A level set method for cupping artifact correction in cone-beam CT

A novel hardware duo of beam modulation and shielding to reduce scatter acquisition and dose in cone-beam breast CT

PURPOSE

In cone-beam breast CT, scattered photons form a large portion of the acquired signal, adversely impacting image quality throughout the frequency response of the imaging system. Prior simulation studies provided proof of concept for utilization of a hardware solution to prevent scatter acquisition. Here, we report the design, implementation, and characterization of an auxiliary apparatus of fluence modulation and scatter shielding that does indeed lead to projections with a reduced level of scatter.

METHODS

An apparatus was designed for permanent installation within an existing cone-beam CT system. The apparatus is composed of two primary assemblies: a "Fluence Modulator" (FM) and a "Scatter Shield" (SS). The design of the assemblies enables them to operate in synchrony during image acquisition, converting the sourced x-rays into a moving narrow beam. During a projection, this narrow beam sweeps the entire fan angle coverage of the imaging system. As the two assemblies are contingent on one another, their joint implementation is described in the singular as apparatus FM-SS. The FM and the SS assemblies are each comprised a metal housing, a sensory system, and a robotic system. A controller unit handles their relative movements. A series of comparative studies were conducted to evaluate the performance of a cone-beam CT system in two "modes" of operation: with and without FM-SS installed, and to compare the results of physical implementation with those previously simulated. The dynamic range requirements of the utilized detector in the cone-beam CT imaging system were first characterized, independent of the mode of operation. We then characterized and compared the spatial resolution of the imaging system with, and without, FM-SS. A physical breast phantom, representative of an average size breast, was developed and imaged. Actual differences in signal level obtained with, versus without, FM-SS were then compared to the expected level gains based on previously reported simulations. Following these initial assessments, the scatter acquisition in each projection in both modes of operation was investigated. Finally, as an initial study of the impact of FM-SS on radiation dose in an average size breast, a series of Monte Carlo simulations were coupled with physical measurements of air kerma, with and without FM-SS.

RESULTS

With implementation of FM-SS, the detector's required dynamic range was reduced by a factor of 5.5. Substantial reduction in the acquisition of the scattered rays, by a factor of 5.1 was achieved. With the implementation of FM-SS, deposited dose was reduced by 27% in the studied breast.

CONCLUSIONS

The disclosed implementation of FM-SS, within a cone-beam breast CT system, results in reduction of scatter-components in acquired projections, reduction of dose deposit to the breast, and relaxation of requirements for the detector's dynamic range. Controlling or correcting for patient motion occurring during image acquisition remains an open problem to be solved prior to practical clinical usage of FM-SS cone-beam breast CT.

Contextual loss based artifact removal method on CBCT image

Purpose

Cone beam computed tomography (CBCT) offers advantages such as high ray utilization rate, the same spatial resolution within and between slices, and high precision. It is one of the most actively studied topics in international computed tomography (CT) research. However, its application is hindered owing to scatter artifacts. This paper proposes a novel scatter artifact removal algorithm that is based on a convolutional neural network (CNN), where contextual loss is employed as the loss function.

Methods

In the proposed method, contextual loss is added to a simple CNN network to correct the CBCT artifacts in the pelvic region. The algorithm aims to learn the mapping from CBCT images to planning CT images. The 627 CBCT‐CT pairs of 11 patients were used to train the network, and the proposed algorithm was evaluated in terms of the mean absolute error (MAE), average peak signal‐to‐noise ratio (PSNR) and so on. The proposed method was compared with other methods to illustrate its effectiveness.

Results

The proposed method can remove artifacts (including streaking, shadowing, and cupping) in the CBCT image. Furthermore, key details such as the internal contours and texture information of the pelvic region are well preserved. Analysis of the average CT number, average MAE, and average PSNR indicated that the proposed method improved the image quality. The test results obtained with the chest data also indicated that the proposed method could be applied to other anatomies.

Conclusions

Although the CBCT‐CT image pairs are not completely matched at the pixel level, the method proposed in this paper can effectively correct the artifacts in the CBCT slices and improve the image quality. The average CT number of the regions of interest (including bones, skin) also exhibited a significant improvement. Furthermore, the proposed method can be applied to enhance the performance on such applications as dose estimation and segmentation.

Metal artifact reduction of orthopedics metal artifact reduction algorithm in total hip and knee arthroplasty

The purpose of this study was to investigate metal artifact reduction effect of orthopedics metal artifact reduction (O-Mar) algorithm in computer tomography (CT) image of patients who have undergone total hip arthroplasty (THA) or total knee arthroplasty (TKA).35 cases of patients who underwent TKA or THA have been recruited in this study. CT image of hip or knee joint was obtained with Philips 256-row CT scanner. Tube voltages of 120 and 140 kilovolt peak (KVP) were set. Afterwards, CT image was reconstructed by O-Mar algorithm to reduce metal artifact. Grade of image quality and severity of metal artifact would be taken into qualitative evaluation. While, quantitative evaluation mainly included measurement of metal artifact volume and 2D measurement of average CT value in region of interest (ROI). The visibility of interface between bone-prostheses was also estimated.Result of qualitative analysis indicated that score of CT quality was improved and grade of metal artifact was decreased significantly with O-Mar. Quantitative analysis illustrated that volume of beam-hardening (B-H) metal artifact decreased remarkably after reconstruction of O-Mar (P < .001). In addition, O-Mar algorithm reduced 83.3% to 83.7% volume of photon-starvation (P-S) metal artifact. As for result of 2D measurement, CT value in ROI was closer to standard value in O-Mar group CT image (P < .001). Meanwhile, error of CT value also decreased significantly after reconstruction of O-Mar algorithm. Visibility rate of bone-prosthesis interface improved from 34.3% (Non-O-Mar) to 66.7% (O-Mar).O-Mar algorithm could significantly reduce metal artifact in CT image of THA and TKA in both 2D and three-dimensional (3D) level. Therefore, better image quality and visibility of bone-prostheses interface could be presented. In this study, O-Mar was proved as an efficient metal artifact reduction method in CT image of THA and TKA.

Correction of Cupping Artifacts in Axial Cone-Beam Computed Tomography Images by Using Image Processing Algorithms

Objectives:

One of the most important problems of cone-beam computed tomography (CBCT) imaging technique is the presence of dense objects, such as implants, amalgam fillings, and metal veneers, which result in beam-hardening artifacts. With an increase in the application of CBCT images and considering the problems in relation to cupping artifacts, some algorithms have been presented to reduce these artifacts. The aim was to present an algorithm to eliminate cupping artifacts from axial and other reconstructed CBCT images.

Materials and Methods:

We used CBCT images of NewTom VG imaging system (Verona, Italy, at Dentistry Faculty, Medical Sciences University, Tabriz, Iran) in which every image has a resolution of 366 × 320 in DICOM format. 50 images of patients with cupping artifacts were selected. Using Sobel edge detector and nonlinear gamma correction coefficient, the difference was calculated between the density of axial images in the main image and the image resulting from nonlinear gamma correction at the exact location of the radiopaque dental materials detected by Sobel. The points at which this density difference was out of a definite limit were treated as image artifacts and were eliminated from the main image by the inpainting method.

Results:

The resultant axial images, for producing reconstructed cross-sectional, panoramic images without cupping artifacts, were imported into NTT viewer V5.6 and utilized.

Conclusions:

With comparison, acquired images observed that the offering algorithm is practical and effective for reducing the cupping artifacts and preserving the quality of the reconstructed images. This algorithm does not need any additional equipment.

Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood polynomial fitting approach

PURPOSE

The purpose of this work was twofold: (a) To provide a robust and accurate method for image segmentation of dedicated breast CT (bCT) volume data sets, and (b) to improve Hounsfield unit (HU) accuracy in bCT by means of a postprocessing method that uses the segmented images to correct for the low-frequency shading artifacts in reconstructed images.

METHODS

A sequential and iterative application of image segmentation and low-order polynomial fitting to bCT volume data sets was used in the interleaved correction (IC) method. Image segmentation was performed through a deep convolutional neural network (CNN) with a modified U-Net architecture. A total of 45 621 coronal bCT images from 111 patient volume data sets were segmented (using a previously published segmentation algorithm) and used for neural network training, validation, and testing. All patient data sets were selected from scans performed on four different prototype breast CT systems. The adipose voxels for each patient volume data set, segmented using the proposed CNN, were then fit to a three-dimensional low-order polynomial. The polynomial fit was subsequently used to correct for the shading artifacts introduced by scatter and beam hardening in a method termed "flat fielding." An interleaved utilization of image segmentation and flat fielding was repeated until a convergence criterion was satisfied. Mathematical and physical phantom studies were conducted to evaluate the dependence of the proposed algorithm on breast size and the distribution of fibroglandular tissue. In addition, a subset of patient scans (not used in the CNN training, testing or validation) were used to investigate the accuracy of the IC method across different scanner designs and beam qualities.

RESULTS

The IC method resulted in an accurate classification of different tissue types with an average Dice similarity coefficient > 95%, precision > 97%, recall > 95%, and F1-score > 96% across all tissue types. The flat fielding correction of bCT images resulted in a significant reduction in either cupping or capping artifacts in both mathematical and physical phantom studies as measured by the integral nonuniformity metric with an average reduction of 71% for cupping and 30% for capping across different phantom sizes, and the Uniformity Index with an average reduction of 53% for cupping and 34% for capping.

CONCLUSION

The validation studies demonstrated that the IC method improves Hounsfield Units (HU) accuracy and effectively corrects for shading artifacts caused by scatter contamination and beam hardening. The postprocessing approach described herein is relevant to the broad scope of bCT devices and does not require any modification in hardware or existing scan protocols. The trained CNN parameters and network architecture are available for interested users.

An energy minimization method for the correction of cupping artifacts in cone-beam CT

The purpose of this study was to reduce cupping artifacts and improve quantitative accuracy of the images in cone-beam CT (CBCT). An energy minimization method (EMM) is proposed to reduce cupping artifacts in reconstructed image of the CBCT. The cupping artifacts are iteratively optimized by using efficient matrix computations, which are verified to be numerically stable by matrix analysis. Moreover, the energy in our formulation is convex in each of its variables, which brings the robustness of the proposed energy minimization algorithm. The cupping artifacts are estimated as a result of minimizing this energy. The results indicate that proposed algorithm is effective for reducing the cupping artifacts and preserving the quality of the reconstructed image. The proposed method focuses on the reconstructed image without requiring any additional physical equipment; it is easily implemented and provides cupping correction using a single scan acquisition. The experimental results demonstrate that this method can successfully reduce the magnitude of cupping artifacts. The correction algorithm reported here may improve the uniformity of the reconstructed images, thus assisting the development of perfect volume visualization and threshold-based visualization techniques for reconstructed images.

A model-based scatter artifacts correction for cone beam CT

PURPOSE

Due to the increased axial coverage of multislice computed tomography (CT) and the introduction of flat detectors, the size of x-ray illumination fields has grown dramatically, causing an increase in scatter radiation. For CT imaging, scatter is a significant issue that introduces shading artifact, streaks, as well as reduced contrast and Hounsfield Units (HU) accuracy. The purpose of this work is to provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging.

METHODS

The method starts with an estimation of coarse scatter profiles for a set of CBCT data in either image domain or projection domain. A denoising algorithm designed specifically for Poisson signals is then applied to derive the final scatter distribution. Qualitative and quantitative evaluations using thorax and abdomen phantoms with Monte Carlo (MC) simulations, experimental Catphan phantom data, and in vivo human data acquired for a clinical image guided radiation therapy were performed. Scatter correction in both projection domain and image domain was conducted and the influences of segmentation method, mismatched attenuation coefficients, and spectrum model as well as parameter selection were also investigated.

RESULTS

Results show that the proposed algorithm can significantly reduce scatter artifacts and recover the correct HU in either projection domain or image domain. For the MC thorax phantom study, four-components segmentation yields the best results, while the results of three-components segmentation are still acceptable. The parameters (iteration number K and weight β) affect the accuracy of the scatter correction and the results get improved as K and β increase. It was found that variations in attenuation coefficient accuracies only slightly impact the performance of the proposed processing. For the Catphan phantom data, the mean value over all pixels in the residual image is reduced from -21.8 to -0.2 HU and 0.7 HU for projection domain and image domain, respectively. The contrast of the in vivo human images is greatly improved after correction.

CONCLUSIONS

The software-based technique has a number of advantages, such as high computational efficiency and accuracy, and the capability of performing scatter correction without modifying the clinical workflow (i.e., no extra scan/measurement data are needed) or modifying the imaging hardware. When implemented practically, this should improve the accuracy of CBCT image quantitation and significantly impact CBCT-based interventional procedures and adaptive radiation therapy.