Advances in deep learning for multimodal brain imaging: A comprehensive survey

Abstract

In recent years, the field of medical brain imaging has witnessed remarkable advancements with the integration of artificial intelligence (AI) and deep learning techniques. Traditional unimodal imaging methods, such as MRI and CT, often fall short in providing comprehensive insights into neurological disorders. To address these limitations, multimodal imaging, which combines various imaging modalities like MRI, CT, PET, and SPECT, has emerged as a powerful tool for enhanced diagnosis and treatment planning. This survey presents an in-depth review of the state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), used for brain tumor classification, segmentation, forecasting, and object detection. We also explore the potential of hybrid models that integrate machine learning and deep learning approaches. Furthermore, we highlight the significant developments in multimodal brain imaging techniques from 2019 to 2024 and discuss the future research directions needed to advance this field. By synthesizing the latest findings, this survey aims to provide a comprehensive understanding of the current landscape and future possibilities in multimodal medical brain imaging.