ArticleMagnetic resonance in medicine2024
The "hidden noise" problem in MR image reconstruction.
Article in Magnetic resonance in medicine, 2024. 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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Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Zero-Shot Self-Supervised Learning of Single Breath-Hold Magnetic Resonance Cholangiopancreatography (MRCP) Reconstruction.Magnetic resonance in medicine · 2026Article
- Well-designed k-space coverage is important for good MRI denoising.Magma (New York, N.Y.) · 2026Article
- Agreement of image quality metrics with radiological evaluation in the presence of motion artifacts.Magma (New York, N.Y.) · 2025Article
- Robust multi-coil MRI reconstruction via self-supervised denoising.Magnetic resonance in medicine · 2025Article
- Diffusion probabilistic generative models for accelerated, in-NICU permanent magnet neonatal MRI.Magnetic resonance in medicine · 2025Article
- MRI acquisition and reconstruction cookbook: recipes for reproducibility, served with real-world flavour.Magma (New York, N.Y.) · 2025Review
- Using deep feature distances for evaluating the perceptual quality of MR image reconstructions.Magnetic resonance in medicine · 2025Article
- Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts.ArXiv · 2025Article
- Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data.NMR in biomedicine · 2025Article
- Spherical echo-planar time-resolved imaging (sEPTI) for rapid 3D quantitativeMagnetic resonance in medicine · 2025Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
purposeThe performance of modern image reconstruction methods is commonly judged using quantitative error metrics like root mean squared-error and the structural similarity index, which are calculated by comparing reconstructed images against fully sampled reference data. In practice, the reference data will contain noise and is not a true gold standard. In this work, we demonstrate that the "hidden noise" present in reference data can substantially confound standard approaches for ranking different image reconstruction results.
methodsUsing both experimental and simulated k-space data and several different image reconstruction techniques, we examined whether there was correlation between performance metrics obtained with typical noisy reference data versus those obtained with higher-quality reference data.
resultsFor conventional performance metrics, the reconstructions that matched best with the higher-quality reference data were substantially different from the reconstructions that matched best with typical noisy reference data. This leads to suboptimal reconstruction results if the performance with respect to noisy reference data is used to select which reconstruction methods/parameters to employ. These issues were reduced when employing alternative error metrics that better account for noise.
conclusionReference data containing hidden noise can substantially mislead the ranking of image reconstruction methods when using conventional error metrics, but this issue can be mitigated with alternative error metrics.
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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.