Magnetic Particle Imaging Super Resolution Using Transfer Learning with Sparse Transforms

In this paper, we examine and propose a super resolution technique for Magnetic Particle Imaging (MPI) developed using transfer learning and sparse transforms. MPI is a new modality for medical imaging that relies on tracking of super-paramagnetic nanoparticles and offers a superior alternative to medical imaging methods such as MRI and X-Ray in some applications. Like other imaging modalities, and despite being faster than other methods, MPI has limitations in spatial and temporal resolution. Super resolution techniques can considerably widen the applications of MPI, for real-time and invivo scanning and analysis. The current lack of MPI data prevents direct development of machine learning and deep neural network (DNN) based methods for super resolution. To overcome this issue, we study the application of transfer learning and show that utilizing some existing large datasets of natural images, and then retraining on a small dataset of MPI images allows for successful development of a super resolution DNN. We also propose to use sparsifying transforms in the early stages of the DNN, for improved quality of the inferred increased resolution image. We show that for such networks a 4–5 dB improvement is achieved over the common bicubic interpolation techniques for super resolution.