The effects of spatial leakage correction on the reliability of EEG-based functional connectivity networks.

Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero-lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject-level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting-state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting-state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model-based, as well as individual head model-based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.

[Advancements in Magnetic Resonance Neurography: Techniques, Sequences, and Standardization].

Magnetic resonance neurography requires varying imaging techniques based on the site of imaging and anticipated disease. In assessing the brachial and lumbosacral plexus, a three-dimensional (3D) spin echo method, such as 3D-short tau inversion recovery imaging, is frequently employed. It's beneficial to familiarize oneself with the imaging sequence and understand the appearance of normal images in advance. The imaging parameters used in our institute are provided below as a reference. When interpreting the images, pay close attention to nerve thickening, signal intensity changes, asymmetry between the left and right sides, and irregularities in nerve caliber. Efforts are underway to standardize qualitative assessments and quantify signals through technological advancements.

Boston Criteria v2.0 for Cerebral Amyloid Angiopathy Without Hemorrhage: An MRI-Neuropathologic Validation Study.

BACKGROUND AND OBJECTIVES: Updated criteria for the clinical-MRI diagnosis of cerebral amyloid angiopathy (CAA) have recently been proposed. However, their performance in individuals without symptomatic intracerebral hemorrhage (ICH) presentations is less defined. We aimed to assess the diagnostic performance of the Boston criteria version 2.0 for CAA diagnosis in a cohort of individuals ranging from cognitively normal to dementia in the community and memory clinic settings. METHODS: Fifty-four participants from the Mayo Clinic Study of Aging or Alzheimer's Disease Research Center were included if they had an antemortem MRI with gradient-recall echo sequences and a brain autopsy with CAA evaluation. Performance of the Boston criteria v2.0 was compared with v1.5 using histopathologically verified CAA as the reference standard. RESULTS: The median age at MRI was 75 years (interquartile range 65-80) with 28/54 participants having histopathologically verified CAA (i.e., moderate-to-severe CAA in at least 1 lobar region). The sensitivity and specificity of the Boston criteria v2.0 were 28.6% (95% CI 13.2%-48.7%) and 65.3% (95% CI 44.3%-82.8%) for probable CAA diagnosis (area under the receiver operating characteristic curve [AUC] 0.47) and 75.0% (55.1-89.3) and 38.5% (20.2-59.4) for any CAA diagnosis (possible + probable; AUC 0.57), respectively. The v2.0 Boston criteria were not superior in performance compared with the prior v1.5 criteria for either CAA diagnostic category. DISCUSSION: The Boston criteria v2.0 have low accuracy in patients who are asymptomatic or only have cognitive symptoms. Additional biomarkers need to be explored to optimize CAA diagnosis in this population.

SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites.

In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex-level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner-invariant and scanner-specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.

The Current Diagnostic Performance of MRI-Based Radiomics for Glioma Grading: A Meta-Analysis.

BACKGROUND: Multiple radiomics models have been proposed for grading glioma using different algorithms, features, and sequences of magnetic resonance imaging. The research seeks to assess the present overall performance of radiomics for grading glioma. METHODS: A systematic literature review of the databases Ovid MEDLINE PubMed, and Ovid EMBASE for publications published on radiomics for glioma grading between 2012 and 2023 was performed. The systematic review was carried out following the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analysis. RESULTS: In the meta-analysis, a total of 7654 patients from 40 articles, were assessed. R-package mada was used for modeling the joint estimates of specificity (SPE) and sensitivity (SEN). Pooled event rates across studies were performed with a random-effects meta-analysis. The heterogeneity of SPE and SEN were based on the χ2 test. Overall values for SPE and SEN in the differentiation between high-grade gliomas (HGGs) and low-grade gliomas (LGGs) were 84% and 91%, respectively. With regards to the discrimination between World Health Organization (WHO) grade 4 and WHO grade 3, the overall SPE was 81% and the SEN was 89%. The modern non-linear classifiers showed a better trend, whereas textural features tend to be the best-performing (29%) and the most used. CONCLUSIONS: Our findings confirm that present radiomics' diagnostic performance for glioma grading is superior in terms of SEN and SPE for the HGGs vs. LGGs discrimination task when compared to the WHO grade 4 vs. 3 task.

Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer's disease.

Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from different data generation protocols, imaging equipment, radiological artifacts, and shifts in demographic distributions. Domain generalization (DG) techniques show promise in addressing these challenges by enabling the model to learn from one or more source domains and apply this knowledge to new, unseen target domains. Here we present a framework that utilizes model interpretability to enhance the generalizability of classification models across various cohorts. We used MRI scans and clinical diagnoses from four independent cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 1821), the Framingham Heart Study (FHS, n = 304), the Australian Imaging Biomarkers & Lifestyle Study of Ageing (AIBL, n = 661), and the National Alzheimer's Coordinating Center (NACC, n = 4647). With this data, we trained a deep neural network to focus on areas of the brain identified as relevant to the disease for model training. Our approach involved training a classifier to differentiate between structural neurodegeneration in individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer's disease (AD). This was achieved by aligning class-wise attention with a unified visual saliency prior, which was computed offline for each class using all the training data. Our method not only competes with state-of-the-art approaches but also shows improved correlation with postmortem histology. This alignment with the gold standard evidence is a significant step towards validating the effectiveness of DG frameworks, paving the way for their broader application in the field.

The additive value of complementing diagnostic idiopathic intracranial hypertension criteria by MRI - an external validation study.

BACKGROUND: Recently, diagnostic criteria including a standardized MRI criterion were presented to identify patients suffering from idiopathic intracranial hypertension (IIH) proposing that IIH might be defined by two out of three objective findings (papilledema, ≥ 25 cm cerebrospinal fluid opening pressure (CSF-OP) and ≥ 3/4 neuroimaging signs). METHODS: To provide independent external validation, we retrospectively applied the proposed diagnostic criteria to our cohort of patients with clinical suspicion of IIH from the Vienna IIH database. Neuroimaging was reevaluated for IIH signs according to standardized definitions by a blinded expert neuroradiologist. We determined isolated diagnostic accuracy of the neuroimaging criterion (≥ 3/4 signs) as well as overall accuracy of the new proposed criteria. RESULTS: We included patients with IIH (n = 102) and patients without IIH (no-IIH, n = 23). Baseline characteristics were balanced between IIH and no-IIH groups, but papilledema and CSF-OP were significantly higher in IIH. For the presence of ≥ 3/4 MRI signs, sensitivity was 39.2% and specificity was 91.3% with positive predictive value (PPV) of 95.2% and negative predictive value (NPV) 25.3%. Reclassifying our cohort according to the 2/3 IIH definition correctly identified 100% of patients without IIH, with definite IIH and suggested to have IIH without papilledema by Friedman criteria, respectively. CONCLUSION: The standardized neuroimaging criteria are easily applicable in clinical routine and provide moderate sensitivity and excellent specificity to identify patients with IIH. Defining IIH by 2/3 criteria significantly simplifies diagnosis without compromising accuracy.

Effect of scanning duration and sample size on reliability in resting state fMRI dynamic causal modeling analysis.

Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.

Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort.

INTRODUCTION: The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort. METHODS: Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures. RESULTS: Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone. CONCLUSION: Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.

Evaluation of MRI post-processing methods combined with PET in detecting focal cortical dysplasia lesions for patients with MRI-negative epilepsy.

OBJECTIVE: Accurate detection of focal cortical dysplasia (FCD) through magnetic resonance imaging (MRI) plays a pivotal role in the preoperative assessment of epilepsy. The integration of multimodal imaging has demonstrated substantial value in both diagnosing FCD and devising effective surgical strategies. This study aimed to enhance MRI post-processing by incorporating positron emission tomography (PET) analysis. We sought to compare the diagnostic efficacy of diverse image post-processing methodologies in patients presenting MRI-negative FCD. METHODS: In this retrospective investigation, we assembled a cohort of patients with negative preoperative MRI results. T1-weighted volumetric sequences were subjected to morphometric analysis program (MAP) and composite parametric map (CPM) post-processing techniques. We independently co-registered images derived from various methods with PET scans. The alignment was subsequently evaluated, and its correlation was correlated with postoperative seizure outcomes. RESULTS: A total of 41 patients were enrolled in the study. In the PET-MAP(p = 0.0189) and PET-CPM(p = 0.00041) groups, compared with the non-overlap group, the overlap group significantly associated with better postoperative outcomes. In PET(p = 0.234), CPM(p = 0.686) and MAP(p = 0.672), there is no statistical significance between overlap and seizure-free outcomes. The sensitivity of using the CPM alone outperformed the MAP (0.65 vs 0.46). The use of PET-CPM demonstrated superior sensitivity (0.96), positive predictive value (0.83), and negative predictive value (0.91), whereas the MAP displayed superior specificity (0.71). CONCLUSIONS: Our findings suggested a superiority in sensitivity of CPM in detecting potential FCD lesions compared to MAP, especially when it is used in combination with PET for diagnosis of MRI-negative epilepsy patients. Moreover, we confirmed the superiority of synergizing metabolic imaging (PET) with quantitative maps derived from structural imaging (MAP or CPM) to enhance the identification of subtle epileptogenic zones (EZs). This study serves to illuminate the potential of integrated multimodal techniques in advancing our capability to pinpoint elusive pathological features in epilepsy cases.

Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

Automated protocols for delineating human hippocampal subfields from 3 Tesla and 7 Tesla magnetic resonance imaging data.

Researchers who study the human hippocampus are naturally interested in how its subfields function. However, many researchers are precluded from examining subfields because their manual delineation from magnetic resonance imaging (MRI) scans (still the gold standard approach) is time consuming and requires significant expertise. To help ameliorate this issue, we present here two protocols, one for 3T MRI and the other for 7T MRI, that permit automated hippocampus segmentation into six subregions, namely dentate gyrus/cornu ammonis (CA)4, CA2/3, CA1, subiculum, pre/parasubiculum, and uncus along the entire length of the hippocampus. These protocols are particularly notable relative to existing resources in that they were trained and tested using large numbers of healthy young adults (n = 140 at 3T, n = 40 at 7T) whose hippocampi were manually segmented by experts from MRI scans. Using inter-rater reliability analyses, we showed that the quality of automated segmentations produced by these protocols was high and comparable to expert manual segmenters. We provide full open access to the automated protocols, and anticipate they will save hippocampus researchers a significant amount of time. They could also help to catalyze subfield research, which is essential for gaining a full understanding of how the hippocampus functions.

Contribution of MRI for the Early Diagnosis of Parkinsonism in Patients with Diagnostic Uncertainty.

BACKGROUND: International clinical criteria are the reference for the diagnosis of degenerative parkinsonism in clinical research, but they may lack sensitivity and specificity in the early stages. OBJECTIVES: To determine whether magnetic resonance imaging (MRI) analysis, through visual reading or machine-learning approaches, improves diagnostic accuracy compared with clinical diagnosis at an early stage in patients referred for suspected degenerative parkinsonism. MATERIALS: Patients with initial diagnostic uncertainty between Parkinson's disease (PD), progressive supranuclear palsy (PSP), and multisystem atrophy (MSA), with brain MRI performed at the initial visit (V1) and available 2-year follow-up (V2), were included. We evaluated the accuracy of the diagnosis established based on: (1) the international clinical diagnostic criteria for PD, PSP, and MSA at V1 ("Clin1"); (2) MRI visual reading blinded to the clinical diagnosis ("MRI"); (3) both MRI visual reading and clinical criteria at V1 ("MRI and Clin1"), and (4) a machine-learning algorithm ("Algorithm"). The gold standard diagnosis was established by expert consensus after a 2-year follow-up. RESULTS: We recruited 113 patients (53 with PD, 31 with PSP, and 29 with MSA). Considering the whole population, compared with clinical criteria at the initial visit ("Clin1": balanced accuracy, 66.2%), MRI visual reading showed a diagnostic gain of 14.3% ("MRI": 80.5%; P = 0.01), increasing to 19.2% when combined with the clinical diagnosis at the initial visit ("MRI and Clin1": 85.4%; P < 0.0001). The algorithm achieved a diagnostic gain of 9.9% ("Algorithm": 76.1%; P = 0.08). CONCLUSION: Our study shows the use of MRI analysis, whether by visual reading or machine-learning methods, for early differentiation of parkinsonism. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Improving 3D edge detection for visual inspection of MRI coregistration and alignment.

BACKGROUND: Visualizing edges is critical for neuroimaging. For example, edge maps enable quality assurance for the automatic alignment of an image from one modality (or individual) to another. NEW METHOD: We suggest that using the second derivative (difference of Gaussian, or DoG) provides robust edge detection. This method is tuned by size (which is typically known in neuroimaging) rather than intensity (which is relative). RESULTS: We demonstrate that this method performs well across a broad range of imaging modalities. The edge contours produced consistently form closed surfaces, whereas alternative methods may generate disconnected lines, introducing potential ambiguity in contiguity. COMPARISON WITH EXISTING METHODS: Current methods for computing edges are based on either the first derivative of the image (FSL), or a variation of the Canny Edge detection method (AFNI). These methods suffer from two primary limitations. First, the crucial tuning parameter for each of these methods relates to the image intensity. Unfortunately, image intensity is relative for most neuroimaging modalities making the performance of these methods unreliable. Second, these existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image. CONCLUSION: The second derivative is well suited for neuroimaging edge detection. We include this method as part of both the AFNI and FSL software packages, standalone code and online.

Non-navigated 2D intraoperative ultrasound: An unsophisticated surgical tool to achieve high standards of care in glioma surgery.

PURPOSE: In an era characterized by rapid progression in neurosurgical technologies, traditional tools such as the non-navigated two-dimensional intraoperative ultrasound (nn-2D-IOUS) risk being overshadowed. Against this backdrop, this study endeavors to provide a comprehensive assessment of the clinical efficacy and surgical relevance of nn-2D-IOUS, specifically in the context of glioma resections. METHODS: This retrospective study undertaken at a single center evaluated 99 consecutive, non-selected patients diagnosed with both high-grade and low-grade gliomas. The primary objective was to assess the proficiency of nn-2D-IOUS in generating satisfactory image quality, identifying residual tumor tissue, and its influence on the extent of resection. To validate these results, early postoperative MRI data served as the reference standard. RESULTS: The nn-2D-IOUS exhibited a high level of effectiveness, successfully generating good quality images in 79% of the patients evaluated. With a sensitivity rate of 68% and a perfect specificity of 100%, nn-2D-IOUS unequivocally demonstrated its utility in intraoperative residual tumor detection. Notably, when total tumor removal was the surgical objective, a resection exceeding 95% of the initial tumor volume was achieved in 86% of patients. Additionally, patients in whom residual tumor was not detected by nn-2D-IOUS, the mean volume of undetected tumor tissue was remarkably minimal, averaging at 0.29 cm3. CONCLUSION: Our study supports nn-2D-IOUS's invaluable role in glioma surgery. The results highlight the utility of traditional technologies for enhanced surgical outcomes, even when compared to advanced alternatives. This is particularly relevant for resource-constrained settings and emphasizes optimizing existing tools for efficient patient care. NCT05873946 - 24/05/2023 - Retrospectively registered.

Grading system for assessing the confidence in the epileptogenic zone reported in published studies: A Delphi consensus study.

OBJECTIVE: This study was undertaken to develop a standardized grading system based on expert consensus for evaluating the level of confidence in the localization of the epileptogenic zone (EZ) as reported in published studies, to harmonize and facilitate systematic reviews in the field of epilepsy surgery. METHODS: We conducted a Delphi study involving 22 experts from 18 countries, who were asked to rate their level of confidence in the localization of the EZ for various theoretical clinical scenarios, using different scales. Information provided in these scenarios included one or several of the following data: magnetic resonance imaging (MRI) findings, invasive electroencephalography summary, and postoperative seizure outcome. RESULTS: The first explorative phase showed an overall interrater agreement of .347, pointing to large heterogeneity among experts' assessments, with only 17% of the 42 proposed scenarios associated with a substantial level of agreement. A majority showed preferences for the simpler scale and single-item scenarios. The successive Delphi voting phases resulted in a majority consensus across experts, with more than two thirds of respondents agreeing on the rating of each of the tested single-item scenarios. High or very high levels of confidence were ascribed to patients with either an Engel class I or class IA postoperative seizure outcome, a well-delineated EZ according to all available invasive EEG (iEEG) data, or a well-delineated focal epileptogenic lesion on MRI. MRI signs of hippocampal sclerosis or atrophy were associated with a moderate level of confidence, whereas a low level was ascribed to other MRI findings, a poorly delineated EZ according to iEEG data, or an Engel class II-IV postoperative seizure outcome. SIGNIFICANCE: The proposed grading system, based on an expert consensus, provides a simple framework to rate the level of confidence in the EZ reported in published studies in a structured and harmonized way, offering an opportunity to facilitate and increase the quality of systematic reviews and guidelines in the field of epilepsy surgery.

A virtual phantom for patient-specific QA On A 1.5T MR-linac.

Create a virtual ArcCHECK-MR phantom, customized for a 1.5T MR-linac, with consideration of the different density regions within the quality assurance (QA) phantom, aiming to streamline the utilization of this specialized QA device. A virtual phantom was constructed in the treatment planning system (TPS) to replicate the ArcCHECK-MR's composition, consisting of five distinct layers: "Outer" (representing the outer PMMA ring), "Complex" (simulating the printed circuit boards), "Detectors" (encompassing the detector area), "Inner" (signifying the inner PMMA ring) and "Insert" (representing the PMMA insert). These layers were defined based on geometric data and represented as contour points on a set of dummy CT images. Additionally, a setup platform was integrated as contoured structures. To determine the relative electron density (RED) values of the external and internal PMMA components, measurements were taken at 25 points in the insert using an ion chamber. A novel method for establishing the exit/entrance dose ratio (EEDR) for ArcCHECK-MR was introduced. The RED of higher density region was derived by evaluating the local gamma index passing rate results with criteria of 2% dose difference and 2 mm distance-to-agreement. The performance of the virtual phantom was assessed for Unity 7 FFF beams with a 1.5T magnetic field. The radii of the five ring structures within the virtual phantom measured 133.0 mm, 110.0 mm, 103.4 mm, 100.0 mm, and 75.0 mm for the "Outer," "Complex," "Detectors," "Inner" and "Insert" regions, respectively. The RED values were as follows: ArcCHECK-MR PMMA had a RED of 1.130, "Detectors" were assumed to have a RED of 1.000, "Complex" had a RED of 1.200, and the setup QA phantom justified a RED of 1.350. Early validation results demonstrate that the 5-layer virtual phantom, when compared to the commonly used bulk overridden phantom, offers improved capability in MR-linac environments. This enhancement led to an increase in passing rates for the local gamma index by approximately 5 ∼ 6%, when applying the criteria of 2%, 2 mm. We have successfully generated a virtual representation of the distinct regions within the ArcCHECK-MR using a TPS, addressing the challenges associated with its use in conjunction with a 1.5T MR-linac. We consistently observed favorable local gamma index passing rates across two 1.5T MR-linac and ArcCHECK-MR unit combinations. This approach has the potential to minimize uncertainties in the creation of the QA phantom for ArcCHECK-MR across various institutions.

Cross-cultural Diagnostic Validity of the Multilingual Naming Test (MINT) in a Sample of Older Adults.

OBJECTIVE: We aimed to evaluate the psychometric properties and diagnostic accuracy of the 32-item version of the Multilingual Naming Test (MINT) in participants from 2 ethnic groups (European Americans [EA; n = 106] and Hispanic Americans [HA; n = 175]) with 3 diagnostic groups (cognitively normal [CN], n = 94, mild cognitive impairment [MCI], n = 148, and dementia, n = 39). METHOD: An Item Response Theory model was used to evaluate items across ethnicity and language groups (Spanish and English), resulting in a 24-item version. We analyzed the MINT discriminant and predictive validity across diagnostic groups. RESULTS: A total of 8 items were differentially difficult between languages in the 32-item version of the MINT. EA scored significantly higher than HA, but the difference was not significant when removing those 8 items (controlling for Education). The Receiver Operating Characteristics showed that the MINT had poor accuracy when identifying CN participants and was acceptable in identifying dementia participants but unacceptable in classifying MCI participants. Finally, we tested the association between MINT scores and magnetic resonance imaging volumetric measures of language-related areas in the temporal and frontal lobes. The 32-item MINT in English and Spanish and the 24-item MINT in Spanish were significantly correlated with the bilateral middle temporal gyrus. The left fusiform gyrus correlated with MINT scores regardless of language and MINT version. We also found differential correlations depending on the language of administration. CONCLUSIONS: Our results highlight the importance of analyzing cross-cultural samples when implementing clinical neuropsychological tests such as the MINT.

An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation.

High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In magnetic resonance imaging (MRI), restricted by hardware capacity, scan time, and patient cooperation ability, isotropic 3-dimensional (3D) HR image acquisition typically requests long scan time and, results in small spatial coverage and low signal-to-noise ratio (SNR). Recent studies showed that, with deep convolutional neural networks, isotropic HR MR images could be recovered from low-resolution (LR) input via single image super-resolution (SISR) algorithms. However, most existing SISR methods tend to approach scale-specific projection between LR and HR images, thus these methods can only deal with fixed up-sampling rates. In this paper, we propose ArSSR, an Arbitrary Scale Super-Resolution approach for recovering 3D HR MR images. In the ArSSR model, the LR image and the HR image are represented using the same implicit neural voxel function with different sampling rates. Due to the continuity of the learned implicit function, a single ArSSR model is able to achieve arbitrary and infinite up-sampling rate reconstructions of HR images from any input LR image. Then the SR task is converted to approach the implicit voxel function via deep neural networks from a set of paired HR and LR training examples. The ArSSR model consists of an encoder network and a decoder network. Specifically, the convolutional encoder network is to extract feature maps from the LR input images and the fully-connected decoder network is to approximate the implicit voxel function. Experimental results on three datasets show that the ArSSR model can achieve state-of-the-art SR performance for 3D HR MR image reconstruction while using a single trained model to achieve arbitrary up-sampling scales.