A Gaussian Mixture MRF for Model-Based Iterative Reconstruction With Applications to Low-Dose X-Ray CT

Zhang, Ruoqiao; Ye, Dong Hye; Pal, Debashish; Thibault, Jean‐Baptiste; Sauer, Ken D.; Bouman, Charles A.

doi:10.1109/tci.2016.2582042

Cited by 28 publications

(30 citation statements)

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“…13 To minimize patient exposure while optimizing the utility of the radiation dose, a normal-dose/high-quality scan can be performed first to establish a reference, followed by a series of low-dose CT (LDCT) scans. The LDCT scans can be reconstructed independently by statistical image reconstruction (SIR) methods [14][15][16][17][18][19][20][21][22][23] to suppress noise and streak artifacts, using information from current acquisition only. Nevertheless, the previously acquired normal-dose image can be exploited as prior information due to similarities between the normal-dose image and the series of reconstructed images from these scans.…”

Section: Introductionmentioning

confidence: 99%

Assessment of prior image induced nonlocal means regularization for low‐dose CT reconstruction: Change in anatomy

Zhang

Wang

et al. 2017

Medical Physics

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Purpose Repeated computed tomography (CT) scans are prescribed for some clinical applications such as lung nodule surveillance. Several studies have demonstrated that incorporating a high-quality prior image into the reconstruction of subsequent low-dose CT (LDCT) acquisitions can either improve image quality or reduce data fidelity requirements. Our proposed previous normal-dose image induced nonlocal means (ndiNLM) regularization method for LDCT is an example of such a method. However, one major concern with prior image based methods is that they might produce false information when the prior image and the current LDCT image show different structures (for example, if a lung nodule emerges, grows, shrinks or disappears over time). This study aims to assess the performance of the ndiNLM regularization method in situations with change in anatomy. Method We incorporated the ndiNLM regularization into the statistical image reconstruction (SIR) framework for reconstruction of subsequent LDCT images. Because of its patch-based search mechanism, a rough registration between the prior image and the current LDCT image is adequate for the SIR-ndiNLM method. We assessed the performance of the SIR-ndiNLM method in lung nodule surveillance for two different scenarios: (1) the nodule was not found in a baseline exam but appears in a follow-up LDCT scan; (2) the nodule was present in a baseline exam but disappears in a follow-up LDCT scan. We further investigated the effect of nodule size on the performance of the SIR-ndiNLM method. Results We found that a relatively large search-window (e.g., 33×33) should be used for the SIR-ndiNLM method to account for misalignment between the prior image and the current LDCT image, and to ensure that enough similar patches can be found in the prior image. With proper selection of other parameters, experimental results with two patient datasets demonstrated that the SIR-ndiNLM method did not miss true nodules nor introduce false nodules in the lung nodule surveillance scenarios described above. We also found that the SIR-ndiNLM reconstruction shows improved image quality when the prior image is similar to the current LDCT image in anatomy. These gains in image quality might appear small upon visual inspection, but they can be detected using quantitative measures. Finally, the SIR-ndiNLM method also performed well in ultra-low-dose conditions and with different nodule sizes. Conclusions This study assessed the performance of the SIR-ndiNLM method in situations in which the prior image and the current LDCT image show substantial anatomical differences, specifically, changes in lung nodules. The experimental results demonstrate that the SIR-ndiNLM method does not introduce false lung nodules nor miss true nodules, which relieves the concern that this method might produce false information. However, there is insufficient evidence that these findings will hold true for all kinds of anatomical changes.

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Section: Introductionmentioning

confidence: 99%

Assessment of prior image induced nonlocal means regularization for low‐dose CT reconstruction: Change in anatomy

Zhang

Wang

et al. 2017

Medical Physics

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“…The essential idea of MAP estimation is to reconstruct images with the maximum posterior observing projection data p [14,36].…”

Section: A Maximum a Posterior (Map) Estimation For Ldctmentioning

confidence: 99%

“…They model statistical noise, image priors and system physics into reconstruction thus better reconstruction results are obtained. For example, introducing manually designed image priors as constraints to statistical iterative reconstruction (SIR) for LDCT reconstruction is commonly adopted to restrain noise and artifacts [10][11][12][13][14]. Markov random field (MRF) reduces image noise by measuring the distribution of neighboring pixels [14].…”

Section: Introductionmentioning

confidence: 99%

“…For example, introducing manually designed image priors as constraints to statistical iterative reconstruction (SIR) for LDCT reconstruction is commonly adopted to restrain noise and artifacts [10][11][12][13][14]. Markov random field (MRF) reduces image noise by measuring the distribution of neighboring pixels [14]. Based on compressed sensing (CS) theory, total-variation (TV) minimization makes use of the sparsity of NDCT images in variation domain and has an advantage in removing LDCT streak artifacts [12].…”

Section: Introductionmentioning

confidence: 99%

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A Model-Based Unsupervised Deep Learning Method for Low-Dose CT Reconstruction

et al. 2020

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Low-dose CT (LDCT) is of great significance due to the concern about the potential radiation risk. With the fast development of deep learning, neural networks have become powerful tools in LDCT enhancement. Current deep neural networks for LDCT reconstruction are often trained with paired LDCT dataset and normal-dose CT (NDCT) dataset. However, high quality NDCT dataset paired with LDCT dataset is expensive to acquire or even not available sometimes in reality. In this work, we proposed an unsupervised model-based deep learning (MBDL) for LDCT reconstruction. The network is trained based on group-wise maximum a posterior (G-MAP) loss function with LDCT dataset only. The MBDL is a general framework. It also allows us to combine with supervised training if a small number of paired NDCT dataset accessible to help optimizing the network parameters, i.e. works in a semi-supervised mode. During inference, LDCT images are reconstructed end-to-end by the trained network. We verified the proposed method with simulated projection data from clinical CT images. The proposed method restrained noise well while restoring anatomical structures and it achieved better results than model-based iterative reconstruction (MBIR) with significantly less computational cost. The performances of MBDL were further enhanced by integrating a small paired NDCT dataset for semi-supervised training. The results suggested that MBDL is an efficient and flexible method for LDCT deep learning based reconstruction in the situations lacking of enough high quality NDCT data.

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“…ij is the coefficient for the mean vector as shown in (29). Thus, β The fact that the full Sinkhorn-Knopp does not resemble a complete EM algorithm offers some insights into the perfor-mance gain phenomenon.…”

Section: Full Sinkhorn-knoppmentioning

confidence: 99%

Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective

Chan

Zickler

2017

IEEE Trans. on Image Process.

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Abstract-Many patch-based image denoising algorithms can be formulated as applying a smoothing filter to the noisy image. Expressed as matrices, the smoothing filters must be row normalized so that each row sums to unity. Surprisingly, if we apply a column normalization before the row normalization, the performance of the smoothing filter can often be significantly improved. Prior works showed that such performance gain is related to the Sinkhorn-Knopp balancing algorithm, an iterative procedure that symmetrizes a row-stochastic matrix to a doublystochastic matrix. However, a complete understanding of the performance gain phenomenon is still lacking.In this paper, we study the performance gain phenomenon from a statistical learning perspective. We show that SinkhornKnopp is equivalent to an Expectation-Maximization (EM) algorithm of learning a Gaussian mixture model of the image patches. By establishing the correspondence between the steps of Sinkhorn-Knopp and the EM algorithm, we provide a geometrical interpretation of the symmetrization process. This observation allows us to develop a new denoising algorithm called Gaussian mixture model symmetric smoothing filter (GSF). GSF is an extension of the Sinkhorn-Knopp and is a generalization of the original smoothing filters. Despite its simple formulation, GSF outperforms many existing smoothing filters and has a similar performance compared to several state-of-the-art denoising algorithms.

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A Gaussian Mixture MRF for Model-Based Iterative Reconstruction With Applications to Low-Dose X-Ray CT

Cited by 28 publications

References 59 publications

Assessment of prior image induced nonlocal means regularization for low‐dose CT reconstruction: Change in anatomy

Assessment of prior image induced nonlocal means regularization for low‐dose CT reconstruction: Change in anatomy

A Model-Based Unsupervised Deep Learning Method for Low-Dose CT Reconstruction

Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective

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