PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 1556927

Magnetization transfer effects in multislice RARE sequences.

P S Melki·R V Mulkern
View original source ↗Export metadata ↓

Abstract

Magnetization transfer effects are demonstrated to be significant in determining the signal intensity from brain tissues on images acquired with multislice rapid acquisition relaxation enhanced (RARE) sequences. We report studies designed to determine how the signal intensities vary with slice number or, equivalently, off-resonance power deposition. The results obtained in fat, gray matter, and white matter are similar in form to those reported in kidney tissues during classic magnetization transfer experiments (J. Eng, T. L. Ceckler, and R. S. Balaban, Magn. Reson. Med. 17, 304 (1991)). Of clinical significance to RARE practitioners is the increase of contrast-to-noise ratios between gray and white matter on proton density-weighted images with increasing slice number.

Explore related subjects

ArtifactsBrainHumansImage EnhancementMagnetic Resonance ImagingMagneticsModels, StructuralJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

P S Melki, R V Mulkern. 1992. Magnetization transfer effects in multislice RARE sequences.. https://doi.org/10.1002/mrm.1910240122

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↗