Imaging value of intracranial steno-occlusive disease based on silent MR angiography modified with hybrid-arterial spin labeling / 中华放射学杂志

Objective:To investigate the stability and feasibility of improved silent MRA technique based on hybrid-arterial spin labeling(ASL) for imaging intracranial arterial stenosis.Methods:From September 2019 to May 2020, totally 35 patients with suspected intracranial vascular stenosis in Department of Neurology of Northern Jiangsu People′s Hospital were enrolled in this study. Silent MRA and improved silent MRA based on hybrid-ASL technique were performed respectively. The acquisition noise (noise measurement and subjective score) of two kinds of MRA examination were evaluated respectively. Two neuroradiologists performed image quality scoring and signal-to-noise ratio (SNR) measurement of intracranial arteries (including internal carotid artery, vertebrobasilar artery, anterior cerebral artery, middle cerebral artery, and posterior cerebral artery) in the two kinds of MRA images using a double-blind, completely randomized method. Independent sample t-test was used to compare the image quality and SNR of two kinds of MRA images in each segment. Two experts assessed the degree of stenosis at the site of confirmed intracranial artery stenosis. Kappa test was used to assess interobserver and intermodel agreement. Results:There was no significant difference in acquisition noise between improved silent MRA and silent MRA ( P>0.05). In all five segments measured, the image quality scores of internal carotid artery [(4.40±0.49)scores], anterior cerebral artery[(4.30±0.33)scores] and middle cerebral artery [(4.46±0.34)scores] in improved silent MRA were higher than those in silent MRA images [(4.02±0.43)scores, (4.02±0.31)scores, (4.02±0.31)scores; t=2.825, 2.877, 1.683, all P<0.05)]. The SNR of internal carotid artery (9.11±1.23) and middle cerebral artery (8.77±1.87) in improved silent MRA images was higher than that in silent MRA images (7.83±1.33, 8.06±2.67, respectively; t=11.154, 3.268, both P<0.05). A total of 24 patients (38 lesions) with intracranial vascular stenosis were diagnosed by CTA. Improved silent MRA (Kappa=0.89, 95%CI 0.82-0.95) and silent MRA (Kappa=0.85, 95%CI 0.77-0.92) were highly consistent among observers in evaluating the degree of cerebrovascular stenosis.The results of improved silent MRA were highly consistent with those of CTA (Kappa=0.92, 95%CI 0.87-0.98), and those of silent MRA were highly consistent with those of CTA (Kappa=0.85, 95%CI 0.77-0.92). Conclusions:The improved silent MRA is feasible to improve the imaging quality and signal uniformity through efficient marking based on keeping the low noise features. In the diagnosis of intracranial stenosis and occlusive disease, the stability of improved silent MRA imaging improves the diagnostic efficiency of stenosis to a certain extent.

Prediction of the onset time of acute stroke by deep learning based on DWI and FLAIR / 中华放射学杂志

Objective:To evaluate the effect of deep learning based on DWI and fluid attenuated inversion recovery (FLAIR) to construct a prediction model of the onset time in acute stroke.Methods:A total of 324 cases of acute stroke with clear onset time, from January 2017 to May 2020 in Nanjing First Hospital, were retrospectively enrolled and analyzed. The patients were divided into a training set of 226 patients and a test set of 98 patients according to the complete randomization method using a 7∶3 ratio, and the patients were divided into ≤ 4.5 h and >4.5 h according to symptom onset time in each group. The acute infarction areas on DWI and the corresponding high signal area on FLAIR were manually outlined by physician. Using the InceptionV3 model as the basic model for image features extraction, the deep learning prediction model based on single sequence (DWI, FLAIR) and multi sequences (DWI+FLAIR) were established and verified. Then the area under curve (AUC), accuracy of human readings, single sequence model and multi sequence model in predicting the acute stroke onset time from imaging were compared.Results:DWI-FLAIR mismatch was found in 94 cases (94/207) of patients with symptom onset time from imaging ≤ 4.5 h, while in 28 cases (28/117) of patients with symptom onset time from imaging >4.5 h. ROC analysis showed that the AUC of DWI-FLAIR mismatch in predicting acute stroke onset time from imaging was 0.607, and the accuracy was 60.2%. The prediction model of deep learning based on single sequence showed that the AUC of FLAIR was 0.761 and the accuracy was 71.4%; the AUC of DWI was 0.836 and the accuracy was 81.6%. The AUC of predicting stroke onset time based on the multi-sequence (DWI+FLAIR) deep learning model was 0.852, which was significantly better than that of manual identification ( Z = 0.617, P = 0.002), FLAIR sequence deep learning model ( Z = 2.133, P = 0.006) and DWI sequence deep learning model ( Z = 1.846, P = 0.012). Conclusion:The deep learning model based on DWI and FLAIR is superior to human readings in predicting acute stroke onset time from imaging, which could provide guidance for intravenous thrombolytic therapy for acute stroke patients with unknown onset time.

Silent MR angiography in the detection of intracranial aneurysm: a feasibility study / 中华放射学杂志

Objective:To evaluate image quality and diagnostic performance of silent MR angiography (MRA) and discuss the feasibility of silent MRA in diagnosing intracranial aneurysms.Methods:Twenty seven patients suspected with cerebrovascular disorders and 30 intracranial aneurysms in Northern Jiangsu People's Hospital, were enrolled prospectively in this study from December 2015 to December 2018. Silent and time of flight (TOF) MRA were performed on the same day prior to CTA examination. The corresponding MRA images were independently and blindly evaluated by two experienced neuroradiologists in the aspects of signal homogeneity, lesion conspicuity, venous signal/artifact and diagnostic confidence (4-point scale). The aneurysms were divided into tiny (≤ 3 mm) and non-tinyaneurysm groups(> 3 mm) according to the measured diameters of aneurysms. The differences in image quality ratings between silent MRA and TOF MRA were analyzed using Wilcoxon signed rank tests. Intra-class correlation coefficients (ICC) were used to test the consistency of measurements between MRAs (silent MRA, TOF MRA) and CTA.Results:CTA revealed 32 intracranial aneurysms. For silent MRA and TOF MRA, the scores of signal homogeneity were 3.38±0.49 and 3.00±0.62, andthe scores of venous signal/artifact were 3.77±0.42 and 2.65±0.48.Significant differences were found between the two MRAs in these aspects ( Z=-2.21, P=0.02; Z=-5.69, P=0.01). The scores of lesion conspicuity were 3.19±0.56 and 3.15±0.46, and the scores of diagnostic confidence were 3.27±0.44 and 3.12±0.51.There were no significant differences found in these aspects ( P>0.05).The ICC coefficient was excellentfor silent MRA (0.94, 95%CI 0.82- 0.98)and was good for TOF MRA (0.72, 95%CI 0.30-0.91) in tiny aneurysm group. The ICC coefficient was excellent (silent MRA, 0.98, 95%CI 0.95-0.99; TOF MRA, 0.95, 95%CI 0.87-0.98) for both MRA in non-tiny aneurysm group. Conclusions:Compared with TOF MRA, silent MRA could achieve higher image quality and higher diagnostic confidence, and higher consistency with CTA. Silent MRA can be a promising non-contrast-enhanced alternative MRA technique in clinical setting.

Value of MRI histogram analysis in differential diagnosis of brain high-grade glioblastomas,primary lymphoma and metastatic tumor / 实用放射学杂志

Objective To investigate the feasibility of histogram analysis in differentiating brain high-grade glioblastomas,primary lymphoma from metastatic tumor.Methods 26 cases of brain high-grade glioblastomas,22 cases of primary lymphoma and 18 cases of metastatic tumor confirmed by postoperative pathological were analyzed retrospectively.Delineation of ROI and extraction of texture parameters were performing by using Mazda software.The histogram parameters,including Mean,Variance,Skewness,Kurtosis,Perc0.1%,Perc10%,Perc50%, Perc90% and Perc99% were analyzed statistically,and the ROC was then established.Results Mean,Perc0.1%,Perc10%and Perc50% exhibited statistically significant differences (P＜0.05).The best diagnostic parameters for differentiation between brain high-grade glioblastomas and primary lymphoma,primary lymphoma and metastatic tumor,and brain high-grade glioblastomas and metastatic tumor were Perc0.1%,Perc0.1%and Kurtosis.The AUC for these preferred diagnostic parameters were 0.937,0.879 and 0.7 1 8,respectively,with optimal thresholds of 50,70 and －0.43,sensitivity and specificity of 90.9% and 88.5%,77.3% and 88.9%,and 61.5% and 77.8%.Conclusion The histogram analysis of MRI images contributes to differentiate quantitatively between brain high-grade glioblastomas,primary lymphoma and metastatic tumor.

DWI in differential diagnosis between dysembryoplastic neuroepithelial tumors and low-grade glioma / 中华全科医师杂志

Clinical and imaging data of 11 patients with dysembryoplastic neuroepithelial tumors (DNET) and 15 patients with low-grade glioma (LGG) admitted in Northern Jiangsu People's Hospital were analyzed retrospectively.Routine MRI scan,diffusion weighted imaging (DWI) and enhanced scan were performed.The workstation automatically generated apparent diffusion coefficient (ADC) maps and then to obtain ADC values of the tumor parenchymal area and the contralateral normal reference area.Relative tumor/reference ADC values (rADC) were also calculated.The ADC values of parenchymal regions of tumor and contralateral normal reference areas and the rADC between DNET and LGG were compared.There was significant difference in age distribution between the two groups [(16.6± 13.1) vs.(43.0± 19.2) years,t=3.938,P＜0.01].Six out of 11 DNET cases and none of 15 LGG cases were cuneiform or fan-shaped (P＜0.01);5/11 DNET and 0/15 LGG showed circular high signal in fluid attenuated inversion recovery-T2 weighted imaging (T2FLAIR) sequence (P＜0.01),while there no significant differences in intracapsular segmentation,peritumor edema and mass effect,enhancement,and skull compression between two groups (all P＞0.05).The ADC values of tumor parenchymal regions in both groups were significantly higher than those in contralateral reference regions (both P＜0.01),the rADC of DNET was significantly higher than that of LGG (P＜0.01).It is difficult to identify DNET and LGG by conventional image morphology,however the rADC value of DNET in DWI is significantly higher than that of LGG,and can provide important reference for differential diagnosis between them.

The diagnosis value of multiGparameter spectral CT imaging in fat quantitative measurement on nonalcoholic fatty liver disease / 实用放射学杂志

Objective To explore the value of single source dual energy CT for quantitative measurement of liver fat fraction in the rabbit model of nonalcoholic fatty liver disease(NAFLD).Methods Thirty male New Zealand rabbits were randomly divided into five groups.Six rabbits were fed with standard chow as a control group for 3 weeks.TwentyGfour rabbits were divided into four groups and fed with highGfat, highGcholesterol diet to reach different stage of NAFLD model for 1 ,3 ,4 and 8 weeks respectively before dualGenergy CT scanning.1 40 keV polychromatic CT values (QC),70 keV monochromatic CT values (Mono 70 keV),slope,effective atomic number (EffectiveGZ)and fat concentration based on dualGenergy CT fat decomposition (Fat/Water)were measured.Liver samples were obtained to measure the fat fraction and staged according to Burnt staging system.Correlations between different CT indexes and fat fraction were analyzed.ROC was used to evaluate the diagnosis efficacy of different parameters.Results Correlation between fat concentration based on dualGenergy CT fat decomposition and fat fraction (r＝0.936)was better than that between 140 keV polychromatic CT values (r＝－0.838)and 70 keV monochromatic CT values (r＝－0.906),as well as effective atomic number (r＝－0.858)and slope (r＝0.863).In terms of diagnostic performance of material decomposition fat imaging,the values of area under the curve were 0.944 (stage 0 vs.stage 1 or more severe),0.995 (stage 1 or less severe vs.stage 2 or more severe)and 1 (stage 2 or less severe vs.stage 3)with optimal cutoff values of 59.310,99.5 17 and 22 3.02 3 mg/cm3 ,respectively.Conclusion The dualGenergy CT can quantitatively measure liver fat concentration as a noninvasive surrogate bioGmarker in the rabbit model of nonalcoholic fatty liver disease.DualGenergy CT derived material decomposition fat images can provide more diagnostic information at the early stage of NAFLD.