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Abstract: Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep learning reconstruction (DLR) and quantitative MRI techniques beyond conventional structural imaging. DLR using deep neural networks facilitates accelerated imaging with improved signal-to-noise ratio and spatial resolution, enhancing image quality with short scan times. DLR focuses on supervised learning applied to clinical implementation and applications. Quantitative MRI techniques, exemplified by 2D multidynamic multiecho, 3D quantification using interleaved Look-Locker acquisition sequences with T2 preparation pulses, and magnetic resonance fingerprinting, enable precise calculation of brain-tissue parameters and further advance diagnostic accuracy and efficiency. Potential DLR instabilities and quantification and bias limitations will be discussed. This review underscores the synergistic potential of DLR and quantitative MRI, offering prospects for improved brain imaging beyond conventional methods., Competing Interests: Conflicts of interest and sources of funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI22C1723) and National Research Foundation of Korea (RS-2023-00305153)., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Abstract: The aging process induces a variety of changes in the brain detectable by magnetic resonance imaging (MRI). These changes include alterations in brain volume, fluid-attenuated inversion recovery (FLAIR) white matter hyperintense lesions, and variations in tissue properties such as relaxivity, myelin, iron content, neurite density, and other microstructures. Each MRI technique offers unique insights into the structural and compositional changes occurring in the brain due to normal aging or neurodegenerative diseases. Age-related brain volume changes encompass a decrease in gray matter and an increase in ventricular volume, associated with cognitive decline. White matter hyperintensities, detected by FLAIR, are common and linked to cognitive impairments and increased risk of stroke and dementia. Tissue relaxometry reveals age-related changes in relaxivity, aiding the distinction between normal aging and pathological conditions. Myelin content, measurable by MRI, changes with age and is associated with cognitive and motor function alterations. Iron accumulation, detected by susceptibility-sensitive MRI, increases in certain brain regions with age, potentially contributing to neurodegenerative processes. Diffusion MRI provides detailed insights into microstructural changes such as neurite density and orientation. Neurofluid imaging, using techniques like gadolinium-based contrast agents and diffusion MRI, reveals age-related changes in cerebrospinal and interstitial fluid dynamics, crucial for brain health and waste clearance. This review offers a comprehensive overview of age-related brain changes revealed by various MRI techniques. Understanding these changes helps differentiate between normal aging and pathological conditions, aiding the development of interventions to mitigate age-related cognitive decline and other symptoms. Recent advances in machine learning and artificial intelligence have enabled novel methods for estimating brain age, offering also potential biomarkers for neurological and psychiatric disorders., Competing Interests: Conflicts of interest: none declared. This work was supported by Cross-ministerial Strategic Innovation Promotion Program (SIP) on “Integrated Health Care System” grant number JPJ012425; JSPS KAKENHI grant numbers 23K07189, 24K18774, and 23K14903; the Juntendo Research Branding Project; a grant from the Accreditation Organization for Management of Radiologic Imaging (AOMRI); and Japan Medical Image Database., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

We aimed to determine the intra-site repeatability and cross-site reproducibility of T1 and T2* relaxation times and quantitative susceptibility (χ) values obtained through quantitative parameter mapping (QPM) at 3 T. This prospective study included three 3-T scanners with the same hardware and software platform at three sites. The brains of twelve healthy volunteers were scanned three times using QPM at three sites. Intra-site repeatability and cross-site reproducibility were evaluated based on voxel-wise and region-of-interest analyses. The within-subject coefficient of variation (wCV), within-subject standard deviation (wSD), linear regression, Bland-Altman plot, and intraclass correlation coefficient (ICC) were used for evaluation. The intra-site repeatability wCV was 11.9 ± 6.86% for T1 and 3.15 ± 0.03% for T2*, and wSD of χ at 3.35 ± 0.10 parts per billion (ppb). Intra-site ICC(1,k) values for T1, T2*, and χ were 0.878-0.904, 0.972-0.976, and 0.966-0.972, respectively, indicating high consistency within the same scanner. Linear regression analysis revealed a strong agreement between measurements from each site and the site-average measurement, with R-squared values ranging from 0.79 to 0.83 for T1, 0.94-0.95 for T2*, and 0.95-0.96 for χ. The cross-site wCV was 13.4 ± 5.47% for T1 and 3.69 ± 2.25% for T2*, and cross-site wSD of χ at 4.08 ± 3.22 ppb. The cross-site ICC(2,1) was 0.707, 0.913, and 0.902 for T1, T2*, and χ, respectively. QPM provides T1, T2*, and χ values with an intra-site repeatability of <12% and cross-site reproducibility of <14%. These findings may contribute to the development of multisite studies., Competing Interests: Declaration of competing interest YT is an employee of FUJIFILM Corporation. KI, HN, and MT are employees of FUJIFILM Healthcare Corporation. The remaining authors have no conflict of interest. This study was partially supported by the Juntendo Research Branding Project, the Sportology Center of Juntendo UniversityGraduate School of Medicine, Leading-edge Research Project for Sports Medicine and Science (LRP) of the Japan Sports Agency, the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI; grant numbers JP19K17244, JP 23 K07189, JP19K17177), the Japan Agency for Medical Research and Development (AMED; grant numbers JP18dm0307004, JP19dm0307101 and JP21wm0425006), the Japan Radiological Society, and a Bayer Research Grant., (Copyright © 2024. Published by Elsevier Inc.)

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI., (© 2024. Italian Society of Medical Radiology.)

Purpose: To develop and evaluate methods for (1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T 2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T 1 and T 2 mapping, and (2) improving the fidelity of subspace QALAS by combining scan-specific deep-learning-based reconstruction and subspace modeling., Theory and Methods: A low-rank subspace method for 3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method (i.e., Zero-DeepSub) were proposed for rapid and high fidelity T 1 and T 2 mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T 1 and T 2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques. The reconstruction performance of the proposed subspace QALAS using Zero-DeepSub was evaluated in vivo and compared with conventional QALAS at high reduction factors of up to nine-fold., Results: Phantom experiments showed that subspace QALAS had good linearity with respect to the reference methods while reducing biases and improving precision compared to conventional QALAS, especially for T 2 maps. Moreover, in vivo results demonstrated that subspace QALAS had better g-factor maps and could reduce voxel blurring, noise, and artifacts compared to conventional QALAS and showed robust performance at up to nine-fold acceleration with Zero-DeepSub, which enabled whole-brain T 1 , T 2 , and PD mapping at 1 mm isotropic resolution within 2 min of scan time., Conclusion: The proposed subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid whole-brain multiparametric quantification and time-resolved imaging., (© 2024 International Society for Magnetic Resonance in Medicine.)

Purpose To evaluate the feasibility of liver MR fingerprinting (MRF) for quantitative characterization and diagnosis of focal liver lesions. Materials and Methods This single-site, prospective study included 89 participants (mean age, 62 years ± 15 [SD]; 45 women, 44 men) with various focal liver lesions who underwent MRI between October 2021 and August 2022. The participants underwent routine clinical MRI, non-contrast-enhanced liver MRF, and reference quantitative MRI with a 1.5-T MRI scanner. The bias and repeatability of the MRF measurements were assessed using linear regression, Bland-Altman plots, and coefficients of variation. The diagnostic capability of MRF-derived T1, T2, T2*, proton density fat fraction (PDFF), and a combination of these metrics to distinguish benign from malignant lesions was analyzed according to the area under the receiver operating characteristic curve (AUC). Results Liver MRF measurements showed moderate to high agreement with reference measurements (intraclass correlation = 0.94, 0.77, 0.45, and 0.61 for T1, T2, T2*, and PDFF, respectively), with underestimation of T2 values (mean bias in lesion = -0.5%, -29%, 5.8%, and -8.2% for T1, T2, T2*, and PDFF, respectively). The median coefficients of variation for repeatability of T1, T2, and T2* values were 2.5% (IQR, 3.6%), 3.1% (IQR, 5.6%), and 6.6% (IQR, 13.9%), respectively. After considering multicollinearity, a combination of MRF measurements showed a high diagnostic performance in differentiating benign from malignant lesions (AUC = 0.92 [95% CI: 0.86, 0.98]). Conclusion Liver MRF enabled the quantitative characterization of various focal liver lesions in a single breath-hold acquisition. Keywords: MR Imaging, Abdomen/GI, Liver, Imaging Sequences, Technical Aspects, Tissue Characterization, Technology Assessment, Diagnosis, Liver Lesions, MR Fingerprinting, Quantitative Characterization Supplemental material is available for this article. © RSNA, 2023.

Relaxation times and morphological information are fundamental magnetic resonance imaging-derived metrics of the human brain that reflect the status of the underlying tissue. Magnetic resonance fingerprinting (MRF) enables simultaneous acquisition of T1 and T2 maps inherently aligned to the anatomy, allowing whole-brain relaxometry and morphometry in a single scan. In this study, we revealed the feasibility of 3D MRF for simultaneous brain structure-wise morphometry and relaxometry. Comprehensive test-retest scan analyses using five 1.5-T and three 3.0-T systems from a single vendor including different scanner types across 3 institutions demonstrated that 3D MRF-derived morphological information and relaxation times are highly repeatable at both 1.5 T and 3.0 T. Regional cortical thickness and subcortical volume values showed high agreement and low bias across different field strengths. The ability to acquire a set of regional T1, T2, thickness, and volume measurements of neuroanatomical structures with high repeatability and reproducibility facilitates the ability of longitudinal multicenter imaging studies to quantitatively monitor changes associated with underlying pathologies, disease progression, and treatments., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Purpose: To develop a rigid real-time prospective motion-corrected multiparametric mapping technique and to test the performance of quantitative estimates., Methods: Motion tracking and correction were performed by integrating single-shot spiral navigators into a multiparametric imaging technique, three-dimensional quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). The spiral navigator was optimized, and quantitative measurements were validated using a standard system phantom. The effect of motion correction on whole-brain T1 and T2 mapping under different types of head motion during the scan was evaluated in 10 healthy volunteers. Finally, six patients with Parkinson's disease, which is known to be associated with a high prevalence of motion artifacts, were scanned to evaluate the effectiveness of our method in the real world., Results: The phantom study demonstrated that the proposed motion correction method did not introduce quantitative bias. Improved parametric map quality and repeatability were shown in volunteer experiments with both in-plane and through-plane motions, comparable to the no-motion ground truth. In real-life validation in patients, the approach showed improved parametric map quality compared to images obtained without motion correction., Conclusions: Real-time prospective motion-corrected multiparametric relaxometry based on 3D-QALAS provided robust and repeatable whole-brain multiparametric mapping., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Current clinical MR imaging practices rely on the qualitative assessment of images for diagnosis and treatment planning. While contrast in MR images is dependent on the spin parameters of the imaged tissue, pixel values on MR images are relative and are not scaled to represent any tissue properties. Synthetic MR is a fully featured imaging workflow consisting of efficient multi-parameter mapping acquisition, synthetic image generation, and volume quantitation of brain tissues. As the application becomes more widely available on multiple vendors and scanner platforms, it has also gained widespread adoption as clinicians begin to recognize the benefits of rapid quantitation. This review will provide details about the sequence with a focus on the physical principles behind its relaxometry mechanisms. It will present an overview of the products in their current form and some potential issues when implementing it in the clinic. It will conclude by highlighting some recent advances of the technique, including a 3D mapping method and its associated applications., (© 2022 American Association of Physicists in Medicine.)

Objectives: We aimed to investigate the influence of magnetic resonance fingerprinting (MRF) dictionary design on radiomic features using in vivo human brain scans., Methods: Scan-rescans of three-dimensional MRF and conventional T1-weighted imaging were performed on 21 healthy volunteers (9 males and 12 females; mean age, 41.3 ± 14.6 years; age range, 22-72 years). Five patients with multiple sclerosis (3 males and 2 females; mean age, 41.2 ± 7.3 years; age range, 32-53 years) were also included. MRF data were reconstructed using various dictionaries with different step sizes. First- and second-order radiomic features were extracted from each dataset. Intra-dictionary repeatability and inter-dictionary reproducibility were evaluated using intraclass correlation coefficients (ICCs). Features with ICCs > 0.90 were considered acceptable. Relative changes were calculated to assess inter-dictionary biases., Results: The overall scan-rescan ICCs of MRF-based radiomics ranged from 0.86 to 0.95, depending on dictionary step size. No significant differences were observed in the overall scan-rescan repeatability of MRF-based radiomic features and conventional T1-weighted imaging (p = 1.00). Intra-dictionary repeatability was insensitive to dictionary step size differences. MRF-based radiomic features varied among dictionaries (overall ICC for inter-dictionary reproducibility, 0.62-0.99), especially when step sizes were large. First-order and gray level co-occurrence matrix features were the most reproducible feature classes among different step size dictionaries. T1 map-derived radiomic features provided higher repeatability and reproducibility among dictionaries than those obtained with T2 maps., Conclusion: MRF-based radiomic features are highly repeatable in various dictionary step sizes. Caution is warranted when performing MRF-based radiomics using datasets containing maps generated from different dictionaries., Key Points: • MRF-based radiomic features are highly repeatable in various dictionary step sizes. • Use of different MRF dictionaries may result in variable radiomic features, even when the same MRF acquisition data are used. • Caution is needed when performing radiomic analysis using data reconstructed from different dictionaries., (© 2022. The Author(s).)

Surface-based morphometry (SBM) is extremely useful for estimating the indices of cortical morphology, such as volume, thickness, area, and gyrification, whereas voxel-based morphometry (VBM) is a typical method of gray matter (GM) volumetry that includes cortex measurement. In cases where SBM is used to estimate cortical morphology, it remains controversial as to whether VBM should be used in addition to estimate GM volume. Therefore, this review has two main goals. First, we summarize the differences between the two methods regarding preprocessing, statistical analysis, and reliability. Second, we review studies that estimate cortical morphological changes using VBM and/or SBM and discuss whether using VBM in conjunction with SBM produces additional values. We found cases in which detection of morphological change in either VBM or SBM was superior, and others that showed equivalent performance between the two methods. Therefore, we concluded that using VBM and SBM together can help researchers and clinicians obtain a better understanding of normal neurobiological processes of the brain. Moreover, the use of both methods may improve the accuracy of the detection of morphological changes when comparing the data of patients and controls.In addition, we introduce two other recent methods as future directions for estimating cortical morphological changes: a multi-modal parcellation method using structural and functional images, and a synthetic segmentation method using multi-contrast images (such as T1- and proton density-weighted images).

Introduction: Recently, three-dimensional (3D) quantitative synthetic magnetic resonance imaging (MRI), which quantifies tissue properties and creates multiple contrast-weighted images, has been enabled by 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). However, the relatively long scan time has hindered its introduction into clinical practice. A hybrid of compressed sensing and parallel imaging (Compressed sensing-sensitivity encoding: CS-SENSE) can accelerate 3D-QALAS; however, whether CS-SENSE affects the quantitative values acquired by 3D-QALAS remains unexplored. Therefore, this study aimed to examine the effects of reduction factors of CS-SENSE (R CSS ) on the quantitative values derived from 3D-QALAS, by assessing the signal-to-noise ratio (SNR) of the quantitative maps, as well as accuracy (linearity and bias) and repeatability of measured quantitative values., Methods: In this study, the ISMRM/NIST standardized phantom was scanned on a 1.5-T MRI scanner with 3D-QALAS using R CSS in the range between 1 and 3, with intervals of 0.2, and between 3 and 10 with intervals of 0.5. The T1, T2, and proton density (PD) values were calculated from the imaging data. For each quantitative value, the SNR, the coefficient of determination (R 2 ) of a linear regression model, the error rate, and the within-subject coefficient of variation (wCV) were calculated for each R CSS and compared., Results: Within the clinically-relevant dynamic range of the brain of T1 and T2 (T1: 200-1400 ms; T2; 50-400 ms) and PD value of 15-100% calculated from 3D-QALAS, the effects of R CSS on quantitative values was small between 1 and 2.8, with SNR ≧ 10, R 2 ≧ 0.9, error rate ≦ 10%, and wCV ≦ 10%, except for T2 values of 186.1 and 258.4 ms., Conclusions: CS-SENSE enabled the reduction of the scan time of 3D-QALAS by 63.5% (R CSS  = 2.8) while maintaining the SNR of quantitative maps and accuracy and repeatability of the quantitative values., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objectives: The aims of this study were to develop an accelerated multiparametric magnetic resonance imaging method based on 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) combined with compressed sensing (CS) and to evaluate the effect of CS on the quantitative mapping, tissue segmentation, and quality of synthetic images., Materials and Methods: A magnetic resonance imaging system phantom, containing multiple compartments with standardized T1, T2, and proton density (PD) values; 10 healthy volunteers; and 12 patients with multiple sclerosis were scanned using the 3D-QALAS sequence with and without CS and conventional contrast-weighted imaging. The scan times of 3D-QALAS with and without CS were 5:56 and 11:11, respectively. For healthy volunteers, brain volumetry and myelin estimation were performed based on the measured T1, T2, and PD. For patients with multiple sclerosis, the mean T1, T2, PD, and the amount of myelin in plaques and contralateral normal-appearing white matter (NAWM) were measured. Simple linear regression analysis and Bland-Altman analysis were performed for each metric obtained from the datasets with and without CS. To compare overall image quality and structural delineations on synthetic and conventional contrast-weighted images, case-control randomized reading sessions were performed by 2 neuroradiologists in a blinded manner., Results: The linearity of both phantom and volunteer measurements in T1, T2, and PD values obtained with and without CS was very strong (R2 = 0.9901-1.000). The tissue segmentation obtained with and without CS also had high linearity (R2 = 0.987-0.999). The quantitative tissue values of the plaques and NAWM obtained with CS showed high linearity with those without CS (R2 = 0.967-1.000). There were no significant differences in overall image quality between synthetic contrast-weighted images obtained with and without CS (P = 0.17-0.99)., Conclusions: Multiparametric imaging of the whole brain based on 3D-QALAS can be accelerated using CS while preserving tissue quantitative values, tissue segmentation, and quality of synthetic images., Competing Interests: Conflicts of interest and sources of funding: N.T. is an employee of GE Healthcare Japan. This work was supported by Japan Agency for Medical Research and Development under grant number JP19lk1010025h9902; JSPS KAKENHI grant numbers 19K17150, 19K17177, 18H02772, and 18K07692; Health, Labor and Welfare Policy Research Grants for Research on Region Medical; and a grant-in-aid for special research in subsidies for ordinary expenses of private schools from The Promotion and Mutual Aid Corporation for Private Schools of Japan; Brain/MINDS beyond program from Japan Agency for Medical Research and Development grant numbers JP19dm0307024 and JP19dm0307101., (Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc.)

Multiple sclerosis and neuromyelitis optica spectrum disorders are both neuroinflammatory diseases and have overlapping clinical manifestations. We developed a convolutional neural network model that differentiates between the two based on magnetic resonance imaging data. Thirty-five patients with relapsing-remitting multiple sclerosis and eighteen age-, sex-, disease duration-, and Expanded Disease Status Scale-matched patients with anti-aquaporin-4 antibody-positive neuromyelitis optica spectrum disorders were included in this study. All patients were scanned on a 3-T scanner using a multi-dynamic multi-echo sequence that simultaneously measures R1 and R2 relaxation rates and proton density. R1, R2, and proton density maps were analyzed using our convolutional neural network model. To avoid overfitting on a small dataset, we aimed to separate features of images into those specific to an image and those common to the group, based on SqueezeNet. We used only common features for classification. Leave-one-out cross validation was performed to evaluate the performance of the model. The area under the receiver operating characteristic curve of the developed convolutional neural network model for differentiating between the two disorders was 0.859. The sensitivity to multiple sclerosis and neuromyelitis optica spectrum disorders, and accuracy were 80.0%, 83.3%, and 81.1%, respectively. In conclusion, we developed a convolutional neural network model that differentiates between multiple sclerosis and neuromyelitis optica spectrum disorders, and which is designed to avoid overfitting on small training datasets. Our proposed algorithm may facilitate a differential diagnosis of these diseases in clinical practice., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Purpose: The aim of this study was to investigate whether the detectability of brain volume change in voxel-based morphometry (VBM) with gray matter images is affected by mild white matter lesions (MWLs)., Methods: Three-dimensional T 1 -weighted images (3D-T 1 WIs) of 11 healthy subjects were obtained using a 3T MR scanner. We initially created 3D-T 1 WIs with focal cortical atrophy simulated cortical atrophy in left amygdala (type A) and the left medial frontal lobe (type B) from control 3D-T 1 WIs. Next, the following three types of MWL images were created: type A + 1L and type B + 1L images, only one white matter lesion; type A + 4L and type B + 4L images, four white matter lesions at distant positions; and type A + 4L * and type B + 4L * images, four white matter lesions at clustered positions. Comparisons between the control group and the other groups were performed with VBM using segmented gray matter images., Results: The gray matter volume was significantly lower in the type A group than in the control group, and similar results were observed in the type A + 1L, type A + 4L, and type A + 4L * groups. Additionally, the gray matter volume was significantly lower in the type B group than in the control group, and similar results were observed in the type B + 1L, type B + 4L, and type B + 4L * groups, but the cluster size in type B + 4L * was smaller than that in type B., Conclusion: Our study showed that the detectability of brain volume change in VBM with gray matter images was not decreased by MWLs as lacunar infarctions. Therefore, we think that group comparisons with VBM should be analyzed by groups including and excluding subjects with MWLs, respectively.

Three-dimensional (3D) Magnetic resonance fingerprinting (MRF) permits whole-brain volumetric quantification of T1 and T2 relaxation values, potentially replacing conventional T1-weighted structural imaging for common brain imaging analysis. The aim of this study was to evaluate the repeatability and reproducibility of 3D MRF in evaluating brain cortical thickness and subcortical volumetric analysis in healthy volunteers using conventional 3D T1-weighted images as a reference standard. Scan-rescan tests of both 3D MRF and conventional 3D fast spoiled gradient recalled echo (FSPGR) were performed. For each sequence, the regional cortical thickness and volume of the subcortical structures were measured using standard automatic brain segmentation software. Repeatability and reproducibility were assessed using the within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), and mean percent difference and ICC, respectively. The wCV and ICC of cortical thickness were similar across all regions with both 3D MRF and FSPGR. The percent relative difference in cortical thickness between 3D MRF and FSPGR across all regions was 8.0 ± 3.2%. The wCV and ICC of the volume of subcortical structures across all structures were similar between 3D MRF and FSPGR. The percent relative difference in the volume of subcortical structures between 3D MRF and FSPGR across all structures was 7.1 ± 3.6%. 3D MRF measurements of human brain cortical thickness and subcortical volumes are highly repeatable, and consistent with measurements taken on conventional 3D T1-weighted images. A slight, consistent bias was evident between the two, and thus careful attention is required when combining data from MRF and conventional acquisitions., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

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) T 1 -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 myelin by magnetic resonance imaging (MRI) is a difficult challenge, but holds promise in demyelinating diseases, such as multiple sclerosis (MS). Although multiple techniques have been developed, no gold standard has been established. This study aims to evaluate the correlation between synthetic MRI myelin volume fraction (SyMRI MVF ) and myelin fraction estimated by other techniques, i.e., magnetization transfer saturation (MTsat), T1-weighted images divided by T2-weighted images (T1w/T2w), and radial diffusivity (RD) in patients with MS. We also compared the sensitivities of these techniques for detecting MS-related myelin damage. SyMRI MVF , MTsat, T1w/T2w, and RD were averaged on plaque, periplaque white matter, and normal-appearing white matter (NAWM). Pairwise correlation was calculated using Spearman's correlation analysis. For all segmented regions, strong correlations were found between SyMRI MVF and T1w/T2w (Rho = 0.89), MTsat (Rho = 0.82), or RD (Rho = -0.75). For each technique, the average estimated myelin differed significantly among regions, but the percentage change of NAWM from both periplaque white matter and plaque were highest in SyMRI MVF . SyMRI MVF might be suitable for myelin evaluation in MS patients, with relevant results as compared to other well-studied techniques. Moreover, it presented better sensitivity for the detection of the difference between plaque or periplaque white matter and NAWM.

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 T 1 -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., (© 2019 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: Synthetic MRI (SyMRI) enables automatic brain tissue and myelin volumetry based on the quantification of R1 and R2 relaxation rates and proton density. This study aimed to determine the validity of SyMRI brain tissue and myelin volumetry using various in-plane resolutions at 3T in patients with multiple sclerosis (MS)., Methods: We scanned 19 MS patients and 10 healthy age- and gender-matched controls using a 3T MR scanner with in-plane resolutions of 0.8, 1.8, and 3.6 mm. The acquisition times were 5 min 8 s, 2 min 52 s, and 2 min 1 s, respectively. White matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and myelin and non-WM/GM/CSF (NoN) volumes; brain parenchymal volume (BPV); and intracranial volume (ICV) were compared between different in-plane resolutions. These parameters were also compared between both groups, after ICV normalization., Results: No significant differences in measured volumes were noted between the 0.8 and 1.8 mm in-plane resolutions, except in NoN and CSF for healthy controls and NoN for MS patients. Meanwhile, significant volumetric differences were noted in most brain tissues when compared between the 3.6 and 0.8 or 1.8 mm resolution for both healthy controls and MS patients. The normalized WM volume, myelin volume, and BPV showed significant differences between controls and MS patients at in-plane resolutions of 0.8 and 1.8 mm., Conclusions: SyMRI brain tissue and myelin volumetry with in-plane resolution as low as 1.8 mm can be useful in the evaluation of MS with a short acquisition time of < 3 min.

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 ± 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 (R 2  = 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., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Gadolinium-based contrast agents (GBCA) provide valuable information for assessing and differentiating lesions in the body. However, contrast enhancement evaluation on conventional MRI is qualitative because the signal intensity uses an arbitrary scale. An approach that allows more quantitative assessment of tissue enhancement that can be integrated into clinical use is desirable. This study aimed to provide a method that can estimate GBCA concentration in a clinically applicable scan-time., Methods: Gadolinium-based contrast agent concentrations were quantified in phantoms containing water and nine different concentrations of Gadoteridol (Gd-HP-DO3A), ranging from 0.02 to 1.00 mmol/L, using quantitative synthetic MRI. Simple linear regression analysis between the estimated GBCA concentration and the reference values were performed to assess the accuracy. We performed region of interest analysis on each phantom, and recorded the mean and standard deviation. We evaluated the precision of the GBCA map by calculating the coefficient of variation (CV) for each concentration. The GBCA concentration quantification method was applied for 10 patients with metastatic brain tumors to demonstrate the feasibility of this method., Results: For the phantom study, estimated GBCA concentrations were in a strong linear relationship (R 2 = 0.998) with reference values, with a slope and intercept on simple linear regression analysis of 0.98 and 0.02, respectively. On precision assessment, the CV was <5%, with the exception of concentrations under 0.07 mmol/L. In the range of 0.07-0.99 mmol/L, the mean CV was 1.5 ± 1.2%. For application to brain metastases, the maximum estimated GBCA concentration in the metastases was 0.73 mmol/L, which was under the upper limit evaluated in the phantom study (i.e. -0.99 mmol/L)., Conclusion: The concentration of Gd-HP-DO3A in the range of 0.07-0.99 mmol/L can be measured in a clinically applicable scan time using quantitative synthetic MRI, even though this study's results are only preliminary due to several limitations.

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.

The comprehension of the glymphatic system, a postulated mechanism responsible for the removal of interstitial solutes within the central nervous system (CNS), has witnessed substantial progress recently. While direct measurement techniques involving fluorescence and contrast agent tracers have demonstrated success in animal studies, their application in humans is invasive and presents challenges. Hence, exploring alternative noninvasive approaches that enable glymphatic research in humans is imperative. This review primarily focuses on several noninvasive magnetic resonance imaging (MRI) techniques, encompassing perivascular space (PVS) imaging, diffusion tensor image analysis along the PVS, arterial spin labeling, chemical exchange saturation transfer, and intravoxel incoherent motion. These methodologies provide valuable insights into the dynamics of interstitial fluid, water permeability across the blood–brain barrier, and cerebrospinal fluid flow within the cerebral parenchyma. Furthermore, the review elucidates the underlying concept and clinical applications of these noninvasive MRI techniques, highlighting their strengths and limitations. It addresses concerns about the relationship between glymphatic system activity and pathological alterations, emphasizing the necessity for further studies to establish correlations between noninvasive MRI measurements and pathological findings. Additionally, the challenges associated with conducting multisite studies, such as variability in MRI systems and acquisition parameters, are addressed, with a suggestion for the use of harmonization methods, such as the combined association test (COMBAT), to enhance standardization and statistical power. Current research gaps and future directions in noninvasive MRI techniques for assessing the glymphatic system are discussed, emphasizing the need for larger sample sizes, harmonization studies, and combined approaches. In conclusion, this review provides invaluable insights into the application of noninvasive MRI methods for monitoring glymphatic system activity in the CNS. It highlights their potential in advancing our understanding of the glymphatic system, facilitating clinical applications, and paving the way for future research endeavors in this field. Evidence Level: 3 Technical Efficacy: Stage 5 [ABSTRACT FROM AUTHOR]

Background: Two‐dimensional synthetic MRI of the breast has limited spatial coverage. Three‐dimensional (3D) synthetic MRI could provide volumetric quantitative parameters that may reflect the immunohistochemical (IHC) status in invasive ductal carcinoma (IDC) of the breast. Purpose: To evaluate the feasibility of 3D synthetic MRI using an interleaved Look–Locker acquisition sequence with a T2 preparation pulse (QALAS) for discriminating the IHC status, including hormone receptor (HR), human epidermal growth factor receptor 2 (HER 2), and Ki‐67 expression in IDC. Study Type: Prospective observational study. Population: A total of 33 females with IDC of the breast (mean, 52.3 years). Field Strength/Sequence: A 3‐T, 3D‐QALAS gradient‐echo and fat‐suppressed T1‐weighted 3D fast spoiled gradient‐echo sequences. Assessment: Two radiologists semiautomatically delineated 3D regions of interest (ROIs) of the whole tumors on the dynamic MRI that was registered to the synthetic T1‐weighted images acquired from 3D‐QALAS. The mean T1 and T2 were measured for each IDC. Statistical Tests: Intraclass correlation coefficient for assessing interobserver agreement. Mann–Whitney U test to determine the relationship between the mean T1 or T2 and the IHC status. Multivariate logistic regression analysis followed by receiver operating characteristics (ROC) analysis for discriminating IHC status. A P value <0.05 was considered statistically significant. Results: The interobserver agreement was good to excellent. There was a significant difference in the mean T1 between HR‐positive and HR‐negative lesions, while the mean T2 value differed between HR‐positive and HR‐negative lesions, between the triple‐negative and HR‐positive or HER2‐positive lesions, and between the Ki‐67 level > 14% and ≤ 14%. Multivariate analysis showed that the mean T2 was higher in HR‐negative IDC than in HR‐positive IDC. ROC analysis revealed that the mean T2 was predictive for discriminating HR status, triple‐negative status, and Ki‐67 level. Data Conclusion: 3D synthetic MRI using QALAS may be useful for discriminating IHC status in IDC of the breast. Evidence Level: 1. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Background: Voxel-based morphometry (VBM) using magnetic resonance imaging (MR) has been used to estimate cortical atrophy associated with various diseases. However, there are mis-segmentations of segmented gray matter image in VBM. Purpose: To study a twofold evaluation of single- and multi-channel segmentation using synthetic MR images: (1) mis-segmentation of segmented gray matter images in transverse and cavernous sinuses; and (2) accuracy and repeatability of segmented gray matter images. Material and Methods: A total of 13 healthy individuals were scanned with 3D quantification using an interleaved Look–Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) sequence on a 1.5-T scanner. Three of the 13 healthy participants were scanned five consecutive times for evaluation of repeatability. We used SyMRI software to create images with three contrasts: T1-weighted (T1W), T2-weighted (T2W), and proton density-weighted (PDW) images. Manual regions of interest (ROI) on T1W imaging were individually set as the gold standard in the transverse sinus, cavernous sinus, and putamen. Single-channel (T1W) and multi-channel (T1W + T2W, T1W + PDW, and T1W + T2W + PDW imaging) segmentations were performed with statistical parametric mapping 12 software. Results: We found that mis-segmentations in both the transverse and cavernous sinuses were large in single-channel segmentation compared with multi-channel segmentations. Furthermore, the accuracy of segmented gray matter images in the putamen was high in both multi-channel T1W + PDW and T1W + T2W + PDW segmentations compared with other segmentations. Finally, the highest repeatability of left putamen volumetry was found with multi-channel segmentation T1WI + PDWI. Conclusion: Multi-channel segmentation with T1WI + PDWI provides good results for VBM compared with single-channel and other multi-channel segmentations. [ABSTRACT FROM AUTHOR]

Purpose: We hypothesize that myelin is more susceptible to damage over time than axons. We investigated the association between the estimated duration from the onset of multiple sclerosis (MS) plaques and myelin- and axon-related quantitative synthetic magnetic resonance imaging (SyMRI) and neurite orientation dispersion and density imaging (NODDI) metrics. Methods: We analyzed 31 patients with MS with 73 newly appeared plaques. Simple linear regression analysis was performed to assess the association between the estimated duration from the onset of plaques and quantitative MRI metrics. These metrics included the myelin volume fraction (MVF), axon volume fraction, and g-ratio in plaque and normal-appearing white matter. Results: MS plaques with a longer estimated duration from onset were significantly correlated with a lower MVF (slope = − 0.0070, R2 = 0.0970), higher g-ratio (slope = 0.0078, R2 = 0.0842) (all P values < 0.05). Conclusion: These results suggested that myelin in plaques undergoes continuous damage, more so than axons. Myelin imaging with SyMRI and NODDI may be useful for the quantitative assessment of temporal changes in MS plaques. [ABSTRACT FROM AUTHOR]

Differentiating corticobasal degeneration presenting with corticobasal syndrome (CBD-CBS) from progressive supranuclear palsy with Richardson's syndrome (PSP-RS), particularly in early stages, is often challenging because the neurodegenerative conditions closely overlap in terms of clinical presentation and pathology. Although volumetry using brain magnetic resonance imaging (MRI) has been studied in patients with CBS and PSP-RS, studies assessing the progression of brain atrophy are limited. Therefore, we aimed to reveal the difference in the temporal progression patterns of brain atrophy between patients with CBS and those with PSP-RS purely based on cross-sectional data using Subtype and Stage Inference (SuStaIn)—a novel, unsupervised machine learning technique that integrates clustering and disease progression modeling. We applied SuStaIn to the cross-sectional regional brain volumes of 25 patients with CBS, 39 patients with typical PSP-RS, and 50 healthy controls to estimate the two disease subtypes and trajectories of CBS and PSP-RS, which have distinct atrophy patterns. The progression model and classification accuracy of CBS and PSP-RS were compared with those of previous studies to evaluate the performance of SuStaIn. SuStaIn identified distinct temporal progression patterns of brain atrophy for CBS and PSP-RS, which were largely consistent with previous evidence, with high reproducibility (99.7%) under cross-validation. We classified these diseases with high accuracy (0.875) and sensitivity (0.680 and 1.000, respectively) based on cross-sectional structural brain MRI data; the accuracy was higher than that reported in previous studies. Moreover, SuStaIn stage correctly reflected disease severity without the label of disease stage, such as disease duration. Furthermore, SuStaIn also showed the genialized performance of differentiation and reflection for CBS and PSP-RS. Thus, SuStaIn has potential for improving our understanding of disease mechanisms, accurately stratifying patients, and providing prognoses for patients with CBS and PSP-RS. [ABSTRACT FROM AUTHOR]

Purpose: The reproducibility of neurite orientation dispersion and density imaging (NODDI) metrics in the human brain has not been explored across different magnetic resonance (MR) scanners from different vendors. This study aimed to evaluate the scan–rescan and inter-vendor reproducibility of NODDI metrics in white and gray matter of healthy subjects using two 3-T MR scanners from two vendors. Methods: Ten healthy subjects (7 males; mean age 30 ± 7 years, range 23–37 years) were included in the study. Whole-brain diffusion-weighted imaging was performed with b-values of 1000 and 2000 s/mm2 using two 3-T MR scanners from two different vendors. Automatic extraction of the region of interest was performed to obtain NODDI metrics for whole and localized areas of white and gray matter. The coefficient of variation (CoV) and intraclass correlation coefficient (ICC) were calculated to assess the scan–rescan and inter-vendor reproducibilities of NODDI metrics. Results: The scan–rescan and inter-vendor reproducibility of NODDI metrics (intracellular volume fraction and orientation dispersion index) were comparable with those of diffusion tensor imaging (DTI) metrics. However, the inter-vendor reproducibilities of NODDI (CoV = 2.3–14%) were lower than the scan–rescan reproducibility (CoV: scanner A = 0.8–3.8%; scanner B = 0.8–2.6%). Compared with the finding of DTI metrics, the reproducibility of NODDI metrics was lower in white matter and higher in gray matter. Conclusion: The lower inter-vendor reproducibility of NODDI in some brain regions indicates that data acquired from different MRI scanners should be carefully interpreted. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Oscillating gradient spin-echo (OGSE) sequences provide access to short diffusion times and may provide insight into micro-scale internal structures of pathologic lesions based on an analysis of changes in diffusivity with differing diffusion times. We hypothesized that changes in diffusivity acquired with a shorter diffusion time may permit elucidation of properties related to the internal structure of extra-axial brain tumors. This study aimed to investigate the utility of changes in diffusivity between short and long diffusion times for characterizing extra-axial brain tumors. In total, 12 patients with meningothelial meningiomas, 13 patients with acoustic neuromas, and 11 patients with pituitary adenomas were scanned with a 3 T magnetic resonance imaging (MRI) scanner with diffusion-weighted imaging (DWI) using OGSE and pulsed gradient spin-echo (PGSE) (effective diffusion times [Δ eff ]: 6.5 ms and 35.2 ms) with b-values of 0 and 1000 s/mm2. Relative percentage changes between shorter and longer diffusion times were calculated using region-of-interest (ROI) analysis of brain tumors on λ 1 , λ 2 , λ 3 , and mean diffusivity (MD) maps. The diffusivities of PGSE, OGSE, and relative percentage changes were compared among each tumor type using a multiple comparisons Steel-Dwass test. The mean (standard deviation) MD at Δ eff of 6.5 ms was 1.07 ± 0.23 10−3 mm2/s, 1.19 ± 0.18 10−3 mm2/s, 1.19 ± 0.21 10−3 mm2/s for meningothelial meningiomas, acoustic neuromas, and pituitary adenomas, respectively. The mean (standard deviation) MD at Δ eff of 35.2 ms was 0.93 ± 0.22 10−3 mm2/s, 1.07 ± 0.19 10−3 mm2/s, 0.82 ± 0.21 10−3 mm2/s for meningothelial meningiomas, acoustic neuromas, and pituitary adenomas, respectively. The mean (standard deviation) of the relative percentage change was 15.7 ± 4.4%, 12.4 ± 8.2%, 46.8 ± 11.3% for meningothelial meningiomas, acoustic neuromas, and pituitary adenomas, respectively. Compared to meningiomas and acoustic neuromas, pituitary adenoma exhibited stronger diffusion time-dependence with diffusion times between 6.5 ms and 35.2 ms (P < 0.05). In conclusion, differences in diffusion time-dependence may be attributed to differences in the internal structures of brain tumors. DWI with a short diffusion time may provide additional information on the microstructure of each tumor and contribute to tumor diagnosis. [ABSTRACT FROM AUTHOR]