Fig. 1: Schematic representation of the core neural Bloch McConnell fitting (NBMF) pipeline.
From: Multi-parameter molecular MRI quantification using physics-informed self-supervised learning
A quantitative parameter reconstructor parameterized as a multi-layer perceptron (MLP) and a differentiable Bloch-McConnell simulator are serially connected into a single computational graph. Single-subject Magnetic Resonance Fingerprinting (MRF) data serves both as the input and as the regression target for the reconstructor-simulator circuit. The network convergence (a) provides the fitted exchange parameter maps for the examined subject as well as a trained NN reconstructor; the latter can be used to extract parameter maps for new subjects within seconds (b). The simulator can be realized using the exact numerical Bloch McConnell ODE solver or using analytical approximations when available (e.g., for 2-pool semisolid Magnetization Transfer (MT) quantification33). While not shown in the diagram, auxiliary per-voxel data such as T1, T2, B0, and B1 maps can be added as input to the neural reconstructor and the simulator. Furthermore, the pipeline main block can be serially repeated so that estimated semisolid MT volume fraction (fss) and proton exchange rate (kssw) maps inferred at the first stage are joined to the raw data used in a second reconstructor aimed to quantify the amide proton exchange parameters (fs, ksw).
