APS-USCT: Ultrasound Computed Tomography on Sparse Data via AI-Physic Synergy

• URL: http://arxiv.org/abs/2407.14564v1
• Date: Thu, 18 Jul 2024 20:30:41 GMT
• Title: APS-USCT: Ultrasound Computed Tomography on Sparse Data via AI-Physic Synergy
• Authors: Yi Sheng, Hanchen Wang, Yipei Liu, Junhuan Yang, Weiwen Jiang, Youzuo Lin, Lei Yang,
• Abstract summary: Ultrasound computed tomography (USCT) is a promising technique that achieves superior medical imaging reconstruction resolution. Despite its advantages, high-quality USCT reconstruction relies on extensive data acquisition by a large number of transducers. We propose a new USCT method called APS-USCT, which facilitates imaging with sparse data.
• Score: 13.805519939012889
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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