# A Novel Implicit Neural Representation for Volume Data
## 2 Related Work
### Deep Neural Network in Medical Image Restoration  
In the Ref. [16] the authors suggest a technique to use transfer learning to reconstruct the accelerated MR images. This work shows the capability of transfer learning for sparse training data. To accelerate dynamic MRI, the authors in the Ref. [17] used a convolutional recurrent neural network (CRNN-MRI). Through this method they reconstructed high-resolution MR image sequences. To accelerate MR image acquisition, the Ref. [18] introduced a self-attention CNN architecture to reconstruct MRI. Image quality can be easily degraded by noises and artefacts in low-dose computed tomography (LDCT). To address this issue, the Ref. [19] proposes a deep iterative reconstruction estimation (DIRE) with a 3D residual convolutional network (ResNet) architecture to improve the quality of images. Researchers in the Ref. [20] proposed a simultaneous algebraic reconstruction technique (SART) to reconstruct images for translational CT. Then they used a pre-trained CNN to remove artefacts and noise. To reduce the radiation dose, the Ref. [21] suggests a deep encoder-decoder adversarial reconstruction (DEAR) network to reconstruct 3D CT images directly from real clinical cone beam image data. To extract 3D details from generated slices with an adversarial network, they use DEAR-3D which is based on 3D convolutional layers. In low-dose CT imaging, noise and artefacts are inevitable. To address that issue, the Ref. [22] proposes an improved version of GoogLeNet to remove artefacts that are caused by missing projection during image reconstruction. To reconstruct an X-ray super-resolution image, the Ref. [23] suggests a GANs-based approach. It proposes spectral normalization super-resolution medical images (SNSR-GAN) to reconstruct high-resolution X-ray images. To reconstruct an ultra-fast CT image, the Ref. [24] suggests a multi-receptive field densely connected CNN (MRDC-CNN). They also use dense skip modules instead of simple skip modules to flow the information between the encoder and decoder. To decrease the memory footprint they ignore batch normalization. The Ref. [25] presents a deep regularization method to overcome the reconstruction of photo-acoustic computed tomography (PACT) with sparse view measurements. A non-local deep image before reconstructing the Positron Emission Tomography (PET) images is proposed by the Ref. [26]. They use a prior image of the patients as the input of the network. They suggest the 3D U-Net [27] as the backbone of their network structure. DeepPET is introduced by the Ref. [28] to reconstruct PET images from sinograms. They use a deep encoder-decoder network to reconstruct high-quality images. DeepPET is 108 times faster than its counterparts. DUG-RECON [29] is an Unet-based deep learning pipeline to reconstruct PET and CT images directly. It uses a convolutional generative network with three stages, denoising, image reconstruction and super-resolution segments. To reconstruct dynamic PET images, the Ref. [30] suggests non-negative matrix factorization (NMF) with a deep image prior. They also show the capabilities of DIP for PET image reconstruction. The Ref. [31] suggests a Deep Residual Error Iterative Minimization Network to reconstruct sparse-view CT. They optimize a hand-crafted function to reconstruct high-quality images. In CNN, the size of the convolutional kernel is smaller than the image size. In this case, they cannot understand the whole of the image. The Ref. [32] suggests using vision transformersto remove restrictions of CNNs in image reconstruction. The slice-by-slice transformer network (SSTrans-3D) is a transformer-based technique that reconstructs 3D single-photon emission computed tomography (SPECT) images. In image reconstruction, incomplete projection data can cause considerable artefacts. Researchers in the Ref. [33] suggest Deep Iterative Optimization-based Residual-learning (DIOR) to reconstruct limited-angle CT. To improve generalization ability, DIOR combines deep learning and iterative optimization.