Evidence map›Paper›PMID 41081829›Full record

ArticlePediatric radiology2026

Accelerated magnetic resonance imaging of hippocampal sclerosis in pediatric patients with deep learning-based reconstruction: comparison of image quality and diagnostic performance with conventional reconstruction.

Xuehua Peng, Baiqi Zhu, Jianbo Shao

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Pediatric radiology, 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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5 · Who and what money

Authors and funding

3 authors.

Xuehua PengWuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), No. 100 Hong Kong road, Jiang'an District, 430000, Wuhan, Hubei Province, China.
Baiqi ZhuWuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), No. 100 Hong Kong road, Jiang'an District, 430000, Wuhan, Hubei Province, China.
Jianbo ShaoWuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), No. 100 Hong Kong road, Jiang'an District, 430000, Wuhan, Hubei Province, China. shaojianboped@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMagnetic resonance imaging (MRI) plays an important role in the diagnosis and treatment of hippocampal sclerosis. However, this exam presents challenges due to long scan times and image quality variability in pediatric patients.

objectiveThis study aims to compare conventional reconstructed MRI and accelerated sequences with and without deep learning-based reconstruction (DLR) with regard to image quality and diagnostic performance in pediatric hippocampal sclerosis patients. MATERIALS AND

methodsA total of 68 pediatric patients proven or suspected to have temporal lobe epilepsy with hippocampal sclerosis who underwent recommended epilepsy structural MRI were included in this study. MRI examination included standard sequences and accelerated sequences with and without DLR. Standard sequences were reconstructed using the conventional pipeline, while accelerated sequences were reconstructed using both the conventional pipeline and DLR pipeline. Two experienced pediatric radiologists independently evaluated the following parameters of three reconstructed image sets on a 5-point scale: image quality, anatomic structure visibility, motion artifact, truncation artifact, image noise, and detectability of hippocampal abnormalities. Signal-to-noise ratio (SNR) measurements of the hippocampus were performed in all sequences and compared between the three sets of images. Inter-reader agreement and agreement between image sets for detecting hippocampal abnormalities were assessed using Cohen's kappa.

resultsImages reconstructed with DLR received significantly higher scores of overall image quality, presence of lesion, and image noise than with conventional or original accelerated reconstructions (all P<0.05), while there was no statistical difference of artifacts between the three groups (all P>0.05). The SNR for all sequences with DLR was significantly higher than conventional or original reconstructions without DLR (all P<0.001). Inter-reader agreement showed almost perfect agreement (κ=0.803-0.963) of the imaging manifestations, while agreement between image sets showed substantial agreement to almost perfect agreement (κ=0.778-0.965) of the imaging manifestations.

conclusionAccelerated sequences with DLR provide a 44% scan time reduction with similar subjective image quality, artifacts, and diagnostic performance to conventional reconstruction sequences.

Indexed as

Deep LearningEpilepsy, Temporal LobeHippocampusImage Interpretation, Computer-AssistedMagnetic Resonance ImagingAdolescentChildChild, PreschoolFemaleHippocampal SclerosisHumansMaleRetrospective StudiesSclerosisSignal-To-Noise RatioDeep learningHippocampal sclerosisInter-reader agreementMagnetic resonance imagingPediatric

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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.