A preliminary investigation of reduced-view image reconstruction from low dose breast CT data

2012 ◽  
Author(s):  
Junguo Bian ◽  
Xiao Han ◽  
Kai Yang ◽  
Emil Y. Sidky ◽  
John M. Boone ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Low Dose ◽  
Preliminary Investigation ◽  
Breast Ct ◽  
Ct Data

scholarly journals Investigation of iterative image reconstruction in low-dose breast CT

2014 ◽  
Vol 59 (11) ◽  
pp. 2659-2685 ◽  
Author(s):  
Junguo Bian ◽  
Kai Yang ◽  
John M Boone ◽  
Xiao Han ◽  
Emil Y Sidky ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Low Dose ◽  
Iterative Image Reconstruction ◽  
Breast Ct

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.

A preliminary investigation of ROI-image reconstruction with the rebinned BPF algorithm

2008 ◽  
Author(s):  
Junguo Bian ◽  
Dan Xia ◽  
Lifeng Yu ◽  
Emil Y. Sidky ◽  
Xiaochuan Pan
Keyword(s):  
Image Reconstruction ◽  
Preliminary Investigation

Block-matching sparsity regularization-based image reconstruction for low-dose computed tomography

2018 ◽  
Vol 45 (6) ◽  
pp. 2439-2452 ◽  
Author(s):  
Ailong Cai ◽  
Lei Li ◽  
Zhizhong Zheng ◽  
Linyuan Wang ◽  
Bin Yan
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
Block Matching ◽  
Sparsity Regularization ◽  
Low Dose Computed Tomography

Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT

2014 ◽  
Vol 24 (6) ◽  
pp. 1239-1250 ◽  
Author(s):  
SayedMasoud Hashemi ◽  
Hatem Mehrez ◽  
Richard S. C. Cobbold ◽  
Narinder S. Paul
Keyword(s):  
Image Reconstruction ◽  
Low Dose ◽  
Pulmonary Nodules ◽  
Low Dose Ct ◽  
Small Pulmonary Nodules

scholarly journals Characteristic performance evaluation of a photon counting Si strip detector for low dose spectral breast CT imaging

2014 ◽  
Vol 41 (9) ◽  
pp. 091903 ◽  
Author(s):  
Hyo-Min Cho ◽  
William C. Barber ◽  
Huanjun Ding ◽  
Jan S. Iwanczyk ◽  
Sabee Molloi
Keyword(s):  
Performance Evaluation ◽  
Low Dose ◽  
Photon Counting ◽  
Ct Imaging ◽  
Breast Ct ◽  
Strip Detector

Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection

2021 ◽  
Vol 94 (1121) ◽  
pp. 20201329
Author(s):  
Yoshifumi Noda ◽  
Tetsuro Kaga ◽  
Nobuyuki Kawai ◽  
Toshiharu Miyoshi ◽  
Hiroshi Kawada ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Low Dose ◽  
Standard Dose ◽  
Lesion Detection ◽  
Whole Body ◽  
Hybrid Iterative Reconstruction ◽  
Whole Body Ct ◽  
Attenuation Values

Objectives: To evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body computed tomography (CT) using deep learning image reconstruction (DLIR). Methods: The study cohort of 59 consecutive patients (mean age, 67.2 years) who underwent whole-body LD CT and a prior standard-dose (SD) CT reconstructed with hybrid iterative reconstruction (SD-IR) within one year for surveillance of malignancy were assessed. The LD CT images were reconstructed with hybrid iterative reconstruction of 40% (LD-IR) and DLIR (LD-DLIR). The radiologists independently evaluated image quality (5-point scale) and lesion detection. Attenuation values in Hounsfield units (HU) of the liver, pancreas, spleen, abdominal aorta, and portal vein; the background noise and signal-to-noise ratio (SNR) of the liver, pancreas, and spleen were calculated. Qualitative and quantitative parameters were compared between the SD-IR, LD-IR, and LD-DLIR images. The CT dose-index volumes (CTDIvol) and dose-length product (DLP) were compared between SD and LD scans. Results: The image quality and lesion detection rate of the LD-DLIR was comparable to the SD-IR. The image quality was significantly better in SD-IR than in LD-IR (p < 0.017). The attenuation values of all anatomical structures were comparable between the SD-IR and LD-DLIR (p = 0.28–0.96). However, background noise was significantly lower in the LD-DLIR (p < 0.001) and resulted in improved SNRs (p < 0.001) compared to the SD-IR and LD-IR images. The mean CTDIvol and DLP were significantly lower in the LD (2.9 mGy and 216.2 mGy•cm) than in the SD (13.5 mGy and 1011.6 mGy•cm) (p < 0.0001). Conclusion: LD CT images reconstructed with DLIR enable radiation dose reduction of >75% while maintaining image quality and lesion detection rate and superior SNR in comparison to SD-IR. Advances in knowledge: Deep learning image reconstruction algorithm enables around 80% reduction in radiation dose while maintaining the image quality and lesion detection compared to standard-dose whole-body CT.

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