Sequential-Scanning Dual-Energy CT Imaging Using High Temporal Resolution Image Reconstruction and Error-Compensated Material Basis Image Generation

Qiaoxin Li, Ruifeng Chen, Peng Wang, Guotao Quan, Yanfeng Du +2 more
8/27/2024
physics.med-phcs.AIcs.CV

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Abstract

Dual-energy computed tomography (DECT) has been widely used to obtain quantitative elemental composition of imaged subjects for personalized and precise medical diagnosis. Compared with DECT leveraging advanced X-ray source and/or detector technologies, the use of the sequential-scanning data acquisition scheme to implement DECT may make a broader impact on clinical practice because this scheme requires no specialized hardware designs and can be directly implemented into conventional CT systems. However, since the concentration of iodinated contrast agent in the imaged subject varies over time, sequentially scanned data sets acquired at two tube potentials are temporally inconsistent. As existing material basis image reconstruction approaches assume that the data sets acquired at two tube potentials are temporally consistent, the violation of this assumption results in inaccurate quantification of material concentration. In this work, we developed sequential-scanning DECT imaging using high temporal resolution image reconstruction and error-compensated material basis image generation, ACCELERATION in short, to address the technical challenge induced by temporal inconsistency of sequentially scanned data sets and improve quantification accuracy of material concentration in sequential-scanning DECT. ACCELERATION has been validated and evaluated using numerical simulation data sets generated from clinical human subject exams and experimental human subject studies. Results demonstrated the improvement of quantification accuracy and image quality using ACCELERATION.

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Cite this paper

@article{li2024sequentialscanning,
  title  = {Sequential-Scanning Dual-Energy CT Imaging Using High Temporal Resolution Image Reconstruction and Error-Compensated Material Basis Image Generation},
  author = {Qiaoxin Li and Ruifeng Chen and Peng Wang and Guotao Quan and Yanfeng Du and Dong Liang and Yinsheng Li},
  year   = {2024},
  eprint = {2408.14754},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.14754v1}
}

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