ArticleNeuroradiology2019
Evaluation of 3D fat-navigator based retrospective motion correction in the clinical setting of patients with brain tumors.
Article in Neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The Quantification of Radiation Fibrosis Using Clinically Indicated Magnetic Resonance Imaging for Head and Neck Cancer Patients.Dysphagia · 2024Article
- Analysis of the effect of motion on highly accelerated 3D FatNavs in 3D brain images acquired at 3T.PloS one · 2024Article
- Accuracy investigations for volumetric head-motion navigators with and without EPI at 7 T.Magnetic resonance in medicine · 2022Article
- Tracking of rigid head motion during MRI using an EEG system.Magnetic resonance in medicine · 2022Article
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5 authors.
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Abstract
purposeA 3D fat-navigator (3D FatNavs)-based retrospective motion correction is an elegant approach to correct for motion as it requires no additional hardware and can be acquired during existing 'dead-time' within common 3D protocols. The purpose of this study was to clinically evaluate 3D FatNavs in the work-up of brain tumors.
methodsAn MRI-based fat-excitation motion navigator incorporated into a standard MPRAGE sequence was acquired in 40 consecutive patients with (or with suspected) brain tumors, pre and post-Gadolinium injection. Each case was categorized into key anatomical landmarks, the temporal lobes, the infra-tentorial region, the basal ganglia, the bifurcations of the middle cerebral artery, and the A2 segment of the anterior cerebral artery. First, the severity of motion in the non-corrected MPRAGE was assessed for each landmark, using a 5-point score from 0 (no artifacts) to 4 (non-diagnostic). Second, the improvement in image quality in each pair and for each landmark was assessed blindly using a 4-point score from 0 (identical) to 3 (strong correction).
resultsThe mean image improvement score throughout the datasets was 0.54. Uncorrected cases with light and no artifacts displayed scores of 0.50 and 0.13, respectively, while cases with moderate artifacts, severe artifacts, and non-diagnostic image quality revealed a mean score of 1.17, 2.25, and 1.38, respectively.
conclusionFat-navigator-based retrospective motion correction significantly improved MPRAGE image quality in restless patients during MRI acquisition. There was no loss of image quality in patients with little or no motion, and improvements were consistent in patients who moved more.
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