Research on spectral CT reconstruction and material decomposition method based on implicit neural representation

Research on spectral CT reconstruction and material decomposition method based on implicit neural representation

Yingxian Xia^a^,^b, Hao Zhou^a^,^b, Yifan Deng^a^,^b, Zhilei Wang^a^,^b, Zhiqiang Chen^a^,^b, Hewei Gao^a^,^b^,^*

Modern Instruments & Medical Treatment | 2026, 32(2) : 1 - 6

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Modern Instruments & Medical Treatment | 2026, 32(2): 1-6

Research on spectral CT reconstruction and material decomposition method based on implicit neural representation

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Yingxian Xia^a^,^b, Hao Zhou^a^,^b, Yifan Deng^a^,^b, Zhilei Wang^a^,^b, Zhiqiang Chen^a^,^b, Hewei Gao^a^,^b^,^*

Affiliations

^a.Department of Engineering Physics, Tsinghua University, Beijing 100084, China

^b.Key Laboratory of Particle & Radiation Imaging, Ministry of Education, Tsinghua University, Beijing 100084, China

Published: 2026-04-10 doi: 10.11876/mimt202602001

Outline

Abstract

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Objective

To address the technical challenges of traditional spectral CT reconstruction methods, including reliance on discrete voxel grids, high computational complexity, degraded reconstruction quality, and insufficient material decomposition accuracy under low-dose and under-sampling conditions.

Methods

A spectral CT reconstruction and material decomposition method based on implicit neural representation (INR) is proposed. This method adopts multi-resolution hash encoding to map spatial coordinates into high-dimensional features, decodes base material densities via a lightweight multi-layer perceptron (MLP), and converts the reconstruction process into a nonlinear optimization problem minimizing projection consistency loss.

Results

Performance verification was conducted based on simulation data from fast kV switching and actual data from a dual-layer flat-panel detector. Compared with the traditional two-step method combining projection-domain material decomposition and filtered back projection (FBP), the proposed method effectively improves the image signal-to-noise ratio and material decomposition accuracy, demonstrating stronger robustness under sparse-view conditions.

Conclusion

The present invention can provide a new algorithmic path and theoretical basis for the low-dose clinical application of next-generation spectral CT technology.

Key words

Implicit neural representation / Spectral CT / Material decomposition / Image reconstruction

Cite this Article

夏颖贤, 周浩, 邓一凡, 王志磊, 陈志强, 高河伟. 基于隐式神经表示的能谱CT重建与物质分解方法研究. 现代仪器与医疗, 2026 , 32 (2) : 1 -6 . DOI: 10.11876/mimt202602001

Yingxian Xia, Hao Zhou, Yifan Deng, Zhilei Wang, Zhiqiang Chen, Hewei Gao. Research on spectral CT reconstruction and material decomposition method based on implicit neural representation[J]. Modern Instruments & Medical Treatment, 2026 , 32 (2) : 1 -6 . DOI: 10.11876/mimt202602001

Appendix

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Year 2026 volume 32 Issue 2

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Article Info

doi: 10.11876/mimt202602001

Online Date：2026-10-23
Published：2026-04-10

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Funding

Affiliations

^a.Department of Engineering Physics, Tsinghua University, Beijing 100084, China

^b.Key Laboratory of Particle & Radiation Imaging, Ministry of Education, Tsinghua University, Beijing 100084, China

References

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表12种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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