ArticleNeuroImage2014
Optimizing RetroICor and RetroKCor corrections for multi-shot 3D FMRI acquisitions.
Article in NeuroImage, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Servo Navigation and Phase Equalization Enhanced by Run-Time Stabilization (PEERS) for 3D EPI Time Series.Magnetic resonance in medicine · 2026Article
- Impact of thermal and physiological denoising on laminar functional connectivity.Scientific reports · 2026Article
- ISME-incoherent sampling of multi-echo data to minimize cardiac-induced noise in brain maps ofMagnetic resonance in medicine · 2026Article
- Physiological Confounds in BOLD-fMRI and Their Correction.NMR in biomedicine · 2025Review
- Improving robustness of 3D multi-shot EPI by structured low-rank reconstruction of segmented CAIPI sampling for fMRI at 7T.NeuroImage · 2023Article
- A 3D Convolutional Encapsulated Long Short-Term Memory (3DConv-LSTM) Model for Denoising fMRI Data.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory.Medical image analysis · 2020Article
- Article
- Methods for cleaning the BOLD fMRI signal.NeuroImage · 2017Review
- Physiological noise in human cerebellar fMRI.Magma (New York, N.Y.) · 2015Article
- Neuroimaging of epilepsy: lesions, networks, oscillations.Clinical neuroradiology · 2014Review
- Physiological noise in brainstem FMRI.Frontiers in human neuroscience · 2013Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Physiological noise, if unaccounted for, can drastically reduce the statistical significance of detected activation in FMRI. In this paper, we systematically optimize physiological noise regressions for multi-shot 3D FMRI data. First, we investigate whether 3D FMRI data are best corrected in image space (RetroICor) or k-space (RetroKCor), in which each k-space segment can be assigned its unique physiological phase. In addition, the optimal regressor set is determined using the Bayesian Information Criterion (BIC) for a variety of 3D acquisitions corresponding to different image contrasts and k-space readouts. Our simulations and experiments indicate that: (a) k-space corrections are more robust when performed on real/imaginary than magnitude/phase data; (b) k-space corrections do not outperform image-space corrections, despite the ability to synchronize physiological phase to acquisition time more accurately; and (c) the optimal model varied considerably between the various acquisition techniques. These results suggest the use of a tailored set of volume-wide regressors, determined by BIC or other selection criteria, that achieves optimal balance between variance reduction and potential over-fitting.
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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.