ACCELERATION: Sequentially-scanning DECT Imaging Using High Temporal Resolution Image Reconstruction And Temporal Extrapolation (2408.06163v1)
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 existing high-end DECT leveraging advanced X-ray source and/or detector technologies, the use of the sequentially-scanning data acquisition scheme to implement DECT may make broader impact on clinical practice because this scheme requires no specialized hardware designs. 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 decomposition approaches for DECT assume that the data sets acquired at two tube potentials are temporally consistent, the violation of this assumption results in inaccurate quantification accuracy of iodine concentration. In this work, we developed a technique to achieve sequentially-scanning DECT imaging using high temporal resolution image reconstruction and temporal extrapolation, ACCELERATION in short, to address the technical challenge induced by temporal inconsistency of sequentially-scanned data sets and improve iodine quantification accuracy in sequentially-scanning DECT. ACCELERATION has been validated and evaluated using numerical simulation data sets generated from clinical human subject exams. Results demonstrated the improvement of iodine quantification accuracy using ACCELERATION.
- “Principles and applications of multienergy ct: Report of aapm task group 291,” Medical physics, vol. 47, no. 7, pp. e881–e912, 2020.
- “A new method for determination of left-to-right cardiac shunts,” Journal of nuclear medicine technology, vol. 19, no. 4, pp. 216–219, 1991.
- “Implicit neural representation in medical imaging: A comparative survey,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 2381–2391.
- “Nerp: implicit neural representation learning with prior embedding for sparsely sampled image reconstruction,” IEEE Transactions on Neural Networks and Learning Systems, 2022.
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