Magnetic source imaging registration based on self-supervised learning and multi-view differentiable rendering

High-sensitivity miniature optically pumped magnetometers (OPMs) and magnetoencephalography (MEG) technologies enable precise localization of brain magnetic field sources and their activity. Magnetic source imaging (MSI) relies on the accurate registration of MEG with Magnetic Resonance Imaging (MRI) to achieve high localization precision. Current registration algorithms typically depend on head point clouds reconstructed by optical scanners, which are then used to compute the coordinate transformation matrix between MEG and MRI. However, the complex process of reconstructing head point clouds not only increases the operational difficulty and the complexity of automation but also results in an unavoidable preparation time of 2–3 min, which is not user-friendly for non-expert operators or emergency patients. To address this, we propose a new MSI registration method based on self-supervised learning and multi-view differentiable rendering. This method eliminates the cumbersome head point cloud reconstruction process and instead achieves registration through several images taken from different angles. We treat the transformation matrix as an optimizable model parameter, and by comparing the differences between the captured images and the rendered images, we use differentiable rendering techniques to propagate gradients and optimize the transformation matrix, gradually bringing it closer to the true value. Experimental results show that this method reduces the preparation time to 30 s, significantly simplifies the operation, and achieves registration accuracy surpassing that of structured light scanners, approaching the precision of laser scanners. Furthermore, the required equipment cost is only 56% of that of a structured light scanner and 17% of a laser scanner. This advantage facilitates the widespread application of wearable OPM-MEG systems.