A COMPRESSED SENSING RECONSTRUCTION METHOD BASED ON UNTRAINED CONVOLUTIONAL NEURAL NETWORK FOR MEDICAL IMAGING

Medical imaging holds significant importance in disease diagnosis and surgical preparation, necessitating higher standards for the storage and transmission of medical images. We propose a method for compressed sensing reconstruction of medical imaging based on untrained convolutional neural networks. This method does not require any training data, offering vast potential in the field of medical imaging. We employ the generator model of Deep Convolutional Generative Adversarial Networks (DCGAN) as the structure for the untrained convolutional neural network. We utilized the Musculoskeletal Radiographs (MURA) and Open Access Series of Imaging Studies (OASIS) datasets to demonstrate that our method significantly improved the reconstruction effect in medical imaging, regardless of whether the sampling rate was high or low.