Evidence map›Paper›PMID 41446781›Full record

ArticleRadiology advances2025

Automated synthetic contrast-enhanced MRI improves choroid plexus segmentation in Parkinsonian syndromes.

Dagnachew Tessema Ambaye, Sungyang Jo, Huseyin Enes Candan, Abel Worku Tessema, Nepes Myratgeldiyev, Chong Hyun Suh, Jihong Ryu, Sun Ju Chung, Hansol Lee, Jae-Hyeok Lee and 2 more

Abstract read
In one paragraph

Article in Radiology advances, 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
–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

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

12 authors.

Dagnachew Tessema AmbayeDepartment of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.ORCID https://orcid.org/0009-0005-5067-9570
Sungyang JoDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
Huseyin Enes CandanGraduate School of Health Science and Technology, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
Abel Worku TessemaDepartment of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
Nepes MyratgeldiyevDepartment of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
Chong Hyun SuhDepartment of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, 05505, Republic of Korea.
Jihong RyuDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
Sun Ju ChungDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
Hansol LeeDepartment of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
Jae-Hyeok LeeDepartment of Neurology, Pusan National University School of Medicine, Pusan National University Yangsan Hospital, Yangsan, 50612, Republic of Korea.
Eun-Jae LeeDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
HyungJoon ChoDepartment of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.ORCID https://orcid.org/0000-0001-5380-7674

Funding

NCIRD CDC HHS U01 IP000110
6 · The paper itself

Abstract

Background: Choroid plexus (ChP) has gained attention as a potential biomarker in neurodegenerative diseases, yet its segmentation remains challenging. Gadolinium-based contrast-enhanced MRI (CE-MRI) is the reference standard, as non-contrast MRI images lack sufficient contrast. However, gadolinium deposition, risk of nephrogenic system fibrosis in renally impaired patients, and patient discomfort limit its repeated administration. Purpose: To develop deep learning-based synthetic-contrast-enhanced MRI (SynCE-MRI) using T1-weighted images to improve ChP visualization and evaluate its ability to detect morphological changes in Parkinsonian syndromes. Materials and methods: This retrospective study included 265 (mean age = 65.7 ± 7.00 years, males/females: 120/145) consecutive patients in the internal cohort (174 with Parkinson's disease [PD], 46 with essential tremor, and 45 with atypical Parkinsonian disorder [APD]) who underwent T1W and CE-MRI at 3T from Asan Medical Center (June 2021-December 2023), and an external cohort of 58 (mean age = 60.7 ± 7.8 years, males/females: 40/18) patients (29/29 PD/APD) from Pusan National University, Yangsan Hospital (April 2011-December 2014). Nested-UNet was used for SynCE-MRI synthesis from T1W images. The 3D-UNet ChP segmentation model was trained by CE-MRI and tested using SynCE-MRI. Kruskal-Wallis and Bonferroni-corrected Mann-Whitney Results: SynCE-MRI achieved high-fidelity images with peak signal-to-noise ratio (PSNR) 35.37 ± 1.32 and structural similarity index measure (SSIM) 0.970 ±0.0054. Segmentation accuracy for SynCE-MRI (dice score = 0.803 ± 0.029, 95% CI: 0.797-0.810) significantly outperformed manual (dice score = 0.59 ± 0.057, 95% CI: 0.578-0.603; Conclusion: SynCE-MRI accurately replicates CE-MRI for ChP imaging and morphometry, outperforms T1W imaging in segmentation, and detects ChP enlargement in APD versus PD across internal and external cohorts, consistent with CE-MRI findings.

Indexed as

choroid plexus morphometrycontrast-enhanced MRIdeep learningParkinsonian syndromessynthetic contrast-enhanced MRIvolumetric analysis

Identifiers

PMID41446781
PMCPMC12724087

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.