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Background: The majority of heart failure (HF) in hypertrophic cardiomyopathy (HCM) manifests as a phenotype with preserved left ventricular (LV) ejection fraction; however, the exact contribution of left atrial (LA) phasic function to HF with preserved ejection fraction (HFpEF) in HCM remains unresolved. Purpose: To define the association between LA function and HFpEF in HCM patients using cardiac magnetic resonance imaging (MRI) feature tracking. Study Type: Retrospective. Population: One hundred and fifty‐four HCM patients (HFpEF vs. non‐HF: 55 [34 females] vs. 99 [43 females]). Field Strength/Sequence: 3.0 T/balanced steady‐state free precession. Assessment: LA reservoir function (reservoir strain [εs], total ejection fraction [EF]), conduit function (conduit strain [εe], passive EF), booster‐pump function (booster strain [εa] and active EF), LA volume index, and LV global longitudinal strain (LV GLS) were evaluated in HCM patients. Statistical Tests: Chi‐square test, Student's t‐test, Mann–Whitney U test, multivariate linear regression, logistic regression, and net reclassification analysis were used. Two‐sided P < 0.05 was considered statistically significant. Results: No significant difference was found in LV GLS between the non‐HF and HFpEF group (−10.67 ± 3.14% vs. −10.14 ± 4.01%, P = 0.397), whereas the HFpEF group had more severely impaired LA phasic strain (εs: 27.40 [22.60, 35.80] vs. 18.15 [11.98, 25.90]; εe: 13.80 [9.20, 18.90] vs. 7.95 [4.30, 14.35]; εa: 13.50 [9.90, 17.10] vs. 7.90 [5.40, 14.15]). LA total EF (37.91 [29.54, 47.94] vs. 47.49 [39.18, 55.01]), passive EF (14.70 [7.41, 21.49] vs. 18.07 [9.32, 24.78]), and active EF (27.19 [17.79, 36.60] vs. 36.64 [26.63, 42.71]) were all significantly decreased in HFpEF patients compared with non‐HF patients. LA reservoir (β = 0.90 [0.85, 0.96]), conduit (β = 0.93 [0.87, 0.99]), and booster (β = 0.86 [0.78, 0.95]) strain were independently associated with HFpEF in HCM patients. The model including reservoir strain (Net Reclassification Index [NRI]: 0.260) or booster strain (NRI: 0.325) improved the reclassification of HFpEF based on LV GLS and minimum left atrial volume index (LAVImin). Data Conclusion: LA phasic function was severely impaired in HCM patients with HFpEF, whereas LV function was not further impaired compared with non‐HF patients. Level of Evidence: 4 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

The safety profiles when performing stress oxygenation‐sensitive magnetic resonance imaging (OS‐MRI) have raised concerns in clinical practice. Adenosine infusion can cause side effects such as chest pain, dyspnea, arrhythmia, and even cardiac death. The aim of this study was to investigate the feasibility of breathing maneuvers‐induced OS‐MRI in acute myocardial infarction (MI). This was a prospective study, which included 14 healthy rabbits and nine MI rabbit models. This study used 3 T MRI/modified Look‐Locker inversion recovery sequence for native T1 mapping, balanced steady‐state free precession sequence for OS imaging, and phase‐sensitive inversion recovery sequence for late gadolinium enhancement. The changes in myocardial oxygenation (ΔSI) were assessed under two breathing maneuvers protocols in healthy rabbits: a series of extended breath‐holding (BH), and a combined maneuver of hyperventilation followed by the extended BH (HVBH). Subsequently, OS‐MRI with HVBH in acute MI rabbits was performed, and the ΔSI was compared with that of adenosine stress protocol. Student's t‐test, Wilcoxon rank test, and Friedman test were used to compare ΔSI in different subgroups. Pearson and Spearman correlation was used to obtain the association of ΔSI between breathing maneuvers and adenosine stress. Bland–Altman analysis was used to assess the bias of ΔSI between HVBH and adenosine stress. In healthy rabbits, BH maneuvers from 30 to 50 s induced significant increase in SI compared with the baseline (all p < 0.05). By contrast, hyperventilation for 60 s followed by 10 s‐BH (HVBH 10 s) exhibited a comparable ΔSI to that of stress test (p = 0.07). In acute MI rabbits, HVBH 10 s‐induced ΔSIs among infarcted, salvaged, and the remote myocardial area were no less effectiveness than adenosine stress when performing OS‐MRI (r = 0.84; p < 0.05). Combined breathing maneuvers with OS‐MRI have the potential to be used as a nonpharmacological alternative for assessing myocardial oxygenation in patients with acute MI. Level of Evidence: 2 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Background: End-stage renal disease (ESRD) patients are at high cardiovascular risk, and myocardial fibrosis (MF) accounts for most of their cardiac events. The purpose of this study is to investigate the prognostic value and risk stratification of MF as measured by extracellular volume (ECV) on cardiac magnetic resonance (CMR) for heart failure (HF) in patients with hemodialysis-dependent ESRD.Methods: Sixty-six hemodialysis ESRD patients and 25 matched healthy volunteers were prospectively enrolled and underwent CMR to quantify multiple parameters of MF by T1 mapping and late gadolinium enhancement (LGE). All ESRD patients were followed up for 11-30 months, and the end-point met the 2016 ESC guidelines for the definition of HF.Results: Over a median follow-up of 18 months (range 11-30 months), there were 26 (39.39%) guideline-diagnosed HF patients in the entire cohort of ESRD subjects. The native T1 value was elongated, and ECV was enlarged in the HF cohort relative to the non-HF cohort and normal controls (native T1, 1360.10 ± 50.14 ms, 1319.39 ± 55.44 ms and 1276.35 ± 56.56 ms; ECV, 35.42 ± 4.42%, 31.85 ± 3.01% and 26.97 ± 1.87%; all p<0.05). In the cardiac strain analysis, ECV was significantly correlated with global radial strain (GRS) (r = - 0.501, p = 0.009), global circumferential strain (GCS) (r = 0.553, p = 0.005) and global longitudinal strain (GLS) (r = 0.507, p = 0.008) in ESRD patients with HF. Cox proportional hazard regression models revealed that ECV (hazard ratio [HR] = 1.160, 95% confidence interval: 1.022 to 1.318, p = 0.022) was the only independent predictor of HF in ESRD patients. It also had a higher diagnostic accuracy for detecting MF (area under the curve [AUC] = 0.936; 95% confidence interval: 0.864 to 0.976) than native T1 and post T1 (all p ≤ 0.002). Kaplan-Meier analysis revealed that the high-ECV group had a shorter median overall survival time than the low-ECV group (18 months vs. 20 months, log-rank p = 0.046) and that ESRD patients with high ECV were more likely to have HF.Conclusions: Myocardial fibrosis quantification by ECV on CMR T1 mapping was shown to be an independent risk factor of heart failure, providing incremental prognostic value and risk stratification for cardiac events in ESRD patients.Trial Registration: Chinese Clinical Trial Registry ChiCTR-DND-17012976, 13/12/2017, Retrospectively registered. [ABSTRACT FROM AUTHOR]

Background: Both acute and chronic myocardial infarction (AMI and CMI, respectively) exhibit delayed enhancement; however, clinical decision-making processes frequently require the differentiation of these two types of myocardial injury.Purpose: To investigate the reliability of AMI and CMI characterization using native T1 mapping and its feasibility for discriminating AMI from CMI.Study Type: Case-control.Animal Model: The study cohort comprised 12 AMI (mean post-MI, 3.75 ± 1.29 days) and 15 CMI (mean post-MI, 39.53 ± 6.10 days) Bama mini-pigs.Field Strength/sequence: Balanced steady-state free precession (bSSFP), segmented-turbo-FLASH-PSIR, and modified Look-Locker inversion recovery (MOLLI) sequences at 3.0T.Assessment: The infarct sizes were compared on matching short-axis slices of late-gadolinium-enhanced (LGE) images and T1 maps by two experienced radiologists.Statistical Tests: The infarct sizes were compared on matching short-axis slices of LGE images and T1 maps, and agreement was determined using linear regression and Bland-Altman analyses. The native T1 values were compared between AMI and CMI models (independent sample t-test). The intraclass correlation coefficient was used to assess inter- and intraobserver variability.Results: Measured infarct sizes did not differ between native T1 mapping and LGE images (AMI: P = 0.913; CMI: P = 0.233), and good agreement was observed between the two techniques (AMI: bias, -3.38 ± 19.38%; R2  = 0.96; CMI: bias, -10.55 ± 10.90%; R2  = 0.90). However, the native infarction myocardium T1 values and the T1 signal intensity ratio of infarct and remote myocardium (T1 SI ratio) did not differ significantly between AMI and CMI (P = 0.173).Data Conclusion: Noncontrast native T1 mapping can accurately determine acute and chronic infarct areas as well as conventional LGE imaging; however, it cannot distinguish acute from chronic MI.Level Of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1406-1414. [ABSTRACT FROM AUTHOR]

Objective: To explore the diagnostic performance of deep learning (DL) model in early detection of the interstitial myocardial fibrosis using native T1 maps of hypertrophic cardiomyopathy (HCM) without late gadolinium enhancement (LGE)., Methods: Sixty HCM patients and 44 healthy volunteers who underwent cardiac magnetic resonance were enrolled in this study. Each native T1 map was labeled according to its LGE status. Then, native T1 maps of LGE (-) and those of the controls were preprocessed and entered in the SE-ResNext-50 model as the matrix for the DL model for training, validation and testing., Results: A total of 241 native T1 maps were entered in the SE-ResNext-50 model. The model achieved a specificity of 0.87, sensitivity of 0.79, and area under curve ( AUC ) of 0.83 ( P <0.05) in distinguishing native T1 maps of LGE (-) from those of the controls in the testing set., Conclusion: The DL model based on SE-ResNext-50 could be used for identifying native T1 maps of LGE (-) with relatively high accuracy. It is a promising approach for early detection of myocardial fibrosis in HCM without the use of contrast agent., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Objective: To explore the clinical feasibility of applying deep learning (DL) reconstruction algorithm in low-dose thin-slice liver CT examination of healthy volunteers by comparing the reconstruction algorithm based on DL, filtered back projection (FBP) reconstruction algorithm and iterative reconstruction (IR) algorithm., Methods: A standard water phantom with a diameter of 180 mm was scanned, using the 160 slice multi-detector CT scanning of United Imaging Healthcare, to compare the noise power spectrums of DL, FBP and IR algorithms. 100 healthy volunteers were prospectively enrolled, with 50 assigned to the normal dose group (ND) and 50 to the low dose group (LD). IR algorithm was used in the ND group to reconstruct images, while DL, FBP and IR algorithms were used in the LD group to reconstruct images. One-way analysis of variance was used to compare the liver CT values, the liver noise, liver signal-to-noise ratio (SNR), contrast noise ratio (CNR) and figure of merit (FOM) of the images of ND-IR, LD-FBP, LD-IR and LD-DL. The Kruskal-Wallis test was used to analyse subjective scores of anatomical structures., Results: The DL algorithm had the lowest average peak value of noise power spectrum, and its shape was similar to that of medium-level IR algorithm. Liver CT values of ND-IR, LD-FBP, LD-IR and LD-DL did not show statistically significant difference. The noise of LD-DL was lower than that of LD-FBP, LD-IR and ND-IR ( P <0.05), and the SNR, CNR and FOM of LD-DL were higher than those of LD-FBP, LD-IR and ND-IR ( P <0.05). The subjective scores of anatomical structures of LD-DL did not show significant difference compared to those of ND-IR ( P >0.05), and were higher than those of LD-FBP and LD-IR. The radiation dose of the LD group was reduced by about 50.2% compared with that of the ND group., Conclusion: The DL algorithm with noise shape similar to the medium iterative grade IR commonly used in clinical practice showed higher noise reduction ability than IR did. Compared with FBP, the DL algorithm had smoother noise shape, but much better noise reduction ability. The application of DL algorithm in low-dose thin-slice liver CT of healthy volunteers can help achieve the standard image quality of liver CT., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Objective: To explore the radiomics features of T2 weighted image (T2WI) and readout-segmented echo-planar imaging (RS-EPI) plus difusion-weighted imaging (DWI), to develop an automated mahchine-learning model based on the said radiomics features, and to test the value of this model in predicting preoperative T staging of rectal cancer., Methods: The study retrospectively reviewed 131 patients who were diagnosed with rectal cancer confirmed by the pathology results of their surgical specimens at West China Hospital of Sichuan University between October, 2017 and December, 2018. In addition, these patients had preoperative rectal MRI. Tumor regions from preoperative MRI were manually segmented by radiologists with the ITK-SNAP software from T2WI and RS-EPI DWI images. PyRadiomics was used to extract 200 features-100 from T2WI and 100 from the apparent diffusion coefficient (ADC) calculated from the RS-EPI DWI. MWMOTE and NEATER were used to resample and balance the dataset, and 13 cases of T 1-2 stage simulation cases were added. The overall dataset was divided into a training set (111 cases) and a test set (37 cases) by a ratio of 3∶1. Tree-based Pipeline Optimization Tool (TPOT) was applied on the training set to optimize model parameters and to select the most important radiomics features for modeling. Five independent T stage models were developed accordingly. Accuracy and the area under the curve ( AUC ) of receiver operating characteristic (ROC) were used to pick out the optimal model, which was then applied on the training set and the original dataset to predict the T stage of rectal cancer., Results: The performance of the the five T staging models recommended by automated machine learning were as follows: The accuracy for the training set ranged from 0.802 to 0.838, sensitivity, from 0.762 to 0.825, specificity, from 0.833 to 0.896, AUC , from 0.841 to 0.893, and average precision (AP) from 0.870 to 0.901. After comparison, an optimal model was picked out, with sensitivity, specificity and AUC for the training set reaching 0.810, 0.875, and 0.893, respectively. The sensitivity, specificity and AUC for the test set were 0.810, 0.813, and 0.810, respectively. The sensitivity, specificity and AUC for the original dataset were 0.810, 0.830, and 0.860, respectively., Conclusion: Based on the radiomics data of T2WI and RS-EPI DWI, the model established by automated machine learning showed a fairly high accuracy in predicting rectal cancer T stage., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Objective: To assess the clinical effectiveness of boundary recognition of upper abdomen organs on CT images based on neural network model and the combination of different slices., Methods: A total of 2 000 patients who underwent upper abdomen enhanced CT scans from March 2018 to March 2019 were included in the study. The quality of the CT images met the requirements for clinical diagnosis. Eight boundary layers (the upper and lower edge of liver, the upper and lower edge of spleen, the lower edge of left kidney, the lower edge of right kidney, the lower edge of the stomach and the lower edge of the gallbladder) of the main organs in the upper abdomen were labeled. The model training (training set, verification set and test set) based on different neural network methods and combinations of different slices were then performed to assess the accuracy of boundary recognition. Furthermore, clinical data from 50 cases were used as test group for assessing the accuracy and clinical effectiveness of this model., Results: The fusion model created by integrating the two models according to different weight ratios yielded the highest accuracy, and then followed the EfficientNet-b3 model, with the Xception model showing the lowest accuracy. In each model, the boundary recognition accuracy of 5-slice image is higher than that of 3-silce image, and that of 1-slice image is the lowest. The recognition accuracy of fusion model of the 5-continuous-slice image for upper edge of liver, lower edge of liver, upper edge of spleen, lower edge of spleen, lower edge of left kidney, lower edge of right kidney, lower edge of stomach and lower edge of gallbladder was 91%, 87%, 92%, 85%, 92%, 95%, 76% and 74%, respectively. The fusion model was checked with the effectiveness data of 50 cases, yielding 88%, 86%, 88%, 80%, 82%, 80%, 69%, and 65% accuracy for 8-slice image, respectively, and the accuracy of meeting clinical application requirement was as high as 98%, 98%, 95%, 98%, 99%, 98%, 80% and 77%, respectively., Conclusion: By increasing boundary change logics in the continuous slices, the fusion model integrating different weight proportions demonstrates the highest accuracy for identifying the boundary of upper abdominal organs on CT images, achieving high examination effectiveness in clinical practice., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Objective: A predictive model of Alzheimer's disease (AD) was established based on brain surface meshes and geometric deep learning, and its performance was evaluated., Methods: Seventy-six clinically diagnosed AD patients and 83 healthy older adults were enrolled and randomly assigned to the training set and the test set according to a 4-to-1 ratio. Brain surface mesh was constructed from 3-D T1-weighted high-resolution structural MR volumes of each participant. After applying a series of simplification to the surface meshes, the training set was fed into the geometric deep neural network for training. The performance of the prediction model was evaluated with the test set, and the evaluation metrics included accuracy, sensitivity and specificity., Results: The prediction model trained on the right brain surface meshes with 6 000 faces achieved the best performance, with accuracy reaching 93.8%, sensitivity, 91.7%, and specificity, 94.1%. The evolution of the brain surface meshes during convolution and pooling revealed that AD patients had diffuse brain tissue loss compared with healthy older adults., Conclusion: Morphological brain analysis based on mesh data and geometric deep learning has great potential in the differential diagnosis of AD., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Objective: To evaluate the noise reduction effect of deep learning-based reconstruction algorithms in thin-section chest CT images by analyzing images reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and deep learning image reconstruction (DLIR) algorithms., Methods: The chest CT scan raw data of 47 patients were included in this study. Images of 0.625 mm were reconstructed using six reconstruction methods, including FBP, ASIR hybrid reconstruction (ASIR50%, ASIR70%), and deep learning low, medium and high modes (DL-L, DL-M, and DL-H). After the regions of interest were outlined in the aorta, skeletal muscle and lung tissue of each group of images, the CT values, SD values and signal-to-noise ratio (SNR) of the regions of interest were measured, and two radiologists evaluated the image quality., Results: CT values, SD values and SNR of the images obtained by the six reconstruction methods showed statistically significant difference ( P <0.001). There were statistically significant differences in the image quality scores of the six reconstruction methods ( P <0.001). Images reconstruced with DL-H have the lowest noise and the highest overall quality score., Conclusion: The model based on deep learning can effectively reduce the noise of thin-section chest CT images and improve the image quality. Among the three deep-learning models, DL-H showed the best noise reduction effect., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Objective: To compare the noise reduction performance of conventional filtering and artificial intelligence-based filtering and interpolation (AIFI) and to explore for optimal parameters of applying AIFI in the noise reduction of abdominal magnetic resonance imaging (MRI)., Methods: Sixty patients who underwent upper abdominal MRI examination in our hospital were retrospectively included. The raw data of T1-weighted image (T1WI), T2-weighted image (T2WI), and dualecho sequences were reconstructed with two image denoising techniques, conventional filtering and AIFI of different levels of intensity. The difference in objective image quality indicators, peak signal-to-noise ratio (pSNR) and image sharpness, of the different denoising techniques was compared. Two radiologists evaluated the image noise, contrast, sharpness, and overall image quality. Their scores were compared and the interobserver agreement was calculated., Results: Compared with the original images, improvement of varying degrees were shown in the pSNR and the sharpness of the images of the three sequences, T1W1, T2W2, and dual echo sequence, after denoising filtering and AIFI were used (all P <0.05). In addition, compared with conventional filtering, the objective quality scores of the reconstructed images were improved when conventional filtering was combined with AIFI reconstruction methods in T1WI sequence, AIFI level≥3 was used in T2WI and echo1 sequence, and AIFI level≥4 was used in echo2 sequence (all P <0.05). The subjective scores given by the two radiologists for the image noise, contrast, sharpness, and overall image quality in each sequence of conventional filtering reconstruction, AIFI reconstruction (except for AIFI level=1), and two-method combination reconstruction were higher than those of the original images (all P <0.05). However, the image contrast scores were reduced for AIFI level=5. There was good interobserver agreement between the two radiologists (all r >0.75, P <0.05). After multidimensional comparison, the optimal parameters of using AIFI technique for noise reduction in abdominal MRI were conventional filtering+AIFI level=3 in the T1WI sequence and AIFI level=4 in the T2WI and dualecho sequences., Conclusion: AIFI is superior to filtering in imaging denoising at medium and high levels. It is a promising noise reduction technique. The optimal parameters of using AIFI for abdominal MRI are Filtering+AIFI level=3 in the T1WI sequence and AIFI level=4 in T2WI and dualecho sequences., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Obejective: To explore the clinical value of using radiomics models based on different MRI sequences in the assessment of hepatic metastasis of rectal cancer., Methods: 140 patients with pathologically confirm edrectal cancer were included in the study. They underwent baseline magnetic resonance imaging (MRI) between April 2015 and May 2018 before receiving any treatment. According to the results of liver biopsy, surgical pathology, and imaging, patients were put into two groups, the patients with hepatic metastasis and those without. T2 weighted images (T2WI), diffusion weighted images (DWI) and apparent diffusion coefficient (ADC) images were used to draw the region of interest (ROI) of primary lesions on consecutive slices on ITK-SNAP. 3-D ROIs were generated and loaded into Artificial Intelligent Kit for extraction of radiomics features and 396 features were extracted for each sequence. The feature data were preprocessed on Python and the samples were oversampled, using Support Vector Machine-Synthetic Minority Over-Sampling Technique (SVM-SMOTE) to balance the number of samples in the group with liver metastasis and the group with no liver metastasis at the end of the follow-up. Then, the samples were divided into the training cohort and the test cohort at a ratio of 2∶1. The logistic regression models were developed with selected radionomic features on R software. The receiver operating characteristics (ROC) curves and calibration curves were used to evaluate the performance of the models., Results: In total, 52 patients with liver metastasis and 88 patients without liver metastasis at the end of follow-up were enrolled. Carcinoembryonic antigen (CEA) and T stage and N stage evaluated on the MRI images showed statistically significant difference between the two groups ( P <0.05). After data preprocessing and selecting, except for 17 non-radiomic features, the model combining T2WI, DWI and ADC features, the model of T2WI features alone, the model of DWI features alone and the model of ADC features alone were developed with 32 features, 10 features, 30 features and 15 features, respectively. The combined model (T2WI+DWI+ADC), the T2WI model, and the ADC model can assess hepatic metastasis accurately, with the area under curve ( AUC ) on the train set reaching 93.5%, 89.2%, 90.6% and that of the test set reaching 80.8%, 80.5%, 81.4%, respectively. The combined model did not show a higher AUC than those of the T2WI and ADC alone models. Model based on DWI features has a slightly insufficient AUC of 90.3% in the train set and 75.1% in the test set. The calibration curve showed the smallest fluctuation in the combined model, which is closest fit to the diagonal reference line. The fluctuation in the three independent data set models were similar. The calibration curves of all the four models showed that as the risk increased, the prediction of the models turned from an underestimation to an overestimating the risk. In brief, the combined model showed the best performance, with the best fit to the diagonal reference line in calibration curve and high AUC comparable to the AUC of the T2WI model and ADC model. The performance of T2WI and ADC alone models were second to that of the combined model, while the DWI alone model showed relatively poor performance., Conclusion: Radiomics models based on MRI could be effectively used in assessing liver metastasis in rectal cancer, which may help determine clinical staging and treatment., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Objective: To evaluate the diagnostic value of 3.0T time-of-flight MR angiography with sparse undersampling and iterative reconstruction (TOFu-MRA) for unruptured intracranial aneurysms (UIAs) on the basis of using digital subtraction angiography (DSA) as the reference standard., Methods: A total of 65 patients with suspected UIAs were prospectively enrolled and all patients underwent TOFu-MRA and DSA. Relying on DSA as the reference standard, the sensitivity (SEN), specificity (SPE), positive predictive value (PPV) and negative predictive value (NPV) of using TOFu-MRA in UIA diagnosis were calculated, and the inter-observer agreement between two doctors was determined. Comparison of maximum intensity projection (MIP) and volume rendering (VR) image datasets was made to evaluate the agreement between DSA results and TOFu-MRA in the measurement of UIA morphological parameters, including the neck width (D neck ), height (H) , and width (D width ) of UIAs., Results: The study covered 55 UIAs from 46 patients. The SEN, SPE, PPV and NPV of the two doctors using TOFu-MRA in UIA diagnosis were as follows: (95.7%, 95.7%), (94.7%, 94.7%), (97.8%, 97.8%) and (90.0%, 90.0%), respectively for patient-based assessment; (96.4%, 94.5%), (94.7%, 94.7%), (98.1%, 98.1%) and (90.0%, 85.7%), respectively, for aneurysm-based assessment. There is a strong inter-observer agreement (Kappa=0.93 for patient-based assessment and 0.96 for aneurysm-based assessment) between the two doctors. Moreover, Bland-Altman analysis showed that more than 95% points fell within the limits of agreement (LoA), suggesting strong agreement between the two examination methods for the measurement of UIAs morphological parameters., Conclusion: TOFu-MRA showed good diagnostic efficacy for UIAs and the results were in good agreement with those of DSA, the reference standard, for assessing UIA morphological parameter. TOFu-MRA can be used as a first choice for noninvasive diagnostic evaluation of UIAs., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Obstract Purpose "One-stop" CT myocardial perfusion imaging (CT-MPI) was compared with cardiac magnetic resonance(CMR) to investigate its application value in evaluating patients with severe coronary artery stenosis. Methods Fifty patients with coronary artery stenosis≥90% of at least one major coronary arteries comfirmed by coronary angiography (CAG) in the department of cardiology in our hospital, who referred for coronary artery stent implantation were prospectively enrolled. All the patients underwent "One-stop" CT-MPI within a week before surgery, among which 22 patients underwent CMR examination simultaneously. The postprocessing software Ziostation2 was used to obatin and compare the perfusion parameters of patients with normal and perfusion defect myocardium, including blood flow (BF), blood volume (BV), peak time (TTP), and mean transit time (MTT). Pearson correlation analysis was used to compare the correlation of relative perfusion parameters (defect/normal myocardium) between CT and CMR. Bland-Altman analysis was used to analyze the consistency between CT and CMR in left ventricular (LV) function parameters measurements. Results Compared with normal myocardium, BV and BF of perfusion defect myocardium were significantly decreased, while MTT and TTP were significantly prolonged (all P < 0.05). The rBV, rBF, rMTT and rTTP were medium to high positive correlated between CT and CMR ( r =0.685, 0.641, 0.871, 0.733, respectively, all P < 0.05). Bland-Altman analysis showed that 95% (21/22) points were within the 95% limits of agreement (LoA), suggesting the LV function parameters measurements between two methods were highly consistent. Conclusion "One-stop" CT-MPI can simultaneously obtain the information about coronary anatomy, myocardial perfusion and LV function. It is of great value in the evaluation of patients with severe coronary artery stenosis, with shorter scan time and less contraindications compared with CMR., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Science Edition).)

Objective: To determine the value of automated detection in computed tomography angiography (CTA) for cases with greater than 70% coronary stenosis., Methods: Fifty-seven patients who had both coronary CTA and digital subtraction angiography (DSA) were retrospectively recruited in this study. The patients were categorized into two groups using a cutoff value of 70% stenosis in DSA. The AW4.6 software was used to estimate the diameter and square values from the data obtained from CTA. The sensitivity (SE), specificity (SPE), positive predictive value (PPV) and negative predictive value (NPV) of the automated CTA estimations were calculated., Results: A total of 178 vessels from the 57 patients were analyzed. The automated CTA estimations had moderate to high levels of agreements ( Kappa value: 0.716-0.804, P < 0.001) with the DSA diagnoses, compared with low to moderate levels of agreements ( Kappa value: 0.385-0.533, P < 0.001) in manual interpretations. The square estimations generated high SE (100%) and NPV (100%) for patient diagnoses ( P < 0.016 7 vs. manual interpretations). The diameter estimations generated high SPE (90.48%) and PPV (94.12%) for patient diagnoses ( P < 0.016 7, vs. manual interpretations). Similarly, high SE (96.92%) and NPV (97.89%) were found for square estimations in vessel diagnoses, while high SPE (94.69%) and PPV (90.16%) were found for diameter estimations in vessel diagnoses., Conclusions: Both automated diameter and square algorithms have high accuracy for diagnosing patients with greater than 70% coronary artery stenosis. The AW4.6 can improve the detection of severe stenosis that needs stent interventions., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Science Edition).)

Objective: To determine the value of diffusion kurtosis imaging for predicting one-year early recurrence (ER) of hepatocellular carcinoma (HCC) after curative resection., Methods: 55 HCC patients were enrolled into this prospective study and received preoperative magnetic resonance (MR) examination including diffusion kurtosis imaging (DKI). The patients were followed up for at least one year after curative resection. The morphological features of HCC were assessed using the conventional contrast-enhanced MR images. Mean diffusivity (MD) and mean kurtosis (MK) were calculated for the intratumoral and peritumoral regions. Univariate and multivariate logistic regression analyses were performed to assess the relative value of these parameters as a potential predictor of ER. Receiver operating characteristic (ROC) curve analyses were used to determine the diagnostic performance of these quantitative parameters., Results: Increased alpha-fetoprotein (AFP), BCLC stage, tumor number (≥2), high grade HCC, peritumoral MK, and decreased peritumoral MD were associated with higher one-year ER of HCC ( P <0.05). The multivariate analyses confirmed that high grade HCC 〔odds ratio ( OR )=5.37,95% confidence interval ( CI ):1.01-28.50, P =0.048〕 and increased peritumoral MK ( OR =5.38,95% CI :1.53-18.92, P =0.009) were independent risk factors for the ER of HCC. The area under curve was 0.79 ( P <0.001) for peritumoral MK, with an optimal sensitivity of 85.2% and specificity of 64.3% at the cut-off of 0.96., Conclusions: Peritumoral MK has moderate diagnostic performance in predicting ER of HCC. Higher peritumoral MK value in combination with high-grade HCC are potential biomarkers for predicting one-year ER of HCC., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Science Edition).)

Objective: To determine consistencies between MR perfusion weighted imaging (PW-MRI) and CT perfusion imaging (CTP) in assessing hemodynamics of patients with moyamoya disease (MMD)., Methods: Images of PWI and CTP scan [including the map of cerebral blood flow (CBF), cerebral blood volume (CBV), time to peak (TTP), and mean transmit time (MTT)] of 24 MMD patients diagnosed by digital subtraction angiography(DSA) or time of flight-MR angiography (TOF-MRA) were obtained in the week before vascular reconstruction operations. Relative perfusion parameters (rCBF, rCBV, rMTT, rTTP) at the operative side within the middle cerebral artery territory and cerebellar hemisphere were compared with those of 17 patients without hypoperfusion and cerebrovascular disease using independent sample t test. Paired t test and Pearson correlation analyses were performed between the results of PWI-MRI and CTP., Results: Different parameters were found in the MMD patients compared with the controls ( P <0.05). The parameter results of PWI-MRI differed from those of CTP, but they were strongly correlated., Conclusion: Both PWI-MRI and CTP can detect the area and degree of hypoperfusion of MMD patients., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Science Edition).)

Background: Both acute and chronic myocardial infarction (AMI and CMI, respectively) exhibit delayed enhancement; however, clinical decision-making processes frequently require the differentiation of these two types of myocardial injury., Purpose: To investigate the reliability of AMI and CMI characterization using native T 1 mapping and its feasibility for discriminating AMI from CMI., Study Type: Case-control., Animal Model: The study cohort comprised 12 AMI (mean post-MI, 3.75 ± 1.29 days) and 15 CMI (mean post-MI, 39.53 ± 6.10 days) Bama mini-pigs., Field Strength/sequence: Balanced steady-state free precession (bSSFP), segmented-turbo-FLASH-PSIR, and modified Look-Locker inversion recovery (MOLLI) sequences at 3.0T., Assessment: The infarct sizes were compared on matching short-axis slices of late-gadolinium-enhanced (LGE) images and T 1 maps by two experienced radiologists., Statistical Tests: The infarct sizes were compared on matching short-axis slices of LGE images and T 1 maps, and agreement was determined using linear regression and Bland-Altman analyses. The native T 1 values were compared between AMI and CMI models (independent sample t-test). The intraclass correlation coefficient was used to assess inter- and intraobserver variability., Results: Measured infarct sizes did not differ between native T 1 mapping and LGE images (AMI: P = 0.913; CMI: P = 0.233), and good agreement was observed between the two techniques (AMI: bias, -3.38 ± 19.38%; R 2  = 0.96; CMI: bias, -10.55 ± 10.90%; R 2  = 0.90). However, the native infarction myocardium T 1 values and the T 1 signal intensity ratio of infarct and remote myocardium (T 1 SI ratio) did not differ significantly between AMI and CMI (P = 0.173)., Data Conclusion: Noncontrast native T 1 mapping can accurately determine acute and chronic infarct areas as well as conventional LGE imaging; however, it cannot distinguish acute from chronic MI., Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1406-1414., (© 2017 International Society for Magnetic Resonance in Medicine.)

Objective: To investigate the feasibility of low concentration contrast agent combined double low dose in CT pulmonary angiography., Methods: 60 patients with clinically suspected pulmonary embolism examed by CT pulmonary angiography (CTPA) were divided into two groups (experimental group: n =30,80 kV, 15 mL,320 mg I/mL;control group: n =30,120 kV,50 mL,370 mg I/mL). The average CT value of main right and left pulmonary arteries,lobar arteries was calculated. Imaging post processing techniques included curved plannar reconstruction (CPR),volume rendering (VR) and maximal intensity projection (MIP). The artifact of the remaining contract in the superior vena cava and overall quality of the image were observed and analyzed by two senior doctors who were double blinded., Results: All patients in two groups completed CTPA successfully. The image qualities of two groupssatisfy clinical diagnostic requirements and no difference of the image qualities was observed between two groups ( P >0.05). The evaluation of venous pollution in experimental group was better than that of control group ( P <0.01).No difference of CT values were observed between two groups [experimental group (423.2±89.4) HU,control group (465.7±85.6) HU]( P >0.05). The SNR and CNR in experimental group were lower than those in control group ( P <0.01 both).The CT dose index volume (CTDIvol),dose-length product (DLP) and size-specific dose estimates (SSDE) in experimental group were significantly lower than those incontrol group ( P <0.01 all)., Conclusion: The low concentration contrast agent combined double low dose in CT pulmonary angiography satisfies clinical diagnostic requirements. It has good clinical value for it could reduce venous pollution,iodine contrast agent and radiation exposure., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Science Edition).)