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We aimed to evaluate myocardial fibrosis using cardiac magnetic resonance (CMR) T1 mapping in type 2 diabetes mellitus (T2DM) patients and investigate the association between left ventricular (LV) subclinical myocardial dysfunction and myocardial fibrosis.The study included 37 short-term (≤ 5 years) and 44 longer-term (5 years) T2DM patients and 41 healthy controls. The LV global strain parameters and T1 mapping parameters were compared between the abovementioned three groups. The association of T1 mapping parameters with diabetes duration, in addition to other risk factors, was determined using multivariate linear regression analysis. The correlation between LV strain parameters and T1 mapping parameters was evaluated using Pearson's correlation.The peak diastolic strain rates (PDSRs) were significantly lower in longer-term T2DM patients compared to those in healthy subjects and short-term T2DM patients (p0.05). The longitudinal peak systolic strain rate and peak strain were significantly lower in the longer-term T2DM compared with the short-term T2DM group (p0.05). The extracellular volumes (ECVs) were higher in both subgroups of T2DM patients compared with control subjects (all p0.05). Multivariate linear regression analysis showed that diabetes duration was independently associated with ECV (β = 0.413, p0.001) by taking covariates into account. Pearson's analysis showed that ECV was associated with longitudinal PDSR (r = - 0.441, p0.001).T1 mapping could detect abnormal myocardial fibrosis early in patients with T2DM, which can cause a decline in the LV diastolic function.• CMR T1 mapping could detect abnormal myocardial fibrosis early in patients with T2DM. • The diabetes duration was independently associated with ECV. • Myocardial fibrosis can cause a decline in the LV diastolic function in T2DM patients.

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.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.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 (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.

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).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.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 (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.

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.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 TThe 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,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.

To explore the clinical value of using radiomics models based on different MRI sequences in the assessment of hepatic metastasis of rectal cancer.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.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 (Radiomics models based on MRI could be effectively used in assessing liver metastasis in rectal cancer, which may help determine clinical staging and treatment.

A predictive model of Alzheimer's disease (AD) was established based on brain surface meshes and geometric deep learning, and its performance was evaluated.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.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.Morphological brain analysis based on mesh data and geometric deep learning has great potential in the differential diagnosis of AD.

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).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.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 (allAIFI 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.

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.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.CT values, SD values and SNR of the images obtained by the six reconstruction methods showed statistically significant difference (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.

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.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.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.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.

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.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 (DThe 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.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.

To determine the value of diffusion kurtosis imaging for predicting one-year early recurrence (ER) of hepatocellular carcinoma (HCC) after curative resection.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.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 (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.

Additional file 1: Table S1. Intra-observer and Inter-observer variability of perfusion parameters.

Background: Achilles tendon (AT) defects frequently occur in trauma and chronic injuries. Currently, no method can satisfactorily reconstruct the AT with completely restored function. Purpose: To evaluate the postoperative outcomes of AT defect reconstruction with decellularized bovine tendon sheets (DBTSs) in a rabbit model. Study Design: Controlled laboratory study. Methods: DBTSs were prepared from bovine tendons after compression, decellularization, antigen extraction, freeze drying, and sterilization. Platelet-rich plasma (PRP) was obtained by differential centrifugation. Sixty-three rabbits were used in this study, and the AT defect model was created bilaterally. All rabbits were divided into 3 groups (n = 21). In the DBTS group and the DBTS + PRP group, 2-cm-long AT was excised and reconstructed by DBTSs or PRP-treated DBTSs. In the control group, the rabbits underwent AT transection, and stumps were sutured. After surgery, all rabbits were assessed by ultrasonography and magnetic resonance imaging and then sacrificed for histological examination and biomechanical testing at 4, 8, or 12 weeks. Results: Gross observations demonstrated the absence of immunologic incompatibility and rejection. Histological examination showed that DBTSs promoted host cell infiltration and new fibrous tissue integration as compared with the control group. In each group, there was an AT-like structure formation and aligned collagen fiber deposition at 12 weeks. Mechanical properties of the reconstructed AT were not significantly different among the 3 groups at 4, 8, and 12 weeks after surgery ( P > .05). Ultrasonography and magnetic resonance imaging results illustrated that the reconstructed AT from each group maintained remodeling, and there was no significant difference in the echogenicity scoring ( P > .05) and percentages of good and excellent ( P > .05) among the 3 groups. Conclusion: DBTSs, which retain the native tendon structure and bioactive factors, had the ability to remodel and integrate into the rabbit AT and improve the healing process. Clinical Relevance: DBTSs could serve as an effective bioscaffold to reconstruct AT defects.

To determine the value of automated detection in computed tomography angiography (CTA) for cases with greater than 70% coronary stenosis.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.A total of 178 vessels from the 57 patients were analyzed. The automated CTA estimations had moderate to high levels of agreements (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.

To determine consistencies between MR perfusion weighted imaging (PW-MRI) and CT perfusion imaging (CTP) in assessing hemodynamics of patients with moyamoya disease (MMD).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 sampleDifferent parameters were found in the MMD patients compared with the controls (Both PWI-MRI and CTP can detect the area and degree of hypoperfusion of MMD patients.

To test the capability of intratumoral susceptibility signal intensity (ITSS) in the differentiation of hepatocellular carcinoma (HCC) grades.Retrospective reviews of 53 patients with pathologically proven HCCs were performed. All patients underwent both conventional MRI and SWI examinations. The morphological score of ITSS and the number of ITSS per square centimeter were calculated,then compared among low-grade and high-grade HCCs. Spearman correlation coefficients were determined between the morphological scores of ITSS,the number of ITSS per square centimeter and HCC grades. Receiver operating characteristic (ROC) curve analysiss was performed to determine the diagnostic accuracy for HCC differentiation.The morphological scores of ITSS and the number of ITSS per square centimeter in low-grade HCCs were significantly lower than that in the high-grade HCCs (ITSS of SWI had the potential to differentiate HCC grades.

To investigate the feasibility of low concentration contrast agent combined double low dose in CT pulmonary angiography.60 patients with clinically suspected pulmonary embolism examed by CT pulmonary angiography (CTPA) were divided into two groups (experimental group: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 (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.

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.To investigate the reliability of AMI and CMI characterization using native TCase-control.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.Balanced steady-state free precession (bSSFP), segmented-turbo-FLASH-PSIR, and modified Look-Locker inversion recovery (MOLLI) sequences at 3.0T.The infarct sizes were compared on matching short-axis slices of late-gadolinium-enhanced (LGE) images and TThe infarct sizes were compared on matching short-axis slices of LGE images and TMeasured infarct sizes did not differ between native TNoncontrast native T1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1406-1414.

To explore the clinical value of readout-segmented echo planar imaging (Rs-EPI) sequence in the assessment of intracranial mass lesions compared to the standard single-shot EPI (Ss-EPI) sequence.We included 21 patients with intracranial mass lesions who underwent both Ss-EPI diffusion weighted imaging (DWI) and Rs-EPI DWI at 3.0T MR scanner with a twenty-channel head-neck coil. The quality of images was assessed by two experienced radiologists independently. The differences in image quality between two sequences were analyzed using Wilcoxon signed-rank test. Inter-observer agreements were analyzed using interclass correlation coefficient (ICC) and Kappa test.All objectives were completed on 3.0T MR. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in Rs-EPI DWI were higher than those in Ss-EPI DWI (130.46±49.10 vs. 71.58±30.43,Rs-EPI DWI sequence is a potential technique to improve the imaging quality in the diagnosis of intracranial mass lesions.

To investigate the radiation dose of body surface at sensitive organs using different protective precaution in head CT scanning.The standard phantom received head routine sequence scanning with dual-source computed tomography.The phantoms were scanned with three different protective precaution:group A (without protective clothing),group B (wearing lead collar and cover lead clothes),group C (wearing lead collar and clothes without cuffs).The thermoluminescence dosimeters (TLD) was placed on the body surface corresponding to sensitive organs to measure radiation dose.The volume of CT dose index (CTDICompared to traditional lead coverage,lead collar and cloth can decrease the radiation dose of body surface and sensitive organs in head scan with dual source CT.

To determine the feasibility of using chemical exchange saturation transfer (CEST) imaging to measure creatine (Cr) metabolites with 3.0 T MR.Phantoms containing different concentrations of Cr under various pH conditions were studied with CEST sequence on 3.0 T MR imaging. CEST effect and Z spectra were analyzed.Cr exhibited significant CEST effect (± 1.8 ppm, F = 99.08, P0.001) on 3.0 T MR imaging, and positive correlation was found between the signal intensity and concentration of Cr (r = 0.963, P0.001). The CEST effect showed pH dependency of Cr (r = 0.41, P = 0.035).Creatine CEST imaging can be performed on 3.0 T MR imaging. Creatine concentrations and pH influence CEST effect.

To compare the two sequences [single shot true-FISP-PSIR (single shot-PSIR) and segmented-turbo-FLASH-PSIR (segmented-PSIR)] in the value of quantification for myocardial infarct size at 3. 0 tesla MRI.38 patients with clinical confirmed myocardial infarction were served a comprehensive gadonilium cardiac MRI at 3. 0 tesla MRI system (Trio, Siemens). Myocardial delayed enhancement (MDE) were performed by single shot-PSIR and segmented-PSIR sequences separatedly in 12-20 min followed gadopentetate dimeglumine injection (0. 15 mmol/kg). The quality of MDE images were analysed by experienced physicians. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) between the two techniques were compared. Myocardial infarct size was quantified by a dedicated software automatically (Q-mass, Medis).All objectives were scanned on the 3. 0T MR successfully. No significant difference was found in SNR and CNR of the image quality between the two sequences (P0. 05), as well as the total myocardial volume, between two sequences (P0. 05). Furthermore, there were still no difference in the infarct size [single shot-PSIR (30. 87 ± 15. 72) mL, segmented-PSIR (29. 26±14. 07) ml], ratio [single shot-PSIR (22. 94%±10. 94%), segmented-PSIR (20. 75% ± 8. 78%)] between the two sequences (P0. 05). However, the average aquisition time of single shot-PSIR (21. 4 s) was less than that of the latter (380 s).Single shot-PSIR is equal to segmented-PSIR in detecting the myocardial infarct size with less acquisition time, which is valuable in the clinic application and further research.

To examine the efficacy of fresh blood imaging (FBI) in detecting vascular diseases in lower extremity.Thirty-six patients suspected of having lower extremity vascular diseases were imaged with a 1.5-T MRI system (Toshiba Excelart Vantage). Contrast-enhanced MRA (CEMRA) and FBI technology, with maximum intensity projection (MIP) reconstruction were adopted to visualize lower extremity vascular. Signal to noise ratios (SNR) were measured on the FBI and CEMRA images. Two experienced radiologists assessed the imaging quality of peripheral artery MRA on MIP reconstructed images.All patients successfully underwent both FBI and CEMRA. All arterial segments were obtained in the 36 patients. The SNR values on FBI and CEMRA were 108.39 +/- 9.76 and 87.46 +/- 14.77 (t = - 6.782, P = 0.001), respectively. There were no significant differences in the overall image quality, arterial anatomy and venous overlap (chi2 = 0.004, P = 0.947; chi2 = 0.000, P = 1; chi2 = 0.681, P = 0.409). The CEMRA motion artifacts were less than FBI (chi2 = 8.744, P = 0.03).The FBI technique, which shows the vascular disease in lower extremity without contrast medium with ECG gating, is considered clinically useful.

To investigate the diagnostic efficacy of gadobutrol for assessing brain metastases in lung cancer patients in comparison with multihance.21 patients with lung cancer suspected of brain metastasis were enrolled in this study. All patients underwent twice MRI scans on a 3.0T MRI scanner (Siemens MAGENETOM Trio) with 8-channel head coil, first with 0.5 mol/L multihance and then with 1.0 mol/L gadobutrol as contrast agent. The dosage of contrast agent was set at 0.1 mmol/kg body mass. The interval between the two scans was 24-72 hours. The detection and delineation of lesions were evaluated visually. The signal-to-noise ratios (SNR), contrast-to-noise ratios (CNR) of lesion to normal brain white matter and the percentage of lesion enhancement were calculated and compared between the two scans by 2 experienced neuroradiologists.One patient was excluded because he received radiation therapy between the two scans. 15 patients were found to have brain metastases with a total of 35 lesions. There were no statistical differences between the two scans in SNR, CNR and percentage enhancement for both normal brain and lesions (t = 0.545, P = 0.592; t = 1.143, P = 2.267; t = 0.592, P = 0.557; t = 0.473, P = 0.639).Half-dose gadobutrol (1.0 mol/L) can achieve the same enhancement effects compared with full-dose multihance (0.5 mol/L).

To evaluate the feasibility of nonenhanced renal artery MR angiography (MRA) with respiratory-gated time spatial labeling inversion pulse (time-slip) and its clinical application.Thirty-two consecutive patients underwent the 1.5 T MRI (TOSHIBA EXCELART Vantage) examination to visualize renal artery with contrast-enhanced MRA (CEMRA) and the examination with time-slip technology. Signal to noise ratio (SNR) and contrast to noise ratio (CNR) were measured on CEMRA and Time-slip images, respectively. Two experienced radiologists assessed the imaging quality of renal artery MRA on 3D-MIPs reconstructed images.Sixty-four renal arteries and 3 accessory renal arteries in 32 patients were visualized on both time-slip and CEMRA. SNR values on Time-slip and CEMRA were 102.7 +/- 16.2 and 83.9 +/- 17.7 (t = -4.625, P0.001); CNR were 97.8 +/- 16.1 and 78.8 +/- 18.3 (t = -4.554, P0.001), respectively. Time-slip MIP images obtained higher quality than CEMRA did (Z = -2.318, P = 0.02), and venous contamination was sharply reduced on Time-slip technology when compared with CEMRA (Z = -4.895, P = 0.001).Non-enhanced renal artery MRA with respiratory-gated time-slip technology can provide higher SNR images of renal artery than conventional CEMRA, which can provide more information for clinical decision-making.