Resolution generalization of deep learning-based dipole inversion networks for QSM

Deep learning-based dipole inversion networks for quantitative susceptibility mapping (QSM) display low performance when test data resolution is different from network-trained data resolution. While several approaches were proposed to enhance the dipole inversion networks' resolution generalizability, they modify network architecture or parameter, limiting direct application to existing pre-trained dipole inversion networks. This study presents a novel pipeline that enables pre-trained dipole inversion networks to reconstruct QSM from input local field maps of various resolutions. The developed pipeline consisted of four steps. (ⅰ) The local field map was re-sampled at multiple different spatial locations, generating multiple local field maps at network-trained resolution. (ⅱ) The re-sampled local field maps were inferred through the network, generating QSM maps. (ⅲ) These QSM maps were combined, and then (ⅳ) compensated for systematic errors, introduced by the previous re-sampling and combining process, by "dipole compensation". The performance of the proposed pipeline was compared with two alternative pipelines using the same network: interpolating the input data to the trained resolution prior to inference (interpolation pipeline), and naïvely inferencing (naïve-input pipeline). Through qualitative and quantitative evaluations, we demonstrate that the proposed pipeline displays superior performance compared to the alternative pipelines. Specifically, when a local field map of 1 mm³ resolution was tested using QSMnet pre-trained at 1.5 mm³ resolution, the proposed pipeline outperformed the two alternative pipelines (NRMSE: 43.1/49.3/56.0, SSIM: 0.933/0.910/0.920, PSNR: 47.1/46.0/44.8, HFEN: 39.9/40.8/48.0 for proposed/interpolation/naïve-input pipeline). This study provides a promising solution for enhancing the generalizability of pre-trained dipole inversion networks to different input data resolutions, widening their applications in clinical settings.