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 and Huaqiang Wu
Additional contact information
Han Zhao: Tsinghua University
Zhengwu Liu: Tsinghua University
Jianshi Tang: Tsinghua University
Bin Gao: Tsinghua University
Qi Qin: Tsinghua University
Jiaming Li: Tsinghua University
Ying Zhou: Tsinghua University
Peng Yao: Tsinghua University
Yue Xi: Tsinghua University
Yudeng Lin: Tsinghua University
He Qian: Tsinghua University
Huaqiang Wu: Tsinghua University

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-38021-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38021-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-38021-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().