scholarly journals Learned Shrinkage Approach for Low-Dose Reconstruction in Computed Tomography

2013 ◽  
Vol 2013 ◽  
pp. 1-20 ◽  
Author(s):  
Joseph Shtok ◽  
Michael Elad ◽  
Michael Zibulevsky
Keyword(s):  
Computed Tomography ◽  
Nonlinear Filtering ◽  
Low Dose ◽  
Reconstruction Algorithm ◽  
Image Sharpness ◽  
Image Domain ◽  
Shrinkage Method ◽  
Gradient Based ◽  
Noisy Measurements ◽  
Adaptive Nonlinear Filtering

We propose a direct nonlinear reconstruction algorithm for Computed Tomography (CT), designed to handle low-dose measurements. It involves the filtered back-projection and adaptive nonlinear filtering in both the projection and the image domains. The filter is an extension of the learned shrinkage method by Hel-Or and Shaked to the case of indirect observations. The shrinkage functions are learned using a training set of reference CT images. The optimization is performed with respect to an error functional in the image domain that combines the mean square error with a gradient-based penalty, promoting image sharpness. Our numerical simulations indicate that the proposed algorithm can manage well with noisy measurements, allowing a dose reduction by a factor of 4, while reducing noise and streak artifacts in the FBP reconstruction, comparable to the performance of a statistically based iterative algorithm.

scholarly journals Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Hsuan-Ming Huang ◽  
Ing-Tsung Hsiao
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Acceleration Techniques ◽  
Reconstruction Methods ◽  
Speed Up ◽  
Phantom Studies

Background and Objective. Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques.Methods. First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively.Results. Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method.Conclusions. In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT.

scholarly journals Mutual Information-Based Non-Local Total Variation Denoiser for Low-Dose Cone-Beam Computed Tomography

2021 ◽  
Vol 11 ◽  
Author(s):  
Ho Lee ◽  
Jiwon Sung ◽  
Yeonho Choi ◽  
Jun Won Kim ◽  
Ik Jae Lee
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Mutual Information ◽  
Low Dose ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
Statistical Measure ◽  
Cbct Image ◽  
Non Local ◽  
Analytical Reconstruction

Conventional non-local total variation (NLTV) approaches use the weight of a non-local means (NLM) filter, which degrades performance in low-dose cone-beam computed tomography (CBCT) images generated with a low milliampere-seconds (mAs) parameter value because a local patch used to determine the pixel weights comprises noisy-damaged pixels that reduce the similarity between corresponding patches. In this paper, we propose a novel type of NLTV based on a combination of mutual information (MI): MI-NLTV. It is based on a statistical measure for a similarity calculation between the corresponding bins of non-local patches vs. a reference patch. The weight is determined in terms of a statistical measure comprising the MI value between corresponding non-local patches and the reference-patch entropy. The MI-NLTV denoising process is applied to CBCT images generated by the analytical reconstruction algorithm using a ray-driven backprojector (RDB). The MI-NLTV objective function is minimized based on the steepest gradient descent optimization to augment the difference between a real structure and noise, cleaning noisy pixels without significant loss of the fine structure and details that remain in the reconstructed images. The proposed method was evaluated using patient data and actual phantom measurement data acquired with lower mAs. The results show that integrating the RDB further enhances the MI-NLTV denoising-based analytical reconstruction algorithm to achieve a higher CBCT image quality when compared with those generated by NLTV denoising-based approach, with an average of 15.97% higher contrast-to-noise ratio, 2.67% lower root mean square error, 0.12% lower spatial non-uniformity, 1.14% higher correlation, and an average of 18.11% higher detectability index. These quantitative results indicate that the incorporation of MI makes the NLTV more stable and robust than the conventional NLM filter for low-dose CBCT imaging. In addition, achieving clinically acceptable CBCT image quality despite low-mAs projection acquisition can reduce the burden on common online CBCT imaging, improving patient safety throughout the course of radiotherapy.

scholarly journals Low-dose computed tomography with 4th-generation iterative reconstruction algorithm in assessment of oncologic patients

2017 ◽  
Vol 9 (10) ◽  
pp. 423-430 ◽  
Author(s):  
Davide Ippolito ◽  
Alessandra Silvia Casiraghi ◽  
Cammillo Talei Franzesi ◽  
Davide Fior ◽  
Franca Meloni ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Reconstruction Algorithm ◽  
Iterative Reconstruction Algorithm ◽  
Oncologic Patients ◽  
Low Dose Computed Tomography

Computed tomography of the urinary tract: optimalization of low-dose stone protocol in a clinical setting

2005 ◽  
Vol 46 (7) ◽  
pp. 764-768 ◽  
Author(s):  
A. S. F. Larsen ◽  
R. Pedersen ◽  
G. Sandbaek
Keyword(s):  
Computed Tomography ◽  
Low Dose ◽  
Reconstruction Algorithm ◽  
Abdominal Fat ◽  
Urinary Stones ◽  
Slice Thickness ◽  
Intravenous Contrast ◽  
Related Factors ◽  
Low Dose Computed Tomography ◽  
Intravenous Contrast Media

Purpose: To establish whether information would be lost if slice reconstruction thickness was increased from 3 to 5 mm, and whether this altered how difficult it was to interpret the examinations. Material and Methods: Twenty-three consecutive patients referred with suspected or known urinary stones were included. All examinations were performed without intravenous contrast media. The original series, with effective mAs 50, were reconstructed with slice thickness 3 and 5 mm, respectively. All demographic and examination data were removed and the series reviewed in PACS by two independent radiologists. Objective findings, i.e. number and size of stones, signs of obstruction, and evaluation of interpretation difficulty, were registered. Results: Identical findings were registered in 18 of the series of 3 mm ( n = 23) and 19 of the series of 5 mm ( n = 23). In two series reconstructed with 3 mm slice thickness and in one series with 5 mm slice thickness, the observers disagreed on the presence of urinary stones. Main reasons for interpretation difficulties were given as “lack of intra-abdominal fat” and “many phleboliths in the pelvic region”, but never “disturbing noise”. Conclusion: To determine the presence and size of urinary stones at low-dose computed tomography, 5 mm reconstruction algorithm seems equal to 3 mm. Patient-related factors influence the interpretation more than image quality.

Liver density in routine and low-dose computed tomography: the effect of image noise on measurement accuracy

2020 ◽  
Vol 24 (1) ◽  
pp. 39-47
Author(s):  
A. P. Gonchar ◽  
V. A. Gombolevskij ◽  
A. B. Elizarov ◽  
N. S. Kulberg ◽  
V. G. Klyashtorny ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Measurement Accuracy ◽  
Low Dose ◽  
Image Noise ◽  
Low Dose Computed Tomography
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