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PurposeTo evaluate isocitrate dehydrogenase (IDH) status in clinically diagnosed grade II~IV glioma patients using the 2016 World Health Organization (WHO) classification based on MRI parameters.Materials and MethodsOne hundred and seventy-six patients with confirmed WHO grade II~IV glioma were retrospectively investigated as the study set, including lower-grade glioma (WHO grade II, n = 64; WHO grade III, n = 38) and glioblastoma (WHO grade IV, n = 74). The minimum apparent diffusion coefficient (ADCmin) in the tumor and the contralateral normal-appearing white matter (ADCn) and the rADC (ADCmin to ADCn ratio) were defined and calculated. Intraclass correlation coefficient (ICC) analysis was carried out to evaluate interobserver and intraobserver agreement for the ADC measurements. Interobserver agreement for the morphologic categories was evaluated by Cohen’s kappa analysis. The nonparametric Kruskal-Wallis test was used to determine whether the ADC measurements and glioma subtypes were related. By univariable analysis, if the differences in a variable were significant (P0.8; κ >0.6), then it was chosen as a predictor variable. The performance of the area under the receiver operating characteristic curve (AUC) was evaluated using several machine learning models, including logistic regression, support vector machine, Naive Bayes and Ensemble. Five evaluation indicators were adopted to compare the models. The optimal model was developed as the final model to predict IDH status in 40 patients with glioma as the subsequent test set. DeLong analysis was used to compare significant differences in the AUCs.ResultsIn the study set, six measured variables (rADC, age, enhancement, calcification, hemorrhage, and cystic change) were selected for the machine learning model. Logistic regression had better performance than other models. Two predictive models, model 1 (including all predictor variables) and model 2 (excluding calcification), correctly classified IDH status with an AUC of 0.897 and 0.890, respectively. The test set performed equally well in prediction, indicating the effectiveness of the trained classifier. The subgroup analysis revealed that the model predicted IDH status of LGG and GBM with accuracy of 84.3% (AUC = 0.873) and 85.1% (AUC = 0.862) in the study set, and with the accuracy of 70.0% (AUC = 0.762) and 70.0% (AUC = 0.833) in the test set, respectively.ConclusionThrough the use of machine-learning algorithms, the accurate prediction of IDH-mutant versus IDH-wildtype was achieved for adult diffuse gliomas via noninvasive MR imaging characteristics, including ADC values and tumor morphologic features, which are considered widely available in most clinical workstations.

Objective To evaluate alterations in phase-shift values in the gray matter of patients with amyotrophic lateral sclerosis (ALS) using susceptibility-weighted imaging (SWI). Methods Twenty patients with definite or probable ALS and 19 age- and sex-matched healthy controls were enrolled. SWI was performed using a 3.0 T magnetic resonance imaging scanner. Phase-shift values were measured in corrected phase images using regions of interest, which were placed on the bilateral precentral gyrus, frontal cortex, caudate nucleus, globus pallidus, and putamen. Results Phase-shift values of the precentral gyrus were significantly lower in ALS patients (−0.176 ± 0.050) than in the control group (−0.119 ± 0.016) on SWI. The average phase-shift values of the frontal cortex, caudate nucleus, globus pallidus, and putamen in ALS patients (−0.089 ± 0.023, −0.065 ± 0.016, −0.336 ± 0.191, and −0.227 ± 0.101, respectively) were not significantly different from those in the healthy controls (−0.885 ± 0.015, −0.079 ± 0.018, −0.329 ± 0.136, and −0.229 ± 0.083, respectively). Conclusions Compared with healthy controls, ALS patients had a lower phase-shift value in the precentral gyrus, which may be related to abnormal iron overload. Thus, SWI is a potential method for identifying ALS patients.

Purpose To evaluate the efficacy of 3D pseudocontinuous arterial spin labeling (3D pCASL) in the differential diagnosis between mitochondrial encephalopathy with lactic acidosis and stroke-like episodes (MELAS) and acute ischemic stroke (AIS). Materials and Methods Conventional magnetic resonance imaging (MRI) including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), and 3D pCASL perfusion data were obtained on a 3.0T MR scanner in 16 newly appearing lesions in nine patients with MELAS and 14 acute lesions in 12 patients with AIS. A postlabeling delay (PLD) time of 2025 msec was applied. The cerebral blood flow (CBF) values were measured in the central part and the peripheral part of the lesions and the CBF values were compared between MELAS and AIS patients. Results The lesions of both MELAS and AIS showed high signal intensity on T2WI and demonstrated hyperintensity on DWI. Compared with the perfusion defects or hypoperfusion in all AIS, hyperperfusion was revealed in 16 acute MELAS lesions, especially in the peripheral part of the lesions. The CBF values of 16 lesions in MELAS were 11.20–73.11 ml/100g/min in the central part and 65.33–169.11 ml/100g/min in the peripheral part. The CBF values of 14 lesions in AIS were 12.32–19.94 ml/100g/min in the central part and 11.66–18.37 ml/100g/min in the peripheral part. The CBF value of the peripheral part (119.80 ± 35.41) in MELAS was significantly higher than that (14.66 ± 2.61) in AIS (P = 0.0001). Conclusion The whole-brain 3D pCASL technique might be useful in differentiating MELAS from AIS. J. Magn. Reson. Imaging 2016.

This study was performed to validate the efficacy of three-dimensional pseudocontinuous arterial spin labeling (pCASL) compared with dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) in distinguishing radiation-induced brain injury from glioma recurrence in patients with glioma.Both 3D pCASL and DSC-PWI were performed using a 3.0 Tesla scanner in 69 patients with previously resected and irradiated glioma who displayed newly developed abnormal contrast-enhanced lesions. The included patients were classified into a radiation-induced brain injury group (n = 34) and a glioma recurrence group (n = 35) based on subsequent pathologic analysis or clinical-radiological follow-up. Lesion perfusion parameter values (CBF and nCBF on pCASL, nrCBV and nrCBF on DSC-PWI) were measured and compared between the two groups using Student's t test. Pearson correlation analysis was performed to evaluate the correlation between pCASL (CBF and nCBF) and DSC-PWI (nrCBV and nrCBF) values in the contrast-enhanced lesions and in the perifocal edema regions.For the contrast-enhanced lesions, the CBF, nCBF, nrCBV, and nrCBF (29.46 ± 15.08 ml/100 g/min, 1.11 ± 0.50, 1.39 ± 1.15, and 1.30 ± 0.74) in the radiation-induced brain injury group were significantly lower than those (64.52 ± 33.92 ml/100 g/min, 2.73 ± 1.71, 3.39 ± 2.12, and 3.20 ± 1.95) in the glioma recurrence group (P 0.001). The CBF and nCBF demonstrated strong correlation with nrCBV and nrCBF in the contrast-enhanced lesions.Radiation-induced brain injury and glioma recurrence can be reliably distinguished using both 3D pCASL and DSC-PWI. Contrast-free 3D pCASL is a suitable alternative to DSC-PWI for long-term follow-up in glioma patients with postoperative radiotherapy.

Objectives We hypothesized that three-dimensional pseudocontinuous arterial spin labelling (pCASL) may have similar efficacy in astrocytic tumour grading as dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI), and the grading accuracy may be further improved when combined with apparent diffusion coefficient (ADC) values. Methods Forty-three patients with astrocytic tumours were studied using diffusion weighted imaging (DWI), pCASL, and DSC-PWI. Histograms of ADC and normalized tumour cerebral blood flow values (nCBF on pCASL and nrCBF on DSC-PWI) were measured and analyzed. Results The mean 10 % ADC value was the DWI parameter that provided the best differentiation between low-grade astrocytoma (LGA) and high-grade astrocytoma (HGA). The nCBF and nrCBF (1.810 ± 0.979 and 2.070 ± 1.048) in LGA were significantly lower than those (4.505 ± 2.270 and 5.922 ± 2.630) in HGA. For differentiation between LGA and HGA, the cutoff values of 0.764 × 10-3 mm2/s for mean 10 % ADC, 2.374 for nCBF, and 3.464 for nrCBF provided the optimal accuracy (74.4 %, 86.1 %, and 88.6 %, respectively). Combining the ADC values with nCBF or nrCBF could further improve the grading accuracy to 97.7 % or 95.3 %, respectively. Conclusions pCASL is an alternative to DSC-PWI for astrocytic tumour grading. The combination of DWI and contrast-free pCASL offers a valuable choice in patients with risk factors. Key Points • pCASL shows positive correlation with DSC-PWI in astrocytic tumour grading. • ADC values based on ADC histograms can be an objective method. • Combination of DWI and pCASL or DSC-PWI can improve grading accuracy.

Objective To evaluate the diagnostic value of magnetic resonance diffusion-weighted imaging (DWI) and three-dimensional arterial spin labelling perfusion imaging (3D-ASL) in distinguishing cavernous haemangioma from parasellar meningioma, using histological data as a reference standard. Methods Patients with parasellar meningioma or parasellar cavernous haemangioma underwent conventional T1- and T2-weighted magnetic resonance imaging (MRI) followed by DWI and 3D-ASL using a 3.0 Tesla MRI. The minimum apparent diffusion coefficient (minADC) from DWI and the maximal normalized cerebral blood flow (nCBF) from 3D-ASL were measured in each tumour. Diagnosis was confirmed by histology. Results MinADC was significantly lower and nCBF significantly higher in meningioma ( n = 19) than cavernous haemangioma ( n = 15). There was a significant negative correlation between minADC and nCBF ( r = −0.605). Conclusion DWI and 3D-ASL are useful in differentiating cavernous haemangiomas from parasellar meningiomas, particularly in situations when the appearance on conventional MRI sequences is otherwise ambiguous.

With the development of economy, there are an increasing number of cars as well as traffic accidents, thus intensifying the need to take measures to reduce traffic accidents and protect the safety of life and property. Vehicle distance is one of the most important indexes of traffic safety. The measurement of safety vehicle distance has become an increasingly hot research area of intelligent transportation. Through analyzing the basic principle of stereo vision and calibrating the parameters of the CCD sensors both inside and outside, this paper comes up with a method to measure the former vehicle distance based on stereo vision and DSP. Once the vehicle speed and distance form a non-security association, it will give a warning, and upload data or force speed-limiting. According to the different coordinates of the obtained images of the target vehicle from the left and the right sensor, this method can identify feature points, calculate distance to the target vehicle, and analyze the security of vehicle distance. The experimental results show that this method has wide measurement range, high measurement accuracy, and fast operation rate, thus it can meet the actual needs of the measurement of safe vehicle distance in intelligent transportation.

When car is in the process of high-speed, the faults caused by tire pressure anomalies are the most worried and difficult to prevent for all drivers. It is one of the important reasons for the sudden traffic accidents. Tire pressure related to ride comfort, fuel consumption, tire wear and vehicle safety, so tire pressure’s monitoring has been one of the important research directions in the safe driving of vehicle. The anomalies of tire pressure made the vehicles appear to tilt and shorten the distance between rim centres and the ground. Using image technology can detect intuitively and fast. This paper measures the dynamic tire pressure based on image processing technology. The vehicles tires goal was extracted by three image differences. After morphological processing, it used three methods to make comprehensive analysis and judgement of whether the tire pressure is normal, to realize the automation and intelligence. This method had realized real-time non-contact detection for dynamic tire pressure, which has extended significance to popularize.