Preliminary study of substantia nigra analysis by tensorial feature extraction.

PURPOSE: Parkinson disease (PD) is a common progressive neurodegenerative disorder in our ageing society. Early-stage PD biomarkers are desired for timely clinical intervention and understanding of pathophysiology. Since one of the characteristics of PD is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, we propose a feature extraction method for analysing the differences in the substantia nigra between PD and non-PD patients. METHOD: We propose a feature-extraction method for volumetric images based on a rank-1 tensor decomposition. Furthermore, we apply a feature selection method that excludes common features between PD and non-PD. We collect neuromelanin images of 263 patients: 124 PD and 139 non-PD patients and divide them into training and testing datasets for experiments. We then experimentally evaluate the classification accuracy of the substantia nigra between PD and non-PD patients using the proposed feature extraction method and linear discriminant analysis. RESULTS: The proposed method achieves a sensitivity of 0.72 and a specificity of 0.64 for our testing dataset of 66 non-PD and 42 PD patients. Furthermore, we visualise the important patterns in the substantia nigra by a linear combination of rank-1 tensors with selected features. The visualised patterns include the ventrolateral tier, where the severe loss of neurons can be observed in PD. CONCLUSIONS: We develop a new feature-extraction method for the analysis of the substantia nigra towards PD diagnosis. In the experiments, even though the classification accuracy with the proposed feature extraction method and linear discriminant analysis is lower than that of expert physicians, the results suggest the potential of tensorial feature extraction.

Reduced Diffusivity along Perivascular Spaces on MR Imaging Associated with Younger Age of First Use and Cognitive Impairment in Recreational Marijuana Users.

BACKGROUND AND PURPOSE: The impairment of the glymphatic system, a perivascular network crucial for brain waste clearance, has been linked to cognitive impairment, potentially attributed to the accumulation of brain waste. Although marijuana use has been associated with poorer cognitive performance, particularly in adolescents, its influence on the glymphatic system remains unexplored. This study evaluated the influence of the age of first marijuana use and the total number of lifetime uses on the glymphatic system, measured using the index of DTI along the perivascular space (DTI-ALPS). Furthermore, we explored the correlation between glymphatic clearance and cognitive performance among marijuana users. MATERIALS AND METHODS: In this study, 125 individuals who reported using marijuana at least once in their lifetime (43 men; mean age, 28.60 [SD, 3.84] years) and 125 individuals with zero lifetime cannabis use (nonusers; 44 men; mean age, 28.82 [SD, 3.56] years) were assessed. ALPS indices of all study participants were calculated using 3T diffusion MR imaging data (b = 1000 s/mm2). RESULTS: After we adjusted for age, sex, education years, Pittsburgh Sleep Quality Index, alcohol use, tobacco use, and intracranial volume, our analysis using a univariate General Linear Model revealed no significant difference in the ALPS index among nonusers and marijuana users with different ages of first use or various frequencies of lifetime usage. However, in marijuana users, multiple linear regression analyses showed associations between a lower ALPS index and earlier age of first marijuana use (standardized ß, -0.20; P = .041), lower accuracy in the working memory 0-back task (standardized ß, 0.20; P = .042), and fewer correct responses in the Fluid Intelligence Test (standardized ß, 0.19; P = .045). CONCLUSIONS: This study shows the potential use of DTI-ALPS as a noninvasive indirect indicator of the glymphatic clearance in young adults. Our findings show novel adverse effects of younger age at first use of marijuana on the glymphatic system function, which is associated with impaired working memory and fluid intelligence. Gaining insight into the alterations in glymphatic function following marijuana use could initiate novel strategies to reduce the risk of cognitive impairment.

Revolutionizing radiation therapy: the role of AI in clinical practice.

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

Advancements in Diffusion MRI Tractography for Neurosurgery.

ABSTRACT: Diffusion magnetic resonance imaging tractography is a noninvasive technique that enables the visualization and quantification of white matter tracts within the brain. It is extensively used in preoperative planning for brain tumors, epilepsy, and functional neurosurgical procedures such as deep brain stimulation. Over the past 25 years, significant advancements have been made in imaging acquisition, fiber direction estimation, and tracking methods, resulting in considerable improvements in tractography accuracy. The technique enables the mapping of functionally critical pathways around surgical sites to avoid permanent functional disability. When the limitations are adequately acknowledged and considered, tractography can serve as a valuable tool to safeguard critical white matter tracts and provides insight regarding changes in normal white matter and structural connectivity of the whole brain beyond local lesions. In functional neurosurgical procedures such as deep brain stimulation, it plays a significant role in optimizing stimulation sites and parameters to maximize therapeutic efficacy and can be used as a direct target for therapy. These insights can aid in patient risk stratification and prognosis. This article aims to discuss state-of-the-art tractography methodologies and their applications in preoperative planning and highlight the challenges and new prospects for the use of tractography in daily clinical practice.

MR Fingerprinting for Contrast Agent-free and Quantitative Characterization of Focal Liver Lesions.

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.

Deep Learning-Driven Transformation: A Novel Approach for Mitigating Batch Effects in Diffusion MRI Beyond Traditional Harmonization.

BACKGROUND: "Batch effect" in MR images, due to vendor-specific features, MR machine generations, and imaging parameters, challenges image quality and hinders deep learning (DL) model generalizability. PURPOSE: We aim to develop a DL model using contrast adjustment and super-resolution to reduce diffusion-weighted images (DWIs) diversity across magnetic field strengths and imaging parameters. STUDY TYPE: Retrospective. SUBJECTS: The DL model was built using an open dataset from one individual. The MR machine identification model was trained and validated on a dataset of 1134 adults (54% females, 46% males), with 1050 subjects showing no DWI abnormalities and 84 with conditions like stroke and tumors. The 21,000 images were divided into 80% for training, 20% for validation, and 3500 for testing. FIELD STRENGTH/SEQUENCE: Seven MR scanners from four manufacturers with 1.5 T and 3 T magnetic field strengths. DWIs were acquired using spin-echo sequences and high-resolution T2WIs using the T2-SPACE sequence. ASSESSMENT: An experienced, board-certified radiologist evaluated the effectiveness of restoring high-resolution T2WI and harmonizing diverse DWI with metrics such as PSNR and SSIM, and the texture and frequency attributes were further analyzed using gray-level co-occurrence matrix and 1-dimensional power spectral density. The model's impact on machine-specific characteristics was gauged through the performance metrics of a ResNet-50 model. Comprehensive statistical tests were employed for statistical robustness, including McNemar's test and the Dice index. RESULTS: Our DL protocol reduced DWI contrast and resolution variation. ResNet-50 model's accuracy decreased from 0.9443 to 0.5786, precision from 0.9442 to 0.6494, recall from 0.9443 to 0.5786, and F1 score from 0.9438 to 0.5587. The t-SNE visualization indicated more consistent image features across multiple MR devices. Autoencoder halved learning iterations; Dice coefficient >0.74 confirmed signal reproducibility in 84 lesions. CONCLUSION: This study presents a DL strategy to mitigate batch effects in diffusion MR images, improving their quality and generalizability. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

From FDG and beyond: the evolving potential of nuclear medicine.

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

Accuracy of skull stripping in a single-contrast convolutional neural network model using eight-contrast magnetic resonance images.

In automated analyses of brain morphometry, skull stripping or brain extraction is a critical first step because it provides accurate spatial registration and signal-intensity normalization. Therefore, it is imperative to develop an ideal skull-stripping method in the field of brain image analysis. Previous reports have shown that convolutional neural network (CNN) method is better at skull stripping than non-CNN methods. We aimed to evaluate the accuracy of skull stripping in a single-contrast CNN model using eight-contrast magnetic resonance (MR) images. A total of 12 healthy participants and 12 patients with a clinical diagnosis of unilateral Sturge-Weber syndrome were included in our study. A 3-T MR imaging system and QRAPMASTER were used for data acquisition. We obtained eight-contrast images produced by post-processing T1, T2, and proton density (PD) maps. To evaluate the accuracy of skull stripping in our CNN method, gold-standard intracranial volume (ICVG) masks were used to train the CNN model. The ICVG masks were defined by experts using manual tracing. The accuracy of the intracranial volume obtained from the single-contrast CNN model (ICVE) was evaluated using the Dice similarity coefficient [= 2(ICVE ⋂ ICVG)/(ICVE + ICVG)]. Our study showed significantly higher accuracy in the PD-weighted image (WI), phase-sensitive inversion recovery (PSIR), and PD-short tau inversion recovery (STIR) compared to the other three contrast images (T1-WI, T2-fluid-attenuated inversion recovery [FLAIR], and T1-FLAIR). In conclusion, PD-WI, PSIR, and PD-STIR should be used instead of T1-WI for skull stripping in the CNN models.

Clinical applications of artificial intelligence in liver imaging.

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.

Increased Parenchymal Free Water May Be Decreased by Revascularization Surgery in Patients with Moyamoya Disease.

PURPOSE: Moyamoya disease (MMD) is a cerebrovascular disease associated with steno-occlusive changes in the arteries of the circle of Willis and with hemodynamic impairment. Previous studies have reported that parenchymal extracellular free water levels may be increased and the number of neurites may be decreased in patients with MMD. The aim of the present study was to investigate the postoperative changes in parenchymal free water and neurites and their relationship with cognitive improvement. METHODS: Multi-shell diffusion MRI (neurite orientation dispersion and density imaging and free water imaging using a bi-tensor model) was performed in 15 hemispheres of 13 adult patients with MMD (11 female, mean age 37.9 years) who had undergone revascularization surgery as well as age- and sex-matched normal controls. Parameter maps of free water and free-water-eliminated neurites were created, and the regional parameter values were compared among controls, patients before surgery, and patients after surgery. RESULTS: The anterior and middle cerebral artery territories of patients showed higher preoperative free water levels (P ≤ 0.007) and lower postoperative free water levels (P ≤ 0.001) than those of normal controls. The change in the dispersion of the white matter in the anterior cerebral artery territory correlated with cognitive improvement (r = -0.75; P = 0.004). CONCLUSION: Our study suggests that increased parenchymal free water levels decreased after surgery and that postoperative changes in neurite parameters are related to postoperative cognitive improvement in adult patients with MMD. Diffusion analytical methods separately calculating free water and neurites may be useful for unraveling the pathophysiology of chronic ischemia and the postoperative changes that occur after revascularization surgery in this disease population.

Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.

ABSTRACT: With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.

Neuroimaging findings related to glymphatic system alterations in older adults with metabolic syndrome.

OBJECTIVE: The glymphatic system is a glial-based perivascular network that promotes brain metabolic waste clearance. Reduced glymphatic flow has been observed in rat models of type 2 diabetes and hypertension, indicating the role of vascular risk factors in the glymphatic system. However, little is known about how vascular risk factors affect the human glymphatic system. The present study aims to assess the relationships between metabolic syndrome (MetS), a cluster of vascular risk factors, and the glymphatic system function using diffusion magnetic resonance imaging (MRI)-based measures of water diffusivity in the glymphatic compartments, including the brain interstitial space and perivascular spaces around the deep medullary vein. We hypothesized that vascular risk factors are associated with glymphatic dysfunction, leading to cognitive impairment in older adults. METHODS: This cross-sectional study assessed 61 older adults (age range, 65-82 years) who had participated in the Bunkyo Health Study, including 15 healthy controls (mean age, 70.87 ± 4.90 years) and 46 individuals with MetS (mean age, 71.76 ± 4.61 years). Fractional volume of extracellular-free water (FW) and an index of diffusion tensor imaging along the perivascular space (DTI-ALPS) were used as indirect indicators of water diffusivity in the interstitial extracellular and perivenous spaces of white matter, respectively. RESULTS: After adjusting for age, sex, years of education, total Fazekas scale, Pittsburgh sleep quality index (PSQI) score, and intracranial volume (ICV), a significantly (P = 0.030; Cohen's d = 1.01) higher FW was observed in individuals with MetS than in the healthy controls. Furthermore, individuals with MetS had a significantly (P = 0.031; Cohen's d = 0.86) lower ALPS index than the healthy controls, with age, sex, years of education, total Fazekas scale, PSQI score, ICV, fractional anisotropy, and mean diffusivity included as confounding factors. Higher FW was significantly associated with lower ALPS index (r = -0.37; P = 0.004). Multiple linear regression (MLR) with backward elimination analyses showed that higher diastolic blood pressure (BP; standardized ß = 0.33, P = 0.005) was independently associated with higher FW, whereas higher fasting plasma glucose levels (standardized ß = -0.63, P = 0.002) or higher Brinkman index of cigarette consumption cumulative amount (standardized ß = -0.27, P = 0.022) were associated with lower ALPS index. The lower ALPS index (standardized ß, 0.28; P = 0.040) was associated with poorer global cognitive performance, which was determined using the Japanese version of the Montreal Cognitive Assessment (MOCA-J) scores. Finally, partial correlation analyses showed a significant correlation between higher FW and lower MOCA-J scores (r = -0.35; P = 0.025) and between higher FW and higher diastolic BP (r = 0.32, P = 0.044). CONCLUSION: The present study shows the changes in diffusion MRI-based measures reflected by the higher FW and lower ALPS index in older adults with MetS, possibly due to the adverse effect of vascular risk factors on the glymphatic system. Our findings also indicate the associations between the diffusion MRI-based measures and elevated diastolic BP, hyperglycemia, smoking habit, and poorer cognitive performance. However, owing to the limitations of this study, the results should be cautiously interpreted.

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.

Investigation of time-dependent diffusion in extra-axial brain tumors using oscillating-gradient spin-echo.

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 [Δeff]: 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 λ1, λ2, λ3, 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 Δeff 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 Δeff 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.

MR Fingerprinting for Liver Tissue Characterization: A Histopathologic Correlation Study.

Background Liver MR fingerprinting (MRF) enables simultaneous quantification of T1, T2, T2*, and proton density fat fraction (PDFF) maps in single breath-hold acquisitions. Histopathologic correlation studies are desired for its clinical use. Purpose To compare liver MRF-derived metrics with separate reference quantitative MRI in participants with diffuse liver disease, evaluate scan-rescan repeatability of liver MRF, and validate MRF-derived measurements for histologic grading of liver biopsies. Materials and Methods This prospective study included participants with diffuse liver disease undergoing MRI from July 2021 to January 2022. Participants underwent two-dimensional single-section liver MRF and separate reference quantitative MRI. Linear regression, Bland-Altman plots, and coefficients of variation were used to assess the bias and repeatability of liver MRF measurements. For participants undergoing liver biopsy, the association between mapping and histologic grading was evaluated by using the Spearman correlation coefficient. Results Fifty-six participants (mean age, 59 years ± 15 [SD]; 32 women) were included to compare mapping techniques and 23 participants were evaluated with liver biopsy (mean age, 52.7 years ± 12.7; 14 women). The linearity of MRF with reference measurements in participants with diffuse liver disease (R2 value) for T1, T2, T2*, and PDFF maps was 0.86, 0.88, 0.54, and 0.99, respectively. The overall coefficients of variation for repeatability in the liver were 3.2%, 5.5%, 7.1%, and 4.6% for T1, T2, T2*, and PDFF maps, respectively. MRF-derived metrics showed high diagnostic performance in differentiating moderate or severe changes from mild or no changes (area under the receiver operating characteristic curve for fibrosis, inflammation, steatosis, and siderosis: 0.62 [95% CI: 0.52, 0.62], 0.92 [95% CI: 0.88, 0.92], 0.97 [95% CI: 0.96, 0.97], and 0.74 [95% CI: 0.57, 0.74], respectively). Conclusion Liver MR fingerprinting provided repeatable T1, T2, T2*, and proton density fat fraction maps in high agreement with reference quantitative mapping and may correlate with pathologic grades in participants with diffuse liver disease. © RSNA, 2022 Online supplemental material is available for this article.

White matter fiber-specific degeneration in older adults with metabolic syndrome.

OBJECTIVE: Metabolic syndrome (MetS) is defined as a complex of interrelated risk factors for type 2 diabetes and cardiovascular disease, including glucose intolerance, abdominal obesity, hypertension, and dyslipidemia. Studies using diffusion tensor imaging (DTI) have reported white matter (WM) microstructural abnormalities in MetS. However, interpretation of DTI metrics is limited primarily due to the challenges of modeling complex WM structures. The present study used fixel-based analysis (FBA) to assess the effect of MetS on the fiber tract-specific WM microstructure in older adults and its relationship with MetS-related measurements and cognitive and locomotor functions to better understand the pathophysiology of MetS. METHODS: Fixel-based metrics, including microstructural fiber density (FD), macrostructural fiber-bundle cross-section (FC), and a combination of FD and FC (FDC), were evaluated in 16 healthy controls (no components of MetS; four men; mean age, 71.31 ± 5.06 years), 57 individuals with premetabolic syndrome (preMetS; one or two components of MetS; 29 men; mean age, 72.44 ± 5.82 years), and 46 individuals with MetS (three to five components of MetS; 27 men; mean age, 72.15 ± 4.97 years) using whole-brain exploratory FBA. Tract of interest (TOI) analysis was then performed using TractSeg across 14 selected WM tracts previously associated with MetS. The associations between fixel-based metrics and MetS-related measurements, neuropsychological, and locomotor function tests were also analyzed in individuals with preMetS and MetS combined. In addition, tensor-based metrics (i.e., fractional anisotropy [FA] and mean diffusivity [MD]) were compared among the groups using tract-based spatial statistics (TBSS) analysis. RESULTS: In whole-brain FBA, individuals with MetS showed significantly lower FD, FC, and FDC compared with healthy controls in WM areas, such as the splenium of the corpus callosum (CC), corticospinal tract (CST), middle cerebellar peduncle (MCP), and superior cerebellar peduncle (SCP). Meanwhile, in fixel-based TOI, significantly reduced FD was observed in individuals with preMetS and MetS in the anterior thalamic radiation, CST, SCP, and splenium of the CC compared with healthy controls, with relatively greater effect sizes observed in individuals with MetS. Compared with healthy controls, significantly reduced FC and FDC were only demonstrated in individuals with MetS, including regions with loss of FD, inferior cerebellar peduncle, inferior fronto-occipital fasciculus, MCP, and superior longitudinal fasciculus part I. Furthermore, negative correlations were observed between FD and Brinkman index of cigarette consumption cumulative amount and between FC or FDC and the Trail Making Test (parts B-A), which is a measure of executive function, waist circumference, or low-density lipoprotein cholesterol. Finally, TBSS analysis revealed that FA and MD were not significantly different among all groups. CONCLUSIONS: The FBA results demonstrate that substantial axonal loss and atrophy in individuals with MetS and early axonal loss without fiber-bundle morphological changes in those with preMetS within the WM tracts are crucial to cognitive and motor function. FBA also clarified the association between executive dysfunction, abdominal obesity, hyper-low-density lipoprotein cholesterolemia, smoking habit, and compromised WM neural tissue microstructure in MetS.

Water Diffusivity Changes Along the Perivascular Space After Lumboperitoneal Shunt Surgery in Idiopathic Normal Pressure Hydrocephalus.

Background: The aim of this study was to evaluate the water diffusivity changes along the perivascular space after lumboperitoneal shunt (LPS) surgery in idiopathic normal pressure hydrocephalus. Methods: Nine patients diagnosed with idiopathic normal pressure hydrocephalus (iNPH; three men and six women, mean age ± SD = 75.22 ± 5.12 years) according to the guidelines for iNPH in Japan were included in the study. Post-LPS surgery, six patients with iNPH who exhibited improvement in symptoms were defined as responder subjects, while three patients with iNPH who did not were defined as non-responder subjects. We calculated the mean analysis along the perivascular space (ALPS) index of the left and right hemispheres and compared the differences between pre- and post-LPS surgery mean ALPS indices in iNPH patients. In the responder or non-responder subjects, the mean ALPS indices in the pre- and post-operative iNPH groups were compared using Wilcoxon signed-rank tests. Next, correlation analyses between pre- and post-operation changes in the mean ALPS index and clinical characteristics were conducted. Results: The mean ALPS index of the post-operative iNPH group was significantly higher than that of the pre-operative iNPH group (p = 0.021). In responder subjects, the mean ALPS index of the post-operative iNPH group was significantly higher than that of the pre-operative iNPH group (p = 0.046). On the other hand, in the non-responder subjects, the mean ALPS index of the post-operative iNPH group was not significantly different compared to the pre-operative iNPH group (p = 0.285). The mean ALPS index change was not significantly correlated with changes in the Mini-Mental State Examination (MMSE) score (r = -0.218, p = 0.574), Frontal Assessment Battery (FAB) score (r = 0.185, p = 0.634), Trail Making Test A (TMTA) score (r = 0.250, p = 0.516), and Evans' index (r = 0.109, p = 0.780). In responder subjects, the mean ALPS index change was significantly correlated with Evans' index in pre-operative patients with iNPH (r = 0.841, p = 0.036). Conclusion: This study demonstrates the improved water diffusivity along perivascular space in patients with iNPH after LPS surgery. This could be indicative of glymphatic function recovery following LPS surgery.

Estimation of intracranial volume: A comparative study between synthetic MRI and FSL-brain extraction tool (BET)2.

Brain extraction represents an important step in numerous neuroimaging analyses. The brain extraction tool (BET)2 is a widely used deformable model-based approach for extraction of intracranial volume (ICV). The aim of this study is to estimate the ICV extraction accuracy using synthetic MR(SyMRI) method and BET2 in healthy adult participants and patients with Sturge-Weber Syndrome (SWS), including infants. 'Quantification of relaxation times and proton density by multi-echo acquisition of saturation recovery with turbo-spin-echo readout' (QRAPMASTER) with a 3.0 T magnetic resonance image (MRI) system was used for data acquisition. Statistical evaluations were performed with linear regression analysis and the Jaccard similarity coefficient (J). ICV extraction accuracy with synthetic MR method is found to be higher than BET2, for both aged healthy participants and SWS.

Differentiation of high-grade and low-grade intra-axial brain tumors by time-dependent diffusion MRI.

INTRODUCTION: Oscillating gradient spin-echo (OGSE) sequences enable acquisitions with shorter diffusion times. There is growing interest in the effect of diffusion time on apparent diffusion coefficient (ADC) values in patients with cancer. However, little evidence exists regarding its usefulness for differentiating between high-grade and low-grade brain tumors. The purpose of this study is to investigate the utility of changes in the ADC value between short and long diffusion times in distinguishing low-grade and high-grade brain tumors. MATERIAL AND METHODS: Eleven patients with high-grade brain tumors and ten patients with low-grade brain tumors were scanned using a 3 T magnetic resonance imaging with diffusion-weighted imaging (DWI) using OGSE and PGSE (effective diffusion time [Δeff]: 6.5 ms and 35.2 ms) and b-values of 0 and 1000 s/mm2. Using a region of interest (ROI) analysis of the brain tumors, we measured the ADC for two Δeff (ADCΔeff) values and computed the subtraction ADC (ΔADC = ADC6.5 ms - ADC35.2 ms) and the relative ADC (ΔADC = (ADC6.5 ms - ADC35.2 ms) / ADC35.2 ms × 100). The maximum values for the subtraction ADC (ΔADCmax) and the relative ADC (rADCmax) on the ROI were compared between low-grade and high-grade tumors using the Wilcoxon rank-sum test. A P-value <.05 was considered significant. The ROIs were also placed in the normal white matter of patients with high- and low-grade brain tumors, and ΔADCmax values were determined. RESULTS: High-grade tumors had significantly higher ΔADCmax and rADCmax than low-grade tumors. The ΔADCmax values of the normal white matter were lower than the ΔADCmax of high- and low-grade brain tumors. CONCLUSION: The dependence of ADC values on diffusion time between 6.5 ms and 35.2 ms was stronger in high-grade tumors than in low-grade tumors, suggesting differences in internal tissue structure. This finding highlights the importance of reporting diffusion times in ADC evaluations and might contribute to the grading of brain tumors using DWI.

Effect of changing the analyzed image contrast on the accuracy of intracranial volume extraction using Brain Extraction Tool 2.

The aim of this study was to evaluate the effect of changing the contrast of an analyzed image on the accuracy of intracranial volume (ICV) extraction using the Brain Extraction Tool (BET2) in healthy adults and patients with Sturge-Weber syndrome (SWS), including infants. Twelve SWS patients, including infants, and 12 healthy participants were imaged on a 3.0-T magnetic resonance imaging (MRI) machine. All individuals underwent quantification of relaxation times and proton density using multi-echo acquisition of saturation recovery with turbo-spin-echo readout (QRAPMASTER). Based on the QRAPMASTER data, we created images with seven contrasts (T1-WI, T2-WI, PD-WI, T2 short-tau inversion recovery [STIR], proton density [PD] STIR, T2STIR + PDSTIR, and T1-WI + T2-WI + PD-WI) by post-processing with SyMRI software. ICVs extracted with BET2 from the FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library with each of the seven image contrasts were compared with manually extracted ICVs, which is the gold standard reviewed by a board-certificated neuroradiologist. Manual extraction was performed on T1-WI and T2STIR. Statistical analyses were performed with Jaccard similarity coefficients (J). The highest J score was found in T1-WI + T2-WI + PD-WI in all participants (0.8451); T1-WI in healthy participants (0.8984); T2STIR in participants with SWS (0.8325). Our findings suggest that T1-WI and T2STIR should be used in ICV extraction performed using BET2 on healthy participants and infants, respectively. Additionally, if the analyzed individuals include both healthy participants and infants, T1-WI + T2-WI + PD-WI should be used.