Evidence map›Paper›PMID 41292017›Full record

ArticleMagnetic resonance in medicine2026

Dynamic Transitions for Fast Joint Acquisition and Reconstruction of CEST-

Markus Huemer, Clemens Stilianu, Nick Scholand, Daniel Mackner, Martin Uecker, Moritz Zaiss, Rudolf Stollberger

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
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

7 authors.

Markus HuemerInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.ORCID https://orcid.org/0009-0004-9095-2626
Clemens StilianuInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.ORCID https://orcid.org/0009-0008-5033-9877
Nick ScholandInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.
Daniel MacknerInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.
Martin UeckerInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.ORCID https://orcid.org/0000-0002-8850-809X
Moritz ZaissInstitute of Neuroradiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany.
Rudolf StollbergerInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.ORCID https://orcid.org/0000-0002-4969-3878

Funding

Austrian Science Fund 10.55776/I4870Austrian Science Fund F100800
6 · The paper itself

Abstract

purposeThis work proposes a method for the simultaneous estimation of the exchange-dependent relaxation rate

methodsA novel acquisition scheme was developed that combines CEST saturation with an inversion pulse and a Look-Locker readout to capture the magnetization evolution starting from the inverse transient Z-spectrum. The corresponding signal model, derived from the Bloch-McConnell equations, describes both the transient Z-spectrum and the Look-Locker dynamics. A model-based reconstruction approach is employed to jointly estimate

resultsThe joint estimation approach demonstrated strong agreement with ground truth and conventional methods across a wide range of

conclusionThe proposed technique enables robust and efficient simultaneous quantification of CEST

Indexed as

BrainImage Processing, Computer-AssistedMagnetic Resonance ImagingAlgorithmsHumansPhantoms, ImagingReproducibility of ResultsAPTwAREXCESTLook‐Lockermodel‐based reconstructionQUASSrNOEssMTT1TGV

Identifiers

PMID41292017
PMCPMC12850581

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.