跨模态医学图像预测综述

医学影像技术与设备的进步在生物医学领域的各项研究中发挥着重要作用.跨模态医学图像预测旨在由一种模态图像预测另一种模态图像.本文详细综述了由MRI预测CT图像、7T-Like图像重构、PET预测及其他医学模态预测研究，阐述了各类模态预测的必要性及存在的挑战，说明各类预测方法的特点并进行性能比较，最终得出结论：基于深度学习的跨模态预测在预测精度和预测时间两方面更具优势.

Review of Cross-Modality Medical Image Prediction

Advances in medical imaging technologies and equipment play an important role in the biomedical researches.Cross-modality image-prediction technology predicts one modal image from that of another modal.This paper presents an overview of the literatures on medical imaging prediction technology and its applications,such as predicting Computed Tomography images from Magnetic Resonance (MR) images,7T-like MR image reconstruction,and predicting positron emission tomography images.The aim is twofold:the necessity and challenge for different modality medical image prediction technology;the overview and comparison of various methods in the field.We conclude that the cross-modality image prediction based on the deep learning technology has superiority in both predicting time and precision.