Enhance the Image: Super Resolution using Artificial Intelligence in MRI

Autor:	Li, Ziyu, Li, Zihan, Li, Haoxiang, Fan, Qiuyun, Miller, Karla L., Wu, Wenchuan, Chaudhari, Akshay S., Tian, Qiyuan
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Physics - Medical Physics
Druh dokumentu:	Working Paper
Popis:	This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI, ranging from convolutional neural networks, generative adversarial networks, to more advanced models including transformers, diffusion models, and implicit neural representations. Our exploration extends beyond the methodologies to scrutinize the impact of super-resolved images on clinical and neuroscientific assessments. We also cover various practical topics such as network architectures, image evaluation metrics, network loss functions, and training data specifics, including downsampling methods for simulating low-resolution images and dataset selection. Finally, we discuss existing challenges and potential future directions regarding the feasibility and reliability of deep learning-based MRI super-resolution, with the aim to facilitate its wider adoption to benefit various clinical and neuroscientific applications. Comment: A book chapter in Machine Learning in MRI: From methods to clinical translation. Copyright may be transferred without notice, after which this version may no longer be accessible
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.13625 Zobrazit plný text záznamu View this record from Arxiv