ESORecon‐Net: A Novel Framework for Enhanced Brain MRI Image Reconstruction Using Echo State Networks and Osprey Optimization

This study uses advanced approaches on the enlarged BRATS dataset to increase brain magnetic resonance imaging (MRI) image reconstruction accuracy and reliability. This study addresses MRI image processing issues such as noise, artifacts, and high‐quality reconstruction. These traits are essential for brain tumor detection and analysis. This effort aims to establish a comprehensive image processing pipeline that standardizes MRI images, reduces noise, and improves clarity for better image reconstruction. ESORecon‐Net, which combines the echo state network (ESN) and osprey optimization algorithm (OSPREY), manages raw k‐space data cleverly and improves reconstruction. The model’s dual‐phase optimization ensures accuracy and efficiency in reconstructing high‐quality MRI images. The proposed ESORecon‐Net achieved a peak signal‐to‐noise ratio (PSNR) of 49.12 dB and a structural similarity index measure (SSIM) of 0.993, surpassing existing methods such as the fully sampled k‐space‐trained network (FS‐kNet) and motion‐informed deep learning network (MIDNet). These results confirm ESORecon‐Net’s effectiveness in enhancing brain MRI image reconstruction, improving both image quality and computational performance.