Effect of the Relationship between Respiratory Interval and Temporal Resolution on Image Quality in Free-breathing Abdominal MR Imaging.

PURPOSE: To evaluate how the relationship between respiratory interval (RI) and temporal resolution (TR) impacts image quality in free-breathing abdominal MRI (FB-aMRI) using golden-angle radial sparse parallel (GRASP). METHODS: Ten healthy volunteers (25.9 ± 2.5 years, four women) underwent 2 mins free-breathing fat-suppression T1-weighted imaging using GRASP at RIs of 3 and 5s (RI3 and RI5, respectively) and retrospectively reconstructed at TR of 1.8, 2.9, 4.8, and 7.7s (TR1.8, TR2.9, TR4.8, and TR7.7, respectively) in each patient. The standard deviation (SD) under the diaphragm was measured using SD maps showing the discrepancy for each horizontal section at all TRs. Two radiologists evaluated image quality (visualization of the right hepatic vein at the confluence of the inferior vena cava, posterior segment branch of portal vein, pancreas, left kidney, and artifacts) at all TRs using a 5-point scale. RESULTS: The SD was significantly higher at TR1.8 compared to TR4.8 (P < 0.01) and TR7.7 (P < 0.001), as well as at TR2.9 compared to TR7.7 (P < 0.01) for both RIs. The SD between TR4.8 and TR7.7 did not differ for both RIs. For all visual assessment metrics, the TR1.8 scores were significantly lower than the TR4.8 and TR7.7 scores for both RIs. The pancreas and left kidney scores at TR2.9 were significantly lower than those at TR7.7 (P < 0.05) for RI5. Additionally, the left kidney score at TR1.8 was lower than that at TR2.9 (P < 0.05) for RI3. All scores at TR2.9, TR4.8, and TR7.7 were similar for RI3, while those at TR4.8 and TR7.7 were similar for RI5. CONCLUSION: Prolonging the TRs compared to RIs enhances image quality in FB-aMRI using GRASP.

Connective differences between patients with depression with and without ASD: A case-control study.

BACKGROUND: Researchers find it difficult to distinguish between depression with ASD (Depress-wASD) and without ASD (Depression) in adult patients. We aimed to clarify the differences in brain connectivity between patients with depression with ASD and without ASD. METHODS: From April 2017 to February 2019, 22 patients with suspected depression were admitted to the hospital for diagnosis or follow-up and met the inclusion criteria. The diagnosis was determined according to the Diagnostic and Statistical Manual of Mental Disorders-5 by skilled psychiatrists. The Hamilton Depression Rating Scale (HAM-D), Young Mania Raging Scale (YMRS), Mini-International Neuropsychiatric Interview, Parent-interview ASD Rating Scale-Text Revision (PARS-TR), and Autism-Spectrum Quotient-Japanese version (AQ-J) were used to assess the patients' background and help with diagnosis. Resting-state functional magnetic resonance imaging (rs-fMRI) was performed using the 3-T-MRI system. rs-fMRI was processed using the CONN functional connectivity toolbox. Voxel-based morphometry was performed using structural images. RESULTS: No significant difference was observed between the Depress-wASD and Depression groups using the HAM-D, YMRS, AQ-J, Intelligence Quotient (IQ), and verbal IQ results. rs-fMRI for the Depress-wASD group indicated a positive connection between the salience network (SN) and right supramarginal gyrus (SMG) and a negative connection between the SN and hippocampus and para-hippocampus than that for the Depression group. No significant structural differences were observed between the groups. CONCLUSIONS: We identified differences in the SN involving the SMG and hippocampal regions between the Depress-wASD and Depression groups.

Usefulness of Breath-Hold Fat-Suppressed T2-Weighted Images With Deep Learning-Based Reconstruction of the Liver: Comparison to Conventional Free-Breathing Turbo Spin Echo.

OBJECTIVES: The aim of this study was to evaluate the usefulness of breath-hold turbo spin echo with deep learning-based reconstruction (BH-DL-TSE) in acquiring fat-suppressed T2-weighted images (FS-T2WI) of the liver by comparing this method with conventional free-breathing turbo spin echo (FB-TSE) and breath-hold half Fourier single-shot turbo spin echo with deep learning-based reconstruction (BH-DL-HASTE). MATERIALS AND METHODS: The study cohort comprised 111 patients with suspected liver disease who underwent 3 T magnetic resonance imaging. Fifty-eight focal solid liver lesions ≥10 mm were also evaluated. Three sets of FS-T2WI were acquired using FB-TSE, prototypical BH-DL-TSE, and prototypical BH-DL-HASTE, respectively. In the qualitative analysis, 2 radiologists evaluated the image quality using a 5-point scale. In the quantitative analysis, we calculated the lesion-to-liver signal intensity ratio (LEL-SIR). Friedman test and Dunn multiple comparison test were performed to assess differences among 3 types of FS-T2WI with respect to image quality and LEL-SIR. RESULTS: The mean acquisition time was 4 minutes and 43 seconds ± 1 minute and 21 seconds (95% confidence interval, 4 minutes and 28 seconds to 4 minutes and 58 seconds) for FB-TSE, 40 seconds for BH-DL-TSE, and 20 seconds for BH-DL-HASTE. In the qualitative analysis, BH-DL-HASTE resulted in the fewest respiratory motion artifacts ( P < 0.0001). BH-DL-TSE and FB-TSE exhibited significantly less motion-related signal loss and clearer intrahepatic vessels than BH-DL-HASTE ( P < 0.0001). Regarding the edge sharpness of the left lobe, BH-DL-HASTE scored the highest ( P < 0.0001), and BH-DL-TSE scored higher than FB-TSE ( P = 0.0290). There were no significant differences among 3 types of FS-T2WI with respect to the edge sharpness of the right lobe ( P = 0.1290), lesion conspicuity ( P = 0.5292), and LEL-SIR ( P = 0.6026). CONCLUSIONS: BH-DL-TSE provides a shorter acquisition time and comparable or better image quality than FB-TSE, and could replace FB-TSE in acquiring FS-T2WI of the liver. BH-DL-TSE and BH-DL-HASTE have their own advantages and may be used complementarily.

Optimal Temporal Resolution to Achieve Good Image Quality and Perform Pharmacokinetic Analysis in Free-breathing Dynamic Contrast-enhanced MR Imaging of the Pancreas.

PURPOSE: The optimal temporal resolution for free-breathing dynamic contrast-enhanced MRI (FBDCE-MRI) of the pancreas has not been determined. This study aimed to evaluate the appropriate temporal resolution to achieve good image quality and to perform pharmacokinetic analysis in FBDCE-MRI of the pancreas using golden-angle radial sparse parallel (GRASP). METHODS: Sixteen participants (53 ± 15 years, eight females) undergoing FBDCE-MRI were included in this prospective study. Images were retrospectively reconstructed at four temporal resolutions (1.8, 3.0, 4.8, and 7.8s). Two radiologists (5 years of experience) evaluated the image quality of each reconstructed image by assessing the visualization of the celiac artery (CEA), the common hepatic artery, the splenic artery, each area of the pancreas, and artifacts using a 5-point scale. Using Tissue-4D, pharmacokinetic parameters were calculated for each area in the reconstructed images at each temporal resolution for 16 examinations, excluding two with errors in the pharmacokinetic modeling analysis. Friedman and Bonferroni tests were used for analysis. A P value < 0.05 was considered statistically significant. RESULTS: During vascular assessment, only scores for the CEA at 7.8s were significantly lower than the other temporal resolutions. Scores of all pancreatic regions and artifacts were significantly lower at 1.8s than at 4.8s and 7.8s. In the pharmacokinetic analysis, all volume transfer coefficients (Ktrans), rate constants (Kep), and the initial area under the concentration curve (iAUC) in the pancreatic head and tail were significantly lower at 4.8s and 7.8s than at 1.8s. iAUC in the pancreatic body and extracellular extravascular volume fraction (Ve) in the pancreatic head were significantly lower at 7.8s than at 1.8s. CONCLUSION: A temporal resolution of 3.0s is appropriate to achieve image quality and perform pharmacokinetic analysis in FBDCE-MRI of the pancreas using GRASP.

Simultaneous brain structure segmentation in magnetic resonance images using deep convolutional neural networks.

In brain magnetic resonance imaging (MRI) examinations, rapidly acquired two-dimensional (2D) T1-weighted sagittal slices are typically used to confirm brainstem atrophy and the presence of signals in the posterior pituitary gland. Image segmentation is essential for the automatic evaluation of chronological changes in the brainstem and pituitary gland. Thus, the purpose of our study was to use deep learning to automatically segment internal organs (brainstem, corpus callosum, pituitary, cerebrum, and cerebellum) in midsagittal slices of 2D T1-weighted images. Deep learning for the automatic segmentation of seven regions in the images was accomplished using two different methods: patch-based segmentation and semantic segmentation. The networks used for patch-based segmentation were AlexNet, GoogLeNet, and ResNet50, whereas semantic segmentation was accomplished using SegNet, VGG16-weighted SegNet, and U-Net. The precision and Jaccard index were calculated, and the extraction accuracy of the six convolutional network (DCNN) systems was evaluated. The highest precision (0.974) was obtained with the VGG16-weighted SegNet, and the lowest precision (0.506) was obtained with ResNet50. Based on the data, calculation times, and Jaccard indices obtained in this study, segmentation on a 2D image may be considered a viable and effective approach. We found that the optimal automatic segmentation of organs (brainstem, corpus callosum, pituitary, cerebrum, and cerebellum) on brain sagittal T1-weighted images could be achieved using SegNet with VGG16.

High-resolution three-dimensional T1-weighted hepatobiliary MR cholangiography using Gd-EOB-DTPA for assessment of biliary tree anatomy: Parallel imaging versus compressed sensing.

PURPOSE: To compare the quality of images obtained by T1-weighted hepatobiliary MR cholangiography using Gd-EOB-DTPA with 1-mm isovoxel acquisition and compressed sensing (T1-MRCCS) or parallel imaging (T1-MRCPI) for assessment of biliary tree anatomy. METHOD: We prospectively reviewed T1-MRCCS, T1-MRCPI, and respiratory-triggered 3D T2-weighted MR cholangiography (T2-MRC) images in 58 patients. Two radiologists independently assessed the three sets of images and scored the biliary tree visualization and overall image quality in all cases using a 5-point Likert scale. The resulting scores were compared among T1-MRCCS, T1-MRCPI, and T2-MRC images using a Friedman test followed by a Scheffe test. The inter-reader agreement in scoring was assessed using κ statistics. RESULTS: The image quality scores for the gallbladder on both T1-MRCCS and T1-MRCPI were significantly lower than those on T2-MRC (p < 0.01) for both readers. Meanwhile, the image quality scores for the right and left hepatic ducts and the anterior and posterior branches of the right hepatic duct on both T1-MRCCS and T1-MRCPI were significantly higher than those on T2-MRC (p < 0.05) for both readers. For Reader 2, the overall image quality scores on T1-MRCCS and T1-MRCPI were both significantly higher than those on T2-MRC (p < 0.05). There were no significant differences between the image quality scores on T1-MRCCS and T1-MRCPI for visualization of each bile duct (p < 0.05). CONCLUSIONS: There may be no significant difference in quality between T1-MRCCS images and T1-MRCPI images for assessment of biliary tree anatomy, and both types of images may be better than T2-MRC images, although clinical indication is limited compared with T2-MRC.

[Examination of Gd-EOB-DTPA Liver Dynamic Contrast-enhanced MRI Using Radial VIBE with k-space Weighted Image Contrast Method].

Dynamic contrast-enhanced magnetic imaging (DCE-MRI) is a useful method for detection and diagnosis of liver lesions. However, DCE-MRI using Gd-EOB-DTPA has some problems with arterial phase images. Radial volumetric imaging breath-holding examination (r-VIBE) with k-space weighted image contrast reconstruction (KWIC), which is a modification of Cartesian VIBE (c-VIBE), is a new 3D-gradient echo sequence with a number of advantages compared with c-VIBE, including lower motion sensitivity. This study was performed to evaluate image contrast, blurring, and temporal phase division effects of r-VIBE in comparison with c-VIBE. Image contrast using diluted Gd-EOB-DTPA aqueous solution showed no significant difference between r-VIBE and c-VIBE. Imaging was performed with r-VIBE and c-VIBE during injection of a Gd-EOB-DTPA solution into a serpentine tube. r-VIBE showed a smaller half-width of the signal intensity profile of the tube and less image artifacts by blurring when compared to c-VIBE. The arrival times and durations of the maximum signal strengths of r-VIBE and c-VIBE images during injection of Gd-EOB-DTPA solution into the tube were almost identical. r-VIBE improved the temporal resolution without degradation of liver DCE-MRI using Gd-EOB-DTPA.

Quantitative estimation of progression of chronic liver disease using gadoxetate disodium-enhanced magnetic resonance imaging.

AIM: The purpose of this study was to determine whether the liver stiffness (LS) measured on magnetic resonance (MR) elastography can be estimated by a combination of gadoxetate disodium-enhanced MR imaging (EOB-MRI) and ordinary blood tests. METHODS: We evaluated 33 consecutive patients with suspected liver disease who underwent EOB-MRI using a Differential Subsampling with Cartesian Ordering MR sequence and MR elastography using a 1.5-T MR system in this prospective study. A stepwise multiple linear regression model analysis of LS was performed using various predictive values obtained from two-in-one-uptake, two-compartment model analysis of EOB-MRI (velocity constants of arterial inflow [K1a ], portal venous inflow [K1p ], hepatocellular uptake [Ki ]), and ordinary blood test results (blood platelet count, serum albumin level [ALB], total serum bilirubin level [T-BIL], and prothrombin time [PT%]). RESULTS: Multiple linear regression model analysis revealed that hepatic perfusion-uptake index (HPUI = -K1a + K1p + Ki ) (P < 0.0001), albumin-bilirubin linear predictor (ALBI-LP = 0.66 × log10 T-BIL - 0.085 × ALB) (P = 0.034), and blood platelet count (P = 0.046) were significant independent predictors of LS (r = 0.863). The area under receiver operator characteristics curve of multiple linear regression model in prediction of the liver stiffness corresponding to higher (LS > 5.0 kPa) and lower (LS < 4.2 kPa) risk for developing hepatocellular carcinoma were 0.956 and 0.938, respectively. CONCLUSION: LS can be estimated quantitatively with the use of HPUI obtained from compartment model analysis of EOB-MRI combined with ALBI-LP and blood platelet count.

[Effects of region of interest settings in signal-to-noise ratio measurement of clinical images using arrays of multiple receiver coils].

In many clinical imaging procedures using arrays of multiple receiver coils, a uniform sensitivity process is performed using the sensitivity distribution from the body coil. This causes the noise to be uneven, and background noise cannot be used when measuring the signal-to-noise ratio (SNR). The SNR of clinical images with sensitivity correction using arrays of multiple receiver coils sets the region of interest (ROI) in the region where the signal is uniform, and is limited to the identical ROI method where measurements are taken with noise from the identical region. When SNR is measured with the identical ROI method, uneven noise caused by sensitivity correction as well as the signal strength distribution within the ROI of the object is reflected in the noise. Therefore, evaluation must be performed in as localized a position as possible. Measurement error becomes small on images with higher resolution, and if ROI larger than 10×10 pixels can be set in a region of even signal, SNR measurement of clinical images with less underestimation may be possible.