Correction of severe beam-hardening artifacts via a high-order linearization function using a prior-image-based parameter selection method

An improved dual-domain network for metal artifact reduction in CT images using aggregated contextual transformations

Objective. Metal artifact reduction (MAR) remains a challenging task due to the difficulty of removing artifacts while preserving anatomical details of the tissue. Although current dual-domain networks have shown promising performance in MAR, they heavily rely on the image domain, which can be too smooth and lose important information in the metal-affected area. To address this problem, we propose an improved dual domain network framework.Approach. We enhance sinogram completion performance by utilizing an aggregated contextual transformations network in the sinogram domain. Furthermore, we utilizea prior-projection-based linearized correction method to obtain images with beam-hardening artifacts removed, which are incorporated into the input of the image post-processing network to assist in training the image domain network. Finally, we train the sinogram domain network and the image domain network separately to their respective convergences.Main results. In experiments conducted on a simulated dataset, our method achieves the best average RMSE of 25.1, SSIM of 0.973, and PSNR of 42.1, respectively.Significance. The proposed method is capable of preserving tissue structures near metallic objects while eliminating metal artifacts from the reconstructed images. Related codes will be released athttps://github.com/Corinna-China/AOTDudoNet.

A new dental CBCT metal artifact reduction method based on a dual-domain processing framework

Objective.Cone beam computed tomography (CBCT) has been wildly used in clinical treatment of dental diseases. However, patients often have metallic implants in mouth, which will lead to severe metal artifacts in the reconstructed images. To reduce metal artifacts in dental CBCT images, which have a larger amount of data and a limited field of view compared to computed tomography images, a new dental CBCT metal artifact reduction method based on a projection correction and a convolutional neural network (CNN) based image post-processing model is proposed in this paper. Approach.The proposed method consists of three stages: (1) volume reconstruction and metal segmentation in the image domain, using the forward projection to get the metal masks in the projection domain; (2) linear interpolation in the projection domain and reconstruction to build a linear interpolation (LI) corrected volume; (3) take the LI corrected volume as prior and perform the prior based beam hardening correction in the projection domain, and (4) combine the constructed projection corrected volume and LI-volume slice-by-slice in the image domain by two concatenated U-Net based models (CNN1 and CNN2). Simulated and clinical dental CBCT cases are used to evaluate the proposed method. The normalized root means square difference (NRMSD) and the structural similarity index (SSIM) are used for the quantitative evaluation of the method.Main results.The proposed method outperforms the frequency domain fusion method (FS-MAR) and a state-of-art CNN based method on the simulated dataset and yields the best NRMSD and SSIM of 4.0196 and 0.9924, respectively. Visual results on both simulated and clinical images also illustrate that the proposed method can effectively reduce metal artifacts.Significance. This study demonstrated that the proposed dual-domain processing framework is suitable for metal artifact reduction in dental CBCT images.

Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer

OBJECTIVES

To analyze whether CT image normalization can improve 3-year recurrence-free survival (RFS) prediction performance in patients with non-small cell lung cancer (NSCLC) relative to the use of unnormalized CT images.

METHODS

A total of 106 patients with NSCLC were included in the training set. For each patient, 851 radiomic features were extracted from the normalized and the unnormalized CT images, respectively. After the feature selection, random forest models were constructed with selected radiomic features and clinical features. The models were then externally validated in the test set consisting of 79 patients with NSCLC.

RESULTS

The model using normalized CT images yielded better performance than the model using unnormalized CT images (with an area under the receiver operating characteristic curve of 0.802 vs 0.702, p = 0.01), with the model performing especially well among patients with adenocarcinoma (with an area under the receiver operating characteristic curve of 0.880 vs 0.720, p < 0.01).

CONCLUSIONS

CT image normalization may improve prediction performance among patients with NSCLC, especially for patients with adenocarcinoma.

KEY POINTS

• After CT image normalization, more radiomic features were able to be identified. • Prognostic performance in patients was improved significantly after CT image normalization compared with before the CT image normalization. • The improvement in prognostic performance following CT image normalization was superior in patients with adenocarcinoma.

Quantitative analysis of metal artifact reduction using the auto-edge counting method in cone-beam computed tomography

The metal artifact reduction (MAR) algorithm is used in most CBCT unit to reduce artifact from various dental materials. The performance of MAR program of a CBCT unit according to the dental material type under different imaging mode was evaluated as introducing automatic quantification of the amount of artifact reduced. Four customized phantoms with different dental prostheses (amalgam, gold, porcelain-fused-metal, zirconia) underwent CBCT scanning with and without the MAR option. The imaging was performed under varied scanning conditions; 0.2 and 0.3 mm³ voxel sizes; 70 and 100 kVp. The amount of artifacts reduced by each prosthesis and scanning mode automatically counted using canny edge detection in MATLAB, and statistical analysis was performed. The overall artifact reduction ratio was ranged from 17.3% to 55.4%. The artifact caused by the gold crown was most effectively reduced compared to the other prostheses (p < 0.05, Welch’s ANOVA analysis). MAR showed higher performance in smaller voxel size mode for all prostheses (p < 0.05, independent t-test). Automatic quantification efficiently evaluated MAR performance in CBCT image. The impact of MAR was different according to the prostheses type and imaging mode, suggesting that thoughtful consideration is required when selecting the imaging mode of CBCT.