磁共振T1WI健康成人脑部皮层下灰质核团纹理特征的可重复性及可信度分析
|
摘要
目的探讨正常成人脑部皮层下灰质核团的磁共振T1WI纹理特征测量的可重复性及可信度。资料与方法对16名成年健康志愿者行脑部磁共振T1WI扫描,并采用灰度共生矩阵法对脑部皮层下灰质核团(尾状核、壳核、丘脑)行纹理特征分析,纹理特征参数值包括能量、对比度、自相关、逆差矩、熵。采用组内相关系数(ICC)及Bland-Altman法分析观察者内的可重复性及观察者间的可信度。结果观察者内皮层下灰质结构尾状核头、壳核及丘脑的纹理特征参数值ICC分别为0.970～0.990、0.815～0.996及0.677～0.996,其中丘脑自相关参数ICC为0.677,可重复性等级为良,其余皮层下核团纹理参数ICC均高于0.74,可重复性等级为优。观察者间皮层下灰质结构尾状核头、壳核及丘脑的纹理特征参数值ICC分别为0.960～0.982、0.833～0.994及0.829～0.989,均高于0.74,可信度等级为优。Bland-Altman法分析提示观察者内及观察者间皮层下灰质核团纹理参数差值绝大部分位于95%一致性界限内。结论 T1WI图像可以可靠地评估正常成人脑部皮层下灰质核团纹理特征(能量、对比度、自相关、逆差矩、熵)。
Purpose To investigate the repeatability and reliability of MRI T1WI texture features measurement of cerebral subcortical gray matter nuclei in normal adults. Materials and Methods Sixteen healthy adult volunteers were performed with MRI T1WI scanning and gray-level co-occurrence matrix was taken to analyze texture features of cerebral subcortical gray matter nuclei including caudate nucleus, putamen and thalamus. Texture parameter included energy, contrast, correlation, inverse difference moment(IDM) and entropy.Intraclass correlation coefficient(ICC) and Bland-Altman method were used to evaluate the intra-observer repeatability and inter-observer reliability. Results Texture parameter ICC value of observer subcortical gray matter caput nuclei caudati, putamen and thalamus were0.970-0.990, 0.815-0.996 and 0.677-0.996, respectively. Among, thalamus ICC was 0.677 and repeatability was classified to be good.Texture parameter ICC of remaining subcortical nuclei were all higher than 0.74 and repeatability was classified to be excellent. Texture parameter ICC value of observer subcortical gray matter caput nuclei caudati, putamen and thalamus were 0.960-0.982, 0.833-0.994 and 0.829-0.989, respectively, being all higher than 0.74, reliability was classified to be excellent. Bland-Altman analysis indicated that texture difference value of intra-observer and observer subcortical gray matter nuclei was mostly within 95%. Conclusion T1WI images could reliably be used to assess texture feature(energy, contrast, correlation, IDM and entropy) of cerebral subcortical gray matter nuclei in health adults.
Purpose To investigate the repeatability and reliability of MRI T1WI texture features measurement of cerebral subcortical gray matter nuclei in normal adults. Materials and Methods Sixteen healthy adult volunteers were performed with MRI T1WI scanning and gray-level co-occurrence matrix was taken to analyze texture features of cerebral subcortical gray matter nuclei including caudate nucleus, putamen and thalamus. Texture parameter included energy, contrast, correlation, inverse difference moment(IDM) and entropy.Intraclass correlation coefficient(ICC) and Bland-Altman method were used to evaluate the intra-observer repeatability and inter-observer reliability. Results Texture parameter ICC value of observer subcortical gray matter caput nuclei caudati, putamen and thalamus were0.970-0.990, 0.815-0.996 and 0.677-0.996, respectively. Among, thalamus ICC was 0.677 and repeatability was classified to be good.Texture parameter ICC of remaining subcortical nuclei were all higher than 0.74 and repeatability was classified to be excellent. Texture parameter ICC value of observer subcortical gray matter caput nuclei caudati, putamen and thalamus were 0.960-0.982, 0.833-0.994 and 0.829-0.989, respectively, being all higher than 0.74, reliability was classified to be excellent. Bland-Altman analysis indicated that texture difference value of intra-observer and observer subcortical gray matter nuclei was mostly within 95%. Conclusion T1WI images could reliably be used to assess texture feature(energy, contrast, correlation, IDM and entropy) of cerebral subcortical gray matter nuclei in health adults.
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]Vignati A,Mazzetti S,Giannini V,et al.Texture features on T2-weighted magnetic resonance imaging:new potential biomarkers for prostate cancer aggressiveness.Phys Med Biol,2015,60(7):2685-2701.
[5]Chamming's F,Ueno Y,Ferre R,et al.Features from computerized texture analysis of breast cancers at pretreatment MR imaging are associated with response to neoadjuvant chemotherapy.Radiology,2018,286(2):412-420.
[6]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.
[7]Chen Z,Chen X,Liu M,et al.Texture features of periaqueductal gray in the patients with medication-overuse headache.J Headache Pain,2017,18(1):14.
[8]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.
[9]王敏红,冯湛.MR纹理分析鉴别诊断脑胶质母细胞瘤和单发转移瘤的价值.中国医学影像技术,2017,33(12):1784-1787.
[10]Gourtsoyianni S,Doumou G,Prezzi D,et al.Primary rectal cancer:repeatability of global and local-regional MRimaging texture features.Radiology,2017,284(2):552-561.
[11]刘梦琦,陈志晔,马林.正常成人脑部皮层下灰质结构的3D pc-ASL灌注成像的可重复性.南方医科大学学报,2017,37(9):1242-1247.
[12]Chen Z,Jia Z,Chen X,et al.Volumetric abnormalities of thalamic subnuclei in medication-overuse headache.JHeadache Pain,2017,18(1):82.
[13]Coppola G,Di Renzo A,Tinelli E,et al.Thalamo-cortical network activity between migraine attacks:insights from MRI-based microstructural and functional resting-state network correlation analysis.J Headache Pain,2016,17(1):100.
[14]Maleki N,Becerra L,Nutile L,et al.Migraine attacks the Basal Ganglia.Mol Pain,2011,7:71.
[15]Masuda M,Senda J,Watanabe H,et al.Involvement of the caudate nucleus head and its networks in sporadic amyotrophic lateral sclerosis-frontotemporal dementia continuum.Amyotroph Lateral Scler Frontotemporal Degener,2016,17(7/8):571-579.
[16]Fusar-Poli P,Placentino A,Carletti F,et al.Functional Atlas of emotional faces processing:a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies.JPsychiatry Neurosci,2009,34(6):418-432.
[17]Walker RF,Jackway P,Longstaff ID.Improving cooccurrence matrix feature discrimination//3rd conference on digital image computing:techniques and application,1996:643-648.
[18]Haralick RM,Shanmugam K,Dinstein IH.Textural features for image classification.IEEE Trans Syst Man Cybern,1973,3(6):610-621.
[19]Noble S,Scheinost D,Finn ES,et al.Multisite reliability of MR-based functional connectivity.Neuroimage,2017,146:959-970.
[20]Gao S,Peng Y,Guo H,et al.Texture analysis and classification of ultrasound liver images.Biomed Mater Eng,2014,24(1):1209-1216.
[21]Rajkovic N,Kolarevic D,Kanjer KA,et al.Comparison of monofractal,multifractal and gray level co-occurrence matrix algorithms in analysis of breast tumor microscopic images for prognosis of distant metastasis risk.Biomed Microdevices,2016,18(5):83.
[22]Bartko JJ.The intraclass correlation coefficient as a measure of reliability.Psychol Rep,1966,19(1):3-11.
[23]陈卉.Bland-Altman分析在临床测量方法一致性评价中的应用.中国卫生统计,2007,24(3):308-309,315.
[24]Bland JM,Altman DG.Statistical methods for assessing agreement between two methods of clinical measurement.Lancet,1986,1(8476):307-310.
[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]Vignati A,Mazzetti S,Giannini V,et al.Texture features on T2-weighted magnetic resonance imaging:new potential biomarkers for prostate cancer aggressiveness.Phys Med Biol,2015,60(7):2685-2701.
[5]Chamming's F,Ueno Y,Ferre R,et al.Features from computerized texture analysis of breast cancers at pretreatment MR imaging are associated with response to neoadjuvant chemotherapy.Radiology,2018,286(2):412-420.
[6]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.
[7]Chen Z,Chen X,Liu M,et al.Texture features of periaqueductal gray in the patients with medication-overuse headache.J Headache Pain,2017,18(1):14.
[8]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.
[9]王敏红,冯湛.MR纹理分析鉴别诊断脑胶质母细胞瘤和单发转移瘤的价值.中国医学影像技术,2017,33(12):1784-1787.
[10]Gourtsoyianni S,Doumou G,Prezzi D,et al.Primary rectal cancer:repeatability of global and local-regional MRimaging texture features.Radiology,2017,284(2):552-561.
[11]刘梦琦,陈志晔,马林.正常成人脑部皮层下灰质结构的3D pc-ASL灌注成像的可重复性.南方医科大学学报,2017,37(9):1242-1247.
[12]Chen Z,Jia Z,Chen X,et al.Volumetric abnormalities of thalamic subnuclei in medication-overuse headache.JHeadache Pain,2017,18(1):82.
[13]Coppola G,Di Renzo A,Tinelli E,et al.Thalamo-cortical network activity between migraine attacks:insights from MRI-based microstructural and functional resting-state network correlation analysis.J Headache Pain,2016,17(1):100.
[14]Maleki N,Becerra L,Nutile L,et al.Migraine attacks the Basal Ganglia.Mol Pain,2011,7:71.
[15]Masuda M,Senda J,Watanabe H,et al.Involvement of the caudate nucleus head and its networks in sporadic amyotrophic lateral sclerosis-frontotemporal dementia continuum.Amyotroph Lateral Scler Frontotemporal Degener,2016,17(7/8):571-579.
[16]Fusar-Poli P,Placentino A,Carletti F,et al.Functional Atlas of emotional faces processing:a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies.JPsychiatry Neurosci,2009,34(6):418-432.
[17]Walker RF,Jackway P,Longstaff ID.Improving cooccurrence matrix feature discrimination//3rd conference on digital image computing:techniques and application,1996:643-648.
[18]Haralick RM,Shanmugam K,Dinstein IH.Textural features for image classification.IEEE Trans Syst Man Cybern,1973,3(6):610-621.
[19]Noble S,Scheinost D,Finn ES,et al.Multisite reliability of MR-based functional connectivity.Neuroimage,2017,146:959-970.
[20]Gao S,Peng Y,Guo H,et al.Texture analysis and classification of ultrasound liver images.Biomed Mater Eng,2014,24(1):1209-1216.
[21]Rajkovic N,Kolarevic D,Kanjer KA,et al.Comparison of monofractal,multifractal and gray level co-occurrence matrix algorithms in analysis of breast tumor microscopic images for prognosis of distant metastasis risk.Biomed Microdevices,2016,18(5):83.
[22]Bartko JJ.The intraclass correlation coefficient as a measure of reliability.Psychol Rep,1966,19(1):3-11.
[23]陈卉.Bland-Altman分析在临床测量方法一致性评价中的应用.中国卫生统计,2007,24(3):308-309,315.
[24]Bland JM,Altman DG.Statistical methods for assessing agreement between two methods of clinical measurement.Lancet,1986,1(8476):307-310.