PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 16897692

Artifacts and pitfalls in diffusion MRI.

Denis Le Bihan·Cyril Poupon·Alexis Amadon·Franck Lethimonnier
View original source ↗Export metadata ↓

Abstract

Although over the last 20 years diffusion MRI has become an established technique with a great impact on health care and neurosciences, like any other MRI technique it remains subject to artifacts and pitfalls. In addition to common MRI artifacts, there are specific problems that one may encounter when using MRI scanner gradient hardware for diffusion MRI, especially in terms of eddy currents and sensitivity to motion. In this article we review those artifacts and pitfalls on a qualitative basis, and introduce possible strategies that have been developed to mitigate or overcome them.

Explore related subjects

ArtifactsBrainBrain DiseasesDiffusionDiffusion Magnetic Resonance ImagingHumansImage Processing, Computer-AssistedMotionNeoplasmsSensitivity and SpecificityTemperatureWaterJournal ArticleReview

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Denis Le Bihan, Cyril Poupon, Alexis Amadon, Franck Lethimonnier. 2006. Artifacts and pitfalls in diffusion MRI.. https://doi.org/10.1002/jmri.20683

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Striping artifact removal in VisiumHD data through nuclear counts modeling.

MOTIVATION: 10x Genomics VisiumHD enables spatial transcriptomics at 2 µm × 2 µm resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. RESULTS: We propose a statistical destriping approach that leverages nuclei segmentation from the co-registered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods. AVAILABILITY AND IMPLEMENTATION: All source code and links to publicly available data used for this study are available at https://github.com/paolamalsot/destriping-GLM.

Artifacts↗
PubMed2008

Impairment of echocardiographic acoustic window caused by breast implants.

Cosmetic breast implants are increasing in popularity. The presence of a breast implant overlying the anterior mediastinal space as a cause of impairment of the echocardiographic acoustic window has not been described previously. Here, we report three cases with significant impairment of echocardiographic acoustic window caused by breast implants. Clinicians should be aware of this interference and women should be informed of this dilemma before considering this cosmetic surgery.

Artifacts↗