Browsing by Author "Ma, Xu"

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    Compressive Spectral Imaging via Misalignment Induced Equivalent Grayscale Coded Aperture
    (IEEE Geoscience and Remote Sensing Letters, 2023-02-22) Zhang, Tong; Zhao, Shengjie; Ma, Xu; Ramirez-Jaime, Andres; Zhao, Qile; Arce, Gonzalo R.
    Coded aperture snapshot spectral imager (CASSI) senses the spectral information of a 2-D scene and captures a set of coded measurement data that can be used to reconstruct the 3-D spatio-spectral datacube of the input scene by compressive sensing algorithms. The coded aperture (CA) in CASSI plays a crucial role in modulating the spatial information. The pixels in CA are typically square, switched binary ON–OFF, and aligned with the pixels of focal plane array (FPA). Instead of this binary modulation, this letter explores a simple yet effective approach to enabling an equivalent grayscale modulation, which can increase the sensing degree of freedom in CASSI systems. In particular, we deliberately introduce misalignment between the CA pixels and the FPA pixels, such that the spatial modulation of one FPA pixel is determined by four adjacent CA pixels instead of one. Numerical experiments show that the proposed equivalent grayscale modulation induced by misalignment can significantly improve the CASSI reconstruction when compared with current methods, whether a random CA or an optimal blue noise CA is used. More importantly, it does not incur in any cost to the CASSI system.
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    Compressive Spectral X-Ray CT Reconstruction via Deep Learning
    (IEEE Transactions on Computational Imaging, 2022-10-20) Zhang, Tong; Zhao, Shengjie; Ma, Xu; Restrepo, Carlos; Arce, Gonzalo R.
    Compressive spectral X-ray imaging (CSXI) uses coded illumination projections to reconstruct tomographic images at multiple energy bins. Different K-edge materials are used to modulate the spectrum of the X-ray source at various angles, thereby capturing the projections containing spectral attenuation information. It is a cost-effective and low-dose sensing approach; however, the image reconstruction is a nonlinear and ill-posed problem. Current methods of solving the inverse problem are computationally expensive and require extensive iterations. This paper proposes a deep learning model consisting of a set of convolutional neural networks to reconstruct the CSXI spectral images, which correspond to inpainting the subsampled sinograms, recovering the monoenergetic sinograms, and removing the artifacts from a fast but low-quality analytical reconstruction. Numerical experiments show that the proposed method significantly improves the quality of reconstructed image compared with that attained by the state-of-the-art reconstruction methods. Moreover, it significantly reduces the time-required for CSXI reconstruction.
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    Experimental demonstration and optimization of X-ray StaticCodeCT
    (Applied Optics, 2021-10-18) Cuadros, Angela P.; Liu, Xiaokang; Parsons, Paul E.; Ma, Xu; Arce, Gonzalo R.
    As the use of X-ray computed tomography (CT) grows in medical diagnosis, so does the concern for the harm a radiation dose can cause and the biological risks it represents. StaticCodeCT is a new low-dose imaging architecture that uses a single-static coded aperture (CA) in a CT gantry. It exploits the highly correlated data in the projection domain to estimate the unobserved measurements on the detector. We previously analyzed the StaticCodeCT system by emulating the effect of the coded mask on experimental CT data. In contrast, this manuscript presents test-bed reconstructions using an experimental cone-beam X-ray CT system with a CA holder. We analyzed the reconstruction quality using three different techniques to manufacture the CAs: metal additive manufacturing, cold casting, and ceramic additive manufacturing. Furthermore, we propose an optimization method to design the CA pattern based on the algorithm developed for the measurement estimation. The obtained results point to the possibility of the real deployment of StaticCodeCT systems in practice.
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    Hyperspectral image reconstruction via patch attention driven network
    (Optics Express, 2023-06-01) Qiu, Yechuan; Zhao, Shengjie; Ma, Xu; Zhang, Tong; Arce, Gonzalo R.
    Coded aperture snapshot spectral imaging (CASSI) captures 3D hyperspectral images (HSIs) with 2D compressive measurements. The recovery of HSIs from these measurements is an ill-posed problem. This paper proposes a novel, to our knowledge, network architecture for this inverse problem, which consists of a multilevel residual network driven by patch-wise attention and a data pre-processing method. Specifically, we propose the patch attention module to adaptively generate heuristic clues by capturing uneven feature distribution and global correlations of different regions. By revisiting the data pre-processing stage, we present a complementary input method that effectively integrates the measurements and coded aperture. Extensive simulation experiments illustrate that the proposed network architecture outperforms state-of-the-art methods.
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    Static coded aperture in robotic X-ray tomography systems
    (Optics Express, 2022-02-22) Mao, Tianyi; Ma, Xu; Cuadros, Angela P.; Dai, XiuBin; Wang, Zhiteng; Zhang, Xin; Zhu, Shujin; Zhu, Jianjian; Arce, Gonzalo R.
    Coded aperture X-ray computed tomography is a computational imaging technique capable of reconstructing inner structures of an object from a reduced set of X-ray projection measurements. Coded apertures are placed in front of the X-ray sources from different views and thus significantly reduce the radiation dose. This paper introduces coded aperture X-ray computed tomography for robotic X-ray systems which offer positioning flexibility. While single coded-aperture 3D tomography was recently introduced for standard trajectory CT scanning, it is shown that significant gains in imaging performance can be attained by simple modifications in the CT scanning trajectories enabled by emerging dual robotic CT systems. In particular, the subject is fixed on a plane and the CT system uniformly rotates around the r −axis which is misaligned with the coordinate axes. A single stationary coded aperture is placed on front of the robotic X-ray source above the plane and the corresponding X-ray projections are measured by a two-dimensional detector on the second arm of the robotic system. The compressive measurements with misalignment enable the reconstruction of high-resolution three-dimensional volumetric images from the low-resolution coded projections on the detector at a sub-sampling rate. An efficient algorithm is proposed to generate the rotation matrix with two basic sub-matrices and thus the forward model is formulated. The stationary coded aperture is designed based on the Pearson product-moment correlation coefficient analysis and the direct binary search algorithm is used to obtain the optimized coded aperture. Simulations using simulated datasets show significant gains in reconstruction performance compared to conventional coded aperture CT systems.