ArticleMagnetic resonance in medicine2023
Accelerated 4D-flow MRI with 3-point encoding enabled by machine learning.
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 18 citations in OpenAlex.
- 3D Phase Contrast Using Balanced Steady-State Free Precession (PC-SSFP) for Improved 4D Flow at Clinical Field Strengths.Magnetic resonance in medicine · 2026Article
- Quantifying hepatic arterial blood flow with 4D flow MRI: an in vivo feasibility and repeatability study.Magma (New York, N.Y.) · 2026Article
- Splanchnic hemodynamic parameters measured with 4D flow MRI can diagnose severe portal hypertension.European radiology · 2026Article
- Quantifying physiological variability and improving reproducibility in 4D-flow MRI cerebrovascular measurements with self-supervised deep learning.Magnetic resonance in medicine · 2025Article
- Simultaneous and synchronous characterization of blood and CSF flow dynamics using multiple Venc PC MRI.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Aortopathy associated with bicuspid aortic valve: advances in clinical and hemodynamics research.Frontiers in physiology · 2025Review
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- Macrovascular blood flow and microvascular cerebrovascular reactivity are regionally coupled in adolescence.bioRxiv : the preprint server for biology · 2024Article
- Four-dimensional flow MRI for quantitative assessment of cerebrospinal fluid dynamics: Status and opportunities.NMR in biomedicine · 2024Review
- Deep Learning-Based Reconstruction for Cardiac MRI: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Accelerated 4D-flow MRI with 3-point encoding enabled by machine learning.Magnetic resonance in medicine · 2023Article
- Advances in machine learning applications for cardiovascular 4D flow MRI.Frontiers in cardiovascular medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo investigate the acceleration of 4D-flow MRI using a convolutional neural network (CNN) that produces three directional velocities from three flow encodings, without requiring a fourth reference scan measuring background phase.
methodsA fully 3D CNN using a U-net architecture was trained in a block-wise fashion to take complex images from three flow encodings and to produce three real-valued images for each velocity component. Using neurovascular 4D-flow scans (n = 144), the CNN was trained to predict velocities computed from four flow encodings by standard reconstruction including correction for residual background phase offsets. Methods to optimize loss functions were investigated, including magnitude, complex difference, and uniform velocity weightings. Subsequently, 3-point encoding was evaluated using cross validation of pixelwise correlation, flow measurements in major arteries, and in experiments with data at differing acceleration rates than the training data.
resultsThe CNN-produced 3-point velocities showed excellent agreements with the 4-point velocities, both qualitatively in velocity images, in flow rate measures, and quantitatively in regression analysis (slope = 0.96, R
conclusionThe 4D-flow MRI can be accelerated using machine learning requiring only three flow encodings to produce three-directional velocity maps with small errors.
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.