Evidence map›Paper›PMID 41873471›Full record

ArticleMagnetic resonance in medicine2026

Navigator-Based Slice Tracking for Multiband Accelerated Liver DWI.

Ke Zhang, Simon M F Triphan, Congcong Fu, Na Zhang, Christian H Ziener, Mark E Ladd, Heinz-Peter Schlemmer, Hans-Ulrich Kauczor, Oliver Sedlaczek

Abstract read
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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. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Ke ZhangDepartment of Diagnostic & Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.ORCID https://orcid.org/0000-0002-7244-8483
Simon M F TriphanDepartment of Diagnostic & Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.
Congcong FuDepartment of Diagnostic & Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.
Na ZhangPaul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.ORCID https://orcid.org/0000-0001-9510-4520
Christian H ZienerDivision of Radiology, German Cancer Research Center, Heidelberg, Germany.
Mark E LaddDivision of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.
Heinz-Peter SchlemmerDivision of Radiology, German Cancer Research Center, Heidelberg, Germany.
Hans-Ulrich KauczorDepartment of Diagnostic & Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.
Oliver SedlaczekDepartment of Diagnostic & Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeClinical liver diffusion-weighted imaging (DWI) suffers from long measurement time and respiratory motion. In this study, we employ a navigator-based slice tracking technique (NAV) to prospectively compensate for respiratory motion in multiband (MB) accelerated liver DWI.

methodsA single gradient-echo slice readout at the location of the diaphragm along the inferior-superior direction was acquired as a navigator. Navigator acquisition and fat suppression were inserted before each transverse imaging slice of the readouts of a 2D spin-echo EPI based DWI sequence, with 20 repeats including 32 training navigators (4 preparing scans) at the beginning of the measurement obtained in about 3 min. For comparison, MB accelerated DWI without NAV during free breathing was also acquired. To evaluate the effect of NAV on image quality, coefficients of variation (CoV), signal to noise ratio (SNR), and sharpness were calculated. Six healthy volunteers and three patients with lesions were examined at 1.5 T.

resultsThe motion caused by respiratory activity was effectively computed using the navigator signal. For measurement with NAV, averaged CoV was significantly lower when compared to without NAV (0.256 ± 0.086 vs. 0.262 ± 0.091, p < 0.001). SNR and liver sharpness were also increased. The shapes of both kidney and spleen were continuous and smooth in the coronal view with NAV. Detailed structure in different organs could be found with NAV compared to without NAV in the cyst and tumor patients.

conclusionThis study demonstrates the feasibility of a NAV technique to reduce respiratory motion in MB accelerated liver DWI.

Indexed as

Diffusion Magnetic Resonance ImagingImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedLiverLiver NeoplasmsAdultAlgorithmsArtifactsFemaleHumansImage EnhancementMaleMiddle AgedMotionReproducibility of ResultsRespirationliver diffusion weighted imagingnavigator‐based slice trackingprospective motion compensation

Identifiers

PMID41873471
PMCPMC13269244

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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.