BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation gr...BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.展开更多
目的:探讨宫颈癌术前磁共振T2WI矢状位图的影像特征对近期预后的预测作用,构建并验证SVM预测模型。方法:回顾性选取2020年6月至2022年6月在我院接受宫颈癌手术治疗的患者80例,统计患者2年内预后情况,根据患者预后情况分为良好组(n=40)...目的:探讨宫颈癌术前磁共振T2WI矢状位图的影像特征对近期预后的预测作用,构建并验证SVM预测模型。方法:回顾性选取2020年6月至2022年6月在我院接受宫颈癌手术治疗的患者80例,统计患者2年内预后情况,根据患者预后情况分为良好组(n=40)和不良组(n=40),再按7:3比例分为建模集(n=56)和验证集(n=24),收集患者术前MR-T2WI矢状位图的影像组学特征,使用单变量曲线下面积(Area under curve,AUC)分析及五折交叉验证的最低绝对收缩和选择算子LASSO回归算法进行特征筛选,以此构建SVM支持向量机预测模型。结果:SVM支持向量机结果显示,影响近期预后不良的前6位特征是灰度游程矩阵运行熵、灰度尺寸区域数量、灰度共生矩阵差异熵、一阶特征平均绝对偏差、运行长度不均匀度标准化、最大行2D直径,模型AUC为0.765,最佳截断值0.536对应的灵敏度、特异度分别为0.667、0.828。结论:基于宫颈癌术前T2WI影像组学特征构建的SVM支持向量机模型具有较好的预测效能,可为临床预防宫颈癌术后预后不良提供参考。展开更多
目的探讨腰椎滑脱症棘间韧带MRI的T2WI、T1WI信号情况及临床意义。方法纳入2018年1月~2021年1月本院收治的80例腰椎滑脱症患者,均采用3.0 T MR扫描,获取T2WI、T1WI影像资料。统计Ⅰ度滑脱、Ⅱ度滑脱、未滑脱节段的棘间韧带T2WI、T1WI信...目的探讨腰椎滑脱症棘间韧带MRI的T2WI、T1WI信号情况及临床意义。方法纳入2018年1月~2021年1月本院收治的80例腰椎滑脱症患者,均采用3.0 T MR扫描,获取T2WI、T1WI影像资料。统计Ⅰ度滑脱、Ⅱ度滑脱、未滑脱节段的棘间韧带T2WI、T1WI信号情况,并统计各节段棘间韧带退变的MRI分型。结果80例患者中,Ⅰ度滑脱节段52个,Ⅱ度28个,未滑脱节段320个。Ⅱ度滑脱T2WI均为高信号,T1WI高信号6个;Ⅰ度滑脱T2WI高信号34个,T1WI高信号36个;未滑脱T2WI高信号44个,T1WI高信号38个;Ⅱ度滑脱棘间韧带MRI分型以C型为主,Ⅰ度滑脱B型为主,未滑脱A型为主。结论腰椎滑脱症的滑脱节段棘间韧带MRI常见T2WI高信号,滑脱越严重,则棘间韧带退变情况也更加严重,在MRI诊断中应给予重视。展开更多
Objective To investigate the difference in tumor conventional imaging findings and texture features on T2 weighted images between glioblastoma and primary central neural system(CNS) lymphoma. Methods The pre-operative...Objective To investigate the difference in tumor conventional imaging findings and texture features on T2 weighted images between glioblastoma and primary central neural system(CNS) lymphoma. Methods The pre-operative MRI data of 81 patients with glioblastoma and 28 patients with primary CNS lymphoma admitted to the Chinese PLA General Hospital and Hainan Hospital of Chinese PLA General Hospital were retrospectively collected. All patients underwent plain MR imaging and enhanced T1 weighted imaging to visualize imaging features of lesions. Texture analysis of T2 weighted imaging(T2 WI) was performed by use of GLCM texture plugin of ImageJ software, and the texture parameters including Angular Second Moment(ASM), Contrast, Correlation, Inverse Difference Moment(IDM), and Entropy were measured. Independent sample t-test and Mann-Whitney U test were performed for the between-group comparisons, regression model was established by Binary Logistic regression analysis, and receiver operating characteristic(ROC) curve was plotted to compare the diagnostic efficacy. Results The conventional imaging features including cystic and necrosis changes(P = 0.000), ‘Rosette' changes(P = 0.000) and ‘incision sign'(P = 0.000), except ‘flame-like edema'(P = 0.635), presented significantly statistical difference between glioblastoma and primary CNS lymphoma. The texture features, ASM, Contrast, Correlation, IDM and Entropy, showed significant differences between glioblastoma and primary CNS lympoma(P = 0.006,0.000, 0.002, 0.000, and 0.015 respectively). The area under the ROC curve was 0.671, 0.752, 0.695, 0.720 and 0.646 respectively, and the area under the ROC curve was 0.917 for the combined texture variables(Contrast, cystic and necrosis, ‘Rosette' changes, and ‘incision sign') in the model of Logistic regression. Binary Logistic regression analysis demonstrated that cystic and necrosis changes, ‘Rosette' changes and ‘incision sign' and texture Contrast could be considered as the specific texture variables for the differential diagnosis of glioblastoma and primary CNS lymphoma. Conclusion The texture features of T2 WI and conventional imaging findings may be used to distinguish glioblastoma from primary CNS lymphoma.展开更多
AIM:To review the literature on the assessment of venous vessels to estimate the penumbra on T2*w imaging and susceptibility-weighted imaging (SWI). METHODS:Literature that reported on the assessment of penumbra by T2...AIM:To review the literature on the assessment of venous vessels to estimate the penumbra on T2*w imaging and susceptibility-weighted imaging (SWI). METHODS:Literature that reported on the assessment of penumbra by T2*w imaging or SWI and used a validation method was included. PubMed and relevant stroke and magnetic resonance imaging (MRI) related conference abstracts were searched. Abstracts that had overlapping content with full text articles were excluded. The retrieved literature was scanned for further relevant references. Only clinical literature published in English was considered, patients with Moya-Moya syndrome were disregarded. Data is given as cumulative absolute and relative values, ranges are given where appropriate. RESULTS:Forty-three publications including 1145 patients could be identified. T2*w imaging was used in 16 publications (627 patients), SWI in 26 publications (453 patients). Only one publication used both (65 patients). The cumulative presence of hypointense vessel sign was 54% (range 32%-100%) for T2* (668 patients) and 81% (range 34%-100%) for SWI (334 patients). There was rare mentioning of interrater agreement (6 publications, 210 patients) and reliability (1 publication, 20 patients) but the numbers reported ranged from good to excellent. In most publications (n = 22) perfusion MRI was used as a validation method (617 patients). More patients were scanned in the subacute than in the acute phase (596 patients vs 320 patients). Clinical outcome was reported in 13 publications (521 patients) but was not consistent. CONCLUSION:The low presence of vessels signs on T2*w imaging makes SWI much more promising. More research is needed to obtain formal validation and quantification.展开更多
BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and H...BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and HFS caused by NVC. The judgement of NVC is a critical step in the preoperative evaluation of MVD, which is related to the effect of MVD treatment. Magnetic resonance imaging(MRI) technology has been used to detect NVC prior to MVD for several years. Among many MRI sequences, three-dimensional time-of-flight magnetic resonance angiography(3D TOF MRA) is the most widely used. However, 3D TOF MRA has some shortcomings in detecting NVC. Therefore, 3D TOF MRA combined with high resolution T2-weighted imaging(HR T2WI) is considered to be a more effective method to detect NVC.AIM To determine the value of 3D TOF MRA combined with HR T2WI in the judgment of NVC, and thus to assess its value in the preoperative evaluation of MVD.METHODS Related studies published from inception to September 2022 based on PubMed, Embase, Web of Science, and the Cochrane Library were retrieved. Studies that investigated 3D TOF MRA combined with HR T2WI to judge NVC in patients with TN or HFS were included according to the inclusion criteria. Studies without complete data or not relevant to the research topics were excluded. The Quality Assessment of Diagnostic Accuracy Studies checklist was used to assess the quality of included studies. The publication bias of the included literature was examined by Deeks’ test. An exact binomial rendition of the bivariate mixed-effects regression model was used to synthesize data. Data analysis was performed using the MIDAS module of statistical software Stata 16.0. Two independent investigators extracted patient and study characteristics, and discrepancies were resolved by consensus. Individual and pooled sensitivities and specificities were calculated. The I_(2) statistic and Q test were used to test heterogeneity. The study was registered on the website of PROSERO(registration No. CRD42022357158).RESULTS Our search identified 595 articles, of which 12(including 855 patients) fulfilled the inclusion criteria. Bivariate analysis showed that the pooled sensitivity and specificity of 3D TOF MRA combined with HR T2WI for detecting NVC were 0.96 [95% confidence interval(CI): 0.92-0.98] and 0.92(95%CI: 0.74-0.98), respectively. The pooled positive likelihood ratio was 12.4(95%CI: 3.2-47.8), pooled negative likelihood ratio was 0.04(95%CI: 0.02-0.09), and pooled diagnostic odds ratio was 283(95%CI: 50-1620). The area under the receiver operating characteristic curve was 0.98(95%CI: 0.97-0.99). The studies showed no substantial heterogeneity(I2 = 0, Q = 0.001 P = 0.50).CONCLUSION Our results suggest that 3D TOF MRA combined with HR T2WI has excellent sensitivity and specificity for judging NVC in patients with TN or HFS. This method can be used as an effective tool for preoperative evaluation of MVD.展开更多
Objective:To achieve precision medicine,the use of imaging methods to help the clinical detection of cerebral infarction is conducive to the clinical development of a treatment plan and increase of the cure rate and i...Objective:To achieve precision medicine,the use of imaging methods to help the clinical detection of cerebral infarction is conducive to the clinical development of a treatment plan and increase of the cure rate and improvement of the prognosis of patients.Methods:In this work,T2-weighted imaging(T2WI),diffusion-weighted imaging(DWI),susceptibility-weighted imaging(SWI),and diffusion tensor imaging(DTI)examinations were performed on 34 patients with clinically diagnosed cerebral infarction to measure the difference in signal intensity between the lesion and its mirror area and make a comparative analysis by means of the Student-Newman-Keuls method.Results:The detection rate of T2WI was 79%(27/34),the detection rate of DWI was 97%(33/34),the detection rate of SWI was 88%(30/34),and the detection rate of DTI was 94%(32/34).Conclusion:The imaging performance was in the order DWI>DTI>SWI>T2WI for the diagnosis of cerebral infarction,and combined imaging is better than single imaging.展开更多
基金the Fujian Province Clinical Key Specialty Construction Project,No.2022884Quanzhou Science and Technology Plan Project,No.2021N034S+1 种基金The Youth Research Project of Fujian Provincial Health Commission,No.2022QNA067Malignant Tumor Clinical Medicine Research Center,No.2020N090s.
文摘BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.
文摘目的:探讨宫颈癌术前磁共振T2WI矢状位图的影像特征对近期预后的预测作用,构建并验证SVM预测模型。方法:回顾性选取2020年6月至2022年6月在我院接受宫颈癌手术治疗的患者80例,统计患者2年内预后情况,根据患者预后情况分为良好组(n=40)和不良组(n=40),再按7:3比例分为建模集(n=56)和验证集(n=24),收集患者术前MR-T2WI矢状位图的影像组学特征,使用单变量曲线下面积(Area under curve,AUC)分析及五折交叉验证的最低绝对收缩和选择算子LASSO回归算法进行特征筛选,以此构建SVM支持向量机预测模型。结果:SVM支持向量机结果显示,影响近期预后不良的前6位特征是灰度游程矩阵运行熵、灰度尺寸区域数量、灰度共生矩阵差异熵、一阶特征平均绝对偏差、运行长度不均匀度标准化、最大行2D直径,模型AUC为0.765,最佳截断值0.536对应的灵敏度、特异度分别为0.667、0.828。结论:基于宫颈癌术前T2WI影像组学特征构建的SVM支持向量机模型具有较好的预测效能,可为临床预防宫颈癌术后预后不良提供参考。
文摘目的探讨腰椎滑脱症棘间韧带MRI的T2WI、T1WI信号情况及临床意义。方法纳入2018年1月~2021年1月本院收治的80例腰椎滑脱症患者,均采用3.0 T MR扫描,获取T2WI、T1WI影像资料。统计Ⅰ度滑脱、Ⅱ度滑脱、未滑脱节段的棘间韧带T2WI、T1WI信号情况,并统计各节段棘间韧带退变的MRI分型。结果80例患者中,Ⅰ度滑脱节段52个,Ⅱ度28个,未滑脱节段320个。Ⅱ度滑脱T2WI均为高信号,T1WI高信号6个;Ⅰ度滑脱T2WI高信号34个,T1WI高信号36个;未滑脱T2WI高信号44个,T1WI高信号38个;Ⅱ度滑脱棘间韧带MRI分型以C型为主,Ⅰ度滑脱B型为主,未滑脱A型为主。结论腰椎滑脱症的滑脱节段棘间韧带MRI常见T2WI高信号,滑脱越严重,则棘间韧带退变情况也更加严重,在MRI诊断中应给予重视。
文摘Objective To investigate the difference in tumor conventional imaging findings and texture features on T2 weighted images between glioblastoma and primary central neural system(CNS) lymphoma. Methods The pre-operative MRI data of 81 patients with glioblastoma and 28 patients with primary CNS lymphoma admitted to the Chinese PLA General Hospital and Hainan Hospital of Chinese PLA General Hospital were retrospectively collected. All patients underwent plain MR imaging and enhanced T1 weighted imaging to visualize imaging features of lesions. Texture analysis of T2 weighted imaging(T2 WI) was performed by use of GLCM texture plugin of ImageJ software, and the texture parameters including Angular Second Moment(ASM), Contrast, Correlation, Inverse Difference Moment(IDM), and Entropy were measured. Independent sample t-test and Mann-Whitney U test were performed for the between-group comparisons, regression model was established by Binary Logistic regression analysis, and receiver operating characteristic(ROC) curve was plotted to compare the diagnostic efficacy. Results The conventional imaging features including cystic and necrosis changes(P = 0.000), ‘Rosette' changes(P = 0.000) and ‘incision sign'(P = 0.000), except ‘flame-like edema'(P = 0.635), presented significantly statistical difference between glioblastoma and primary CNS lymphoma. The texture features, ASM, Contrast, Correlation, IDM and Entropy, showed significant differences between glioblastoma and primary CNS lympoma(P = 0.006,0.000, 0.002, 0.000, and 0.015 respectively). The area under the ROC curve was 0.671, 0.752, 0.695, 0.720 and 0.646 respectively, and the area under the ROC curve was 0.917 for the combined texture variables(Contrast, cystic and necrosis, ‘Rosette' changes, and ‘incision sign') in the model of Logistic regression. Binary Logistic regression analysis demonstrated that cystic and necrosis changes, ‘Rosette' changes and ‘incision sign' and texture Contrast could be considered as the specific texture variables for the differential diagnosis of glioblastoma and primary CNS lymphoma. Conclusion The texture features of T2 WI and conventional imaging findings may be used to distinguish glioblastoma from primary CNS lymphoma.
文摘AIM:To review the literature on the assessment of venous vessels to estimate the penumbra on T2*w imaging and susceptibility-weighted imaging (SWI). METHODS:Literature that reported on the assessment of penumbra by T2*w imaging or SWI and used a validation method was included. PubMed and relevant stroke and magnetic resonance imaging (MRI) related conference abstracts were searched. Abstracts that had overlapping content with full text articles were excluded. The retrieved literature was scanned for further relevant references. Only clinical literature published in English was considered, patients with Moya-Moya syndrome were disregarded. Data is given as cumulative absolute and relative values, ranges are given where appropriate. RESULTS:Forty-three publications including 1145 patients could be identified. T2*w imaging was used in 16 publications (627 patients), SWI in 26 publications (453 patients). Only one publication used both (65 patients). The cumulative presence of hypointense vessel sign was 54% (range 32%-100%) for T2* (668 patients) and 81% (range 34%-100%) for SWI (334 patients). There was rare mentioning of interrater agreement (6 publications, 210 patients) and reliability (1 publication, 20 patients) but the numbers reported ranged from good to excellent. In most publications (n = 22) perfusion MRI was used as a validation method (617 patients). More patients were scanned in the subacute than in the acute phase (596 patients vs 320 patients). Clinical outcome was reported in 13 publications (521 patients) but was not consistent. CONCLUSION:The low presence of vessels signs on T2*w imaging makes SWI much more promising. More research is needed to obtain formal validation and quantification.
基金Supported by the Key Research and Development Plan of Shaanxi Province,No.2021SF-298.
文摘BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and HFS caused by NVC. The judgement of NVC is a critical step in the preoperative evaluation of MVD, which is related to the effect of MVD treatment. Magnetic resonance imaging(MRI) technology has been used to detect NVC prior to MVD for several years. Among many MRI sequences, three-dimensional time-of-flight magnetic resonance angiography(3D TOF MRA) is the most widely used. However, 3D TOF MRA has some shortcomings in detecting NVC. Therefore, 3D TOF MRA combined with high resolution T2-weighted imaging(HR T2WI) is considered to be a more effective method to detect NVC.AIM To determine the value of 3D TOF MRA combined with HR T2WI in the judgment of NVC, and thus to assess its value in the preoperative evaluation of MVD.METHODS Related studies published from inception to September 2022 based on PubMed, Embase, Web of Science, and the Cochrane Library were retrieved. Studies that investigated 3D TOF MRA combined with HR T2WI to judge NVC in patients with TN or HFS were included according to the inclusion criteria. Studies without complete data or not relevant to the research topics were excluded. The Quality Assessment of Diagnostic Accuracy Studies checklist was used to assess the quality of included studies. The publication bias of the included literature was examined by Deeks’ test. An exact binomial rendition of the bivariate mixed-effects regression model was used to synthesize data. Data analysis was performed using the MIDAS module of statistical software Stata 16.0. Two independent investigators extracted patient and study characteristics, and discrepancies were resolved by consensus. Individual and pooled sensitivities and specificities were calculated. The I_(2) statistic and Q test were used to test heterogeneity. The study was registered on the website of PROSERO(registration No. CRD42022357158).RESULTS Our search identified 595 articles, of which 12(including 855 patients) fulfilled the inclusion criteria. Bivariate analysis showed that the pooled sensitivity and specificity of 3D TOF MRA combined with HR T2WI for detecting NVC were 0.96 [95% confidence interval(CI): 0.92-0.98] and 0.92(95%CI: 0.74-0.98), respectively. The pooled positive likelihood ratio was 12.4(95%CI: 3.2-47.8), pooled negative likelihood ratio was 0.04(95%CI: 0.02-0.09), and pooled diagnostic odds ratio was 283(95%CI: 50-1620). The area under the receiver operating characteristic curve was 0.98(95%CI: 0.97-0.99). The studies showed no substantial heterogeneity(I2 = 0, Q = 0.001 P = 0.50).CONCLUSION Our results suggest that 3D TOF MRA combined with HR T2WI has excellent sensitivity and specificity for judging NVC in patients with TN or HFS. This method can be used as an effective tool for preoperative evaluation of MVD.
文摘Objective:To achieve precision medicine,the use of imaging methods to help the clinical detection of cerebral infarction is conducive to the clinical development of a treatment plan and increase of the cure rate and improvement of the prognosis of patients.Methods:In this work,T2-weighted imaging(T2WI),diffusion-weighted imaging(DWI),susceptibility-weighted imaging(SWI),and diffusion tensor imaging(DTI)examinations were performed on 34 patients with clinically diagnosed cerebral infarction to measure the difference in signal intensity between the lesion and its mirror area and make a comparative analysis by means of the Student-Newman-Keuls method.Results:The detection rate of T2WI was 79%(27/34),the detection rate of DWI was 97%(33/34),the detection rate of SWI was 88%(30/34),and the detection rate of DTI was 94%(32/34).Conclusion:The imaging performance was in the order DWI>DTI>SWI>T2WI for the diagnosis of cerebral infarction,and combined imaging is better than single imaging.
