The Evaluation of Basal Ganglia in Pediatric Patients with Cerebral Palsy Using Magnetic Resonance Histogram Analysis

Year 2024, Volume: 11 Issue: 2, 97 - 101, 31.08.2024

Ferit Doğan , Mehmet Demir , Hüseyin Gümüş , Celil Yılmaz

https://doi.org/10.47572/muskutd.1440247

Abstract

The aim of this study is to detect changes in the basal ganglia using magnetic resonance imaging (MRI) histogram in patients with cerebral palsy (CP) who do not have pathological signal changes in the basal ganglia on conventional MRI images. A retrospective evaluation was made of the images of 40 children with CP and 60 children with no significant intracranial findings on brain MRI examination. The histogram parameters of mean, variance, skewness, kurtosis, 1st percentile (P), 10th P, 50th P, 90th P and 99th P were calculated for each patient and control group on the areas identified in the head of the thalamus, lentiform nucleus and nucleus caudatus and these were evaluated separately for each case. A significant difference was found between the groups in terms of the mean, kurtosis and 50th P values of histogram parameters obtained from the thalamus (p=0.001, p=0.002, p=0.025, respectively). A significant difference was found between the mean, skewness, kurtosis and 1st P values of histogram parameters obtained from the lentiform nuclei (p=0.021, p=0.005, p=0.015, p=0.035, respectively). A significant difference was found between the mean, kurtosis, 90th P and 99th P values of the histogram parameters obtained from the head section of the nucleus caudatus (p=0.002, p=0.03, p=0.004, p=0.042, respectively). Texture analysis can produce objective features that may indicate differences in the basal ganglia and thalamus in patients with CP. Texture analysis can identify changes in the basal ganglia in patients with CP who do not have pathological signal changes on conventional MRI images.

Keywords

Basal ganglia, Texture analysis, Cerebral palsy, Thalamus

Serebral Palsili Pediatrik Hastalarda Bazal Ganglionların Manyetik Rezonans Histogram Analizi Kullanılarak Değerlendirilmesi

Abstract

Konvansiyonel manyetik rezonans görüntüleme (MRG) görüntülerinde bazal ganglionlarda patolojik sinyal değişiklikleri olmayan serebral palsili (SP) hastalarda MRG histogramını kullanarak bazal ganglionlardaki değişiklikleri tespit etmeyi amaçladık. Serebral palsili 40 çocuk ve beyin MRG incelemesinde anlamlı intrakraniyal bulgusu olmayan 60 çocuğun görüntüleri retrospektif olarak değerlendirildi. Ortalama, varyans, çarpıklık, basıklık, 1. yüzdelik (P), 10. P, 50. P, 90. P ve 99. P histogram parametreleri her hasta ve kontrol grubu için talamus, lentiform ve kaudat nukleuslardan tanımlanan alanlarda hesaplandı ve her vaka için ayrı ayrı değerlendirildi. Talamustan elde edilen histogram parametrelerinin ortalama, basıklık ve 50. P değerleri açısından gruplar arasında anlamlı fark bulundu (sırasıyla p=0.001, p=0.002, p=0.025). Lentiform çekirdeklerden elde edilen histogram parametrelerinin ortalama, çarpıklık, basıklık ve 1. P değerleri arasında anlamlı bir fark bulunmuştur (sırasıyla p=0.021, p=0.005, p=0.015, p=0.035). Nukleus kaudatustan elde edilen histogram parametrelerinin ortalama, basıklık, 90. P ve 99. P değerleri arasında anlamlı farklılık tespit edildi (sırasıyla p=0.002, p=0.03, p=0.004, p=0.042). Doku analizi, serebral palsili hastalarda bazal ganglionlar ve talamustaki farklılıkları gösterebilecek objektif özellikler üretebilir. Doku analizi, konvansiyonel MRG görüntülerinde patolojik sinyal değişikliği olmayan SP'li hastalarda bazal ganglionlardaki değişiklikleri tanımlayabilir.

Keywords

Bazal Ganglionlar, Doku Analizi, Serebral Palsi, Talamus

Ethical Statement

Çalışma için etik kurul onamı alınmıştır

Supporting Institution

yok

References

• 1. Grether JK, Nelson KB, Emery ES, et al. Prenatal and perinatal factors and cerebral palsy in very low birth weight infants. J Pediatr. 1996;128(3):407–14.
• 2. Blair E, Watson L. Epidemiology of cerebral palsy. Semin Fetal Neonatal Med. 2006;11(2):117–25.
• 3. Te Velde A, Morgan C, Novak I, et al. Early diagnosis and classification of cerebral palsy: an historical perspective and barriers to an early diagnosis. J Clin Med. 2019;8(10):1599.
• 4. Bax M, Goldstein M, Rosenbaum P, et al. Proposed definition and classification of cerebral palsy, April 2005. Dev Med Child Neurol. 2005;47(8):571–6.
• 5. McGuire DO, Tian LH, Yeargin-Allsopp M, et al. Prevalence of cerebral palsy, intellectual disability, hearing loss, and blindness, National Health Interview Survey, 2009–2016. Disabil Health J. 2019;12(3):443–51.
• 6. Reid SM, Dagia CD, Ditchfield MR, et al. Population‐based studies of brain imaging patterns in cerebral palsy. Dev Med Child Neurol. 2014;56(3):222–32.
• 7. Kuenzle C, Baenziger O, Martin E, et al. Prognostic value of early mr imaging in term infants with severe perinatal asphyxia. Neuropediatrics. 1994;25(4):191–200.
• 8. Ashwal S, Russman BS, Blasco PA, et al. Practice parameter: diagnostic assessment of the child with cerebral palsy: report of the Quality Standards Subcommittee of the American Academy of Neurology and the Practice Committee of the Child Neurology Society. Neurology. 2004;62(6):851–63.
• 9. Himmelmann K, Horber V, De La Cruz J, et al. MRI classification system (MRICS) for children with cerebral palsy: development, reliability, and recommendations. Dev Med Child Neurol. 2017;59(1):57–64.
• 10. Novak I, Morgan C, Adde L, et al. Early, accurate diagnosis and early intervention in cerebral palsy: advances in diagnosis and treatment. JAMA Pediatr. 2017;171(9):897–907.
• 11. Krägeloh-Mann I, Horber V. The role of magnetic resonance imaging in elucidating the pathogenesis of cerebral palsy: A systematic review. Dev Med Child Neurol. 2007;49(2):144–51.
• 12. Castellano G, Bonilha L, Li LM, et al. Texture analysis of medical images. Clin Radiol. 2004;59(12):1061–9.
• 13. Baykara M, Koca TT, Demirel A, et al. Magnetic resonance imaging evaluation of the median nerve using histogram analysis in carpal tunnel syndrome. Neurol Sci Neurophysiol. 2018;35(3):145–50.
• 14. Ganeshan B, Miles KA, Young RCD, et al. Hepatic entropy and uniformity: additional parameters that can potentially increase the effectiveness of contrast enhancement during abdominal CT. Clin Radiol. 2007;62(8):761–8.
• 15. Sarioglu FC, Sarioglu O, Guleryuz H, et al. The role of MRI-based texture analysis to predict the severity of brain injury in neonates with perinatal asphyxia. Br J Radiol. 2022;95(1132):20210128.
• 16. Suoranta S, Holli-Helenius K, Koskenkorva P, et al. 3D texture analysis reveals imperceptible mri textural alterations in the thalamus and putamen in progressive myoclonic epilepsy type 1, EPM1. Reddy H, editor. PLoS One. 2013;8(7):e69905.
• 17. Wang R, Xı Y, Xu H, et al. The texture analysis of MRI diffusion-weighted imaging for predicting prognosis of neonatal hypoglycemic encephalopathy. Chinese J Gen Pract. 2022;6:367–75.
• 18. Valdés Hernández MDC, González-Castro V, Chappell FM, et al. Application of texture analysis to study small vessel disease and blood–brain barrier integrity. Front Neurol. 2017;8:327.
• 19. Baykara M, Baykara S, Atmaca M. Magnetic resonance imaging histogram analysis of amygdala in functional neurological disorder: Histogram Analysis of Amygdala in Functional Neurological Disorder. Psychiatry Res Neuroimaging. 2022;323:111487.
• 20. Johns SLM, Ishaque A, Khan M, et al. Quantifying changes on susceptibility weighted images in amyotrophic lateral sclerosis using MRI texture analysis. Amyotroph Lateral Scler Front Degener. 2019;20(5–6):396–403.
• 21. Bhattacharya D, Vengalil SK, Sinha N, et al. Structural MRI based texture analysis of corpus callosum in patients with Progressive Supraneuclear Palsy. In: TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). Kochi, India: IEEE; 2019. p. 441–6.