Image reconstruction and scan configurations enabled by optimization-based algorithms in multispectral CT

Chen, Buxin; Zheng, Zhang; Sidky, Emil Y.; Xia, Dan; Pan, Xiaochuan

doi:10.1088/1361-6560/aa8a4b

Cited by 64 publications

(67 citation statements)

References 41 publications

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“…10 In this work we show that the same algorithm can be used for MBMD. This new method differs from previous MBMD methods 5–9 in that it does not require matched projection data (cf. Refs 6,8 ) and is based on a Gaussian noise model (cf.…”

Section: Introductionmentioning

confidence: 94%

“…5–9 Each technique requires datasets at multiple energies, traditionally acquired with either an energy discriminating detector or multiple X-ray sources/spectra. Image domain decomposition first reconstructs each dataset independently, and then decomposes the reconstructed images into different material density images.…”

Section: Introductionmentioning

confidence: 99%

“…Refs 6,8 ) and is based on a Gaussian noise model (cf. Refs 5–9 ). Because the projection data does not need to be matched, this method may be applied to novel acquisition protocols (e.g., kV switching or multiple X-ray sources with non-coincident rays) that can challenge traditional projection-domain and many MBMD methods.…”

Section: Introductionmentioning

confidence: 99%

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A general CT reconstruction algorithm for model-based material decomposition

Tilley

Zbijewski

Siewerdsen

et al. 2018

Medical Imaging 2018: Physics of Medical Imaging

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Material decomposition in CT has the potential to reduce artifacts and improve quantitative accuracy by utilizing spectral models and multi-energy scans. In this work we present a novel Model-Based Material Decomposition (MBMD) method based on an existing iterative reconstruction algorithm derived from a general non-linear forward model. A digital water phantom with inserts containing different concentrations of calcium was scanned on a kV switching system. We used the presented method to simultaneously reconstruct water and calcium material density images, and compared the results to an image domain and a projection domain decomposition method. When switching voltage every other frame, MBMD resulted in more accurate water and calcium concentration values than the image domain decomposition method, and was just as accurate as the projection domain decomposition method. In a second, slower, kV switching scheme (changing voltage every ten frames) which precluded the use of traditional projection domain based methods, MBMD continued to produce quantitatively accurate reconstructions. Finally, we present a preliminary study applying MBMD to a water phantom containing vials of different concentrations of K2HPO4 which was scanned on a cone-beam CT test bench. Both the fast and slow (emulated) kV switching schemes resulted in similar reconstructions, indicating MBMD’s robustness to challenging acquisition schemes. Additionally, the K2HPO4 concentration ratios between the vials were accurately represented in the reconstructed K2HPO4 density image.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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A general CT reconstruction algorithm for model-based material decomposition

Tilley

Zbijewski

Siewerdsen

et al. 2018

Medical Imaging 2018: Physics of Medical Imaging

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show abstract

“…where |∇I(x)| 1, = i ∂ i I(x) 2 + with > 0 small, e.g., = 10 −12 . This is a frequently used modification for TV regularization in image reconstruction [49,18,16]. Then (49) can be solved by the following gradient descent scheme:…”

Section: Template Reconstructionmentioning

confidence: 99%

A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging

Chen¹,

Gris²,

Öktem³

2019

SIAM J. Imaging Sci.

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We propose a new variational model for joint image reconstruction and motion estimation in spatiotemporal imaging, which is investigated along a general framework that we present with shape theory. This model consists of two components, one for conducting modified static image reconstruction, and the other performs sequentially indirect image registration. For the latter, we generalize the large deformation diffeomorphic metric mapping framework into the sequentially indirect registration setting. The proposed model is compared theoretically against alternative approaches (optical flow based model and diffeomorphic motion models), and we demonstrate that the proposed model has desirable properties in terms of the optimal solution. The theoretical derivations and efficient algorithms are also presented for a time-discretized scenario of the proposed model, which show that the optimal solution of the time-discretized version is consistent with that of the time-continuous one, and most of the computational components is the easy-implemented linearized deformation. The complexity of the algorithm is analyzed as well. This work is concluded by some numerical examples in 2D space + time tomography with very sparse and/or highly noisy data.

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“…Direct reconstruction approaches reconstruct basis images directly from data collected at two x-ray energy spectra. 12,13 This type of methods depicts the data model of DECT and incorporates regularization constrains, leading to the flexibility for accommodating different CT scanners. Nevertheless, this type of approaches requires high computational complexity, and the decomposition results are sensitive to parameter selection in different applications.…”

Section: Introductionmentioning

confidence: 99%

Image domain dual material decomposition for dual‐energy CT using butterfly network

Zhang

Wang

et al. 2019

Medical Physics

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Purpose Dual‐energy CT (DECT) has been increasingly used in imaging applications because of its capability for material differentiation. However, material decomposition suffers from magnified noise from two CT images of independent scans, leading to severe degradation of image quality. Existing algorithms exhibit suboptimal decomposition performance because they fail to fully depict the mapping relationship between DECT images and basis materials under noisy conditions. Convolutional neural network exhibits great promise in the modeling of data coupling and has recently become an important technique in medical imaging application. Inspired by its impressive potential, we developed a new Butterfly network to perform the image domain dual material decomposition. Methods The Butterfly network is derived from the model of image domain DECT decomposition by exploring the geometric relationship between the mapping functions of the data model and network components. The network is designed as the double‐entry double‐out crossover architecture based on the decomposition formulation. It enters a pair of dual‐energy images as inputs and defines the ground true decomposed images as each label. The crossover architecture, which plays an important role in material decomposition, is designed to implement the information exchange between the two material generation pathways in the network. The proposed network is further applied on the digital phantom and clinical data to evaluate its performance. Results The qualitative and quantitative evaluations of the material decomposition of digital phantoms and clinical data indicate that the proposed network outperforms its counterparts. For the digital phantom, the proposed network reduces the standard deviation (SD) of noise in tissue, bone, and mixture regions by an average of 95.75% and 86.58% compared with the direct matrix inversion and the conventional iterative method, respectively. The line profiles and image biases of the decomposition results of digital phantom indicate that the proposed network provides the decomposition results closest to the ground truth. The proposed network reduces the SD of the noise in decomposed images of clinical head data by over 90% and 80% compared with the direct matrix inversion and conventional iterative method, respectively. As the modulation transfer function decreases to 50%, the proposed network increases the spatial resolution by average factors of 1.34 and 1.17 compared with the direct matrix inversion and conventional iterative methods, respectively. The proposed network is further applied to the clinical abdomen data. Among the three methods, the proposed method received the highest score from six radiologists in the visual inspection of noise suppression in the clinical data. Conclusions We develop a model‐based Butterfly network to perform image domain material decomposition for DECT. The decomposition results of digital phantom validate its capability of decomposing two basis materials from DECT images. The proposed approach also le...

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Image reconstruction and scan configurations enabled by optimization-based algorithms in multispectral CT

Cited by 64 publications

References 41 publications

A general CT reconstruction algorithm for model-based material decomposition

A general CT reconstruction algorithm for model-based material decomposition

A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging

Image domain dual material decomposition for dual‐energy CT using butterfly network

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