APS-USCT: Ultrasound Computed Tomography on Sparse Data via AI-Physic Synergy
<|reference_start|>APS-USCT: Ultrasound Computed Tomography on Sparse Data via AI-Physic Synergy: Ultrasound computed tomography (USCT) is a promising technique that achieves superior medical imaging reconstruction resolution by fully leveraging waveform information, outperforming conventional ultrasound methods. Despite its advantages, high-quality USCT reconstruction relies on extensive data acquisition by a large number of transducers, leading to increased costs, computational demands, extended patient scanning times, and manufacturing complexities. To mitigate these issues, we propose a new USCT method called APS-USCT, which facilitates imaging with sparse data, substantially reducing dependence on high-cost dense data acquisition. Our APS-USCT method consists of two primary components: APS-wave and APS-FWI. The APS-wave component, an encoder-decoder system, preprocesses the waveform data, converting sparse data into dense waveforms to augment sample density prior to reconstruction. The APS-FWI component, utilizing the InversionNet, directly reconstructs the speed of sound (SOS) from the ultrasound waveform data. We further improve the model's performance by incorporating Squeeze-and-Excitation (SE) Blocks and source encoding techniques. Testing our method on a breast cancer dataset yielded promising results. It demonstrated outstanding performance with an average Structural Similarity Index (SSIM) of 0.8431. Notably, over 82% of samples achieved an SSIM above 0.8, with nearly 61% exceeding 0.85, highlighting the significant potential of our approach in improving USCT image reconstruction by efficiently utilizing sparse data.<|reference_end|>
@article{sheng2024aps-usct:,
    title={APS-USCT: Ultrasound Computed Tomography on Sparse Data via AI-Physic
  Synergy},
    author={Yi Sheng, Hanchen Wang, Yipei Liu, Junhuan Yang, Weiwen Jiang, Youzuo
  Lin, and Lei Yang},
    journal={arXiv preprint arXiv:2407.14564},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14564},
    primaryClass={eess.IV cs.AI cs.CV cs.LG}
}