无需训练数据，自动优化参数实现快速精准的MRI重建。
Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction
- 用双层优化自动调参，针对每次扫描定制重建方案。
- 每幅2D图像重建仅需数秒，比传统方法更高效。
- 适合临床急需快速重建的场景，尤其对新设备兼容性好。
深度学习方法可实现高加速磁共振成像（MRI）重建，但依赖特定应用的大规模训练数据，且对分布外数据泛化能力差。自监督深度学习算法虽能实现扫描特异性重建，但仍需复杂的超参数调优，且加速能力有限。本文提出一种基于双层优化的隐式神经表示（INR）方法，用于扫描特异性MRI重建。该方法将欠采样MRI重建问题明确定义为双层优化问题，自动为给定采集协议优化多维超参数，实现无需训练数据的定制化重建。采用高斯过程回归优化INR超参数，适应多种采集方式。INR包含可训练的位置编码器用于高维特征嵌入，以及小型多层感知机用于解码。双层优化计算高效，典型2D Cartesian扫描仅需几分钟。在扫描仪硬件上，利用离线优化的超参数进行后续扫描特异性重建，可在数秒内完成，图像质量优于以往模型基与自监督学习方法。
原文摘要 · Abstract (English)
Deep Learning (DL) methods can reconstruct highly accelerated magnetic resonance imaging (MRI) scans, but they rely on application-specific large training datasets and often generalize poorly to out-of-distribution data. Self-supervised deep learning algorithms perform scan-specific reconstructions, but still require complicated hyperparameter tuning based on the acquisition and often offer limited acceleration. This work develops a bilevel-optimized implicit neural representation (INR) approach for scan-specific MRI reconstruction. The method automatically optimizes the hyperparameters for a given acquisition protocol, enabling a tailored reconstruction without training data. The proposed algorithm uses Gaussian process regression to optimize INR hyperparameters, accommodating various acquisitions. The INR includes a trainable positional encoder for high-dimensional feature embedding and a small multilayer perceptron for decoding. The bilevel optimization is computationally efficient, requiring only a few minutes per typical 2D Cartesian scan. On scanner hardware, the subsequent scan-specific reconstruction-using offline-optimized hyperparameters-is completed within seconds and achieves improved image quality compared to previous model-based and self-supervised learning methods.
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