Free water in gray matter linked to gut microbiota changes with decreased butyrate producers in Alzheimer's disease and mild cognitive impairment.

Neuroinflammation contributes to the pathology and progression of Alzheimer's disease (AD), and it can be observed even with mild cognitive impairment (MCI), a prodromal phase of AD. Free water (FW) imaging estimates the extracellular water content and has been used to study neuroinflammation across several neurological diseases including AD. Recently, the role of gut microbiota has been implicated in the pathogenesis of AD. The relationship between FW imaging and gut microbiota was examined in patients with AD and MCI. Fifty-six participants underwent neuropsychological assessments, FW imaging, and gut microbiota analysis targeting the bacterial 16S rRNA gene. They were categorized into the cognitively normal control (NC) (n = 19), MCI (n = 19), and AD (n = 18) groups according to the neuropsychological assessments. The correlations of FW values, neuropsychological assessment scores, and the relative abundance of gut microbiota were analyzed. FW was higher in several white matter tracts and in gray matter regions, predominantly the frontal, temporal, limbic and paralimbic regions in the AD/MCI group than in the NC group. In the AD/MCI group, higher FW values in the temporal (superior temporal and temporal pole), limbic and paralimbic (insula, hippocampus and amygdala) regions were the most associated with worse neuropsychological assessment scores. In the AD/MCI group, FW values in these regions were negatively correlated with the relative abundances of butyrate-producing genera Anaerostipes, Lachnospiraceae UCG-004, and [Ruminococcus] gnavus group, which showed a significant decreasing trend in the order of the NC, MCI, and AD groups. The present study showed that increased FW in the gray matter regions related to cognitive impairment was associated with low abundances of butyrate producers in the AD/MCI group. These findings suggest an association between neuroinflammation and decreased levels of the short-chain fatty acid butyrate that is one of the major gut microbial metabolites having a potentially beneficial role in brain homeostasis.

Dosimetric evaluation in Helical TomoTherapy for lung SBRT using Monte Carlo-based independent dose verification software.

PURPOSE: To elucidate the dosimetric errors caused by a model-based algorithm in lung stereotactic body radiation therapy (SBRT) with Helical TomoTherapy (HT) using Monte Carlo (MC)-based dose verification software. METHODS: For 38 plans of lung SBRT, the dose calculation accuracy of a treatment planning system (TPS) of HT was compared with the results of DoseCHECK, the commercial MC-based independent verification software. The following indices were extracted to evaluate the correlation of dosimetric errors: (1) target volume, (2) average computed tomography (CT) value of the planning target volume (PTV) margin, and (3) average CT value of surrounding 2-mm area of the PTV (PTV ring). Receiver operating characteristic (ROC) analyses determined the threshold for 5% of differences in PTV D95%. Then, the 38 plans were classified into two groups using the cutoff values of ROC analysis for these three indices. Dosimetric differences between groups were statistically compared using the Mann-Whitney U test. RESULTS: TPS of HT overestimated by more than 5% in the PTV D95% in 16 of 38 plans. The PTV ring showed the strongest correlation with dosimetric differences. The cutoff value for the target volume, the PTV margin, and the PTV ring was 14.7 cc, -754 HU, and -708 HU, respectively. The area under the curve (AUC) for the target volume, the PTV margin, and the PTV ring were 0.835, 0.878, and 0.932, respectively. Dosimetric errors more than 5% were observed when the PTV volume was less than 15 cc or when the CT value around the target was less than -700 HU. CONCLUSION: The TPS of HT might overestimate the PTV dose by more than 5% if any the three indices in this study were below threshold. Therefore, independent verification with an MC-based algorithm should be strongly recommended for lung SBRT in HT.

Diameters of the optic sheath and superior ophthalmic vein can expand and contract at positional changes: a magnetic resonance imaging study.

PURPOSE: This study aimed to explore the diameters of the optic sheath (OSD) and superior ophthalmic vein (SOVD) in response to positional changes using magnetic resonance imaging (MRI). MATERIALS AND METHODS: Fifty adult outpatients who presented to the hospital underwent thin-slice coronal T2-weighted MRI in the supine position followed by the prone position. RESULTS: The OS and SOV were well delineated in all the patients. The OSD in the anterior orbit was measured in the supine and prone positions on both sides. In addition, the SOVD in the anterior and posterior orbits was measured in the supine and prone positions on both sides. The OSD demonstrated an increase on both sides in 100% of the cases. The SOVD demonstrated an increase on both sides in 94% of the cases, whereas the remaining 6% demonstrated a decrease. The OSD measured at the anterior orbit and the SOVD at the anterior and posterior orbits significantly increased on both sides with positional changes from the supine to the prone position. CONCLUSION: OSD and SOVD may expand and contract in response to alterations in the intracranial pressure and venous flow patterns. MRI examination in the supine position combined with positional changes can help to better understand the OS and SOV as dynamic structures.

Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning.

PURPOSE: Myelination-related MR signal changes in white matter are helpful for assessing normal development in infants and children. A rule-based myelination evaluation workflow regarding signal changes on T1-weighted images (T1WIs) and T2-weighted images (T2WIs) has been widely used in radiology. This study aimed to simulate a rule-based workflow using a stacked deep learning model and evaluate age estimation accuracy. METHODS: The age estimation system involved two stacked neural networks: a target network-to extract five myelination-related images from the whole brain, and an age estimation network from extracted T1- and T2WIs separately. A dataset was constructed from 119 children aged below 2 years with two MRI systems. A four-fold cross-validation method was adopted. The correlation coefficient (CC), mean absolute error (MAE), and root mean squared error (RMSE) of the corrected chronological age of full-term birth, as well as the mean difference and the upper and lower limits of 95% agreement, were measured. Generalization performance was assessed using datasets acquired from different MR images. Age estimation was performed in Sturge-Weber syndrome (SWS) cases. RESULTS: There was a strong correlation between estimated age and corrected chronological age (MAE: 0.98 months; RMSE: 1.27 months; and CC: 0.99). The mean difference and standard deviation (SD) were -0.15 and 1.26, respectively, and the upper and lower limits of 95% agreement were 2.33 and -2.63 months. Regarding generalization performance, the performance values on the external dataset were MAE of 1.85 months, RMSE of 2.59 months, and CC of 0.93. Among 13 SWS cases, 7 exceeded the limits of 95% agreement, and a proportional bias of age estimation based on myelination acceleration was exhibited below 12 months of age (P = 0.03). CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.

Effect of Temporal Sampling Rate on Estimates of the Perfusion Parameters for Patients with Moyamoya Disease Assessed with Simultaneous Multislice Dynamic Susceptibility Contrast-enhanced MR Imaging.

PURPOSE: The effect of temporal sampling rate (TSR) on perfusion parameters has not been fully investigated in Moyamoya disease (MMD); therefore, this study evaluated the influence of different TSRs on perfusion parameters quantitatively and qualitatively by applying simultaneous multi-slice (SMS) dynamic susceptibility contrast-enhanced MR imaging (DSC-MRI). METHODS: DSC-MRI datasets were acquired from 28 patients with MMD with a TSR of 0.5 s. Cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time to peak (TTP), and time to maximum tissue residue function (Tmax) were calculated for eight TSRs ranging from 0.5 to 4.0 s in 0.5-s increments that were subsampled from a TSR of 0.5 s datasets. Perfusion measurements and volume for chronic ischemic (Tmax ≥ 2 s) and non-ischemic (Tmax < 2 s) areas for each TSR were compared to measurements with a TSR of 0.5 s, as was visual perfusion map analysis. RESULTS: CBF, CBV, and Tmax values tended to be underestimated, whereas MTT and TTP values were less influenced, with a longer TSR. Although Tmax values were overestimated in the TSR of 1.0 s in non-ischemic areas, differences in perfusion measurements between the TSRs of 0.5 and 1.0 s were generally minimal. The volumes of the chronic ischemic areas with a TSR ≥ 3.0 s were significantly underestimated. In CBF and CBV maps, no significant deterioration was noted in image quality up to 3.0 and 2.5 s, respectively. The image quality of MTT, TTP, and Tmax maps for the TSR of 1.0 s was similar to that for the TSR of 0.5 s but was significantly deteriorated for the TSRs of ≥ 1.5 s. CONCLUSION: In the assessment of MMD by SMS DSC-MRI, application of TSRs of ≥ 1.5 s may lead to deterioration of the perfusion measurements; however, that was less influenced in TSRs of ≤ 1.0 s.

Effective clinical applications of Monte Carlo-based independent secondary dose verification software for helical tomotherapy.

PURPOSE: To investigate the scope of the effective clinical application of Monte Carlo (MC)-based independent dose verification software for helical tomotherapy. METHODS: DoseCHECK was selected as the MC-based dose calculation software. First, the dose calculation accuracy of DoseCHECK was evaluated with film and chamber measurements in a water-equivalent phantom. Second, the dose calculation accuracy was examined in several heterogeneous materials. Finally, dosimetric comparisons between DoseCHECK and the treatment planning system (TPS) were performed for clinical patient plans. Prostate IMRT, head and neck IMRT (HN), total body irradiation (TBI), and brain stereotactic radiotherapy (SRT) were evaluated. RESULT: The DoseCHECK calculations agreed with the chamber and film measurements in the homogenous phantom. For heterogeneous phantom cases, the dose differences between DoseCHECK and TPS were within 3 %, except in air, in which large dose differences of 20 % were observed. In clinical patient plans, the median dose differences between the lung Dmean in TBI cases and the normal brain Dmean in brain SRT cases were significantly >3 %. For HN and brain SRT cases, the median target dose differences were >3 %. CONCLUSION: Our results show that independent dose verification with the MC algorithm can detect systematic errors caused by the lack of heterogeneity correction in the TPS. In particular, MC-based independent dose verification is required for HN, TBI, and brain SRT cases in helical tomotherapy.

Radiologic criteria of retropharyngeal lymph node metastasis in maxillary sinus cancer.

OBJECTIVE: To determine the most appropriate radiologic criteria of metastatic retropharyngeal lymph nodes (RLNs) in patients with maxillary sinus cancer (MSC). MATERIALS AND METHODS: We retrospectively evaluated 16 consecutive patients who underwent magnetic resonance imaging (MRI) before and after the treatment of locally advanced squamous cell carcinoma of the maxillary sinus. The minimal and maximal diameters of all RLNS were recorded. RLNs were classified as metastatic on the basis of the MRI follow-up (f/u). RLNs were considered non-metastatic if stable disease continued until the final MRI f/u and metastatic in cases with different evaluations (complete response, partial response, progressive disease) determined using Response Evaluation Criteria in Solid Tumours (RECIST) ver. 1.1. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to assess the accuracy of various criteria in the diagnosis of metastatic RLNs. RESULTS: Of the 34 RLNs in 16 cases observed on pretreatment MRI, 7 were classified as metastatic RLNs and 27 as non-metastatic RLNs. Using the radiologic criteria, metastatic RLNs tended to be diagnosed more accurately with the minimal axial diameter than with the maximal axial diameter (AUC; 0.97 vs. 0.73, p = 0.06). The most accurate size criterion of metastatic RLNs was a minimal axial diameter of 5 mm or larger, with an accuracy of 94.1% (32 of 34). CONCLUSIONS: The most appropriate radiologic criterion of metastatic RLNs in MSC is a minimal axial diameter of 5 mm or longer.

Deep Learning Approach for Generating MRA Images From 3D Quantitative Synthetic MRI Without Additional Scans.

OBJECTIVES: Quantitative synthetic magnetic resonance imaging (MRI) enables synthesis of various contrast-weighted images as well as simultaneous quantification of T1 and T2 relaxation times and proton density. However, to date, it has been challenging to generate magnetic resonance angiography (MRA) images with synthetic MRI. The purpose of this study was to develop a deep learning algorithm to generate MRA images based on 3D synthetic MRI raw data. MATERIALS AND METHODS: Eleven healthy volunteers and 4 patients with intracranial aneurysms were included in this study. All participants underwent a time-of-flight (TOF) MRA sequence and a 3D-QALAS synthetic MRI sequence. The 3D-QALAS sequence acquires 5 raw images, which were used as the input for a deep learning network. The input was converted to its corresponding MRA images by a combination of a single-convolution and a U-net model with a 5-fold cross-validation, which were then compared with a simple linear combination model. Image quality was evaluated by calculating the peak signal-to-noise ratio (PSNR), structural similarity index measurements (SSIMs), and high frequency error norm (HFEN). These calculations were performed for deep learning MRA (DL-MRA) and linear combination MRA (linear-MR), relative to TOF-MRA, and compared with each other using a nonparametric Wilcoxon signed-rank test. Overall image quality and branch visualization, each scored on a 5-point Likert scale, were blindly and independently rated by 2 board-certified radiologists. RESULTS: Deep learning MRA was successfully obtained in all subjects. The mean PSNR, SSIM, and HFEN of the DL-MRA were significantly higher, higher, and lower, respectively, than those of the linear-MRA (PSNR, 35.3 ± 0.5 vs 34.0 ± 0.5, P < 0.001; SSIM, 0.93 ± 0.02 vs 0.82 ± 0.02, P < 0.001; HFEN, 0.61 ± 0.08 vs 0.86 ± 0.05, P < 0.001). The overall image quality of the DL-MRA was comparable to that of TOF-MRA (4.2 ± 0.7 vs 4.4 ± 0.7, P = 0.99), and both types of images were superior to that of linear-MRA (1.5 ± 0.6, for both P < 0.001). No significant differences were identified between DL-MRA and TOF-MRA in the branch visibility of intracranial arteries, except for ophthalmic artery (1.2 ± 0.5 vs 2.3 ± 1.2, P < 0.001). CONCLUSIONS: Magnetic resonance angiography generated by deep learning from 3D synthetic MRI data visualized major intracranial arteries as effectively as TOF-MRA, with inherently aligned quantitative maps and multiple contrast-weighted images. Our proposed algorithm may be useful as a screening tool for intracranial aneurysms without requiring additional scanning time.

A Novel Deep Learning Approach with a 3D Convolutional Ladder Network for Differential Diagnosis of Idiopathic Normal Pressure Hydrocephalus and Alzheimer's Disease.

PURPOSE: Idiopathic normal pressure hydrocephalus (iNPH) and Alzheimer's disease (AD) are geriatric diseases and common causes of dementia. Recently, many studies on the segmentation, disease detection, or classification of MRI using deep learning have been conducted. The aim of this study was to differentiate iNPH and AD using a residual extraction approach in the deep learning method. METHODS: Twenty-three patients with iNPH, 23 patients with AD and 23 healthy controls were included in this study. All patients and volunteers underwent brain MRI with a 3T unit, and we used only whole-brain three-dimensional (3D) T1-weighted images. We designed a fully automated, end-to-end 3D deep learning classifier to differentiate iNPH, AD and control. We evaluated the performance of our model using a leave-one-out cross-validation test. We also evaluated the validity of the result by visualizing important areas in the process of differentiating AD and iNPH on the original input image using the Gradient-weighted Class Activation Mapping (Grad-CAM) technique. RESULTS: Twenty-one out of 23 iNPH cases, 19 out of 23 AD cases and 22 out of 23 controls were correctly diagnosed. The accuracy was 0.90. In the Grad-CAM heat map, brain parenchyma surrounding the lateral ventricle was highlighted in about half of the iNPH cases that were successfully diagnosed. The medial temporal lobe or inferior horn of the lateral ventricle was highlighted in many successfully diagnosed cases of AD. About half of the successful cases showed nonspecific heat maps. CONCLUSION: Residual extraction approach in a deep learning method achieved a high accuracy for the differential diagnosis of iNPH, AD, and healthy controls trained with a small number of cases.

Evaluation of white matter microstructure in patients with Parkinson's disease using microscopic fractional anisotropy.

PURPOSE: Micro fractional anisotropy (µFA) is more accurate than conventional fractional anisotropy (FA) for assessing microscopic tissue properties and can overcome limitations related to crossing white matter fibres. We compared µFA and FA for evaluating white matter changes in patients with Parkinson's disease (PD). METHODS: We compared FA and µFA measures between 25 patients with PD and 25 age- and gender-matched healthy controls using tract-based spatial statistics (TBSS) analysis. We also examined potential correlations between changes, revealed by conventional FA or µFA, and disease duration or Unified Parkinson's Disease Rating Scale (UPDRS)-III scores. RESULTS: Compared with healthy controls, patients with PD had significantly reduced µFA values, mainly in the anterior corona radiata (ACR). In the PD group, µFA values (primarily those from the ACR) were significantly negatively correlated with UPDRS-III motor scores. No significant changes or correlations with disease duration or UPDRS-III scores with tissue properties were detected using conventional FA. CONCLUSION: µFA can evaluate microstructural changes that occur during white matter degeneration in patients with PD and may overcome a key limitation of FA.

Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI.

PURPOSE: This study aimed to evaluate the accuracy and diagnostic test performance of the U-net-based segmentation method in neuromelanin magnetic resonance imaging (NM-MRI) compared to the established manual segmentation method for Parkinson's disease (PD) diagnosis. METHODS: NM-MRI datasets from two different 3T-scanners were used: a "principal dataset" with 122 participants and an "external validation dataset" with 24 participants, including 62 and 12 PD patients, respectively. Two radiologists performed SNpc manual segmentation. Inter-reader precision was determined using Dice coefficients. The U-net was trained with manual segmentation as ground truth and Dice coefficients used to measure accuracy. Training and validation steps were performed on the principal dataset using a 4-fold cross-validation method. We tested the U-net on the external validation dataset. SNpc hyperintense areas were estimated from U-net and manual segmentation masks, replicating a previously validated thresholding method, and their diagnostic test performances for PD determined. RESULTS: For SNpc segmentation, U-net accuracy was comparable to inter-reader precision in the principal dataset (Dice coefficient: U-net, 0.83 ± 0.04; inter-reader, 0.83 ± 0.04), but lower in external validation dataset (Dice coefficient: U-net, 079 ± 0.04; inter-reader, 0.85 ± 0.03). Diagnostic test performances for PD were comparable between U-net and manual segmentation methods in both principal (area under the receiver operating characteristic curve: U-net, 0.950; manual, 0.948) and external (U-net, 0.944; manual, 0.931) datasets. CONCLUSION: U-net segmentation provided relatively high accuracy in the evaluation of the SNpc in NM-MRI and yielded diagnostic performance comparable to that of the established manual method.

Free-Water Imaging in White and Gray Matter in Parkinson's Disease.

This study aimed to discriminate between neuroinflammation and neuronal degeneration in the white matter (WM) and gray matter (GM) of patients with Parkinson's disease (PD) using free-water (FW) imaging. Analysis using tract-based spatial statistics (TBSS) of 20 patients with PD and 20 healthy individuals revealed changes in FW imaging indices (i.e., reduced FW-corrected fractional anisotropy (FAT), increased FW-corrected mean, axial, and radial diffusivities (MDT, ADT, and RDT, respectively) and fractional volume of FW (FW) in somewhat more specific WM areas compared with the changes of DTI indices. The region-of-interest (ROI) analysis further supported these findings, whereby those with PD showed significantly lower FAT and higher MDT, ADT, and RDT (indices of neuronal degeneration) in anterior WM areas as well as higher FW (index of neuroinflammation) in posterior WM areas compared with the controls. Results of GM-based spatial statistics (GBSS) analysis revealed that patients with PD had significantly higher MDT, ADT, and FW than the controls, whereas ROI analysis showed significantly increased MDT and FW and a trend toward increased ADT in GM areas, corresponding to Braak stage IV. These findings support the hypothesis that neuroinflammation precedes neuronal degeneration in PD, whereas WM microstructural alterations precede changes in GM.

Three-dimensional high-resolution simultaneous quantitative mapping of the whole brain with 3D-QALAS: An accuracy and repeatability study.

BACKGROUND: Previous methods for the quantification of brain tissue properties by magnetic resonance imaging were mainly based on two-dimensional acquisitions and were thus limited to a relatively low resolution in the slice direction compared to three-dimensional (3D) acquisitions. The 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) sequence may allow for simultaneous acquisition of relaxometry parameters in high spatial resolution. PURPOSE: To evaluate bias, linearity, and day-to-day repeatability of relaxometry parameters, as well as tissue fraction maps, acquired with 3D-QALAS. MATERIALS AND METHODS: Scan-rescan test of the 3D-QALAS sequence was performed on a 1.5-T scanner with the International Society for Magnetic Resonance in Medicine/National institute of Standards and Technology system phantom and 10 healthy volunteers (7 male, 3 female; mean age, 23.2 ±â€¯3.6 years). Simple linear regression analysis, Bland-Altman plots, and intrasubject coefficients of variation (CV) were used to assess the reliability of 3D-QALAS sequence-derived parameters. The T1, T2, proton density (PD), and myelin volume fraction (MVF) of in vivo brain regions were compared with values obtained using the multidynamic multi-echo sequence. RESULTS: In the phantom study, the T1, T2, and PD values measured by 3D-QALAS showed strong linearity with the reference values (R2 = 0.998, 0.998, and 0.960 for T1, T2, and PD, respectively) and high repeatability (mean CV of 1.2%, 2.8%, and 2.9% for T1, T2, and PD, respectively). The T1, T2, PD, and MVF values of in vivo brain regions obtained with 3D-QALAS were highly consistent within subjects, with mean intrasubject CVs of 0.5%, 0.5%, 0.4%, and 1.6% for the T1, T2, PD, and MVF values, respectively. CONCLUSION: 3D-QALAS enables reliable measurement of T1, T2, PD, and MVF values of the whole brain in high spatial resolution across a clinically-relevant dynamic range.

Aberrant myelination in patients with Sturge-Weber syndrome analyzed using synthetic quantitative magnetic resonance imaging.

PURPOSE: Accelerated myelination in the affected hemisphere has been demonstrated previously in patients with Sturge-Weber syndrome (SWS). This prospective study investigated myelin-related changes in patients with unilateral SWS using synthetic quantitative magnetic resonance imaging (qMRI). METHODS: Fourteen children with unilateral SWS were categorized according to age, i.e., ≤ 2 years (group A, n = 5, mean age 1.1 years, 3 males) and > 2 years (group B, n = 9, mean age 3.9 years, 4 males). All children underwent two-dimensional synthetic qMRI. The myelin volume in the cerebral hemisphere and white matter (WM) myelin volume fraction (MVF), proton density (PD), R1 and R2 relaxation rates ipsilateral to the leptomeningeal enhancement, and/or a port-wine birthmark were compared with the corresponding values in the contralateral hemisphere. RESULTS: In group A, 3 patients had a higher myelin volume in the ipsilateral hemisphere and a higher MVF, R1, and R2 and lower PD in the ipsilateral WM than on the contralateral side; the findings were the opposite in the remaining two patients. All patients in group B had a significantly lower myelin volume in the ipsilateral hemisphere (P < 0.05) and a lower MVF and R1 and higher PD in the ipsilateral WM than on the contralateral side (P < 0.0125). CONCLUSION: Higher estimated myelin was observed on the ipsilateral side in some patients aged ≤ 2 years and lower myelin on the ipsilateral side in all older patients. Synthetic qMRI might be useful for showing myelin-related abnormalities in SWS.

Comparison of magnetization transfer contrast of conventional and simultaneous multislice turbo spin echo acquisitions focusing on excitation time interval.

PURPOSE: Image contrast differs between conventional multislice turbo spin echo (conventional TSE) and multiband turbo spin echo (SMS-TSE). Difference in time interval between excitations for adjacent slices (SETI) might cause this difference. This study aimed to evaluate the influence of SETI on MT effect for conventional TSE and compare conventional TSE with SMS-TSE in this respect. MATERIALS AND METHODS: Three different agar concentration phantoms were scanned with conventional TSE by adjusting SETI and TR. Signal change for different SETI was evaluated using Pearson's correlation analysis. SMS-TSE was acquired by changing TR similarly. Three human volunteers were scanned with similar settings to evaluate reproducibility of the phantom results in human brain. RESULTS: In conventional TSE, shorter SETI induced larger signal reduction. Longer TR and higher agar concentration emphasized this characteristic. Significant linear correlation (P < 0.05) was found in the major cases. The SMS-TSE signal intensity in each TR and phantom was smaller than the assumable levels in conventional TSE when the slices were simultaneously excited. Similar characteristic was observed in human brain. CONCLUSION: Shorter SETI results in larger MT effect in conventional TSE. The contrast change in SMS-TSE was larger than the supposable level from simultaneous excitation, which needs consideration in clinics.

White matter alterations in adult with autism spectrum disorder evaluated using diffusion kurtosis imaging.

PURPOSE: Autism spectrum disorder (ASD) is related to impairment in various white matter (WM) pathways. Utility of the recently developed two-compartment model of diffusion kurtosis imaging (DKI) to analyse axial diffusivity of WM is restricted by several limitations. The present study aims to validate the utility of model-free DKI in the evaluation of WM alterations in ASD and analyse the potential relationship between DKI-evident WM alterations and personality scales. METHODS: Overall, 15 participants with ASD and 15 neurotypical (NT) controls were scanned on a 3 T magnetic resonance (MR) scanner, and scores for autism quotient (AQ), systemising quotient (SQ) and empathising quotient (EQ) were obtained for both groups. Multishell diffusion-weighted MR data were acquired using two b-values (1000 and 2000 s/mm2). Differences in mean kurtosis (MK), radial kurtosis (RK) and axial kurtosis (AK) between the groups were evaluated using tract-based spatial statistics (TBSS). Finally, the relationships between the kurtosis indices and personality quotients were examined. RESULTS: The ASD group demonstrated significantly lower AK in the body and splenium of corpus callosum than the NT group; however, no other significant differences were identified. Negative correlations were found between AK and AQ or SQ, predominantly in WM areas related to social-emotional processing such as uncinate fasciculus, inferior fronto-occipital fasciculus, and inferior and superior longitudinal fasciculi. CONCLUSIONS: Model-free DKI and its indices may represent a novel, objective method for detecting the disease severity and WM alterations in patients with ASD.

3D quantitative synthetic MRI-derived cortical thickness and subcortical brain volumes: Scan-rescan repeatability and comparison with conventional T1 -weighted images.

BACKGROUND: Previous quantitative synthetic MRI of the brain has been solely performed in 2D. PURPOSE: To evaluate the feasibility of the recently developed sequence 3D-QALAS for brain cortical thickness and volumetric analysis. STUDY TYPE: Reproducibility/repeatability study. SUBJECTS: Twenty-one healthy volunteers (35.6 ± 13.8 years). FIELD STRENGTH/SEQUENCE: 3D T1 -weighted fast spoiled gradient recalled echo (FSPGR) sequence was performed once, and 3D-QALAS sequence was performed twice with a 3T scanner. ASSESSMENT: FreeSurfer and FIRST were used to measure cortical thickness and volume of subcortical structures, respectively. Agreement with FSPGR and scan-rescan repeatability were evaluated for 3D-QALAS. STATISTICAL TESTS: Percent relative difference and intraclass correlation coefficient (ICC) were used to assess reproducibility and scan-rescan repeatability of the 3D-QALAS sequence-derived measurements. RESULTS: Percent relative difference compared with FSPGR in cortical thickness of the whole cortex was 3.1%, and 89% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.65, and 74% of the structures showed substantial to almost perfect agreement. For volumes of subcortical structures, the median percent relative differences were lower than 10% across all subcortical structures, except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement. For the scan-rescan test, percent relative difference in cortical thickness of the whole cortex was 2.3%, and 97% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.73, and 80% showed substantial to almost perfect agreement. For volumes of subcortical structures, relative differences were less than 10% across all subcortical structures except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement. DATA CONCLUSION: 3D-QALAS could be reliably used for measuring cortical thickness and subcortical volumes in most brain regions. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1834-1842.

Review of synthetic MRI in pediatric brains: Basic principle of MR quantification, its features, clinical applications, and limitations.

Quantitative magnetic resonance imaging (MRI) with multislice, multi-echo, and multi-delay acquisition enables simultaneous quantification of R1 and R2 relaxation rates, proton density, and the B1 field in a single acquisition, and requires only about 6 minutes for full-head coverage. Using dedicated SyMRI software, radiologists can generate any contrast-weighted image by manipulating the acquisition parameters, including repetition time, echo time, and inversion time. Moreover, automatic brain tissue segmentation, volumetry, and myelin measurement can also be performed. Using the SyMRI approach, a shorter scan time, an objective examination, and personalized MR imaging parameters can be obtained in daily clinical pediatric imaging. Here we summarize and review the use of SyMRI in imaging of the pediatric brain, including the basic principles of MR quantification along with its features, clinical applications, and limitations.

Large hospital variation in the utilization of Post-procedural CT to detect pulmonary embolism/Deep Vein Thrombosis in Patients Undergoing Total Knee or Hip Replacement Surgery: Japanese Nationwide Diagnosis Procedure Combination Database Study.

OBJECTIVE: The purpose of the study was to investigate variation in the use of in-hospital CT for venous thromboembolism (VTE) detection after total knee or hip replacement (TKR/THR) among surgical patients, using a nationwide Japanese in-hospital administrative database. METHODS: This retrospective study using a national administrative database (4/2012-3/2013) extracted patients who underwent TKR/THR surgeries at hospitals meeting the annual case-volume threshold of ≥ 30. Hospitals were categorized into three equally sized groups by frequency of postoperative CT use (low, middle, and high CT use group) to compare baseline patient-level and hospital-level characteristics. To further investigate between-hospital variation in CT usage, we fitted a hierarchical logistic regression model including hospital-specific random intercepts and fixed patient- and hospital-level effects. The intra class correlation coefficient was used to measure the amount of variability in CT use attributable to between-hospital variation. RESULTS: A total of 39,127 patients discharged from 447 hospitals met the inclusion criteria. The median hospital stay was 25 days (interquartile range, 20 - 32) and 7,599 (19.4%) patients underwent CT for VTE. CT utilization varied greatly among the hospitals; the crude frequency ranged from 0 to 100 % (median, 7.3 %; interquartile range, 1.8 - 18.3 %). After adjustment for known hospital- and patient-level factors related to CT use, 47 % of the variation in CT use remained attributable to the behavior of individual hospitals. CONCLUSION: We observed large inter hospital variability in the utilization of post-procedure CT for VTE detection in this Japanese TKR/THR cohort, suggesting that CT utilization is not optimized across the nation. ADVANCES IN KNOWLEDGE: We observed significant variability in the utilization of post-procedure CT for VTE detection among inpatients who underwent TKR/THR surgeries in a large sample of Japanese hospitals. The large variation suggests that CT utilization is not optimized across the nation, and that there may be potential overutilization of the technology in the highest CT use hospitals.