where a human developed model is difficult to describe the underlying physics. Real-world applications are expected to be benefit from this methodology.

Efficacy of low-dose cone beam computed tomography and metal artifact reduction tool for assessment of peri-implant bone defects: an in vitro study

BACKGROUND

Early accurate radiographic assessment of peri-implant bone condition is highly important to avoid excessive loss of supporting bone and implant failure. Cone beam computed tomography (CBCT) is the radiographic technique of choice if peri-implant dehiscence and fenestration defects are suspected. The higher radiation dose and the presence of beam hardening artifacts are the main drawbacks of CBCT imaging techniques. This study aims to evaluate the influence of low-dose cone beam computed tomography (LD-CBCT) and metal artifact reduction (MAR) tool on the assessment of peri-implant dehiscence and fenestration.

METHODOLOGY

Thirty titanium implants were inserted into bovine rib blocks. Twenty had standardized bone defects (10 with dehiscence and 10 with fenestration), while the remaining 10 were used as control group with no defects. Radiographic examinations held with high-definition CBCT (HD-CBCT) and LD-CBCT with and without application of MAR tool. Images were assessed by four examiners for the presence or absence of peri-implant defects. The area under the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and accuracy were calculated for all radiographic protocols.

RESULTS

In the absence of MAR tool, there was no difference in AUC and diagnostic values between LD-CBCT and HD-CBCT for detection of both defects. When the MAR tool was applied, the AUC values, sensitivity, and accuracy were higher in HD-CBCT than in LD-CBCT for the detection of both defects, especially for the dehiscence, while specificity remained the same.

CONCLUSION

LD-CBCT can be used in the evaluation of peri-implant dehiscence and fenestration without any decrease in diagnostic accuracy. The application of MAR tool decrease the diagnostic ability of both defects, especially for the detection of dehiscence defects.

A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy

Metal objects in X-ray computed tomography can cause severe artifacts. The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods. This paper proposes a projection-domain algorithm to reduce the metal artifacts. In this algorithm, the unknowns are the metal-affected projections, while the objective function is set up in the image domain. The data fidelity term is not utilized in the objective function. The objective function of the proposed algorithm consists of two terms: the total variation of the metal-removed image and the energy of the negative-valued pixels in the image. After the metal-affected projections are modified, the final image is reconstructed via the filtered backprojection algorithm. The feasibility of the proposed algorithm has been verified by real experimental data.

Reducing metal artifacts by restricting negative pixels

When the object contains metals, its x-ray computed tomography (CT) images are normally affected by streaking artifacts. These artifacts are mainly caused by the x-ray beam hardening effects, which deviate the measurements from their true values. One interesting observation of the metal artifacts is that certain regions of the metal artifacts often appear as negative pixel values. Our novel idea in this paper is to set up an objective function that restricts the negative pixel values in the image. We must point out that the naïve idea of setting the negative pixel values in the reconstructed image to zero does not give the same result. This paper proposes an iterative algorithm to optimize this objective function, and the unknowns are the metal affected projections. Once the metal affected projections are estimated, the filtered backprojection algorithm is used to reconstruct the final image. This paper applies the proposed algorithm to some airport bag CT scans. The bags all contain unknown metallic objects. The metal artifacts are effectively reduced by the proposed algorithm.

Projection-domain iteration to estimate unreliable measurements

Due to the beam-hardening effect of the broad energy spectrum of the X-ray source in computed tomography, the reconstructed images usually suffer from severe artifacts when metallic objects are being imaged. Metal artifact correction methods are usually sophisticated and not practical, especially in some non-medical applications, in which the linear attenuation coefficients are unknown. This paper suggests a simple and effective algorithm to estimate the unreliable measurements. The proposed algorithm is an iterative algorithm, in which the iteration is performed in the projection domain, while the objective function is set up in the image domain. The final image is reconstructed with the conventional filtered backprojection algorithm. The feasibility of the proposed method is verified with airport bags that contain some unknown metals.

Superiorized method for metal artifact reduction

PURPOSE

Metal artifact reduction (MAR) is a challenging problem in computed tomography (CT) imaging. A popular class of MAR methods replace sinogram measurements that are corrupted by metal with artificial data, typically generated from some combination of interpolation along with other heuristics. While these "projection completion" approaches are successful in eliminating severe artifacts, secondary artifacts may be introduced by the artificial data. In this paper, we propose an approach which uses projection completion to generate a prior image, which is then incorporated into an iterative reconstruction algorithm based on the superiorization framework. The rationale is that the image produced by the iterative algorithm can inherit the desirable properties of the prior image, while also reducing secondary artifacts.

METHODS

The prior image is reconstructed using normalized metal artifact reduction (NMAR), a popular projection completion approach. The iterative algorithm is a modified version of the simultaneous algebraic reconstruction technique (SART), which reduces artifacts by incorporating a polyenergetic forward model, least-squares weighting, and superiorization. The penalty function used for superiorization is a weighted average between a total variation (TV) term and a term promoting similarity with the prior image, similar to penalty functions used in prior image constrained compressive sensing (PICCS). Because the prior is largely free of severe metal artifacts, these artifacts are discouraged from arising during iterative reconstruction; additionally, because the iterative approach uses the original projection data, it is able to recover information that is lost during the NMAR process.

RESULTS

We perform numerical experiments modeling a simple geometric object, as well as several more realistic scenarios such as metal pins, bilateral hip implants, and dental fillings placed within an anatomical phantom. The proposed iterative algorithm is largely successful at eliminating severe metal artifacts as well as secondary artifacts introduced by the NMAR process, especially lost edges of bone structures in the neighborhood of the metal regions. In one case modeling severe photon starvation, the NMAR algorithm is found to provide better results.

CONCLUSION

The proposed algorithm is effective in applying the superiorization methodology to the problem of MAR, providing better results than both NMAR and a purely total variation-based superiorization approach in nearly all cases.

The efficacy of metal artifact reduction (MAR) algorithm in cone-beam computed tomography on the diagnostic accuracy of fenestration and dehiscence around dental implants

BACKGROUND

The aim of the present study was to investigate the impact of metal artifact reduction (MAR) algorithm of cone-beam computed tomography (CBCT) on the diagnostic accuracy of fenestration and dehiscence around dental implants.

METHODS

Twenty-three dental implants were placed adjacent to the dehiscence and 23 adjacent to the fenestration defects on bovine bone blocks. The blocks were scanned with CBCT unit in two modes, with and without MAR algorithm. The area under the receiver operator characteristic (ROC) curves (Az value), specificity, sensitivity, positive predictive value, negative predictive value, and accuracy were determined for all modes.

RESULTS

For both defects, the Az values were higher in off MAR condition. The values of sensitivity, positive predictive value, negative predictive value, and accuracy, were higher in off MAR condition for both defects. However, the specificity in both defects in the two modes was equal.

CONCLUSION

The MAR algorithm in CBCT unit may not be helpful in increasing the diagnostic accuracy of fenestration and dehiscence around dental implants.

Improvement of dose calculation in radiation therapy due to metal artifact correction using the augmented likelihood image reconstruction

Background

Metal artifacts caused by high‐density implants lead to incorrectly reconstructed Hounsfield units in computed tomography images. This can result in a loss of accuracy in dose calculation in radiation therapy. This study investigates the potential of the metal artifact reduction algorithms, Augmented Likelihood Image Reconstruction and linear interpolation, in improving dose calculation in the presence of metal artifacts.

Materials and Methods

In order to simulate a pelvis with a double‐sided total endoprosthesis, a polymethylmethacrylate phantom was equipped with two steel bars. Artifacts were reduced by applying the Augmented Likelihood Image Reconstruction, a linear interpolation, and a manual correction approach. Using the treatment planning system Eclipse™, identical planning target volumes for an idealized prostate as well as structures for bladder and rectum were defined in corrected and noncorrected images. Volumetric modulated arc therapy plans have been created with double arc rotations with and without avoidance sectors that mask out the prosthesis. The irradiation plans were analyzed for variations in the dose distribution and their homogeneity. Dosimetric measurements were performed using isocentric positioned ionization chambers.

Results

Irradiation plans based on images containing artifacts lead to a dose error in the isocenter of up to 8.4%. Corrections with the Augmented Likelihood Image Reconstruction reduce this dose error to 2.7%, corrections with linear interpolation to 3.2%, and manual artifact correction to 4.1%. When applying artifact correction, the dose homogeneity was slightly improved for all investigated methods. Furthermore, the calculated mean doses are higher for rectum and bladder if avoidance sectors are applied.

Conclusion

Streaking artifacts cause an imprecise dose calculation within irradiation plans. Using a metal artifact correction algorithm, the planning accuracy can be significantly improved. Best results were accomplished using the Augmented Likelihood Image Reconstruction algorithm.

The effects of metal artifact reduction on the retrieval of attenuation values

BACKGROUND

The quality of CT slices can be drastically reduced in the presence of high-density objects such as metal implants within the patients' body due to the occurrence of streaking artifacts. Consequently, a delineation of anatomical structures might not be possible, which strongly influences clinical examination.

PURPOSE

The aim of the study is to clinically evaluate the retrieval of attenuation values and structures by the recently proposed Augmented Likelihood Image Reconstruction (ALIR) and linear interpolation in the presence of metal artifacts.

MATERIAL AND METHODS

A commercially available phantom was equipped with two steel inserts. At a position between the metal rods, which shows severe streaking artifacts, different human tissue-equivalent inserts are alternately mounted. Using a single-source computer tomograph, raw data with and without metal rods are acquired for each insert. Images are reconstructed using the ALIR algorithm and a filtered back projection with and without linear interpolation. Mean and standard deviation are compared for a region of interest in the ALIR reconstructions, linear interpolation results, uncorrected images with metal rods, and the images without metal rods, which are used as a reference. Furthermore, the reconstructed shape of the inserts is analyzed by comparing different profiles of the image.

RESULTS

The measured mean and standard deviation values show that for all tissue classes, the metal artifacts could be reduced using the ALIR algorithm and the linear interpolation. Furthermore, the HU values for the different classes could be retrieved with errors below the standard deviation in the reference image. An evaluation of the shape of the inserts shows that the reconstructed object fits the shape of the insert accurately after metal artifact correction. Moreover, the evaluation shows a drop in the standard deviation for the ALIR reconstructed images compared to the reference images while reducing artifacts and keeping the shape of the inserts, which indicates a noise reduction ability of the ALIR algorithm.

CONCLUSION

HU values, which are distorted by metal artifacts, can be retrieved accurately with the ALIR algorithm and the linear interpolation approach. After metal artifact correction, structures, which are not perceptible in the original images due to streaking artifacts, are reconstructed correctly within the image using the ALIR algorithm. Furthermore, the ALIR produced images with a reduced noise level compared to reference images and artifact images. Linear interpolation results in a distortion of the investigated shapes and features remaining streaking artifacts.

Efficacy of a cone beam computed tomography metal artifact reduction algorithm for the detection of peri-implant fenestrations and dehiscences

OBJECTIVE

To determine whether the use of a metal artifact reduction (MAR) algorithm improves the detection of peri-implant fenestrations and dehiscences on cone beam computed tomography scans.

STUDY DESIGN

One hundred titanium fixtures were implanted into bovine ribs after the creation of defects simulating fenestrations and dehiscences. Images were acquired using four different protocols, namely, A2 (MAR on, voxel 0.2 mm), A3 (MAR on, voxel 0.3 mm), B2 (MAR off, voxel 0.2 mm), and B3 (MAR off, voxel 0.3 mm). For all protocols, receiver operating characteristic (ROC) curves were determined. Values for the areas under the ROC curves (Az) were subjected to analysis of variance.

RESULTS

Az values were not statistically different among protocols regardless of the defect type (P > .05).

CONCLUSIONS

The MAR algorithm tested by us did not improve the diagnosis of peri-implant fenestrations and dehiscences with use of either the 0.2 mm or the 0.3 mm voxel sizes.

Multi-phase rotational angiography of the left ventricle to assist ablations: feasibility and accuracy of novel imaging

AIMS

Interventional left ventricular (LV) procedures integrating static 3D anatomy visualization are subject to mismatch with dynamic catheter movements due to prominent LV motion. We aimed to evaluate the accuracy of a recently developed acquisition and post-processing protocol for low radiation dose LV multi-phase rotational angiography (4DRA) in patients.

METHODS AND RESULTS

4DRA image acquisition of the LV was performed as investigational acquisition in patients undergoing left-sided ablation (11 men; BMI = 24.7 ± 2.5 kg/m²). Iodine contrast was injected in the LA, while pacing from the RA at a cycle length of 700 ms. 4DRA acquisition and reconstruction were possible in all 11 studies. Reconstructed images were post-processed using streak artefact reduction algorithms and an interphase registration-based filtering method, increasing contrast-to-noise ratio by a factor 8.2 ± 2.1. This enabled semi-automatic segmentation, yielding LV models of five equidistant phases per cardiac cycle. For evaluation, off-line 4DRA fluoroscopy registration was performed, and the 4DRA LV contours of the different phases were compared with the contours of five corresponding phases of biplane LV angiography, acquired in identical circumstances. Of the distances between these contours, 95% were <4 mm in both incidences. Effective radiation dose for 4DRA, calculated by patient-specific Monte-Carlo simulation, was 5.1 ± 1.1 mSv.

CONCLUSION

Creation of 4DRA LV models in man is feasible at near-physiological heart rate and with clinically acceptable radiation dose. They showed high accuracy with respect to LV angiography in RAO and LAO. The presented technology not only opens perspectives for full cardiac cycle dynamic anatomical guidance during interventional procedures, but also for 3DRA without need for very rapid pacing.

Prior-based artifact correction (PBAC) in computed tomography

PURPOSE

Image quality in computed tomography (CT) often suffers from artifacts which may reduce the diagnostic value of the image. In many cases, these artifacts result from missing or corrupt regions in the projection data, e.g., in the case of metal, truncation, and limited angle artifacts. The authors propose a generalized correction method for different kinds of artifacts resulting from missing or corrupt data by making use of available prior knowledge to perform data completion.

METHODS

The proposed prior-based artifact correction (PBAC) method requires prior knowledge in form of a planning CT of the same patient or in form of a CT scan of a different patient showing the same body region. In both cases, the prior image is registered to the patient image using a deformable transformation. The registered prior is forward projected and data completion of the patient projections is performed using smooth sinogram inpainting. The obtained projection data are used to reconstruct the corrected image.

RESULTS

The authors investigate metal and truncation artifacts in patient data sets acquired with a clinical CT and limited angle artifacts in an anthropomorphic head phantom data set acquired with a gantry-based flat detector CT device. In all cases, the corrected images obtained by PBAC are nearly artifact-free. Compared to conventional correction methods, PBAC achieves better artifact suppression while preserving the patient-specific anatomy at the same time. Further, the authors show that prominent anatomical details in the prior image seem to have only minor impact on the correction result.

CONCLUSIONS

The results show that PBAC has the potential to effectively correct for metal, truncation, and limited angle artifacts if adequate prior data are available. Since the proposed method makes use of a generalized algorithm, PBAC may also be applicable to other artifacts resulting from missing or corrupt data.

Metal artifact reduction in CT using fusion based prior image

PURPOSE

In computed tomography, metallic objects in the scanning field create the so-called metal artifacts in the reconstructed images. Interpolation-based methods for metal artifact reduction (MAR) replace the metal-corrupted projection data with surrogate data obtained from interpolation using the surrounding uncorrupted sinogram information. Prior-based MAR methods further improve interpolation-based methods by better estimating the surrogate data using forward projections from a prior image. However, the prior images in most existing prior-based methods are obtained from segmented images and misclassification in segmentation often leads to residual artifacts and tissue structure loss in the final corrected images. To overcome these drawbacks, the authors propose a fusion scheme, named fusion prior-based MAR (FP-MAR).

METHODS

The FP-MAR method consists of (i) precorrect the image by means of an interpolation-based MAR method and an edge-preserving blur filter; (ii) generate a prior image from the fusion of this precorrected image and the originally reconstructed image with metal parts removed; (iii) forward project this prior image to guide the estimation of the surrogate data using well-developed replacement techniques.

RESULTS

Both simulations and clinical image tests are carried out to show that the proposed FP-MAR method can effectively reduce metal artifacts. A comparison with other MAR methods demonstrates that the FP-MAR method performs better in artifact suppression and tissue feature preservation.

CONCLUSIONS

From a wide range of clinical cases to which FP-MAR has been tested (single or multiple pieces of metal, various shapes, and sizes), it can be concluded that the proposed fusion based prior image preserves more tissue information than other segmentation-based prior approaches and can provide better estimates of the surrogate data in prior-based MAR methods.

X-ray CT metal artifact reduction using wavelet domain L0 sparse regularization

X-ray computed tomography (CT) imaging of patients with metallic implants usually suffers from streaking metal artifacts. In this paper, we propose a new projection completion metal artifact reduction (MAR) algorithm by formulating the completion of missing projections as a regularized inverse problem in the wavelet domain. The Douglas-Rachford splitting (DRS) algorithm was used to iteratively solve the problem. Two types of prior information were exploited in the algorithm: 1) the sparsity of the wavelet coefficients of CT sinograms in a dictionary of translation-invariant wavelets and 2) the detail wavelet coefficients of a prior sinogram obtained from the forward projection of a segmented CT image. A pseudo- L0 synthesis prior was utilized to exploit and promote the sparsity of wavelet coefficients. The proposed L0-DRS MAR algorithm was compared with standard linear interpolation and the normalized metal artifact reduction (NMAR) approach proposed by Meyer using both simulated and clinical studies including hip prostheses, dental fillings, spine fixation and electroencephalogram electrodes in brain imaging. The qualitative and quantitative evaluations showed that our algorithm substantially suppresses streaking artifacts and can outperform both linear interpolation and NMAR algorithms.