Speed up implicit neural representation for parallel MRI reconstruction

Parallel imaging is widely used in the clinic to accelerate magnetic resonance imaging (MRI) data collection. However, conventional reconstruction techniques for parallel imaging still face significant challenges in achieving satisfactory performance at high acceleration rates. It results in artifacts and noise that affect the subsequent diagnosis. Recently, implicit neural representation (INR) has emerged as a new deep learning paradigm that represents an object as a continuous function of spatial coordinates. INR’s continuity in representation enhances the model’s capacity to capture redundant information within the object. However, it usually needs thousands of training iterations to reconstruct the image. In this work, we proposed a method to speed up INR for parallel MRI reconstruction using hash-mapping and a pre-trained encoder. It enables INR to achieve better results with fewer training iterations. Benefiting from INR’s powerful representations, the proposed method outperforms existing methods in removing the aliasing artifacts and noise. The experimental results on simulated and real undersampled data demonstrate the model’s potential for further accelerating parallel MRI.