Evidence map›Paper›PMID 40423026›Full record

ArticleJournal of imaging2025

Recovery and Characterization of Tissue Properties from Magnetic Resonance Fingerprinting with Exchange.

Naren Nallapareddy, Soumya Ray

Abstract read
In one paragraph

Article in Journal of imaging, 2025. 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
0cells of the map it votes in
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

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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

2 authors.

Naren NallapareddyDepartment of Computer and Data Sciences, Case Western Reserve University, Olin 516, 2101 Martin Luther King Jr Dr, Cleveland, OH 44106, USA.ORCID 0009-0003-9983-3554
Soumya RayDepartment of Computer and Data Sciences, Case Western Reserve University, Olin 516, 2101 Martin Luther King Jr Dr, Cleveland, OH 44106, USA.ORCID 0000-0002-6673-6796

Funding

U.S. National Science Foundation SES-2120972
6 · The paper itself

Abstract

Magnetic resonance fingerprinting (MRF), a quantitative MRI technique, enables the acquisition of multiple tissue properties in a single scan. In this paper, we study a proposed extension of MRF, MRF with exchange (MRF-X), which can enable acquisition of the six tissue properties T1a,T2a, T1b, T2b, ρ and τ simultaneously. In MRF-X, 'a' and 'b' refer to distinct compartments modeled in each voxel, while ρ is the fractional volume of component 'a', and τ is the exchange rate of protons between the two components. To assess the feasibility of recovering these properties, we first empirically characterize a similarity metric between MRF and MRF-X reconstructed tissue property values and known reference property values for candidate signals. Our characterization indicates that such a recovery is possible, although the similarity metric surface across the candidate tissue properties is less structured for MRF-X than for MRF. We then investigate the application of different optimization techniques to recover tissue properties from noisy MRF and MRF-X data. Previous work has widely utilized template dictionary-based approaches in the context of MRF; however, such approaches are infeasible with MRF-X. Our results show that Simplicial Homology Global Optimization (SHGO), a global optimization algorithm, and Limited-memory Bryoden-Fletcher-Goldfarb-Shanno algorithm with Bounds (L-BFGS-B), a local optimization algorithm, performed comparably with direct matching in two-tissue property MRF at an SNR of 5. These optimization methods also successfully recovered five tissue properties from MRF-X data. However, with the current pulse sequence and reconstruction approach, recovering all six tissue properties remains challenging for all the methods investigated.

Indexed as

inverse problemmedical imagingMRFMRIoptimization

Identifiers

PMID40423026
PMCPMC12113007

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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.