Fractional Sailfish Optimizer with Deep Convolution Neural Network for Compressive Sensing Based Magnetic Resonance Image Reconstruction

Magnetic resonance image (MRI) is extensively adapted in clinical diagnosis due to its improved representation of changes with soft tissue. The current innovations in compressive sensing (CS) showed that it is viable to precisely reconstruct MRI with K-space data with reduced scanning duration and it takes more time for computation and it is complex to capture the fine details. The CS is capable to minimize the imaging time and it uses compressibility for reconstructing the images. In this paper, a novel deep-learning method is devised for reconstructing images using a set of MRI. Here, MRI acquisition and MRIsub sampling is performed for reducing the size of images to perform improved analysis. Here, a convolution layer is utilized for imitating the process of compressed sampling that can learn the sampling matrix to prevent complex designs. In addition, a convolution layer is utilized to execute the initial reconstruction. In addition, Deep Convolutional Neural Network (Deep CNN) is adapted for image reconstruction. The training of Deep CNN is performed using proposed Fractional Sailfish Optimizer (FSO) algorithm. The proposed FSO is designed by incorporating fractional concepts in Sail fish optimization for tuning the optimal weights of Deep CNN. The proposed FSO-based CSNet outperformed other methods with minimal MSE of 1.877 and maximal PSNR of 45.396 dB.