Charting cortical growth trajectories is of paramount importance for understanding brain development. However, such analysis necessitates the collection of longitudinal data, which can be challenging due to subject dropouts and failed scans. In this paper, we will introduce a method for longitudinal prediction of cortical surfaces using a spatial graph convolutional neural network (GCNN), which extends conventional CNNs from Euclidean to curved manifolds. The proposed method is designed to model the cortical growth trajectories and jointly predict inner and outer cortical surfaces at multiple time points. Adopting a binary flag in loss calculation to deal with missing data, we fully utilize all available cortical surfaces for training our deep learning model, without requiring a complete collection of longitudinal data. Predicting the surfaces directly allows cortical attributes such as cortical thickness, curvature, and convexity to be computed for subsequent analysis. We will demonstrate with experimental results that our method is capable of capturing the nonlinearity of spatiotemporal cortical growth patterns and can predict cortical surfaces with improved accuracy.
LoCI-DiffCom: Longitudinal Consistency-Informed Diffusion Model for 3D Infant Brain Image Completion
School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；Shanghai United Imaging Intelligence Co., Ltd（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trail Center（上海临床研究中心）
Individualized Mapping of Aberrant Cortical Thickness via Stochastic Cortical Self-Reconstruction
Lab for Artificial Intelligence in Medical Imaging, Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)（人工智能在医学影像中的实验室、诊断与介入放射学研究所、医学与健康学院、TUM医院、慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Neuroradiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM)（神经放射科、医学与健康学院、TUM医院、慕尼黑技术大学）
Spherical U-Net on Cortical Surfaces: Methods and Applications
Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University（浙江大学教育部生物医学工程重点实验室）；Department of Radiology and BRIC, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校放射科和BRIC（生物医学研究信息中心））；Department of Psychiatry, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校精神病学系）
Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network
Nanjing University of Information Science and Technology（南京信息工程大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Shandong University (Weihai)（山东大学（威海））
Spherical Brownian Bridge Diffusion Models for Conditional Cortical Thickness Forecasting
Lab for AI in Medical Imaging, Technical University of Munich, Munich, Germany（人工智能医学影像实验室，慕尼黑技术大学，慕尼黑，德国）；Munich Center for Machine Learning, Munich, Germany（慕尼黑机器学习中心，慕尼黑，德国）
Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data
Imperial College London（伦敦帝国理工学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究中心）
Open video data sharing in developmental and behavioural science
University Medical Center Göttingen（哥廷根大学医学中心）；Karolinska Institutet（卡罗林斯卡学院）；Medical University of Graz（格拉茨医科大学）；Leibniz-ScienceCampus Primate Cognition（莱布尼茨灵长类认知科学园区）；David Geffen UCLA School of Medicine（加州大学洛杉矶分校大卫·格芬医学院）；Region Stockholm（斯德哥尔摩地区）；Curtin University（科廷大学）；Victoria University of Wellington（惠灵顿维多利亚大学）
SynthBrainGrow: Synthetic Diffusion Brain Aging for Longitudinal MRI Data Generation in Young People
Mass General Brigham, Harvard Medical School（哈佛医学院麻省总医院布里格姆医疗系统）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Maastricht University（马斯特里赫特大学）；Boston Children's Hospital（波士顿儿童医院）
