Validation and feasibility of a deep learning-based reconstruction technology in 5.0 tesla knee joint MR imaging

The DLR technique could improve 5.0 Tesla knee MR images' quality and obtain as least equal diagnostic efficiency without extra scan time, demonstrating its potential clinical applicability.

Authors

Institutions

Publication Details

Journal
Frontiers in Radiology
Published
2026-02-19
DOI
https://doi.org/10.3389/fradi.2026.1776035
Primary Topic
Knee injuries and reconstruction techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Validation and feasibility of a deep learning-based reconstruction technology in 5.0 tesla knee joint MR imaging

Zhigang Li, Lixin Du, Chang Liu, Jing Yang et al.
Frontiers in Radiology
Knee injuries and reconstruction techniques
article

Validation and feasibility of a deep learning-based reconstruction technology in 5.0 tesla knee joint MR imaging

Zhigang Li, Lixin Du, Chang Liu, Jing Yang, Ran Mu, Pan Wang, Chuan Zhu
article en

Abstract

The DLR technique could improve 5.0 Tesla knee MR images' quality and obtain as least equal diagnostic efficiency without extra scan time, demonstrating its potential clinical applicability.

Frontiers in RadiologyVol. 6
Longgang Central Hospital (CN), United Imaging Healthcare (China) (CN)
Quality Education
Openalex Percentile: Top 50%
Knee injuries and reconstruction techniques
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.