Evidence map›Paper›PMID 40445963›Full record

ArticlePloS one2025

Deep learning reconstruction of free-breathing, diffusion-weighted imaging of the liver: A comparison with conventional free-breathing acquisition.

Jiyoung Yoon, Yoonhee Lee, Sungjin Yoon, JaeKon Sung, Thomas Benkert, Jungbok Lee, So Hyun Park

Abstract readComparative Study
In one paragraph

Article in PloS one, 2025. 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.

Jiyoung YoonDepartment of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.
Yoonhee LeeDepartment of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.ORCID 0000-0002-5266-4322
Sungjin YoonDepartment of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.
JaeKon SungSiemens Healthineers Ltd, Seoul, Rebublic of Korea.
Thomas BenkertMR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.ORCID 0000-0002-3794-5580
Jungbok LeeDepartment of Clinical Epidemiology and Biostatistics, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea.
So Hyun ParkDepartment of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.ORCID 0000-0001-9935-2863

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to compare image quality and solid focal liver lesion (FLL) assessments between free-breathing, diffusion-weighted imaging using deep learning reconstruction (FB-DL-DWI) and conventional DWI (FB-C-DWI) in patients undergoing clinically indicated liver MRIs. Our retrospective study included 199 patients who underwent 3 T-liver MRIs with FB-DL-DWI and FB-C-DWI. DWI was performed using a single-shot, spin-echo, echo-planar, fat suppression technique during free-breathing with matching parameters. Three radiologists independently evaluated subjective image quality across two sequences. The apparent diffusion coefficient (ADC) was measured in 15 liver regions. Four radiologists analyzed 138 solid FLLs from 60 patients for the presence of diffusion restriction, lesion conspicuity, and sharpness. Among the 199 patients, 110 (55.3%) had underlying chronic liver disease (CLD). FB-DL-DWI was found to be 43.0% faster than FB-C-DWI (119.4 ± 2.2 sec vs. 209.6 ± 3.7 sec). Furthermore, FB-DL-DWI scored higher than FB-C-DWI for all subjective image quality parameters (all, P < 0.001); however, FB-DL-DWI exhibited greater artificial sensation than FB-C-DWI (P < 0.001). In patients with CLD, FB-DL-DWI exhibited a better subjective image quality (all, P < 0.001) than FB-C-DWI. ADC values ranged from 1.06-1.12 × 10-3 mm2/sec in FB-DL-DWI and 1.06-1.20 × 10-3 mm2/sec in FB-C-DWI. Among the 138 lesions analyzed, 116 malignancies (61 hepatocellular carcinomas, 3 cholangiocarcinomas, 52 metastases) and 22 benignities were included. Four readers identified 88, 93, 93, and 105 diffusion-restricted FLLs in FB-DL-DWI and 84, 80, 98, and 95 in FB-C-DWI. FB-DL-DWI (75.9-90.5%) demonstrated comparable or superior diffusion restriction rates for malignant FLLs compared to FB-C-DWI (68.1-82.8%). Furthermore, FB-DL-DWI presented higher lesion-edge sharpness and lesion-conspicuity compared to FB-C-DWI. Overall, FB-DL-DWI provided better image quality, lesion sharpness, and conspicuity for solid FLLs, with a shorter acquisition time than FB-C-DWI. Therefore, FB-DL-DWI may replace FB-C-DWI as the preferred imaging method for liver evaluations.

Indexed as

Deep LearningDiffusion Magnetic Resonance ImagingImage Processing, Computer-AssistedLiverLiver NeoplasmsAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedRespirationRetrospective Studies

Identifiers

PMID40445963
PMCPMC12124547

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.