GA-GAN: CT reconstruction from Biplanar DRRs using GAN with Guided Attention
This work addresses medical imaging reconstruction for healthcare applications, but it is incremental as it builds on existing GAN methods with specific enhancements.
The paper tackles CT reconstruction from biplanar DRRs by using a GAN with guided attention and vector quantization to improve visual quality and reduce memory usage, showing that their approaches outperform previous works.
This work investigates the use of guided attention in the reconstruction of CTvolumes from biplanar DRRs. We try to improve the visual image quality of the CT reconstruction using Guided Attention based GANs (GA-GAN). We also consider the use of Vector Quantization (VQ) for the CT reconstruction so that the memory usage can be reduced, maintaining the same visual image quality. To the best of our knowledge no work has been done before that explores the Vector Quantization for this purpose. Although our findings show that our approaches outperform the previous works, still there is a lot of room for improvement.