IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation
This work addresses the need for faster and higher-quality MR imaging in medical diagnostics, representing an incremental improvement through a novel method for a known bottleneck.
The authors tackled the problem of reconstructing high-resolution MR images from low-resolution scans by proposing IREM, a network that learns a continuous volumetric function from sparse observations, achieving arbitrary up-sampling rates and reducing scan time while improving SNR and image detail.
For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction. In this work, we suppose the desired HR image as an implicit continuous function of the 3D image spatial coordinate and the thick-slice LR images as several sparse discrete samplings of this function. Then the super-resolution (SR) task is to learn the continuous volumetric function from a limited observations using an fully-connected neural network combined with Fourier feature positional encoding. By simply minimizing the error between the network prediction and the acquired LR image intensity across each imaging plane, IREM is trained to represent a continuous model of the observed tissue anatomy. Experimental results indicate that IREM succeeds in representing high frequency image feature, and in real scene data collection, IREM reduces scan time and achieves high-quality high-resolution MR imaging in terms of SNR and local image detail.