• Laser & Optoelectronics Progress
  • Vol. 62, Issue 2, 0237003 (2025)
Tianhao Ge1,*, Fanning Kong1, Zaifeng Shi1,3, Yichao Jin1, and Qingjie Cao2
Author Affiliations
  • 1School of Microelectronics, Tianjin University, Tianjin 300072, China
  • 2School of Mathematical Sciences, Tianjin Normal University, Tianjin 300387, China
  • 3Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin 300072, China
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    DOI: 10.3788/LOP241085 Cite this Article Set citation alerts
    Tianhao Ge, Fanning Kong, Zaifeng Shi, Yichao Jin, Qingjie Cao. Dual-Energy Computed Tomography Material Decomposition Network Based on Mamba and Channel Attention[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237003 Copy Citation Text show less
    References

    [1] Shikhaliev P M, Fritz S G. Photon counting spectral CT versus conventional CT: comparative evaluation for breast imaging application[J]. Physics in Medicine and Biology, 56, 1905-1930(2011).

    [2] Ouyang S X, Shi Z F, Kong F N et al. Projection domain denoising method for multi-energy computed tomography via dual-stream Transformer[J]. Laser & Optoelectronics Progress, 61, 0837008(2024).

    [3] Zhao W, Niu T Y, Xing L et al. Using edge-preserving algorithm with non-local mean for significantly improved image-domain material decomposition in dual-energy CT[J]. Physics in Medicine and Biology, 61, 1332-1351(2016).

    [4] Li Z P, Ravishankar S, Long Y et al. DECT-MULTRA: dual-energy CT image decomposition with learned mixed material models and efficient clustering[J]. IEEE Transactions on Medical Imaging, 39, 1223-1234(2020).

    [5] Feng J, Yu H J, Wang S Y et al. Image-domain based material decomposition by multi-constraint optimization for spectral CT[J]. IEEE Access, 8, 155450-155458(2020).

    [6] Liu S Z, Tivnan M, Osgood G M et al. Model-based three-material decomposition in dual-energy CT using the volume conservation constraint[J]. Physics in Medicine and Biology, 67, 145006(2022).

    [7] Pan H Y, Zhao S S, Zhang W B et al. Fast iterative reconstruction for multi-spectral CT by a Schmidt orthogonal modification algorithm (SOMA)[J]. Inverse Problems, 39, 085001(2023).

    [8] Hohweiller T, Ducros N, Peyrin F et al. A constrained Gauss-Newton algorithm for material decomposition in spectral computed tomography[C], 336-339(2018).

    [9] Tian F Y, Zhou M Q, Yan F et al. Spinal CT segmentation based on AttentionNet and DenseUnet[J]. Laser & Optoelectronics Progress, 57, 201008(2020).

    [10] Xu Y F, Yan B, Zhang J F et al. Image decomposition algorithm for dual-energy computed tomography via fully convolutional network[J]. Computational and Mathematical Methods in Medicine, 2018, 2527516(2018).

    [11] Wang G S, Liu Z, Huang Z Y et al. Improved GAN: using a transformer module generator approach for material decomposition[J]. Computers in Biology and Medicine, 149, 105952(2022).

    [12] Chang H Y, Liu C K, Huang H M. Material decomposition using dual-energy CT with unsupervised learning[J]. Physical and Engineering Sciences in Medicine, 46, 1607-1617(2023).

    [13] Wu F, Jin T, Zhan G R et al. Dual-energy CT base material decomposition method based on multi-channel cross-convolution UCTransNet[J]. Acta Optica Sinica, 44, 0515001(2024).

    [14] Yang B Q, Feng X F, Dong Y Y et al. Combination of CNN and Transformer for lesion-guided honeycomb lung CT image recognition[J]. Laser & Optoelectronics Progress, 61, 1437014(2024).

    [15] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[EB/OL]. https://arxiv.org/abs/1706.03762

    [16] Chen H T, Wang Y H, Guo T Y et al. Pre-trained image processing transformer[C], 12294-12305(2021).

    [17] Gu A, Dao T. Mamba: linear-time sequence modeling with selective state spaces[EB/OL]. http://arxiv.org/abs/2312.00752v2

    [18] Zhu L H, Liao B C, Zhang Q et al. Vision Mamba: efficient visual representation learning with bidirectional state space model[EB/OL]. http://arxiv.org/abs/2401.09417v2

    [19] Ma J, Li F F, Wang B. U-Mamba: enhancing long-range dependency for biomedical image segmentation[EB/OL]. http://arxiv.org/abs/2401.04722v1

    [20] Xing Z H, Ye T, Yang Y J et al. SegMamba: long-range sequential modeling Mamba for 3D medical image segmentation[EB/OL]. http://arxiv.org/abs/2401.13560v3

    [21] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

    [22] Shazeer N. GLU variants improve Transformer[EB/OL]. http://arxiv.org/abs/2002.05202v1

    [23] Ruan J C, Xiang S C. VM-UNet: vision Mamba UNet for medical image segmentation[EB/OL]. http://arxiv.org/abs/2402.02491v1

    [24] Wang Z, Simoncelli E P, Bovik A C. Multiscale structural similarity for image quality assessment[C], 1398-1402(2003).

    [25] Segars W P, Sturgeon G, Mendonca S et al. 4D XCAT phantom for multimodality imaging research[J]. Medical Physics, 37, 4902-4915(2010).

    [26] Wang J, Lu H B, Liang Z R et al. An experimental study on the noise properties of X-ray CT sinogram data in Radon space[J]. Physics in Medicine and Biology, 53, 3327-3341(2008).

    [27] Niu T Y, Dong X, Petrongolo M et al. Iterative image-domain decomposition for dual-energy CT[J]. Medical Physics, 41, 041901(2014).

    [28] Zhang W K, Zhang H M, Wang L Y et al. Image domain dual material decomposition for dual-energy CT using butterfly network[J]. Medical Physics, 46, 2037-2051(2019).

    [29] Gong H, Tao S Z, Rajendran K et al. Deep-learning-based direct inversion for material decomposition[J]. Medical Physics, 47, 6294-6309(2020).

    [30] Walsh M F, Nik S J, Procz S et al. Spectral CT data acquisition with Medipix3.1[J]. Journal of Instrumentation, 8, P10012(2013).

    [31] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [32] Liu Z, Lin Y T, Cao Y et al. Swin Transformer: hierarchical vision Transformer using shifted windows[C], 9992-10002(2021).

    Tianhao Ge, Fanning Kong, Zaifeng Shi, Yichao Jin, Qingjie Cao. Dual-Energy Computed Tomography Material Decomposition Network Based on Mamba and Channel Attention[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237003
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