Evidence map›Paper›PMID 40883956›Full record

ArticleMagnetic resonance in medicine2026

Liver fat quantification at 0.55 T enabled by locally low-rank enforced deep learning reconstruction.

Majd Helo, Dominik Nickel, Stephan Kannengiesser, Thomas Kuestner

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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.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Majd HeloMedical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany.ORCID 0009-0003-9548-0608
Dominik NickelResearch & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany.ORCID 0000-0002-0360-7233
Stephan KannengiesserResearch & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany.ORCID 0000-0001-6345-9132
Thomas KuestnerMedical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany.ORCID 0000-0002-0353-4898

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe emergence of new medications for fatty liver conditions has increased the need for reliable and widely available assessment of MRI proton density fat fraction (MRI-PDFF). Whereas low-field MRI presents a promising solution, its utilization is challenging due to the low SNR. This work aims to enhance SNR and enable precise PDFF quantification at low-field MRI using a novel locally low-rank deep learning-based (LLR-DL) reconstruction.

methodsLLR-DL alternates between regularized SENSE and a neural network (U-Net) throughout several iterations, operating on complex-valued data. The network processes the spectral projection onto singular value bases, which are computed on local patches across the echoes dimension. The output of the network is recast into the basis of the original echoes and used as a prior for the following iteration. The final echoes are processed by a multi-echo Dixon algorithm. Two different protocols were proposed for imaging at 0.55 T. An iron-and-fat phantom and 10 volunteers were scanned on both 0.55 and 1.5 T systems. Linear regression, t-statistics, and Bland-Altman analyses were conducted.

resultsLLR-DL achieved significantly improved image quality compared to the conventional reconstruction technique, with a 32.7% increase in peak SNR and a 25% improvement in structural similarity index. PDFF repeatability was 2.33% in phantoms (0% to 100%) and 0.79% in vivo (3% to 18%), with narrow cross-field strength limits of agreement below 1.67% in phantoms and 1.75% in vivo.

conclusionAn LLR-DL reconstruction was developed and investigated to enable precise PDFF quantification at 0.55 T and improve consistency with 1.5 T results.

Indexed as

Adipose TissueDeep LearningFatty LiverImage Processing, Computer-AssistedLiverMagnetic Resonance ImagingAdultAlgorithmsFemaleHumansMaleNeural Networks, ComputerPhantoms, ImagingReproducibility of ResultsSignal-To-Noise Ratiodeep learningliver PDFFlocally low‐ranklow‐field MRIreconstruction

Identifiers

PMID40883956
PMCPMC12620156

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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.