ArticleMagnetic resonance in medicine2020
Extreme MRI: Large-scale volumetric dynamic imaging from continuous non-gated acquisitions.
Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed.
- 3D and 4D Free-Breathing Abdominal T1-Weighted MRI in Clinical Practice Using Deep Learning Auto-Navigation and Reconstruction.Magnetic resonance in medicine · 2026Article
- Fully 3D Unrolled Magnetic Resonance Fingerprinting Reconstruction via Staged Pretraining and Implicit Gridding.Magnetic resonance in medicine · 2026Article
- Arc-ZTE: Continuously-Slewed Zero-TE Imaging With Incoherent Temporal Sampling for Near-Silent Dynamic MRI.Magnetic resonance in medicine · 2026Article
- S2V-DREME: a time-resolved slice-to-volume MR image reconstruction framework with dynamic reconstruction and motion estimation.Physics in medicine and biology · 2026Article
- Article
- Motion Mitigation Techniques for Abdominal and Cardiac MR Imaging.Journal of magnetic resonance imaging : JMRI · 2026Review
- Free-running time-resolved first-pass myocardial perfusion using a multi-scale dynamics decomposition: CMR-MOTUS.Magma (New York, N.Y.) · 2026Article
- Coil Sketching for Fast and Efficient 4D Lung MRI Reconstruction.Magnetic resonance in medicine · 2026Article
- Automatic Respiratory and Bulk Patient Motion Corrected 3D Fetal MRI.Magnetic resonance in medicine · 2026Article
- Motion compensated magnetic resonance imaging of an active sun beetle using an in situ treadmill.Scientific reports · 2025Article
- Synchronous quantification of arterial, venous, and cerebrospinal fluid flow dynamics using real-time phase-contrast MRI.Fluids and barriers of the CNS · 2025Article
- A dynamic reconstruction and motion estimation framework for cardiorespiratory motion-resolved real-time volumetric MR imaging (DREME-MR).Physics in medicine and biology · 2025Article
- Higher spatial resolution and sensitivity in whole brain functional MRI at 7T using 3D EPI accelerated with variable density CAIPI sampling and temporal random walk.Magnetic resonance in medicine · 2025Article
- Repeatability-encouraging self-supervised learning reconstruction for quantitative MRI.Magnetic resonance in medicine · 2025Article
- Generalizable, sequence-invariant deep learning image reconstruction for subspace-constrained quantitative MRI.Magnetic resonance in medicine · 2025Article
- Ultra-high temporal resolution 4D angiography using arterial spin labeling with subspace reconstruction.Magnetic resonance in medicine · 2025Article
- 5D image reconstruction exploiting space-motion-echo sparsity for accelerated free-breathing quantitative liver MRI.Medical image analysis · 2025Article
- Electric potential energy optimized 3D radial sampling trajectories for MRI.Scientific reports · 2024Article
- Reproducible Research Practices in Magnetic Resonance Neuroimaging: A Review Informed by Advanced Language Models.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Review
- Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction.IEEE transactions on medical imaging · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
purposeTo develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast-enhanced (DCE) imaging. THEORY AND
methodsThe problem considered here requires recovering 100 gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three-dimensional (3D) trajectories, such as 3D radial and 3D cones, with ordering that incoherently cover time and k-space over the entire acquisition. We then propose two innovations: (a) A compressed representation using multiscale low-rank matrix factorization that constrains the reconstruction problem, and reduces its memory footprint. (b) Stochastic optimization to reduce computation, improve memory locality, and minimize communications between threads and processors. We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory. We compare it with "soft-gated" dynamic reconstruction for DCE and respiratory-resolved reconstruction for pulmonary imaging.
resultsThe proposed technique shows transient dynamics that are not seen in gating-based methods. When applied to datasets with irregular, or non-repetitive motions, the proposed method displays sharper image features.
conclusionsWe demonstrated a method that can reconstruct massive 3D dynamic image series in the extreme undersampling and extreme computation setting.
Indexed as
Identifiers
What OpenQuestion holds
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.