量子混合网络加速磁共振成像重建，提升图像质量。
Quantum Neural Network for Accelerated Magnetic Resonance Imaging
- 构建量子-经典混合神经网络，融合量子卷积优势
- 在模拟量子计算机上实现优异重建效果，显著提升图像质量
- 适合对快速成像与高精度重建有需求的研究者
从欠采样的k空间数据重建磁共振图像需要恢复大量潜在的非线性特征，这对传统算法而言极具挑战。近年来，量子计算的发展表明，量子卷积可能带来性能提升，具备潜在的量子优势。本文提出一种包含量子与经典网络的混合神经网络，用于快速磁共振成像，并在量子计算机模拟系统上进行实验。结果表明，该混合网络实现了出色的重建效果，验证了将混合量子-经典神经网络应用于快速磁共振成像重建的可行性。
原文摘要 · Abstract (English)
Magnetic resonance image reconstruction starting from undersampled k-space data requires the recovery of many potential nonlinear features, which is very difficult for algorithms to recover these features. In recent years, the development of quantum computing has discovered that quantum convolution can improve network accuracy, possibly due to potential quantum advantages. This article proposes a hybrid neural network containing quantum and classical networks for fast magnetic resonance imaging, and conducts experiments on a quantum computer simulation system. The experimental results indicate that the hybrid network has achieved excellent reconstruction results, and also confirm the feasibility of applying hybrid quantum-classical neural networks into the image reconstruction of rapid magnetic resonance imaging.
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