Low-dose dynamic myocardial perfusion CT image reconstruction using pre-contrast normal-dose CT scan induced structure tensor total variation regularization

Gong, Changfei; Han, Ce; Gan, Guanghui; Deng, Zhenmiao; Zhou, Yanfang; Jin, Yi; Zheng, Xiaomin; Xie, Congying; Jin, Xiance

doi:10.1088/1361-6560/aa5d40

Cited by 27 publications

(14 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dataset 1 was used to evaluate the texture enhanced image reconstruction algorithm, LRTP. For comparison study, some well-established methods were employed, including the simultaneous algebraic reconstruction technique (SART), total variation minimization (TV) [33], low-rank representation and total variation regularization (LRTV) [34] as well as tensor dictionary learning (TDL) [35].…”

Section: Results Of Image Reconstructionmentioning

confidence: 99%

Energy enhanced tissue texture in spectral computed tomography for lesion classification

Gao

Shi

Cao

et al. 2019

Vis. Comput. Ind. Biomed. Art

View full text Add to dashboard Cite

Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels, i.e., the tissue heterogeneity, and has been recognized as important biomarkers in various clinical tasks. Spectral computed tomography (CT) is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray energies. Therefore, this paper aims to address two related issues for clinical usage of spectral CT, especially the photon counting CT (PCCT): (1) texture enhancement by spectral CT image reconstruction, and (2) spectral energy enriched tissue texture for improved lesion classification. For issue (1), we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian theory. Reconstruction results showed the proposed method outperforms existing methods of total variation (TV), low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image noise. For issue (2), this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs: one is the spectral images, another is the cooccurrence matrices (CMs) extracted from the spectral images, and the third one is the Haralick features (HF) extracted from the CMs. Studies were performed on simulated photon counting data by introducing attenuationenergy response curve to the traditional CT images from energy integration detectors. Classification results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve (AUC) score by 7.3%, 0.42% and 3.0% for the spectral images, CMs and HFs respectively on the five-energy spectral data over the original single energy data only. The CM-and HF-inputs can achieve the best AUC of 0.934 and 0.927. This texture themed study shows the insight that incorporating clinical important prior information, e.g., tissue texture in this paper, into the medical imaging, such as the upstream image reconstruction, the downstream diagnosis, and so on, can benefit the clinical tasks.

show abstract

Section: Results Of Image Reconstructionmentioning

confidence: 99%

Energy enhanced tissue texture in spectral computed tomography for lesion classification

Gao

Shi

Cao

et al. 2019

Vis. Comput. Ind. Biomed. Art

View full text Add to dashboard Cite

show abstract

“…Several groups have utilized VCTs to evaluate their novel CT image reconstruction algorithms. [305][306][307] Abadi et al 141,308 characterized the noise texture across filtered back projection and iterative reconstruction algorithms. In this study, an XCAT phantom 41,55 was imaged 50 times using a validated CT simulator, setup to mimic the parameters and settings of a specific scanner model (Siemens Definition Flash).…”

Section: Ct Imagingmentioning

confidence: 99%

Virtual clinical trials in medical imaging: a review

et al. 2020

View full text Add to dashboard Cite

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

show abstract

“…Next, we tested ITALE on three 2‐D image examples, corresponding to piecewise‐constant digital phantom images of varying complexity: the Shepp–Logan digital phantom depicted in Figure 6, a digital brain phantom from Fessler and Hero (1994) depicted in Figure 7, and the XCAT chest slice from Gong et al. (2017) as previously depicted in Figure 1.…”

Section: Simulationsmentioning

confidence: 99%

“…Figure 1 compares in simulation

{\hat{boldx}}^{ITALE}

using the

ℓ_{0}

‐regularizer (3) with

{\hat{boldx}}^{TV}

(globally) minimizing the TV‐regularized objective

F^{normalTV} false(boldx false) = \frac{1}{2} {‖ y ‐ A x ‖}_{2}^{2} + λ {‖ \nabla x ‖}_{1} .

The example depicts a synthetic image of a human chest slice, previously generated by Gong et al. (2017) using the XCAT digital phantom (Segars et al., 2010). The design A is an undersampled and reweighted Fourier matrix, using a sampling scheme described in Section 3 and similar to that proposed in Krahmer and Ward (2014) for TV‐regularized compressed sensing.…”

Section: Introductionmentioning

confidence: 99%

Iterative Alpha Expansion for Estimating Gradient-Sparse Signals from Linear Measurements

Fan

2021

Journal of the Royal Statistical Society Series B: Statistical Methodology

View full text Add to dashboard Cite

We consider estimating a piecewise‐constant image, or a gradient‐sparse signal on a general graph, from noisy linear measurements. We propose and study an iterative algorithm to minimize a penalized least‐squares objective, with a penalty given by the “ℓ0‐norm” of the signal’s discrete graph gradient. The method uses a non‐convex variant of proximal gradient descent, applying the alpha‐expansion procedure to approximate the proximal mapping in each iteration, and using a geometric decay of the penalty parameter across iterations to ensure convergence. Under a cut‐restricted isometry property for the measurement design, we prove global recovery guarantees for the estimated signal. For standard Gaussian designs, the required number of measurements is independent of the graph structure, and improves upon worst‐case guarantees for total‐variation (TV) compressed sensing on the 1‐D line and 2‐D lattice graphs by polynomial and logarithmic factors respectively. The method empirically yields lower mean‐squared recovery error compared with TV regularization in regimes of moderate undersampling and moderate to high signal‐to‐noise, for several examples of changepoint signals and gradient‐sparse phantom images.

show abstract

Low-dose dynamic myocardial perfusion CT image reconstruction using pre-contrast normal-dose CT scan induced structure tensor total variation regularization

Cited by 27 publications

References 56 publications

Energy enhanced tissue texture in spectral computed tomography for lesion classification

Energy enhanced tissue texture in spectral computed tomography for lesion classification

Virtual clinical trials in medical imaging: a review

Iterative Alpha Expansion for Estimating Gradient-Sparse Signals from Linear Measurements

Contact Info

Product

Resources

About