Super Resolution Dual-Energy Cone-Beam CT Imaging With Dual-Layer Flat-Panel Detector

Su, Ting; Zhu, Jiongtao; Zhang, Xin; Tan, Yuhang; Cui, Han; Zeng, Dong; Guo, Jinchuan; Zheng, Hairong; Ma, Jianhua; Liang, Dong; Ge, Yongshuai

doi:10.1109/tmi.2023.3319668

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…In this study, an alternative super-resolution CBCT imaging method is suggested for DL-FPD. Instead of manually introducing additional 1D or 2D sub-pixel shift between the top and bottom layers (Su et al 2023), this new approach relies on the over-sampled spatial information at sub-pixel scale between the top and bottom detector layers to generate CBCT images of improved spatial resolution, as shown in figure 1. As illustrated, a certain beamlet carrying the same integration information of the scanned object falls on two different locations of the top and bottom receptor planes.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution dual-layer CBCT imaging with model-guided deep learning

Zhu,

Su,

Zhang

et al. 2023

Phys. Med. Biol.

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Objective. This study aims at investigating a novel super resolution CBCT imaging approach with a dual-layer flat panel detector (DL-FPD). Approach. With DL-FPD, the low-energy and high-energy projections acquired from the top and bottom detector layers contain over-sampled spatial information, from which super-resolution CT images can be reconstructed. A simple mathematical model is proposed to explain the signal formation procedure in DL-FPD, and a dedicated recurrent neural network, named suRi-Net, is developed based upon the above imaging model to nonlinearly retrieve the high-resolution dual-energy information. Physical benchtop experiments are conducted to validate the performance of this newly developed super-resolution CBCT imaging method. Main Results. The results demonstrate that the proposed suRi-Net can accurately retrieve high spatial resolution information from the low-energy and high-energy projections of low spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images from the top and bottom detector layers is increased by about 45% and 54%, respectively. Significance. In the future, suRi-Net will provide a new approach to perform high spatial resolution dual-energy imaging in DL-FPD-based CBCT systems.

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Section: Introductionmentioning

confidence: 99%

Super-resolution dual-layer CBCT imaging with model-guided deep learning

Zhu,

Su,

Zhang

et al. 2023

Phys. Med. Biol.

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Subpixel Registration for Dual-Layer Detectors Based on the Amplitude Response

Lee,

Kim

2024

IEEE Access

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Two-Step Image Registration for Dual-Layer Flat-Panel Detectors

Kim,

Lee

2024

Diagnostics

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Background: For a single exposure in radiography, a dual-layer flat-panel detector (DFD) can provide spectral images and efficiently utilize the transmitted X-ray photons to improve the detective quantum efficiency (DQE) performance. In this paper, to acquire high DQE performance, we present a registration method for X-ray images acquired from a DFD, considering only spatial translations and scale factors. The conventional registration methods have inconsistent estimate accuracies depending on the captured object scene, even when using entire pixels, and have deteriorated frequency performance because of the interpolation method employed. Methods: The proposed method consists of two steps; the first step is conducting a spatial translation according to the Fourier shift theorem with a subpixel registration, and the second step is conducting a scale transformation using cubic interpolation to process the X-ray projections. To estimate the subpixel spatial translation, a maximum-amplitude method using a small portion of the slant-edge phantom is used. Results: The performance of the proposed two-step method is first theoretically analyzed and then observed by conducting extensive experiments and measuring the noise power spectrum and DQE. An example for registering chest images is also shown. For a DFD, the proposed method shows a better registration result than the conventional one-step registration. The DQE improvement was more than 56% under RQA 9 compared to the single flat-panel detector case. Conclusions: The proposed two-step registration method can efficiently provide aligned image pairs from the DFD to improve the DQE performance at low doses and, thus, increase the accuracy of clinical diagnosis.

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Super Resolution Dual-Energy Cone-Beam CT Imaging With Dual-Layer Flat-Panel Detector

Cited by 3 publications

References 40 publications

Super-resolution dual-layer CBCT imaging with model-guided deep learning

Super-resolution dual-layer CBCT imaging with model-guided deep learning

Subpixel Registration for Dual-Layer Detectors Based on the Amplitude Response

Two-Step Image Registration for Dual-Layer Flat-Panel Detectors

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