Evidence map›Paper›PMID 38755452›Full record

ArticleAbdominal radiology (New York)2024

Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging.

Yukihisa Takayama, Keisuke Sato, Shinji Tanaka, Ryo Murayama, Ryotaro Jingu, Kengo Yoshimitsu

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Article in Abdominal radiology (New York), 2024. 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

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yukihisa TakayamaDepartment of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan. ytakayama@fukuoka-u.ac.jp.ORCID 0000-0002-2029-9316
Keisuke SatoDepartment of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan.
Shinji TanakaDepartment of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan.
Ryo MurayamaDepartment of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan.
Ryotaro JinguRadiology Center, Fukuoka University Hospital, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan.
Kengo YoshimitsuDepartment of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the effectiveness of deep learning-based reconstruction (DLR) in improving image quality and tumor detectability of isovoxel high-resolution breath-hold fat-suppressed T1-weighted imaging (HR-BH-FS-T1WI) in the hepatobiliary phase (HBP) of Gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI). MATERIALS AND

methodsThis retrospective evaluated 42 patients with 98 liver tumors who underwent Gd-EOB-MRI between March 2023 and May 2023 using three techniques based on HBP imaging: isovoxel HR-BH-FS-T1WI reconstructed (1) with DLR (BH-DLR +) and (2) without DLR (BH-DLR -) and (3) HR-FS-T1WI scanned with a free-breathing technique using a navigator-echo-triggered technique and DLR (Navi-DLR +). The three techniques were qualitatively and quantitatively compared by the Friedman test and the Bonferroni post-hoc test. Tumor detectability was compared using the McNemar test.

resultsBH-DLR + (3.85, average score of two radiologists) showed significantly better qualitative scores for image noise than BH-DLR - (2.84) and Navi-DLR + (3.37) (p < 0.0167), and Navi-DLR + showed significantly better scores than BH-DLR - (p < 0.0167). BH-DLR + (3.77) and BH-DLR - (3.77) showed significantly better qualitative scores for respiratory motion artifact than Navi-DLR + (2.75) (p < 0.0167), but there was no significant difference in scores between BH-DLR + and BH-DLR - (p > 0.0167). BH-DLR + (0.32) and Navi-DLR + (0.33) showed significantly higher lesion-to-nonlesion CR than BH-DLR - (0.29) (p < 0.0167), but there was no significant difference in lesion-to-nonlesion CR between BH-DLR + and Navi-DLR + (p > 0.0167). BH-DLR + (89.8%) showed significantly better tumor detectability than BH-DLR - (76.0%) and Navi-DLR + (77.6%) (p < 0.05).

conclusionThe use of DLR for isovoxel HR-BH-FS-T1WI was effective in improving image quality and tumor detectability.

Indexed as

Contrast MediaDeep LearningGadolinium DTPALiver NeoplasmsMagnetic Resonance ImagingAdultAgedAged, 80 and overFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedMaleMiddle AgedRetrospective StudiesContrast MediaGadolinium DTPAgadolinium ethoxybenzyl DTPADeep learning-based reconstruction (DLR)Gd-EOB-MRIHepatobiliary phaseLiver tumorMagnetic resonance imaging (MRI)

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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.