ArticleAJNR. American journal of neuroradiology2018
Improved Detection of New MS Lesions during Follow-Up Using an Automated MR Coregistration-Fusion Method.
Article in AJNR. American journal of neuroradiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Limited added value of systematic spinal cord MRI vs brain MRI alone to classify patients with MS as active or inactive during follow-up.Journal of neurology · 2025Article
- Revolutionizing MS Monitoring: The Impact of Postprocessing Techniques on Lesion Detection.AJNR. American journal of neuroradiology · 2023Article
- Evaluation of the Statistical Detection of Change Algorithm for Screening Patients with MS with New Lesion Activity on Longitudinal Brain MRI.AJNR. American journal of neuroradiology · 2023Article
- Automated Registration and Color Labeling of Serial 3D Double Inversion Recovery MR Imaging for Detection of Lesion Progression in Multiple Sclerosis.Journal of digital imaging · 2023Article
- Automated Color-Coding of Lesion Changes in Contrast-Enhanced 3D T1-Weighted Sequences for MRI Follow-up of Brain Metastases.AJNR. American journal of neuroradiology · 2022Article
- Health Economic Impact of Software-Assisted Brain MRI on Therapeutic Decision-Making and Outcomes of Relapsing-Remitting Multiple Sclerosis Patients-A Microsimulation Study.Brain sciences · 2021Article
- A Clinically-Compatible Workflow for Computer-Aided Assessment of Brain Disease Activity in Multiple Sclerosis Patients.Frontiers in medicine · 2021Article
- Article
- Utility of image fusion software in identifying shunt malfunction.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2020Article
- Follow-up MRI in multiple sclerosis patients: automated co-registration and lesion color-coding improves diagnostic accuracy and reduces reading time.European radiology · 2019Article
- PACS Integration of Semiautomated Imaging Software Improves Day-to-Day MS Disease Activity Detection.AJNR. American journal of neuroradiology · 2019Article
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Authors and funding
11 authors.
Funding
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Abstract
background and purposeMR imaging is the key examination in the follow-up of patients with MS, by identification of new high-signal T2 brain lesions. However, identifying new lesions when scrolling through 2 follow-up MR images can be difficult and time-consuming. Our aim was to compare an automated coregistration-fusion reading approach with the standard approach by identifying new high-signal T2 brain lesions in patients with multiple sclerosis during follow-up MR imaging. MATERIALS AND
methodsThis prospective monocenter study included 94 patients (mean age, 38.9 years) treated for MS with dimethyl fumarate from January 2014 to August 2016. One senior neuroradiologist and 1 junior radiologist checked for new high-signal T2 brain lesions, independently analyzing blinded image datasets with automated coregistration-fusion or the standard scroll-through approach with a 3-week delay between the 2 readings. A consensus reading with a second senior neuroradiologist served as a criterion standard for analyses. A Poisson regression and logistic and γ regressions were used to compare the 2 methods. Intra- and interobserver agreement was assessed by the κ coefficient.
resultsThere were significantly more new high-signal T2 lesions per patient detected with the coregistration-fusion method (7 versus 4,
conclusionsOur study showed that an automated coregistration-fusion method was more sensitive for detecting new high-signal T2 lesions in patients with MS and reducing reading time. This method could help to improve follow-up care.
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