Synthetic MRI of Preterm Infants at Term-Equivalent Age: Evaluation of Diagnostic Image Quality and Automated Brain Volume Segmentation

Vanderhasselt, Tim; Naeyaert, Maarten; Watté, Nina; Allemeersch, Gert‐Jan; Raeymaeckers, Steven; Dudink, Jeroen; Mey, Johan De; Raeymaekers, Hubert

doi:10.3174/ajnr.a6533

Cited by 18 publications

(12 citation statements)

References 24 publications

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“…The accuracy and repeatability of quantitative measurements of relaxation time and PD value of brain tissue by SyMRI have been confirmed in previous studies (Krauss et al, 2015;Vanderhasselt et al, 2021;Vanderhasselt et al, 2020).…”

Section: Discussionsupporting

confidence: 73%

Gestational age‐related changes in relaxation times of neonatal brain by quantitative synthetic magnetic resonance imaging

Dong

Deng²,

Xie

et al. 2023

Brain and Behavior

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Objective: This study aimed to explore the correlation between T1 and T2 relaxation times of synthetic MRI (SyMRI) and gestational age (GA) in each hemisphere of preterm and term newborns at the initial 28 days of birth. Methods: Seventy preterm and full-term infants were prospectively included in this study. All subjects completed 3.0 T routine MRI and SyMRI (MAGiC) one-stop scanning within 28 days of birth (aged 34-42 W at examination). The SyMRI postprocessing software (v8.0.4) was used to measure the T1 and T2 relaxation values of each brain region. The linear regression equations of quantitative relaxation values with GA were established to compare the variation speed in each brain region. Results: A significant linear and negative correlation was found between relaxation times and GA in the neonate cerebral cortex and subcortical gray and white matter regions (All p<.05). The relaxation time of the left centrum semiovale decreased with maximum variance with increasing GA among all white matter regions (T1: b = -51.45, β = -0.65, p < .0001; T2: b = -8.77, β = -0.71, p < .0001), whereas the right posterior limb of internal capsule showed minimal variance (T1: b = -27.94, β = -0.60, p < .0001; T2: b = -3.25, β = -0.68, p < .0001). Among all gray matter regions, the right globus pallidus and thalamus indicated the most significant decreasing degree of T1 and T2 relaxation values with GA (right globus pallidus T1: b = -33.14, β = -0.64, p < .0001; right thalamus T2: b = -3.94, β = -0.81, p < .0001), and the right and left occipital lobes indicated the least significant decreasing degree of T1 and T2 relaxation values with GA, respectively (right occipital lobes T1: b = -11.18, β = -0.26, p = .028; left occipital lobes T2: b = -1.22, β = -0.27, p = .024).Conclusions: SyMRI could quantitatively evaluate the linear changes of T1 and T2 relaxation values with GA in brain gray and white matter of preterm and term neonates.

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Section: Discussionsupporting

confidence: 73%

Gestational age‐related changes in relaxation times of neonatal brain by quantitative synthetic magnetic resonance imaging

Dong

Deng²,

Xie

et al. 2023

Brain and Behavior

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“…We used the default settings of TR, TE, and TI to create synthetic contrast-weighted images, except for T1WI (Online Supplemental Data). 19 All anonymized images were independently reviewed on the PACS workstation by 2 radiologists (S.M.L., and H.H.C.) with 10 years of pediatric radiology experience.…”

Section: Image Quality and Lesion Detectabilitymentioning

confidence: 99%

Accelerated Synthetic MRI with Deep Learning–Based Reconstruction for Pediatric Neuroimaging

Kim

Cho

et al. 2022

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Synthetic MR imaging is a time-efficient technique. However, its rather long scan time can be challenging for children. This study aimed to evaluate the clinical feasibility of accelerated synthetic MR imaging with deep learningbased reconstruction in pediatric neuroimaging and to investigate the impact of deep learning-based reconstruction on image quality and quantitative values in synthetic MR imaging. MATERIALS AND METHODS:This study included 47 children 2.3-14.7 years of age who underwent both standard and accelerated synthetic MR imaging at 3T. The accelerated synthetic MR imaging was reconstructed using a deep learning pipeline. The image quality, lesion detectability, tissue values, and brain volumetry were compared among accelerated deep learning and accelerated and standard synthetic data sets. RESULTS:The use of deep learning-based reconstruction in the accelerated synthetic scans significantly improved image quality for all contrast weightings (P , .001), resulting in image quality comparable with or superior to that of standard scans. There was no significant difference in lesion detectability between the accelerated deep learning and standard scans (P . .05). The tissue values and brain tissue volumes obtained with accelerated deep learning and the other 2 scans showed excellent agreement and a strong linear relationship (all, R 2 . 0.9). The difference in quantitative values of accelerated scans versus accelerated deep learning scans was very small (tissue values, ,0.5%; volumetry, À1.46%-0.83%). CONCLUSIONS:The use of deep learning-based reconstruction in synthetic MR imaging can reduce scan time by 42% while maintaining image quality and lesion detectability and providing consistent quantitative values. The accelerated deep learning synthetic MR imaging can replace standard synthetic MR imaging in both contrast-weighted and quantitative imaging.

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“…However, the amount of misclassified voxels without enhancement as T1e* makes the voxel-wise distinction of the T1w-enhancement difficult, since it could easily lead to false positive detection of T1w-enhancement. Although it is possible that it could reflect some leakage in the blood brain barrier not appreciable in conventional images 22 , 23 , also it could be that only applying a threshold to the pre-contrast quantitative images has moderate ability to detect T1w-enhancement, as it was previously published for Multiple Sclerosis lesions 27 . Further investigation could depict more insights regarding the physiological process underlying the leakage in the blood brain barrier.…”

Section: Discussionmentioning

confidence: 95%

Pre-contrast MAGiC in treated gliomas: a pilot study of quantitative MRI

Nunez-Gonzalez

Garderen

Smits

et al. 2022

Sci Rep

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Quantitative MR imaging is becoming more feasible to be used in clinical work since new approaches have been proposed in order to substantially accelerate the acquisition and due to the possibility of synthetically deriving weighted images from the parametric maps. However, their applicability has to be thoroughly validated in order to be included in clinical practice. In this pilot study, we acquired Magnetic Resonance Image Compilation scans to obtain T1, T2 and PD maps in 14 glioma patients. Abnormal tissue was segmented based on conventional images and using a deep learning segmentation technique to define regions of interest (ROIs). The quantitative T1, T2 and PD values inside ROIs were analyzed using the mean, the standard deviation, the skewness and the kurtosis and compared to the quantitative T1, T2 and PD values found in normal white matter. We found significant differences in pre-contrast T1 and T2 values between abnormal tissue and healthy tissue, as well as between T1w-enhancing and non-enhancing regions. ROC analysis was used to evaluate the potential of quantitative T1 and T2 values for voxel-wise classification of abnormal/normal tissue (AUC = 0.95) and of T1w enhancement/non-enhancement (AUC = 0.85). A cross-validated ROC analysis found high sensitivity (73%) and specificity (73%) with AUCs up to 0.68 on the a priori distinction between abnormal tissue with and without T1w-enhancement. These results suggest that normal tissue, abnormal tissue, and tissue with T1w-enhancement are distinguishable by their pre-contrast quantitative values but further investigation is needed.

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Synthetic MRI of Preterm Infants at Term-Equivalent Age: Evaluation of Diagnostic Image Quality and Automated Brain Volume Segmentation

Cited by 18 publications

References 24 publications

Gestational age‐related changes in relaxation times of neonatal brain by quantitative synthetic magnetic resonance imaging

Gestational age‐related changes in relaxation times of neonatal brain by quantitative synthetic magnetic resonance imaging

Accelerated Synthetic MRI with Deep Learning–Based Reconstruction for Pediatric Neuroimaging

Pre-contrast MAGiC in treated gliomas: a pilot study of quantitative MRI

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