Compressed sensing (CS) takes advantage of a low-dimensional signal structure to reduce sampling requirements to far below the Nyquist rate. In magnetic resonance imaging (MRI), this often takes the form of sparsity through wavelet transforms, finite differences, and low-rank extensions. Though powerful, these image priors are phenomenological in nature and do not account for the mechanism behind the image formation. On the other hand, MRI signal dynamics are governed by physical laws, which can be explicitly modeled and used as priors for reconstruction. These explicit and implicit signal priors can be synergistically combined in an inverse-problem framework to recover sharp, multicontrast images from highly accelerated scans. Furthermore, the physics-based constraints provide a recipe for recovering quantitative, biophysical parameters from the data.

中文翻译：

---

具有基于物理约束的计算 MRI：在多重对比和定量成像中的应用

压缩感知 (CS) 利用低维信号结构将采样要求降低到远低于奈奎斯特速率。在磁共振成像 (MRI) 中，这通常通过小波变换、有限差分和低秩扩展以稀疏的形式出现。虽然强大，但这些图像先验本质上是现象学的，并没有解释图像形成背后的机制。另一方面，MRI 信号动力学受物理定律控制，可以明确建模并用作重建的先验。这些显式和隐式信号先验可以在逆问题框架中协同组合，以从高度加速的扫描中恢复清晰的多对比度图像。此外，基于物理的约束提供了恢复定量、