磁共振场强对脑部T1WI结构图像纹理特征的影响
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摘要
目的比较不同磁共振场强对脑部T1WI纹理特征的影响。资料与方法对30例健康成年人分别在磁共振1.5T(MR-1.5T)及3.0T(MR-3.0T)上行脑部3DT1WI结构像扫描。对3D结构像进行脑部灰质及白质分割,并采用灰度共生矩阵方法进行全脑纹理分析,纹理特征参数包括角二阶矩、对比度、自相关、逆差距及熵。结果 MR-3.0T全脑灰质熵高于MR-1.5T(2.170±0.059比2.163±0.054,F=4.465,P=0.039)。MR-3.0T全脑白质角二阶矩(0.734±0.008比0.736±0.007,F=11.368,P=0.001)及熵(1.392±0.051比1.397±0.042,F=10.612,P=0.002)低于MR-1.5T,MR-3.0T全脑白质逆差距高于MR-1.5T(0.875±0.005比0.873±0.004,F=10.776,P=0.002)。结论磁共振场强可影响脑部灰质及白质T1WI结构图像纹理特征。
Purpose To investigate the effect of different magnetic resonance field intensity on the texture features of the brain T1WI structural images. Materials and Methods The 3D T1WI structural image scans of the brain were performed on 30 healthy adults at 1.5T(MR-1.5 T) and 3.0T MR(MR-3.0T). The brain structural images were segmented into gray matter and white matter and the whole brain texture analysis was performed by gray level co-occurrence matrix method. The texture feature parameters included the angular second moment, contrast, autocorrelation, inverse difference moment and entropy. Results The entropy of whole brain gray matter at MR-3.0 T was higher than that at MR-1.5T(2.170±0.059 vs. 2.163±0.054, F=4.465, P=0.039). The angular second moment of whole brain white matter at MR-3.0T was lower than that at MR-1.5 T(0.734±0.008 vs. 0.736±0.007, F=11.368, P=0.001). The entropy of whole brain white matter at MR-3.0T was lower than that at MR-1.5T(1.392±0.051 vs. 1.397±0.042, F=10.612, P=0.002). The inverse difference moment of whole brain white matter at MR-3.0T was higher than that at MR-1.5T(0.875±0.005 vs. 0.8731±0.004, F=10.776,P=0.002). Conclusion The MR field intensity could impact the texture feature of the gray matter and white matter T1WI structure of the brain.
Purpose To investigate the effect of different magnetic resonance field intensity on the texture features of the brain T1WI structural images. Materials and Methods The 3D T1WI structural image scans of the brain were performed on 30 healthy adults at 1.5T(MR-1.5 T) and 3.0T MR(MR-3.0T). The brain structural images were segmented into gray matter and white matter and the whole brain texture analysis was performed by gray level co-occurrence matrix method. The texture feature parameters included the angular second moment, contrast, autocorrelation, inverse difference moment and entropy. Results The entropy of whole brain gray matter at MR-3.0 T was higher than that at MR-1.5T(2.170±0.059 vs. 2.163±0.054, F=4.465, P=0.039). The angular second moment of whole brain white matter at MR-3.0T was lower than that at MR-1.5 T(0.734±0.008 vs. 0.736±0.007, F=11.368, P=0.001). The entropy of whole brain white matter at MR-3.0T was lower than that at MR-1.5T(1.392±0.051 vs. 1.397±0.042, F=10.612, P=0.002). The inverse difference moment of whole brain white matter at MR-3.0T was higher than that at MR-1.5T(0.875±0.005 vs. 0.8731±0.004, F=10.776,P=0.002). Conclusion The MR field intensity could impact the texture feature of the gray matter and white matter T1WI structure of the brain.
引文
[1]周书仁,殷建平.基于Haar特性的LBP纹理特征.软件学报,2013,24(8):1909-1926.
[2]王波涛,刘刚,何蕾,等.纹理特征分析在肺部磨玻璃结节随访中的应用.中国医学影像学杂志,2017,25(6):441-446,451.
[3]王波涛,刘刚,樊文萍,等.纹理特征分析在肝囊肿及肝血管瘤磁共振成像鉴别诊断中的价值.中国医学科学院学报,2017,39(2):169-176.
[4]Chen Z,Chen X,Liu M,et al.Magnetic resonance image texture analysis of the periaqueductal gray matter in episodic migraine patients without T2-visible lesions.Korean JRadiol,2018,19(1):85-92.
[5]Chen ZY,Chen XY,Liu MQ,et al.Texture features of periaqueductal gray in the patients with medication-overuse headache.J Headache Pain,2017,18(1):14.
[6]Chen Z,Chen X,Chen Z,et al.Alteration of gray matter texture features over the whole brain in medication-overuse headache using a 3-dimentional texture analysis.J Headache Pain,2017,18(1):112.
[7]康立丽,余晓锷.MRI图像信噪比影响因素分析.放射学实践,2001,16(6):404.
[8]Gao S,Peng YH,Guo HZ,et al.Texture analysis and classification of ultrasound liver images.Biomed Mater Eng,2014,24(1):1209-1216.
[9]Karahaliou AN,Boniatis IS,Skiadopoulos SG,et al.Breast cancer diagnosis:analyzing texture of tissue surrounding microcalcifications.IEEE Trans Inf Technol Biomed,2008,12(6):731-738.
[10]Lin F,Yu C,Jiang T,et al.Discriminative analysis of relapsing neuromyelitis optica and relapsing-remitting multiple sclerosis based on two-dimensional histogram from diffusion tensor imaging.Neuroimage,2006,31(2):543-549.
[2]王波涛,刘刚,何蕾,等.纹理特征分析在肺部磨玻璃结节随访中的应用.中国医学影像学杂志,2017,25(6):441-446,451.
[3]王波涛,刘刚,樊文萍,等.纹理特征分析在肝囊肿及肝血管瘤磁共振成像鉴别诊断中的价值.中国医学科学院学报,2017,39(2):169-176.
[4]Chen Z,Chen X,Liu M,et al.Magnetic resonance image texture analysis of the periaqueductal gray matter in episodic migraine patients without T2-visible lesions.Korean JRadiol,2018,19(1):85-92.
[5]Chen ZY,Chen XY,Liu MQ,et al.Texture features of periaqueductal gray in the patients with medication-overuse headache.J Headache Pain,2017,18(1):14.
[6]Chen Z,Chen X,Chen Z,et al.Alteration of gray matter texture features over the whole brain in medication-overuse headache using a 3-dimentional texture analysis.J Headache Pain,2017,18(1):112.
[7]康立丽,余晓锷.MRI图像信噪比影响因素分析.放射学实践,2001,16(6):404.
[8]Gao S,Peng YH,Guo HZ,et al.Texture analysis and classification of ultrasound liver images.Biomed Mater Eng,2014,24(1):1209-1216.
[9]Karahaliou AN,Boniatis IS,Skiadopoulos SG,et al.Breast cancer diagnosis:analyzing texture of tissue surrounding microcalcifications.IEEE Trans Inf Technol Biomed,2008,12(6):731-738.
[10]Lin F,Yu C,Jiang T,et al.Discriminative analysis of relapsing neuromyelitis optica and relapsing-remitting multiple sclerosis based on two-dimensional histogram from diffusion tensor imaging.Neuroimage,2006,31(2):543-549.