ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020
A 3D Convolutional Encapsulated Long Short-Term Memory (3DConv-LSTM) Model for Denoising fMRI Data.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- A novel distortion-matched anatomical imaging sequence for high-fidelity functional mapping in submillimeter-resolution fMRI.Scientific reports · 2026Article
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction.ArXiv · 2025Article
- Systematic evaluation of head motion on resting-state functional connectivity MRI in the neonate.Human brain mapping · 2023Article
- Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.Journal of digital imaging · 2023Review
- Explainable Contrastive Multiview Graph Representation of Brain, Mind, and Behavior.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Revealing Continuous Brain Dynamical Organization with Multimodal Graph Transformer.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Predicting Spatio-Temporal Human Brain Response Using fMRI.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Brain disorder prediction with dynamic multivariate spatio-temporal features: Application to Alzheimer's disease and autism spectrum disorder.Frontiers in aging neuroscience · 2022Article
- Deep Generative Analysis for Task-Based Functional MRI Experiments.Proceedings of machine learning research · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Function magnetic resonance imaging (fMRI) data are typically contaminated by noise introduced by head motion, physiological noise, and thermal noise. To mitigate noise artifact in fMRI data, a variety of denoising methods have been developed by removing noise factors derived from the whole time series of fMRI data and therefore are not applicable to real-time fMRI data analysis. In the present study, we develop a generally applicable, deep learning based fMRI denoising method to generate noise-free realistic individual fMRI volumes (time points). Particularly, we develop a fully data-driven 3D convolutional encapsulated Long Short-Term Memory (3DConv-LSTM) approach to generate noise-free fMRI volumes regularized by an adversarial network that makes the generated fMRI volumes more realistic by fooling a critic network. The 3DConv-LSTM model also integrates a gate-controlled self-attention model to memorize short-term dependency and historical information within a memory pool. We have evaluated our method based on both task and resting state fMRI data. Both qualitative and quantitative results have demonstrated that the proposed method outperformed state-of-the-art alternative deep learning methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.