Volume quantification of normal brain tissues based on the magnetic resonance T1 images

doi:10.16098/j.issn.0529-1356.2019.06.014

Abstract

Abstract:

Objective By manual and automatic segmentation, the total volume, cerebrospinal fluid volume and white matter volume of normal human brain were obtained, and the differences between different genders and the patterns of changes with age were analyzed. At the same time, we give the software and parameters that can get better result. Methods The rough brain mask was obtained by software automatic segmentation and it would be refined manually. Then the brain masks would be divided into gray matter, white matter and cerebrospinal fluid by FSL-FAST, and finally statistical analysis was performed with SPSS 25.0 software.
Results The total brain volume of normal Chinese adults was about 1263.24 ml, and the total brain volume of males (1313.84 ml) was lager than the total volume of female brain (1173.11 ml). The difference was statistically significant. There were significant differences in total volume, gray matter, white matter and cerebrospinal fluid volume among different age groups. And there was no correlation between the age and the ratio of gray matter white matter. The total brain volume, gray matter, and white matter volume of males were larger than those of females, and as the age increases, the decline trend of these three sets of data is similar. The changes of total brain volume, gray matter volume and white matter volume with age were also similar in females. They reached their maximum at about 50 years of age, and then the volume gradually decreased. Conclusion The study on the changes of brain volume and the volume of gray matter, white matter and cerebrospinal fluid with age in different sexes can be used as a basis for identifying the changes of brain volume caused by diseases or the age.

Key words: Brain volume, Grey matter, White matter, Volume measurement, Human

WANG Shi-qing SHI Yong-hong ZHUANG Han QIN Chun-li LI Wen-sheng. Volume quantification of normal brain tissues based on the magnetic resonance T1 images[J]. Acta Anatomica Sinica, 2019, 50(6): 780-785.

References

［1］ Zeng QSh, Li ChF, Liu ZQ, et al. Quantitative analysis of brain volume by in vivo magnetic resonance imaging in normal adults［J］. Acta Academiae Medicinae Sinicae, 2006, 28(6): 795-798.(in Chinese)

曾庆师, 李传福, 刘尊齐, 等. 正常成年人脑体积随年龄变化的磁共振成像定量分析［J］. 中国医学科学院学报, 2006, 28(6): 795-798.

［2］ Choi SM, Kim BC, Chang J, et al. Comparison of the Brain Volume in Essential Tremor and Parkinson's Disease Tremor Using an Automated Segmentation Method ［J］. Eur Neurol, 2015, 73(5-6): 303-309.

［3］ Chen JP, He GJ. A quantitative measurement of MR volumes in Alzheimer’s disease and vascular dementia［J］. Chinese Journal of Neuromedicine, 2006, 5(11): 1132-1134. (in Chinese)

陈俊抛, 何国军. 阿尔茨海默病和血管性痴呆的颅脑核磁共振定量研究［J］. 中华神经医学杂志, 2006, 5(11): 1132-1134.

［4］ Reite M, Reite E, Collins D, et al. Brain size and brain/intracranial volume ratio in major mental illness ［J］. BMC Psychiatry, 2010, 10(1): 79.

［5］ Moore MT, Nathan D, Elliott AR, et al. Encephalographic Studies in Mental Disease. An Analysis of 152 Cases ［J］. Am J Psychiatry, 1937,92(1): 43-67.

［6］ Hedman AM, van Haren NE, Schnack HG, et al. Human brain changes across the life span: a review of 56 longitudinal magnetic resonance imaging studies ［J］. Hum Brain Map, 2012, 33(8): 1987-2002.

［7］ Rightmire GR. Brain size and encephalization in early to Mid-Pleistocene Homo ［J］. Am J Phys Anthropol, 2010, 124(2): 109-123.

［8］ Ge Y, Grossman RI, Babb JS, et al. Age-related total gray matter and white matter changes in normal adult brain. Part Ⅰ: volumetric MR imaging analysis ［J］. AJNR Am J Neuroradiol, 2002, 23(8): 1327-1333.

［9］ Shattuck DW, Mirza M, Adisetiyo Ⅴ, et al. Construction of a 3D probabilistic atlas of human cortical structures ［J］. Neuroimage, 2008, 39(3): 1064-1080.

［10］ Li X, Morgan PS, Ashburner J, et al. The first step for neuroimaging data analysis: DICOM to NIfTI conversion ［J］. J Neurosci Methods, 2016, 264:47-56.

［11］ Somasundaram K, Kalaiselvi T. Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images ［J］. Comput Biol Med, 2010, 40(10): 811-822.

［12］ Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D SLICER as an image computing platform for the quantitative imaging network ［J］. Magn Reson Imaging, 2012, 30(9): 1323-1341.

［13］ Yushkevich PA, Piven J, Hazlett HC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability ［J］. Neuroimage, 2006, 31(3): 1116-1128.

［14］ Woolrich MW, Jbabdi S, Patenaude B, et al. Bayesian analysis of neuroimaging data in FSL ［J］. Neuroimage, 2009, 451(Suppl 1): S173-186.

［15］ Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL ［J］. Neuroimage, 2004, 23(Suppl 1): S208-219.

［16］ Jenkinson M, Beckmann CF, Behrens TE, et al. Fsl ［J］. Neuroimage, 2012, 62(2): 782-790.

［17］ Shattuck DW, Leahy RM. BrainSuite: an automated cortical surface identification tool ［J］. Med Image Anal, 2002, 6(2): 129-142.

［18］ Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. Ⅰ. Segmentation and surface reconstruction ［J］. Neuroimage, 1999, 9(2): 179-194.

［19］ Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis Ⅱ: Inflation, flattening, and a surface-based coordinate system ［J］. NeuroImage, 1999, 9（2）: 195-207.

［20］ Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm ［J］. IEEE Trans Med Imaging, 2001, 20(1): 45-57.

［21］ Liu GX，Shi YH，Wang ShQ，et al. Differential analysis of brain structure volume associated with Parkinson’s disease based on nuclear magnetic resonance T1 imaging［J］.Acta Anatomica Sinica, 2018, 49(3):78-85. (in Chinese)

刘桂雪, 史勇红, 王士卿, 等. 基于磁共振T1成像的帕金森病相关脑结构体积差异性分析［J］. 解剖学报, 2018,49(3):78-85.

［22］ Xu J, Kobayashi S, Yamaguchi S, et al. Gender effects on age-related changes in brain structure ［J］. AJNR Am J Neuroradiol, 2000, 21(1): 112-118.

［23］ Cui CX，Zhou CH，Cui HX，et al.Volume measurement of brain using 3.0T MRI in 140 normal Chinese and data analysis［J］. Journal of Brain and Nervous Diseases, 2014, (4): 281-283. (in Chinese)

崔彩霞, 周存河, 崔慧先, 等. 采用3.0T MRI 对正常人脑体积定量测量研究（附140例分析）［J］. 脑与神经疾病杂志, 2014, (4): 281-283.

［24］ Ou ShN，Luo YN，Ye ChQ,et al. MRI quantitative analysis of relation between brain white matter lesion and brain volume［J］. Chinese Journal of Geriatric Heart Brain and Vessel Diseases, 2013, 15(8): 831-834. (in Chinese)

欧世宁, 罗彦妮, 叶长青, 等. MRI定量分析脑白质病变程度与脑体积变化关系的研究［J］. 中华老年心脑血管病杂志, 2013, 15(8): 831-834.

［25］ Lü X, Zou L, Song SS, et al. Volumetric measurement of lateral ventricle of Chinese normal adults based on high-resolution MRI［J］. Chinese Journal of Medical Imaging Technology, 2010, 26(9): 1607-1610. (in Chinese)

吕曦, 邹翎, 宋思思, 等. 基于高分辨力MRI的中国正常成人侧脑室的体积测量［J］. 中国医学影像技术, 2010, 26(9): 1607-1610.

[1]	LIU Xiao-li XU Lin WU Ai-xue WEN Cai-yun LI Ru-hua WU An-ting CHEN Dai-qian CHEN Cheng-chun. Analysis of cerebral gray matter structure in multiple sclerosis and neuromyelitis optica [J]. Acta Anatomica Sinica, 2024, 55(1): 17-24.
[2]	BAI He TENG Ye FENG Lei MENG Hai-wei1 TANG Yu-chun LIU Shu-wei. 3D hippocampal segmentation based on spatial and frequency domain features adaptive fusion and inter-class boundary region enhancement [J]. Acta Anatomica Sinica, 2024, 55(1): 73-81.
[3]	YIN Shi-qin YANG Si-yi WANG Rui-han YOU Gui-xuan YANG Ying-qiu ZHANG Lei. Distal tibiofibular syndesmosis fibular notch typing and its clinical significance based on CT [J]. Acta Anatomica Sinica, 2024, 55(1): 82-87.
[4]	YANG Yang SHI Jun LI Kun MA Yuan ZHANG Shao-jie HOU Er-fei WANG Chao-qun CHEN Jie WANG Xing LI Zhi-jun. Finite element analysis of cervical intervertebral discs after removing different ranges of uncinate processes [J]. Acta Anatomica Sinica, 2024, 55(1): 88-97.
[5]	SUN Lei WANG Xing-yu XIE Shui-hua. Risk analysis of re-fracture after percutaneous kyphoplasty in elderly patients with osteoporotic thoracolumbar compression fractures and construction of acolumnar graph prediction model#br# [J]. Acta Anatomica Sinica, 2024, 55(1): 98-104.
[6]	LIU Li-ping ZHU Meng-ni XU Hui . Effect of interleukin-6 on nucleated erythrocytes in lipopolysaccharide induced preeclampsia rats [J]. Acta Anatomica Sinica, 2023, 54(6): 722-729.
[7]	ZHAO Wei-ming LI Xiao YU Guo-ying WANG Gai-ping CHANG Cui-fang . Effect of serine protease inhibitor Kazaltype 1 on the proliferation of hepatocellular carcinoma cell and its mechanism [J]. Acta Anatomica Sinica, 2023, 54(6): 695-702.
[8]	HAN Yi CAI Li FENG Xiao . Risk analysis of bone cement leakage after percutaneous puncture vertebroplasty for osteoporotic spinal compression fractures and construction of a predictive model with column line drawings [J]. Acta Anatomica Sinica, 2023, 54(6): 710-715.
[9]	DU Da-peng SUN Yu ZHANG Min WANG Han-qin . Shear stress regulateing the phosphorylation of endothelial nitric oxide synthase Ser633 and Ser1177 in human umbilical vein endothelial cells through Pim1/Akt pathway [J]. Acta Anatomica Sinica, 2023, 54(5): 546-552.
[10]	ZHOU Chun-hui XIE Sheng-qiang WANG Jiang-ting LAN Xiao-juan ZHANG Jian-ning ZHAO Hu-lin . Application of multimodal electromagnetic navigation in endoscopic endonasal skull base anatomical measurement of fresh cadavers [J]. Acta Anatomica Sinica, 2023, 54(5): 560-566.
[11]	LIU Yan-jing SHI Yong-hong . Automatic extraction of point cloud on cartilage surface of intraoperative knee using FPFH-PointNet [J]. Acta Anatomica Sinica, 2023, 54(5): 553-559.
[12]	MI Si-rong LIU Guang-xing ZHANG Zhen-wu YU Peng-hui JU Xiao-jun RAO Li-bing LI Li . Accuracy of 3D printing skull under different CT layer thickness [J]. Acta Anatomica Sinica, 2023, 54(5): 575-581.
[13]	ZHU Qiu-liang YU Xiang-ping MA Jun CHEN Yun-yun LIN Fang RUAN Wen-bin . Adjustment of X-ray angle intraoperation based on the anatomic shape of femoral neck section [J]. Acta Anatomica Sinica, 2023, 54(5): 586-592.
[14]	LI Xiao-jun ZHANG Ya-min. Screening ferroptosis related genes influencing prognosis of colon cancer through bioinformatics analysis [J]. Acta Anatomica Sinica, 2023, 54(4): 445-452.
[15]	LIU Hai-lan MEI Yi-xuan ZHANG Yi LIU Cheng LIU Shu-wei. Classification and clinical significance of the branching patterns of the right pulmonary arteries based on the intravital CT data [J]. Acta Anatomica Sinica, 2023, 54(4): 453-459.