Highly Accelerating Joint Intracranial and Carotid Vessel Wall Imaging Using ESPIRiT ‐Driven Diffusion Model Reconstruction

PURPOSE: . THEORY AND METHODS: ESPIRiT-Diffusion exploits the powerful generative capability of the diffusion framework for the reconstruction of large-FOV 3D VWI images, aiming to recover vessel wall details and improve image quality at high acceleration factors. By further incorporating multi-set ESPIRiT coil sensitivity maps into the Langevin equation, it enforces accurate data consistency, thereby improving VWI reconstruction quality while constraining unreliable generation. In addition, diffusion performed directly in the image domain leads to clearer fine details and faster reconstruction. RESULTS: In retrospective experiments with Cartesian, CAIPI, and variable-density undersampling at acceleration factors of 8.8× and 10.7×, ESPIRiT-Diffusion showed improved reconstruction performance compared with ESPIRiT, DL-ESPIRiT, SENSE-Diffusion, and SPIRiT-Diffusion in the evaluated retrospective experiments, with better preservation of fine vessel wall structures. In prospective patient experiments, ESPIRiT-Diffusion provided favorable visualization of vessel wall lesions, with no statistically significant differences in reader scores from the 3-fold CS reference across either individual vascular segments or Overall comparisons. CONCLUSIONS: ESPIRiT-Diffusion for VWI reconstruction mitigates some limitations related to instability and sampling-pattern dependence in unfolding-based methods, while also alleviating image blurring and reducing reconstruction time compared with k-space diffusion. As a result, ESPIRiT-Diffusion showed improved reconstruction quality and clearer fine structural details in the evaluated experiments, while reducing the required acquisition time.