Evidence map›Paper›PMID 42906627›Full record

ArticleNMR in biomedicine2026

Quantitative Chemical Exchange Saturation Transfer Imaging With Golden-Angle Radial k-Space Sampling and Locally Low-Rank Reconstruction.

Ouri Cohen, Elizabeth J Sutton, Robert J Young, Ricardo Otazo

Abstract read
In one paragraph

Article in NMR in biomedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ouri CohenDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.ORCID https://orcid.org/0000-0003-3632-8094
Elizabeth J SuttonDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.ORCID https://orcid.org/0000-0002-1407-7012
Robert J YoungDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Ricardo OtazoDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.ORCID https://orcid.org/0000-0002-3782-4930

Funding

NIH HHS R37-CA262662
6 · The paper itself

Abstract

Chemical exchange saturation transfer magnetic resonance fingerprinting (CEST-MRF) is a promising quantitative molecular imaging technique. To reduce scan time, current CEST-MRF implementations typically use echo-planar imaging (EPI) readouts, which are prone to geometric distortion and signal dropout. In this study, we developed a motion-robust, geometrically accurate CEST-MRF method using radial k-space sampling, locally low-rank reconstruction, and neural network-based quantification. The acquisition schedule was optimized using deep learning, and its accuracy was validated in numerical simulations with digital phantoms. The number of spokes per measurement was determined through simulations and in vivo ablation studies in healthy volunteers. Five healthy subjects underwent repeated scans, and regions of interest were defined for analysis. Tissue maps generated with the proposed method were compared with values obtained from nonlinear least-squares (NLS) fitting of multisaturation-power z-spectra. Motion sensitivity and test-retest reproducibility were evaluated using the coefficient of variation (CV) and intraclass correlation coefficient (ICC). Clinical feasibility was demonstrated in a subject with a history of remote middle cerebral artery infarction. The results show that 3D quantitative CEST maps can be acquired in 11 min using 34 spokes per measurement. Numerical simulations yielded mean errors below 14% for all tissue parameters, while in vivo parameter estimates agreed well with both NLS-derived values and prior brain CEST-MRF studies. The mean ICC across all tissue maps was 0.92 in white matter and 0.87 in gray matter, with mean intersubject CVs of 5.4% and 3.4%, respectively. The mean error between motion and no-motion conditions was 8.6%. In the pathology case, changes in CEST-MRF tissue parameters were consistent with clinically diagnosed cystic gliosis. This approach enables accurate and reproducible 3D quantitative CEST imaging of the brain within clinically feasible scan times.

Indexed as

Image Processing, Computer-AssistedMagnetic Resonance ImagingAdultAlgorithmsBrainComputer SimulationFemaleHumansMalePhantoms, ImagingReproducibility of Results

Identifiers

PMID42906627
PMCPMC13686781

What OpenQuestion holds

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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.