Biometric analysis of fetal cerebellum. (18th April 2012)

• Record Type: Journal Article
• Authors: Vatansever, D; Kyriakopoulou, V; Sirgiovanni, I; Allsop, JM; Fox, M; Hajnal, JV; Rutherford, MA
• Is Part Of: Archives of disease in childhood. Volume 97(2012)Supplement 1
• Page Start: A9
• Page End: A10
• Publication Date: 2012-04-18
• Subjects: Infants -- Diseases -- Periodicals; Newborn infants -- Diseases -- Periodicals; Fetus -- Diseases -- Periodicals; 618.920105
• DOI: 10.1136/fetalneonatal-2012-301809.27
• Languages: English
• ISSNs: 1359-2998

Abstract

Abstract : Background: Cerebellar malformations are one of the main referral reasons for fetal magnetic resonance imaging, with recent studies suggesting associated impairments in higher cognitive functions. The lack of biometric data, motion artifact and inconsistent classification schemes all hinder the detailed characterisation of cerebellar anomalies, while information about volumetric analysis of normal cerebellar development is limited in the current literature. Objectives: The main objectives of this study were to provide volumetric and linear growth trajectories of normal cerebellar development using post-acquisition, Snapshot Imaging with Volume Reconstruction (SVR) technique, and to assess selected cerebellar malformations. Methods: Using a 1.5T scanner, 88 control (mean 29.76, range 21.71-38.86 weeks of age) and 56 abnormal cerebellum (mean 27.39, range 18.57 – 37.53 weeks of age) MR images were obtained from 133 pregnant patients. The cerebellar abnormalities were grouped according to the severity of diagnosis and additional brain regions involved. Reconstructed 3D image datasets were utilised for measurements. Results: Normal growth trajectories of cerebellar hemispheric, vermis, pons and fourth ventricle volumes depicted significant correlations with linear measurements, and increased in exponential and linear trends across gestation (Pearson r = .93, P < .05). Comparison of normal controls to five abnormal cerebellum subgroups showed significant alterations in volumes of cerebellar structures that could not be detected with linear measurements (MANOVA, P < .05). Conclusion: Reconstructed 3D image datasets are suitable for volumetric measurements and the biometric data provided in this study can be utilised by clinicians and researchers in characterising cerebellar abnormalities.