Comparison Study of Regularizations in Spectral Computed Tomography Reconstruction

Salehjahromi, Morteza; Zhang, Yanbo; Yu, Hengyong

doi:10.1007/s11220-018-0200-4

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…On the other hand, this method has the drawback of not being able to correct for the effect of beam hardening. The bin images can be reconstructed with filtered backprojection or with an iterative algorithm similar to those used for single-energy CT (Salehjahromi et al 2018). By summing the images with carefully selected weight factors, the optimal CNR for a specific imaging task can then be attained (Schmidt 2009).…”

Section: Energy Bin Imagesmentioning

confidence: 99%

Photon-counting x-ray detectors for CT

2021

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The introduction of photon-counting detectors is expected to be the next major breakthrough in clinical x-ray computed tomography (CT). During the last decade, there has been considerable research activity in the field of photon-counting CT, in terms of both hardware development and theoretical understanding of the factors affecting image quality. In this article, we review the recent progress in this field with the intent of highlighting the relationship between detector design considerations and the resulting image quality. We discuss detector design choices such as converter material, pixel size, and readout electronics design, and then elucidate their impact on detector performance in terms of dose efficiency, spatial resolution, and energy resolution. Furthermore, we give an overview of data processing, reconstruction methods and metrics of imaging performance; outline clinical applications; and discuss potential future developments.

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Section: Energy Bin Imagesmentioning

confidence: 99%

Photon-counting x-ray detectors for CT

2021

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“…In the former category, material decomposition is carried out in the image domain, while in the latter category, it is carried out in the projection domain (see figure 2). In both approaches, independent methods for material decomposition in the projection domain [22], (multi-channel) spectral reconstruction [23] with various forms of structural or spectral regularization [24][25][26], and material decomposition in the image domain [15] can be plugged in. Although these sequential two-step methods are computationally inexpensive, separating the reconstruction and decomposition steps causes information loss [27,28].…”

Section: Related Workmentioning

confidence: 99%

ADJUST: a dictionary-based joint reconstruction and unmixing method for spectral tomography

Zeegers¹,

Kadu²,

Leeuwen³

et al. 2022

Inverse Problems

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Advances in multi-spectral detectors are causing a paradigm shift in X-ray Computed Tomography (CT). Spectral information acquired from these detectors can be used to extract volumetric material composition maps of the object of interest. If the materials and their spectral responses are known a priori, the image reconstruction step is rather straightforward. If they are not known, however, the maps as well as the responses need to be estimated jointly. A conventional workflow in spectral CT involves performing volume reconstruction followed by material decomposition, or vice versa. However, these methods inherently suffer from the ill-posedness of the joint reconstruction problem. To resolve this issue, we propose ‘A Dictionary-based Joint reconstruction and Unmixing method for Spectral Tomography’ (ADJUST). Our formulation relies on forming a dictionary of spectral signatures of materials common in CT and prior knowledge of the number of materials present in an object. In particular, we decompose the spectral volume linearly in terms of spatial material maps, a spectral dictionary, and the indicator of materials for the dictionary elements. We propose a memory-efficient accelerated alternating proximal gradient method to find an approximate solution to the resulting bi-convex problem. From numerical demonstrations on several synthetic phantoms, we observe that ADJUST performs exceedingly well compared to other state-of-the-art methods. Additionally, we address the robustness of ADJUST against limited and noisy measurement patterns. The demonstration of the proposed approach on a spectral micro-CT dataset shows its potential for real-world applications. Code is available at https://github.com/mzeegers/ADJUST.

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“…In both approaches, independent methods for material decomposition in the projection domain [22], (multi-channel) spectral reconstruction [23] with various forms of structural or spectral regularization [24,25,26], and material decomposition in the image domain [15] can be plugged in. Although these sequential two-step methods are computationally inexpensive, separating the reconstruction and unmixing steps causes information loss [27,28].…”

Section: Related Workmentioning

confidence: 99%

ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography

Zeegers¹,

Kadu²,

Leeuwen³

et al. 2021

Preprint

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Advances in multi-spectral detectors are causing a paradigm shift in X-ray Computed Tomography (CT). Spectral information acquired from these detectors can be used to extract volumetric material composition maps of the object of interest. If the materials and their spectral responses are known a priori, the image reconstruction step is rather straightforward. If they are not known, however, the maps as well as the responses need to be estimated jointly. A conventional workflow in spectral CT involves performing volume reconstruction followed by material decomposition, or vice versa. However, these methods inherently suffer from the ill-posedness of the joint reconstruction problem. To resolve this issue, we propose 'A Dictionary-based Joint reconstruction and Unmixing method for Spectral Tomography' (ADJUST). Our formulation relies on forming a dictionary of spectral signatures of materials common in CT and prior knowledge of the number of materials present in an object. In particular, we decompose the spectral volume linearly in terms of spatial material maps, a spectral dictionary, and the indicator of materials for the dictionary elements.We propose a memory-efficient accelerated alternating proximal gradient method to find an approximate solution to the resulting bi-convex problem. From numerical demonstrations on several synthetic phantoms, we observe that ADJUST performs exceedingly well when compared to other state-of-the-art methods. Additionally, we address the robustness of ADJUST against limited measurement patterns.

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Comparison Study of Regularizations in Spectral Computed Tomography Reconstruction

Cited by 9 publications

References 26 publications

Photon-counting x-ray detectors for CT

Photon-counting x-ray detectors for CT

ADJUST: a dictionary-based joint reconstruction and unmixing method for spectral tomography

ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography

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