Comparison Between Pre-Log and Post-Log Statistical Models in Ultra-Low-Dose CT Reconstruction

Fu, Lin; Lee, Tzu-Cheng; Kim, Soo Mee; Alessio, Adam M.; Kinahan, Paul E.; Chang, Zhiqian; Sauer, Ken D.; Kalra, Mannudeep K.; Man, Bruno De

doi:10.1109/tmi.2016.2627004

Cited by 86 publications

(56 citation statements)

References 78 publications

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“…We chose I 0 = 1 × 10 5 photons per ray and σ = 5 in our experiments. We approximated elements of the diagonal weighting matrix W of the data-fidelity in (1) by y 2 l i y l i +σ 2 [38]. The images are of size 512 × 512 at a resolution of 0.9766 mm × 0.9766 mm, when reconstructed using the FBP method.…”

Section: Methodsmentioning

confidence: 99%

SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction

Long

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Recent years have witnessed growing interest in machine learning-based models and techniques for low-dose X-ray CT (LDCT) imaging tasks. The methods can typically be categorized into supervised learning methods and unsupervised or model-based learning methods. Supervised learning methods have recently shown success in image restoration tasks. However, they often rely on large training sets. Model-based learning methods such as dictionary or transform learning do not require large or paired training sets and often have good generalization properties, since they learn general properties of CT image sets. Recent works have shown the promising reconstruction performance of methods such as PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches into a learned union of transforms. In this paper, we propose a new Supervised-UnsuPERvised (SUPER) reconstruction framework for LDCT image reconstruction that combines the benefits of supervised learning methods and (unsupervised) transform learning-based methods such as PWLS-ULTRA that involve highly image-adaptive clustering. The SUPER model consists of several layers, each of which includes a deep network learned in a supervised manner and an unsupervised iterative method that involves image-adaptive components. The SUPER reconstruction algorithms are learned in a greedy manner from training data. The proposed SUPER learning methods dramatically outperform both the constituent supervised learning-based networks and iterative algorithms for LDCT, and use much fewer it- *

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Section: Methodsmentioning

confidence: 99%

SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction

Long

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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“…Let

f_{i}^{'}

denote the first derivative of f i , this beam hardening correction will change the weighting matrix in Eq. as:

{boldΛ}_{ii} = 1 / var false(y_{i}^{italicbhc} false) \approx {\overset{N}{true}}_{i} / {[f_{i}^{'} false({\bar{y}}_{i} false)]}^{2} \approx {\overset{N}{true}}_{i} / {[f_{i}^{'} false(y_{i} false)]}^{2}

…”

Section: Stastical Image Reconstruction Frameworkmentioning

confidence: 99%

Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review

et al. 2018

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Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.

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“…Indeed, IR techniques are replacing conventional FBP techniques since they are less prone to artifacts and increased image noise at lower radiation doses as compared with FBP, which may have compromised diagnostic quality at low radiation doses. 42,72 IR techniques remove image noise through a process of modeling of the imaging acquisition processes including photon statistics fluctuations, the optics system, and other aspects of x-ray interactions that are ignored by the conventional FBP techniques. IR techniques use and include a simple algebraic reconstruction technique, statistical IR, and more recent model-based IR (MBIR).…”

Section: Iterative Reconstructionsmentioning

confidence: 99%

Recent advances in cardiac computed tomography dose reduction strategies: a review of scientific evidence and technical developments

et al. 2017

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Abstract. Cardiac imagers worldwide are bracing for increased utilization of cardiac computed tomography (CT) in clinical practice. This expanding opportunity brings along a responsibility to produce diagnostic quality images with optimized radiation dose. The following review aims to address the dose reduction strategies in cardiac CT in light of recent scientific evidence and technical developments.

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Comparison Between Pre-Log and Post-Log Statistical Models in Ultra-Low-Dose CT Reconstruction

Cited by 86 publications

References 78 publications

SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction

SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction

Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review

Recent advances in cardiac computed tomography dose reduction strategies: a review of scientific evidence and technical developments

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