Low-dose computed tomography (CT) reconstruction has become the focus of X-ray CT imaging study. In this paper, we propose an improved prior image constrained compressed sensing (PICCS) reconstruction approach. A penalized weighted least-squares approach was adopted to realize the line integral projection (sinogram) data restoration, followed by filtered back-projection (FBP) of the restored sinogram data for image reconstruction. Finally, the FBP image as the prior image was used for PICCS approach for dose reduction. Qualitative and quantitative evaluations were carried out with computer simulation. The results showed that the present approach yielded noticeable gains over the original PICCS approach for dose reduction in terms of noise-induced artifacts suppression and edge detail preservation.

Low-dose computed tomography (CT) reconstruction has become the focus of X-ray CT imaging study. In this paper, we propose an improved prior image constrained compressed sensing (PICCS) reconstruction approach. A penalized weighted least-squares approach was adopted to realize the line integral projection (sinogram) data restoration, followed by filtered back-projection (FBP) of the restored sinogram data for image reconstruction. Finally, the FBP image as the prior image was used for PICCS approach for dose reduction. Qualitative and quantitative evaluations were carried out with computer simulation. The results showed that the present approach yielded noticeable gains over the original PICCS approach for dose reduction in terms of noise-induced artifacts suppression and edge detail preservation.

OBJECTIVETo compare two methods for threshold selection in Huber regularization for low-dose computed tomography imaging.

METHODSHuber regularization-based iterative reconstruction (IR) approach was adopted for low-dose CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge-detecting operators.

RESULTSThe experimental results on the simulation data demonstrated that both of the two threshold selection methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal.

CONCLUSIONBoth of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image iterative reconstruction.

OBJECTIVETo minimize of the radiation dose of cardiovascular CT angiography (CTA) imaging while preserving the image quality.

METHODSTo reduce the radiation dose in CTA imaging, the normal-dose scan induced non-local means (ndiNLM) algorithm was adapted for low-mAs scanned CTA image restoration by using the previous scanned high-quality image.

RESULTSQualitative and quantitative evaluations were carried out on both simulated phantom and clinical CTA scans in terms of accuracy and resolution properties. Compared to the original NLM algorithm, the ndiNLM method could achieve noticeable gains in terms of noise-induced artifacts suppression and enhanced structure preservation.

CONCLUSIONThe ndiNLM algorithm is a potential useful technique to reduce the radiation dose in CTA imaging.

OBJECTIVETo develop a computed tomography myocardial perfusion (CT-MP) deconvolution algorithm by incorporating high-dimension total variation (HDTV) regularization.

METHODSA perfusion deconvolution model was formulated for the low-dose CT-MPI data, followed by HDTV regularization to regularize the consistency of the solution by fusing the spatial correlation of the vascular structure and the temporal continuation of the blood flow signal.

RESULTSBoth qualitative and quantitative studies were conducted using XCAT and pig myocardial perfusion data to evaluate the present algorithm. The experimental results showed that this algorithm achieved hemodynamic parameter maps with better performances than the existing methods in terms of streak-artifacts suppression, noise-resolution tradeoff, and diagnosis structure preservation.

CONCLUSIONThe proposed algorithm can achieve high-quality hemodynamic parameter maps in low-dose CT-MPI.

Low-dose computed tomography (CT) reconstruction has become the focus of X-ray CT imaging study. In this paper, we propose an improved prior image constrained compressed sensing (PICCS) reconstruction approach. A penalized weighted least-squares approach was adopted to realize the line integral projection (sinogram) data restoration, followed by filtered back-projection (FBP) of the restored sinogram data for image reconstruction. Finally, the FBP image as the prior image was used for PICCS approach for dose reduction. Qualitative and quantitative evaluations were carried out with computer simulation. The results showed that the present approach yielded noticeable gains over the original PICCS approach for dose reduction in terms of noise-induced artifacts suppression and edge detail preservation.
Algorithms ; Computer Simulation ; Data Compression ; methods ; Image Processing, Computer-Assisted ; methods ; Least-Squares Analysis ; Phantoms, Imaging ; Radiation Dosage ; Signal-To-Noise Ratio ; Tomography, X-Ray Computed ; methods

Maximum a Posteriori (MAP) method has been widely applied to the ill-posed problem of image reconstruction. The choice of prior is the crucial point on MAP methods. However, the most conventional priors will lead to a blurring of the whole image or cause ladder-like artifacts. We therefore proposed a Tsallis entropy-based prior for positron emission tomography (PET) iterative reconstruction in MAP framework. The method uses a Tsallis entropy-based prior to eliminate the uncertainty between prior information and the estimated images. We tested this method in the phantom image, compared it with the traditional prior methods. the results showed that the proposed algorithm could suppress noise and obtain better reconstructed image quality.
Algorithms ; Artifacts ; Entropy ; Image Processing, Computer-Assisted ; methods ; Phantoms, Imaging ; Positron-Emission Tomography ; methods

Objective To minimize of the radiation dose of cardiovascular CT angiography (CTA) imaging while preserving the image quality. Methods To reduce the radiation dose in CTA imaging, the normal-dose scan induced non-local means (ndiNLM) algorithm was adapted for low-mAs scanned CTA image restoration by using the previous scanned high-quality image. Results Qualitative and quantitative evaluations were carried out on both simulated phantom and clinical CTA scans in terms of accuracy and resolution properties. Compared to the original NLM algorithm, the ndiNLM method could achieve noticeable gains in terms of noise-induced artifacts suppression and enhanced structure preservation. Conclusion The ndiNLM algorithm is a potential useful technique to reduce the radiation dose in CTA imaging.

Objective To minimize of the radiation dose of cardiovascular CT angiography (CTA) imaging while preserving the image quality. Methods To reduce the radiation dose in CTA imaging, the normal-dose scan induced non-local means (ndiNLM) algorithm was adapted for low-mAs scanned CTA image restoration by using the previous scanned high-quality image. Results Qualitative and quantitative evaluations were carried out on both simulated phantom and clinical CTA scans in terms of accuracy and resolution properties. Compared to the original NLM algorithm, the ndiNLM method could achieve noticeable gains in terms of noise-induced artifacts suppression and enhanced structure preservation. Conclusion The ndiNLM algorithm is a potential useful technique to reduce the radiation dose in CTA imaging.

Objective We propose a motion artifact correction algorithm(DMBL)for reducing motion artifacts in reconstructed dental cone-beam computed tomography(CBCT)images based on deep blur learning.Methods A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion,and the obtained motion degradation features were imported in the artifact correction module for artifact removal.The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns.Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets.Results The experimental results with the simulated dataset showed that compared with the existing methods,the PSNR of the proposed method increased by 2.88%,the SSIM increased by 0.89%,and the RMSE decreased by 10.58%.The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417(in a 5-point scale),significantly higher than those of the comparison methods.Conclusion The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.