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Introduction: Arterial spin labeling (ASL) is a non-invasive magnetic resonance imaging (MRI) technique that can measure regional cerebral blood flow (rCBF) without radiation exposure. This study aimed to evaluate rCBF in individuals with autism and their age-matched controls, globally and regionally., Methods: We performed ASL MRI (3 T, pulsed-continuous ASL, 3 delayed ASL imaging sequences) for 33 patients with autism spectrum disorder (ASD) (average age: 7.3 years, range: 2-14 years). Nineteen children (average age: 8.6 years, range: 3-15 years) without ASD and intellectual delay were included as controls. Patients with morphological abnormalities detected on MRI were excluded. Objective analysis was performed with automatic region of interest analysis of the ASL results. The Mann-Whitney U test was used to compare the rCBF results between the groups., Results: Compared to the controls, patients with ASD showed a statistically significant decrease in rCBF, respectively, in the insula [left, rCBF 51.8 ± 9.5 mL/100 g/min (mean ± SD) versus 59.9 ± 9.8, p = 0.0017; right, 51.2 ± 10.1 versus 57.8 ± 8.8, p = 0.0354], superior parietal lobule (left, 44.6 ± 8.4 versus 52.0 ± 7.8, p = 0.003), superior temporal gyrus (left, 50.0 ± 8.6 versus 56.9 ± 8.6, p = 0.007; right, 49.5 ± 8.4 versus 56.4 ± 7.7, p = 0.0058), and inferior frontal gyrus (left, 53.0 ± 9.8 versus 59.3 ± 9.9, p = 0.0279), which are associated with the mirror neuron system., Conclusions: We concluded that patients with ASD showed a statistically significant decline in CBF in regions associated with the mirror neuron system. The advantages of ASL MRI include low invasiveness (no radiation exposure) and short imaging time (approximately 5 min). Studies with larger sample sizes are required to establish the diagnostic value of ASL MRI for ASD., (Copyright © 2020 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.)

The purpose of our study was to clarify the dependence of quantitative susceptibility mapping (QSM) on echo time (TE). We constructed a phantom consisting of six tubes; three tubes were filled with different concentrations (0.5, 1.0, and 2.5 mM) of gadopentetate dimeglumine (Gd-DTPA), and three were filled with different concentrations (100, 200, and 350 mg/mL) of calcium hydroxyapatite. Real and imaginary images from multi-echo spoiled gradient-echo data (12 echoes) were acquired. We then used four datasets with three serial echoes. The QSM procedure consists of four steps: field map estimation, phase unwrapping, background removal, and dipole inversion. For each sample, we compared the measured mean susceptibility value with the theoretical susceptibility value and conducted a linear regression analysis. Accordingly, the relationship between the measured susceptibility and concentration of Gd-DTPA was shown to agree well with the theoretical values (TEs = 16.4, 20.8, and 25.2 ms; slope = 0.24, R 2  = 1.00). Furthermore, the relationship between the measured susceptibility and concentration of hydroxyapatite also showed good linearity (TEs = 16.4, 20.8, and 25.2 ms; slope = - 0.00121, R 2  = 1.00). In conclusion, the optimization of the TE in QSM makes it possible to obtain more detailed information regarding the susceptibility of biomaterials.

Purpose : To compare data on brain tumors derived from intravoxel incoherent motion (IVIM) and arterial spin labeling (ASL) imaging with multiple parameters obtained on dynamic susceptibility contrast (DSC) perfusion MRI and to clarify the characteristics of IVIM and ASL perfusion data from the viewpoint of cerebral blood flow (CBF) analysis. Methods : ASL-CBF and IVIM techniques as well as DSC examination were performed in 24 patients with brain tumors. The IVIM data were analyzed with the two models. The relative blood flow (rBF), relative blood volume (rBV) corrected relative blood volume (crBV), mean transit time (MTT), and leakage coefficient (K2) were obtained from the DSC MRI data. Results : The ASL-CBF had the same tendency as the perfusion parameters derived from the DSC data, but the permeability from the vessels had less of an effect on the ASL-CBF. The diffusion coefficient of the fast component on IVIM contained more information on permeability than the f value. Conclusion : ASL-CBF is more suitable for the evaluation of perfusion in brain tumors than IVIM parameters. ASL-CBF and IVIM techniques should be carefully selected and the biological significance of each parameter should be understood for the correct comprehension of the pathological status of brain tumors. J. Med. Invest. 66 : 308-313, August, 2019.

Objective: This study aims to improve the signal-to-noise ratio (SNR) of the phase image focusing T2 and to develop an improved phase (iPhase) image acquired high SNR., Methods: The iPhase images of phantom and brain were acquired with multi-echo spoiled gradient-echo. The phantom component was a gadopentetate dimeglumine (Gd-DTPA) solution made of different concentrations (0.1, 0.5, and 1 wt%) and Gd-DTPA (0.1, 0.5, and 1 wt%) with agar (1.0 wt%). We applied the iPhase image to susceptibility weighed image (SWI) and evaluated SNR of SWI., Results: In phantom study, SNRs of conventional SWI at each sample were 19.8, 15.7, 7.4, 20.0, 17.4, and 27.3, respectively. Signal-to-noise ratios of SWI derived from iPhase method were 29.5, 33.7, 21.7, 28.5, 24.3, and 14.7, respectively. Then, the SNR showed an improvement of 196% at maximum (for the Gd-DTPA 1 wt% sample). In healthy volunteer study, SWI derived from iPhase method had the good contrast between white matter and gray matter., Conclusions: The iPhase image was able to improve the phase SNR. Moreover, iPhase method makes it possible to obtain a high SNR image when applying to SWI.

Background: To evaluate the degree of cerebral atrophy for Alzheimer's disease (AD), voxel-based morphometry has been performed with magnetic resonance imaging. Detailed morphological changes in a specific tissue area having the most evidence of atrophy were not considered by the machine-learning technique. Purpose: To develop a machine-learning system that can capture morphology features for determination of atrophy of brain tissue in early-stage AD and classification of healthy participants or patients. Material and Methods: Three-dimensional T1-weighted (3D-T1W) data were obtained from AD Neuroimaging Initiative (200 healthy controls and 200 patients with early-stage AD). Automated segmentation of 3D-T1W data was performed. Deep learning (DL) and support vector machine (SVM) were trained using 66-segmented volume values as input and AD diagnosis as output. DL was performed using 66 volume values or gray matter (GM) and white matter (WM) volume values. SVM learning was performed using 66 volume values and six regions with high variable importance. 3D convolutional neural network (3D-CNN) was trained using the segmented images. Accuracy and area under curve (AUC) were obtained. Variable importance was evaluated from logistic regression analysis. Results: DL for GM and WM volume values, accuracy 0.6; SVM for all volume values, accuracy 0.82 and AUC 0.81; DL for all volume values, accuracy 0.82 and AUC 0.8; 3D-CNN using segmental images of the whole brain, accuracy 0.5 and AUC 0.51. SVM using volume values of six regions, accuracy 0.82; image-based 3D-CNN, highest accuracy 0.69. Conclusion: Our results show that atrophic features are more considerable than morphological features in the early detection of AD. [ABSTRACT FROM AUTHOR]

In clinical magnetic resonance imaging, gadolinium-based contrast agents are commonly used for detecting brain tumors and evaluating the extent of malignancy. We present a new method to evaluate relaxivity (r1) and contrast agent concentration separately in contrast-enhanced lesions using quantitative parameter mapping (QPM). Furthermore, we also aimed to estimate the extracellular pH (pHe) of tumor lesions. We demonstrated that it is possible to evaluate pathophysiological tumor changes due to therapeutic efficacy by measuring r1 in contrast-enhanced lesions. In this study, the primary brain tumor group showed significantly higher r1 values than other brain disease groups (P < 0.001). Moreover, mean pHe value showed a trend for tumor malignancy having a lower pHe value and primary brain tumor having a significantly lower pHe than other brain diseases (P < 0.001). Our results might suggest that QPM can separately quantify r1 and CA concentration in brain tumors and that pHe brain tumor mapping could serve as a tumor biomarker. In conclusion, our method has potential clinical applications for assessing the treatment effects. [ABSTRACT FROM AUTHOR]

Purpose: We developed a novel method which is applicable to visualize contrast according to myelin components in the human brain using relaxation time derived from quantitative parameter mapping magnetic resonance imaging (QPM-MRI).Materials and Methods: Using healthy volunteer data (n = 10), we verified that our method demonstrated that the myelin-weighted contrast increased proportionally by products R1 and R2*, i.e., QPM-myelin-weighted image, in which modified T1-weighted/T2-weighted (T1w/T2w) ratio mapping method was applied. We compared measurement values in white matter (WM) and gray matter (GM) regions of the T1w/T2w ratio and R1·R2* product maps of healthy volunteers. Linear regression analysis between each value. Mann Whitney U test between WM and GM signals in each myelin map. In addition, Additionally, QPM-myelin-weighted image was applied to a 32-year-old female MS patient.Results: Linear regression analysis showed a highly significant correlation between conventional T1w/T2w ratios and R1·R2* products derived from QPM (R = 0.73, P < 0.0001). Moreover, there is a significant difference between WM and GM structures in each myelin images (both, P < 0.0001). Additionally, in a clinical case, MS lesions enabled observation of not only MS plaques but also heterogeneous myelin signal loss associated with demyelination more clearly than T2w image and conventional T1w/T2w ratio image.Conclusion: Our myelin-weighted imaging technique using QPM may be useful for myelin visualization and is expected to become independent of measurement conditions due to having quantitative characteristics of QPM itself. [ABSTRACT FROM AUTHOR]