scholarly journals Evaluation of DISORDER: retrospective image motion correction for volumetric brain MRI in a pediatric setting

Author(s):  
Katy Vecchiato ◽  
Alexia Egloff ◽  
Olivia Carney ◽  
Ata Siddiqui ◽  
Emer Hughes ◽  
...  

Background and Purpose: Head motion causes image degradation in brain MRI examinations, negatively impacting image quality, especially in pediatric populations. Here, we used a retrospective motion correction technique in children and assessed image quality improvement for 3D MRI acquisitions. Material and Methods: We prospectively acquired brain MRI at 3T using 3D sequences, T1-weighted MPRAGE, T2-weighted Turbo Spin Echo and FLAIR, in 32 unsedated children, including 7 with epilepsy (age range 2-18 years). We implemented a novel motion correction technique: Distributed and Incoherent Sample Orders for Reconstruction Deblurring using Encoding Redundancy (DISORDER). For each subject and modality, we obtained 3 reconstructions: as acquired (Aq), after DISORDER motion correction (Di), and Di with additional outlier rejection (DiOut). We analyzed 288 images quantitatively, measuring 2 objective no-reference image quality metrics: Gradient Entropy (GE) and MPRAGE White Matter Homogeneity (WM-H). As a qualitative metric, we presented blinded and randomized images to 2 expert neuroradiologists who scored them for clinical readability. Results: Both image quality metrics improved after motion correction for all modalities and improvement correlated with the amount of intrascan motion. Neuroradiologists also considered the motion corrected images as of higher quality (Wilcoxon z=-3.164 MPRAGE, z=-2.066 TSE, z=-2.645 FLAIR, for all p<0.05). Conclusions: Retrospective image motion correction with DISORDER increased image quality both from an objective and qualitative perspective. In 75% of sessions, at least one sequence was improved by this approach, indicating the benefit of this technique in un-sedated children for both clinical and research environments.

Author(s):  
Naima Merzougui ◽  
Leila Djerou

Many objective quality metrics for assessing the visual quality of images have been developed during the last decade. A simple way to fine tune the efficiency of assessment is through permutation and combination of these metrics. The goal of this fusion approach is to take advantage of the metrics utilized and minimize the influence of their drawbacks. In this paper, a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets using a combination of objective scores of a set of image quality metrics (IQM). By learning from image datasets, the MGGP algorithm can determine appropriate image quality metrics, from 21 metrics utilized, whose objective scores employed as predictors in the symbolic regression model, by optimizing simultaneously two competing objectives of model ‘goodness of fit’ to data and model ‘complexity’. Six large image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) that are available in public domain are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.


2014 ◽  
Author(s):  
Lukáš Krasula ◽  
Karel Fliegel ◽  
Patrick Le Callet ◽  
Miloš Klíma

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