A Novel Implicit Neural Representation for Volume Data
Armin Sheibanifard Hongchuan Yu
The storage of medical images is one of the challenges in the medical imaging
field. There are variable works that use implicit neural representation (INR)
to compress volumetric medical images. However, there is room to improve the
compression rate for volumetric medical images. Most of the INR techniques need
a huge amount of GPU memory and a long training time for high-quality medical
volume rendering. In this paper, we present a novel implicit neural
representation to compress volume data using our proposed architecture, that
is, the Lanczos downsampling scheme, SIREN deep network, and SRDenseNet
high-resolution scheme. Our architecture can effectively reduce training time,
and gain a high compression rate while retaining the final rendering quality.
Moreover, it can save GPU memory in comparison with the existing works. The
experiments show that the quality of reconstructed images and training speed
using our architecture is higher than current works which use the SIREN only.
Besides, the GPU memory cost is evidently decreased