Anatomical Atlas Creation Method in Brain Magnetic Resonance Image and Its Contribution to Anatomy Education

Year 2024, , 214 - 238, 30.06.2024

Handan Soysal , Niyazi Acer , Orhan Polat Örs

https://doi.org/10.31832/smj.1469788

Abstract

Objective: The aim was to create a brain atlas by creating 3D visualizations of the structures of the brain tissue in magnetic resonance images and parceling them. It is aimed that the anatomy atlas created for this purpose will contribute to both anatomy education and clinical studies.
Materials and methods: Objective: MriStudio, a fully automatic segmentation software, was used on magnetic resonance images. MriStudio provides fully automatic segmentation of brain images. It consists of three software programs: DTIStudio, DiffeoMap and ROIEditor. Using MriStudio software, the desired brain structure is saved as MASK (separating the image from the background and assigning labels). ROI atlas was applied to obtain ROIs for 189 brain regions. Each brain region image was registered at each slice level and in three dimensions with MricroGL software.
Results: The volume values of three methods (manual, MriStudio and Ibaspm) were obtained on brain magnetic resonance images. By accepting manual measurements as the gold standard, it was observed that the values obtained with MriStudio were closer to manual measurements. The volume and cross-sectional area of 189 regions of the brain were obtained (Table 1).
Conclusion: It is possible to obtain atlases of anatomically normal individuals using magnetic resonance images. Obtaining the volume values of brain structures with such individual atlases will be a second advantage. In addition, we believe that it will be useful in understanding the brain anatomy and contributing to the diagnosis of clinical problems by showing the structures in the brain cross-sectionally and in 3D.

Keywords

MriStudio, IBASPM, Brain Parcellation, Brain 3D Atlas, MRI

Beyin Manyetik Rezonans Görüntüsünde Anatomik Atlas Oluşturma yöntemi ve Anatomi Eğitimine Katkısı

Abstract

Amaç: Manyetik rezonans (MR) görüntülerinde Beyin yapılarının 3D görsellerinde parselasyon yapılarak beyin atlası oluşturulması amaçlanmıştır. Bu amaçla oluşturulan Anatomi atlasının, hem Anatomi eğitimine hem de klinik çalışmalara katkı sunacağı hedeflenmiştir.
Materyal ve Metod: Amaç: Manyetik rezonans görüntüleri üzerinde tam otomatik parselasyon yazılımı olan MriStudio kullanılmıştır. MriStudio beyin görüntülerinde tam otomatik segmentasyon olanağı sağlar. DTIStudio, DiffeoMap ve ROIEditor olmak üzere üç yazılımdan oluşmaktadır. MriStudio yazılımları kullanılarak istenilen beyin yapısı MASK (görüntüyü arka plandan ayrıştırarak etiketlerinin atanması) olarak kaydedilir. Beyinde bulunan 189 bölgeye ait ROI'leri elde etmek için, ROI atlas uygulanmıştır. Her bir beyin bölgesi görüntüsü MricroGL yazılımı ile her bir kesit seviyesinde ve üç boyutlu olarak kayıt edilmiştir.
Bulgular: Beyin Manyetik rezonans görüntüleri üzerinde kullanılan üç yönteme (manuel, MriStudio ve Ibaspm) ait hacim değerleri elde edilmiştir. Manuel ölçümleri altın standart olarak kabul ederek MriStudio ile elde edilen değerlerin manuel ölçümlere daha yakın olduğu görülmüştür. Beyine ait 189 bölgenin hacmi ve kesit alanı elde edilmiştir (Tablo 1).
Sonuç: Anatomik olarak normal bireylere ait Manyetik rezonans görüntüleri kullanılarak atlas elde etmek mümkündür. Oluşturulan bu tür bireysel atlaslarla beyin yapılarının hacim değerlerinin elde edilmesi de ikinci bir avantaj oluşturacaktır. Ayrıca beyin içindeki yapıların kesitsel ve 3 boyutlu gösterilerek beyin anatomisinin anlaşılmasında ve klinik problemlerde teşhise katkı sunmada yararlı olacağı kanaatindeyiz.

Keywords

MriStudio, IBASPM, Beyin Parselasyonu, Beyin 3D Atlas, MRG

References

• 1. Oyar O. Manyetik Rezonans Görüntüleme (MRG)’nin Klinik Uygulamaları ve Endikasyonları. Harran Üniversitesi Tıp Fakültesi Dergisi 2008; 5(2): 31-40.
• 2. Oyar O, Gülsoy UK. Tıbbi Görüntüleme Fiziği. Tisamat Basım, Ankara, 2003: 254-273.
• 3. Grossman CB. Physical Principles of Computed Tomography and Magnetic Resonance Imaging In: Magnetic Resonance Imaging and Computed Tomography of the Head and Spine. 2nd edition. Grossman CB. Williams&Wilkins, 1996: 10-58.
• 4. Konez O. Manyetik Rezonans Görüntüleme: Temel Bilgiler. Nobel Yayınları, İstanbul, 1995: 154.
• 5. Tuncel E. Klinik Radyoloji. 2.Baskı, Nobel Tip Kitabevi, Bursa: 2002: 25–30.
• 6. Edelman RR, Wielopolski PA. Fast MRI. In: Edelman RR, Hessellink JR. eds. Clinical Magnetic Resonance Imaging. 2nd ed, W.B. Saunders Company, Phileadelphia, 1996: 302.
• 7. Müller NL. Computed Tomography and Magnetic Resonance Imaging: past, present and future. Eur Respir J 2002;19 (35): 3-12.
• 8. Oyar O. Radyolojide Temel Fizik Kavramlar. Nobel Tıp Kitabevleri, İstanbul, 1998: 151-210.
• 9. Bushong SC. Radiologic Science for Technologists. Physics, Biology and Protection. Third edition, C.V. Mosby Company, St Luis, 1984: 387-412.
• 10. Looi JC, Lindberg O, Liberg B., Tayham v., Kumar R., Maller J., et al. Volumetrics of the Caudate Nucleus: Reliability and Validity of a New Manual Tracing Protocol. Psychiatry Res 2008; 163: 279-288. doi: 10.1016/j.pscychresns.2007.07.005.
• 11. Keller SS, Roberts N. Measurement of Brain Volume Using MRI: software, techniques, choices and prerequisites. J Anthropol Sci 2009; 87: 127-151.
• 12. Oishi K, Faria A, Jiang H., Li X., Akhter K., Zhang J., et al. Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer's disease participants. Neuroimage 2009; 46(2): 486-499. doi: 10.1016/j.neuroimage.2009.01.002.
• 13. Faria AV, Joel SE, Zhang Y., Oishi K., Van Zjil PCM., et al. Atlas-based analysis of resting-state functional connectivity: evaluation for reproducibility and multi-modal anatomy-function correlation studies. Neuroimage 2012; 61(3): 613-621. Doi: 10.1016/j.neuroimage.2012.03.078
• 14. Faria AV, Landau B, O'Hearn KM., Lii X., Jiang H., Oishi K., et al. Quantitative analysis of gray and white matter in Williams syndrome. Neuroreport 2012; 28; 23: 283-289. doi: 10.1097/WNR.0b013e3283505b62
• 15. Izbudak I, Acer N, Poretti A, Gümüş K, Zararsiz G. Macrocerebellum:Volumetric and Diffusion Tensor Imaging Analysis. Turkish Neurosurgery 2015; 25: 948-953.
• 16. Faria AV, Hoon A, Stashinko E., Lii X., Jiang H., Mashayekh, et al. Quantitative Analysis of Brain Pathology Based on MRI and Brain Atlases-Applications for Cerebral Palsy. Neuroimage 2011; 54(3): 1854–1861. doi: 10.1016/j.neuroimage.2010.09.061
• 17. Yoshida S, Faria AV, Oishi K., Kanda T., Yamori Y., Yoshida N., et al. Anatomical Characterization of Athetotic and Spastic Cerebral Palsy Using An Atlas-Based Analysis. J Magn Reson Imaging 2013; 38(2): 288-298. doi: 10.1002/jmri.23931
• 18. Fischl B, Van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V,Makris N, Rosen B, Dale AM. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004; 14(1):11-22. https://doi.org/10.1093/cercor/bhg087
• 19. Y. Aleman-Gomez, L. Melie-Garcia, P. Valdes-Hernandez, "IBASPM: Toolbox for automatic parcellation of brain structures," Presented at the 12th Annual Meeting of the Organization for Human Brain Mapping, Florence, Italy, Available on CD-Rom in NeuroImage 2006; 27(1): 11-15.
• 20. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J., Zilles K., et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 2001; 356(1412):1293-322. doi: 10.1098/rstb.2001.0915.
• 21. Talairach, J, Tournoux, P. Co-Planar Stereotactic Atlas of the Human Brain. Thieme, Stuttgart/New York: 1988
• 22. Ceritoglu C, Oishi K, Li X, Chou MC, Younes L, Albert M, et al. Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging. NeuroImage 2009 47(2):618-27. doi: 10.1016/j.neuroimage.2009.04.057.
• 23. Lancaster JL, Tordesillas GD, Martinez M, Salinas F, Evans A, Zilles K. et al. Bias between MNI and Talairach Coordinates analyzed using the ICBM-152 brain template. Hum Brain Mapp 2007;28(11):1194-205. doi: 10.1002/hbm.20345.
• 24. Chau W, Mclntosh AR. The Talairach coordinate of a point in the MNI space: how to interpret it. Neuroimage 2005; 25 (2): 408–416.
• 25. Toga AW, Thomason PM, Mori S, Amunts K, Zilles K. Towards multimodal atlases of the human brain. NeuroImaging 2006; 7: 952–966.
• 26. Frıston K., Ashburner J, Kıebel S., Nıchols T., Penny W. Statistical Parametric Mapping, The Analysis of Functional Brain Images. Academic Press 2007: I-XXXII.
• 27. Evans AC, Janke AL, Collins DL, Baillet S. Brain templates and atlases. NeuroImage 2012; 62(2):911-22. doi: 10.1016/j.neuroimage.2012.01.024.
• 28. Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL. Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 2011;1(54):313–327. doi: 10.1016/j.neuroimage.2010.07.033. Epub 2010 Jul 23.
• 29. Sargolzaei S, Sargolzaei A, Cabrerizo M, Chen G, Goryawala M, Pinzon-Ardila A, Gonzalez-Arias SM, Adjouadi M. Estimating Intracranial Volume in Brain Research: An Evaluation of Methods. Neuroinformatics 2015; 13(4):427-41. doi: 10.1007/s12021-015-9266-5
• 30. Malone IB, Leung KK, Clegg S, Barnes J, Whitwell JL, Ashburner J, Fox NC, Ridgway GR. Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance. Neuroimage 2015; 1(104):366-72. doi: 10.1016/j.neuroimage.2014.09.034
• 31. Ashburner J. A fast diffeomorphic image registration algorithm. NeuroImage 2007; 38: 95–113. doi:10.1016/j.neuroimage.2007.07.007
• 32. Ashburner J., Friston KJ. Voxel-Based Morphometry-The Methods. NeuroImage 2000; 11: 805–821. doi: 10.1006/nimg.2000.0582.
• 33. Focke NK., Yogarajah M., Bonelli SB., Bartlett PA., Symms MR., Duncan JS. Voxel-based diffusion tensor imaging in patients with mesial temporal lobe epilepsy and hippocampal sclerosis NeuroImage 2008; 40: 728–737. doi: 10.1016/j.neuroimage.2007.12.031