Acta Optica Sinica, Volume. 44, Issue 5, 0515001(2024)
Dual-Energy CT Base Material Decomposition Method Based on Multi-Channel Cross-Convolution UCTransNet
[1] Wang Y Z, Cai A L, Liang N N et al. Dual plug and play multi-energy computed tomography reconstruction algorithm[J]. Acta Optica Sinica, 43, 1434001(2023).
[2] Große Hokamp N, Lennartz S, Salem J et al. Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study[J]. European Radiology, 30, 1397-1404(2020).
[3] Jacobsen M C, Cressman E N K, Tamm E P et al. Dual-energy CT: lower limits of iodine detection and quantification[J]. Radiology, 292, 414-419(2019).
[4] Chandarana H, Megibow A J, Cohen B A et al. Iodine quantification with dual-energy CT: phantom study and preliminary experience with renal masses[J]. American Journal of Roentgenology, 196, W693-W700(2011).
[5] Wang L, Wang Y, Bian Z et al. A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images[J]. Journal of Southern Medical University, 42, 724-732(2022).
[6] Su T, Sun X D, Yang J C et al. DIRECT-Net: a unified mutual-domain material decomposition network for quantitative dual-energy CT imaging[J]. Medical Physics, 49, 917-934(2022).
[7] Fu H J, Xi X Q, Han Y et al. Micro-CT image denoising algorithm based on deep residual encoding-decoding[J]. Laser & Optoelectronics Progress, 60, 1410014(2023).
[8] Long C, Jin H, Li L et al. CT image denoising with non-local means based on feature fusion[J]. Acta Optica Sinica, 42, 1134024(2022).
[9] Kelcz F, Joseph P M, Hilal S K. Noise considerations in dual energy CT scanning[J]. Medical Physics, 6, 418-425(1979).
[10] 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).
[11] Heo S Y, An B, Kim D et al. Feasibility study of block-matching and 3D filtering denoising algorithm in multi-material decomposition technique for dual-energy computed tomography[J]. Journal of the Korean Physical Society, 82, 305-314(2023).
[12] Lee H, Kim H J, Lee D et al. Improvement with the multi-material decomposition framework in dual-energy computed tomography: a phantom study[J]. Journal of the Korean Physical Society, 77, 515-523(2020).
[13] Xue Y, Ruan R S, Hu X H et al. Statistical image-domain multimaterial decomposition for dual-energy CT[J]. Medical Physics, 44, 886-901(2017).
[14] Harms J, Wang T H, Petrongolo M et al. Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization[J]. Medical Physics, 43, 2676-2686(2016).
[15] 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).
[16] Jiang J R, Yu H J, Gong C C et al. Image-domain multimaterial decomposition for dual-energy CT based on dictionary learning and relative total variation[J]. Acta Optica Sinica, 40, 2111004(2020).
[17] Ding Q Q, Niu T Y, Zhang X Q et al. Image-domain multimaterial decomposition for dual-energy CT based on prior information of material images[J]. Medical Physics, 45, 3614-3626(2018).
[18] Chen Q H, Ding J H, Zhou S et al. Tomographic image reconstruction method combining exponential filtering inverse projection reconstruction and iterative reconstruction algorithms[J]. Laser & Optoelectronics Progress, 59, 2310001(2022).
[19] Lantz M, Ongie G. Learning-based material decomposition in dual energy CT using an unrolled estimator[C](2023).
[20] Wang C X, Chen P, Pan J X et al. Research on material decomposition of dual-energy CT image based on iterative residual network[J]. Computerized Tomography Theory and Applications, 31, 47-54(2022).
[21] Li Z P, Long Y, Chun I Y. An improved iterative neural network for high-quality image-domain material decomposition in dual-energy CT[J]. Medical Physics, 50, 2195-2211(2023).
[22] 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).
[23] 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).
[24] Kawahara D, Saito A, Ozawa S et al. Image synthesis with deep convolutional generative adversarial networks for material decomposition in dual-energy CT from a kilovoltage CT[J]. Computers in Biology and Medicine, 128, 104111(2021).
[25] Shi Z F, Li H L, Cao Q J et al. A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks[J]. Medical Physics, 48, 2891-2905(2021).
[26] Wang H N, Cao P, Wang J Q et al. UCTransNet: rethinking the skip connections in U-net from a channel-wise perspective with transformer[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 2441-2449(2022).
[27] Szczykutowicz T P, Chen G H. Dual energy CT using slow kVp switching acquisition and prior image constrained compressed sensing[J]. Physics in Medicine and Biology, 55, 6411-6429(2010).
[28] Kalender W A, Klotz E, Kostaridou L. An algorithm for noise suppression in dual energy CT material density images[J]. IEEE Transactions on Medical Imaging, 7, 218-224(1988).
[29] Guimarães L S, Fletcher J G, Harmsen W S et al. Appropriate patient selection at abdominal dual-energy CT using 80 kV: relationship between patient size, image noise, and image quality[J]. Radiology, 257, 732-742(2010).
[30] Rutherford R A, Pullan B R, Isherwood I. Measurement of effective atomic number and electron density using an EMI scanner[J]. Neuroradiology, 11, 15-21(1976).
[31] Warp R J, Dobbins J T. Quantitative evaluation of noise reduction strategies in dual-energy imaging[J]. Medical Physics, 30, 190-198(2003).
[32] Duan J Y, Mou X Q. Image quality guided iterative reconstruction for low-dose CT based on CT image statistics[J]. Physics in Medicine and Biology, 66, 185018(2021).
[33] Wang G. A perspective on deep imaging[J]. IEEE Access, 4, 8914-8924(2016).
[34] Zhao H, Gallo O, Frosio I et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 3, 47-57(2017).
[35] Yang Q S, Yan P K, Zhang Y B et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss[J]. IEEE Transactions on Medical Imaging, 37, 1348-1357(2018).
[36] Johnson J, Alahi A, Li F F, Leibe B, Matas J, Sebe N et al. Perceptual losses for real-time style transfer and super-resolution[M]. Computer vision-ECCV 2016, 9906, 694-711(2016).
[38] Di J L, Lin J C, Zhong L Y et al. Review of sparse-view or limited-angle CT reconstruction based on deep learning[J]. Laser & Optoelectronics Progress, 60, 0811002(2023).
[39] Wu W W, Hu D L, Niu C et al. Deep learning based spectral CT imaging[J]. Neural Networks, 144, 342-358(2021).
[40] Niu T Y, Dong X, Petrongolo M et al. Iterative image-domain decomposition for dual-energy CT[J]. Medical Physics, 41, 041901(2014).
Get Citation
Copy Citation Text
Fan Wu, Tong Jin, Guorui Zhan, Jingjing Xie, Jin Liu, Yikun Zhang. Dual-Energy CT Base Material Decomposition Method Based on Multi-Channel Cross-Convolution UCTransNet[J]. Acta Optica Sinica, 2024, 44(5): 0515001
Category: Machine Vision
Received: Oct. 31, 2023
Accepted: Dec. 14, 2023
Published Online: Mar. 19, 2024
The Author Email: Liu Jin (liujin@ahpu.edu.cn)
CSTR:32393.14.AOS231715