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A Novel Implicit Neural Representation for Volume Data

arXiv 2403.08566 ↗First posted 13 Mar 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 56 references that are in the map. First posted on arXiv 13 Mar 2024.

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References · 23

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11 more
  1. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
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  11. 2021Lossy Medical Image Compression using Residual Learning-based Dual Autoencoder Modelcited by 1

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