Nature Communications (Apr 2023)

Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosis

  • Han Zhao,
  • Zhengwu Liu,
  • Jianshi Tang,
  • Bin Gao,
  • Qi Qin,
  • Jiaming Li,
  • Ying Zhou,
  • Peng Yao,
  • Yue Xi,
  • Yudeng Lin,
  • He Qian,
  • Huaqiang Wu

DOI
https://doi.org/10.1038/s41467-023-38021-7
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 10

Abstract

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Abstract Medical imaging is an important tool for accurate medical diagnosis, while state-of-the-art image reconstruction algorithms raise critical challenges in massive data processing for high-speed and high-quality imaging. Here, we present a memristive image reconstructor (MIR) to greatly accelerate image reconstruction with discrete Fourier transformation (DFT) by computing-in-memory (CIM) with memristor arrays. A high-accuracy quasi-analogue mapping (QAM) method and generic complex matrix transfer (CMT) scheme was proposed to improve the mapping precision and transfer efficiency, respectively. High-fidelity magnetic resonance imaging (MRI) and computed tomography (CT) image reconstructions were demonstrated, achieving software-equivalent qualities and DICE scores after segmentation with nnU-Net algorithm. Remarkably, our MIR exhibited 153× and 79× improvements in energy efficiency and normalized image reconstruction speed, respectively, compared to graphics processing unit (GPU). This work demonstrates MIR as a promising high-fidelity image reconstruction platform for future medical diagnosis, and also largely extends the application of memristor-based CIM beyond artificial neural networks.