ArticleNature communications2023
Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosis.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Micro-LED/van der Waals heterointegration for in-pixel processing display architecture.Nature communications · 2026Article
- Privacy-preserving data analysis using a memristor chip with colocated authentication and processing.Science advances · 2026Article
- A memristor-based energy-efficient compressed sensing accelerator with hardware-software co-optimization for edge computing.National science review · 2026Article
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- Molecular crystal memristors.Nature nanotechnology · 2025Article
- Interstitial nanofilament pathway reduces energy consumption in memristors.Nature nanotechnology · 2025Article
- Bioinspired neuromorphic memristor with logical operation for precision sperm detection in seminiferous tubules.Materials today. Bio · 2025Article
- Advancing Flexible Optoelectronic Synapses and Neurons with MXene-Integrated Polymeric Platforms.Nanomaterials (Basel, Switzerland) · 2025Review
- Neuromorphic chips for biomedical engineering.Mechanobiology in medicine · 2025Review
- Real-time signal processing enabled by fused networks on a memristor-based system on a chip.Science advances · 2025Article
- A near-threshold memristive computing-in-memory engine for edge intelligence.Nature communications · 2025Article
- Memristive floating-point Fourier neural operator network for efficient scientific modeling.Science advances · 2025Article
- Rapid detection of brain tumor cells using memristors for biomedical applications.Materials today. Bio · 2025Article
- Benchmarking Stochasticity behind Reproducibility: Denoising Strategies in TaACS applied materials & interfaces · 2025Article
- Random resistive memory-based deep extreme point learning machine for unified visual processing.Nature communications · 2025Article
- Nonlinear memristive computational spectrometer.Light, science & applications · 2025Article
- Two-dimensional fully ferroelectric-gated hybrid computing-in-memory hardware for high-precision and energy-efficient dynamic tracking.Science advances · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.