Improved conspicuity and delineation of high-grade primary and metastatic brain tumors using "restriction spectrum imaging": quantitative comparison with high B-value DWI and ADC

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM2.5) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Tri-Compartmental Restriction Spectrum Imaging Based on 18F-FDG PET/MR for Identification of Primary Benign and Malignant Lung Lesions

BACKGROUND

Restriction spectrum imaging (RSI), as an advanced quantitative diffusion-weighted magnetic resonance imaging technique, has the potential to distinguish primary benign and malignant lung lesions.

OBJECTIVE

To explore how well the tri-compartmental RSI performs in distinguishing primary benign from malignant lung lesions compared with diffusion-weighted imaging (DWI), and to further explore whether positron emission tomography/magnetic resonance imaging (PET/MRI) can improve diagnostic efficacy.

STUDY TYPE

Prospective.

POPULATION

137 patients, including 108 malignant and 29 benign lesions (85 males, 52 females; average age = 60.0 ± 10.0 years).

FIELD STRENGTH/SEQUENCE

T2WI, T1WI, multi-b value DWI, MR-based attenuation correction, and PET imaging on a 3.0 T whole-body PET/MR system.

ASSESSMENT

The apparent diffusion coefficient (ADC), RSI-derived parameters (restricted diffusionf 1 $$ {f}_1 $$ , hindered diffusionf 2 $$ {f}_2 $$ , and free diffusionf 3 $$ {f}_3 $$ ) and the maximum standardized uptake value (SUVmax) were calculated and analyzed for diagnostic efficacy individually or in combination.

STATISTICAL TESTS

Student's t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curves, Delong test, Spearman's correlation analysis. P < 0.05 was considered statistically significant.

RESULTS

Thef 1 $$ {f}_1 $$ , SUVmax were significantly higher, andf 3 $$ {f}_3 $$ , ADC were significantly lower in the malignant group [0.717 ± 0.131, 9.125 (5.753, 13.058), 0.194 ± 0.099, 1.240 (0.972, 1.407)] compared to the benign group [0.504 ± 0.236, 3.390 (1.673, 6.030), 0.398 ± 0.195, 1.485 ± 0.382]. The area under the ROC curve (AUC) values ranked from highest to lowest as follows: AUC (SUVmax) > AUC (f 3 $$ {f}_3 $$ ) > AUC (f 1 $$ {f}_1 $$ ) > AUC (ADC) > AUC (f 2 $$ {f}_2 $$ ) (AUC = 0.819, 0.811, 0.770, 0.745, 0549). The AUC (AUC = 0.900) of the combined model of RSI with PET was significantly higher than that of either single-modality imaging.

CONCLUSION

RSI-derived parameters (f 1 $$ {f}_1 $$ ,f 3 $$ {f}_3 $$ ) might help to distinguish primary benign and malignant lung lesions and the discriminatory utility off 2 $$ {f}_2 $$ was not observed. The RSI exhibits comparable or potentially enhanced performance compared with DWI, and the combined RSI and PET model might improve diagnostic efficacy.

LEVEL OF EVIDENCE: 2

TECHNICAL EFFICACY

Stage 2.

Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model

BACKGROUND

Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF).

PURPOSE

To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model.

METHODS

Thirty mice with carbon tetrachloride (CCl4)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000 s/mm2) diffusion-weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2-5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF.

RESULTS

According to BIC results, a 4-compartment RSI model (RSI4) with optimal ADCs of 0.471 × 10-3, 1.653 × 10-3, 9.487 × 10-3, and > 30 × 10-3, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C1 and C3 compartments were observed between LF and control groups (P = 0.018 and 0.003, respectively). ROC analysis showed that RSI4-C3 was the most effective single diffusion parameter for characterizing LF (AUC = 0.876, P = 0.003). Furthermore, the combination of ADC values and RSI4-C3 value increased the diagnosis performance significantly (AUC = 0.894, P = 0.002).

CONCLUSION

The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI4-C3 showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI4-C3 values further improved the discrimination performance.

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM 2.5 ) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM 2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM 2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM 2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM 2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Application of PET/MRI in Gynecologic Malignancies

The diagnosis, treatment, and management of gynecologic malignancies benefit from both positron emission tomography/computed tomography (PET/CT) and MRI. PET/CT provides important information on the local extent of disease as well as diffuse metastatic involvement. MRI offers soft tissue delineation and loco-regional disease involvement. The combination of these two technologies is key in diagnosis, treatment planning, and evaluating treatment response in gynecological malignancies. This review aims to assess the performance of PET/MRI in gynecologic cancer patients and outlines the technical challenges and clinical advantages of PET/MR systems when specifically applied to gynecologic malignancies.

Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study

Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion magnetic resonance imaging [MRI]). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N = 34,029) and ABCD Study (N = 8607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI in a genome-wide association study (GWAS) with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.

Diagnostic Utility of Restriction Spectrum Imaging in Head and Neck Tumors: A Pilot Study

OBJECTIVE

Imaging is crucial in the assessment of head and neck cancers for site, extension, and enlarged lymph nodes. Restriction spectrum imaging (RSI) is a new diffusion-weighted magnetic resonance imaging (MRI) technique that enhances the ability to differentiate aggressive cancer from low-grade or benign tumors and helps guide treatment and biopsy. Its contribution to imaging of brain and prostate tumors has been previously published. However, there are no prior studies using RSI sequence in head and neck tumors. The purpose of this study was to evaluate the feasibility of performing RSI in head and neck cancer.

METHODS

An additional RSI sequence was added in the routine MRI neck protocol for 13 patients diagnosed with head and neck cancer between November 2018 and April 2019. Restriction spectrum imaging sequence was performed with b values of 0, 500, 1500, and 3000 s/mm 2 and 29 directions on 1.5T magnetic resonance scanners.Diffusion-weighted imaging (DWI) images and RSI images were compared according to their ability to detect the primary malignancy and possible metastatic lymph nodes.

RESULTS

In 71% of the patients, RSI outperformed DWI in detecting the primary malignancy and possible metastatic lymph nodes, whereas in the remaining cases, the 2 were comparable. In 66% of the patients, RSI detected malignant lymph nodes that DWI/apparent diffusion coefficient failed to detect.

CONCLUSIONS

This is the first study of RSI in head and neck imaging and showed its superiority over the conventional DWI sequence. Because of its ability to differentiate benign and malignant lymph nodes in some cases, the addition of RSI to routine head and neck MRI should be considered.

Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer

Purpose

Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy.

Experimental design

Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint.

Results

Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC.

Conclusion

The automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.

Ambient fine particulate exposure and subcortical gray matter microarchitecture in 9- and 10-year-old children across the United States

Neuroimaging studies showing the adverse effects of air pollution on neurodevelopment have largely focused on smaller samples from limited geographical locations and have implemented univariant approaches to assess exposure and brain macrostructure. Herein, we implement restriction spectrum imaging and a multivariate approach to examine how one year of annual exposure to daily fine particulate matter (PM_2.5), daily nitrogen dioxide (NO₂), and 8-h maximum ozone (O₃) at ages 9-10 years relates to subcortical gray matter microarchitecture in a geographically diverse subsample of children from the Adolescent Brain Cognitive Development (ABCD) Study^℠. Adjusting for confounders, we identified a latent variable representing 66% of the variance between one year of air pollution and subcortical gray matter microarchitecture. PM_2.5 was related to greater isotropic intracellular diffusion in the thalamus, brainstem, and accumbens, which related to cognition and internalizing symptoms. These findings may be indicative of previously identified air pollution-related risk for neuroinflammation and early neurodegenerative pathologies.

Linear multi-scale modeling of diffusion MRI data: A framework for characterization of oriented structures across length scales

Diffusion-weighted magnetic resonance imaging (DW-MRI) has evolved to provide increasingly sophisticated investigations of the human brain's structural connectome in vivo. Restriction spectrum imaging (RSI) is a method that reconstructs the orientation distribution of diffusion within tissues over a range of length scales. In its original formulation, RSI represented the signal as consisting of a spectrum of Gaussian diffusion response functions. Recent technological advances have enabled the use of ultra-high b-values on human MRI scanners, providing higher sensitivity to intracellular water diffusion in the living human brain. To capture the complex diffusion time dependence of the signal within restricted water compartments, we expand upon the RSI approach to represent restricted water compartments with non-Gaussian response functions, in an extended analysis framework called linear multi-scale modeling (LMM). The LMM approach is designed to resolve length scale and orientation-specific information with greater specificity to tissue microstructure in the restricted and hindered compartments, while retaining the advantages of the RSI approach in its implementation as a linear inverse problem. Using multi-shell, multi-diffusion time DW-MRI data acquired with a state-of-the-art 3 T MRI scanner equipped with 300 mT/m gradients, we demonstrate the ability of the LMM approach to distinguish different anatomical structures in the human brain and the potential to advance mapping of the human connectome through joint estimation of the fiber orientation distributions and compartment size characteristics.

Morphometric dis-similarity between cortical and subcortical areas underlies cognitive function and psychiatric symptomatology: a preadolescence study from ABCD

Preadolescence is a critical period characterized by dramatic morphological changes and accelerated cortico-subcortical development. Moreover, the coordinated development of cortical and subcortical regions underlies the emerging cognitive functions during this period. Deviations in this maturational coordination may underlie various psychiatric disorders that begin during preadolescence, but to date these deviations remain largely uncharted. We constructed a comprehensive whole-brain morphometric similarity network (MSN) from 17 neuroimaging modalities in a large preadolescence sample (N = 8908) from Adolescent Brain Cognitive Development (ABCD) study and investigated its association with 10 cognitive subscales and 27 psychiatric subscales or diagnoses. Based on the MSNs, each brain was clustered into five modules with distinct cytoarchitecture and evolutionary relevance. While morphometric correlation was positive within modules, it was negative between modules, especially between isocortical and paralimbic/subcortical modules; this developmental dissimilarity was genetically linked to synapse and neurogenesis. The cortico-subcortical dissimilarity becomes more pronounced longitudinally in healthy children, reflecting developmental differentiation of segregated cytoarchitectonic areas. Higher cortico-subcortical dissimilarity (between the isocortical and paralimbic/subcortical modules) were related to better cognitive performance. In comparison, children with poor modular differentiation between cortex and subcortex displayed higher burden of externalizing and internalizing symptoms. These results highlighted cortical-subcortical morphometric dissimilarity as a dynamic maturational marker of cognitive and psychiatric status during the preadolescent stage and provided insights into brain development.

A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases

Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10^-3, 2.8 × 10^-3, and >3.0 ×10^-2 mm²/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.

Quantitative Assessment of Restriction Spectrum MR Imaging for the Diagnosis of Breast Cancer and Association With Prognostic Factors

BACKGROUND

Restriction spectrum imaging (RSI) is an advanced quantitative diffusion-weighted magnetic resonance imaging (DWI) technique to assess breast cancer.

PURPOSE

To investigate the ability of RSI to differentiate the benign and malignant breast lesions and the association with prognostic factors of breast cancer.

STUDY TYPE

Retrospective.

POPULATION

Seventy women (mean age, 49.6 ± 12.3 years) with 56 malignant and 19 benign breast lesions.

FIELD STRENGTH/SEQUENCE

3-T; RSI-based DWI sequence with echo-planar imaging technique.

ASSESSMENT

The apparent diffusion coefficient (ADC) and RSI parameters (restricted diffusion f₁ , hindered diffusion f₂ , free diffusion f₃ , and signal fractions f₁ f₂ ) were calculated by two readers for the whole lesion volume and compared between the benign and malignant groups and the subgroups with different statuses of prognostic factors in breast cancer.

STATISTICAL TESTS

Mann-Whitney U test or Student's t-test was applied to compare the quantitative parameters between the different groups. Intraclass correlation coefficient (ICC) was used to assess readers' reproducibility. Binary logistic regression was used to combine parameters. Area under the curve (AUC) of receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of parameters to distinguish benign from malignant breast lesions. A P-value <0.05 was considered statistically significant.

RESULTS

Malignant breast lesions showed significantly lower ADC and f₃ values, and significantly higher f₁ and f₁ f₂ values than the benign lesions, with AUC of 0.951, 0.877, 0.868, and 0.860, respectively. When RSI-derived parameters and ADC were combined, the diagnostic performance was superior to either single parameter (AUC = 0.973). The f₃ value was significantly differed between estrogen receptor (ER)-positive and ER-negative tumors. The ADC, f₁ , f₃ , and f₁ f₂ values were significantly different progesterone receptor (PR)-positive and PR-negative status.

DATA CONCLUSION

The RSI-derived parameters (f₁ , f₃ , and f₁ f₂ ) may facilitate the differential diagnosis between benign and malignant breast lesions.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

White matter microstructure shows sex differences in late childhood: Evidence from 6797 children

Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.

Associations between MRI-assessed locus coeruleus integrity and cortical gray matter microstructure

The locus coeruleus (LC) is one of the earliest sites of tau pathology, making it a key structure in early Alzheimer's disease (AD) progression. As the primary source of norepinephrine for the brain, reduced LC integrity may have negative consequences for brain health, yet macrostructural brain measures (e.g. cortical thickness) may not be sensitive to early stages of neurodegeneration. We therefore examined whether LC integrity was associated with differences in cortical gray matter microstructure among 435 men (mean age = 67.5; range = 62-71.7). LC structural integrity was indexed by contrast-to-noise ratio (LCCNR) from a neuromelanin-sensitive MRI scan. Restriction spectrum imaging (RSI), an advanced multi-shell diffusion technique, was used to characterize cortical microstructure, modeling total diffusion in restricted, hindered, and free water compartments. Higher LCCNR (greater integrity) was associated with higher hindered and lower free water diffusion in multiple cortical regions. In contrast, no associations between LCCNR and cortical thickness survived correction. Results suggest lower LC integrity is associated with patterns of cortical microstructure that may reflect a reduction in cytoarchitectural barriers due to broader neurodegenerative processes. These findings highlight the potential utility for LC imaging and advanced diffusion measures of cortical microstructure in assessing brain health and early identification of neurodegenerative processes.

Tri-Compartmental Restriction Spectrum Imaging Breast Model Distinguishes Malignant Lesions from Benign Lesions and Healthy Tissue on Diffusion-Weighted Imaging

Diffusion-weighted MRI (DW-MRI) offers a potential adjunct to dynamic contrast-enhanced MRI to discriminate benign from malignant breast lesions by yielding quantitative information about tissue microstructure. Multi-component modeling of the DW-MRI signal over an extended b-value range (up to 3000 s/mm²) theoretically isolates the slowly diffusing (restricted) water component in tissues. Previously, a three-component restriction spectrum imaging (RSI) model demonstrated the ability to distinguish malignant lesions from healthy breast tissue. We further evaluated the utility of this three-component model to differentiate malignant from benign lesions and healthy tissue in 12 patients with known malignancy and synchronous pathology-proven benign lesions. The signal contributions from three distinct diffusion compartments were measured to generate parametric maps corresponding to diffusivity on a voxel-wise basis. The three-component model discriminated malignant from benign and healthy tissue, particularly using the restricted diffusion C₁ compartment and product of the restricted and intermediate diffusion compartments (C₁ and C₂). However, benign lesions and healthy tissue did not significantly differ in diffusion characteristics. Quantitative discrimination of these three tissue types (malignant, benign, and healthy) in non-pre-defined lesions may enhance the clinical utility of DW-MRI in reducing excessive biopsies and aiding in surveillance and surgical evaluation without repeated exposure to gadolinium contrast.

Longitudinal Evidence of a Vicious Cycle Between Nucleus Accumbens Microstructure and Childhood Weight Gain

PURPOSE

Pediatric obesity is a growing public health concern. Previous work has observed diet to impact nucleus accumbens (NAcc) inflammation in rodents, measured by the reactive proliferation of glial cells. Recent work in humans has demonstrated a relationship between NAcc cell density-a proxy for neuroinflammation-and weight gain in youth; however, the directionality of this relationship in the developing brain and association with diet remains unknown.

METHODS

Waist circumference (WC) and NAcc cell density were collected in a large cohort of children (n > 2,000) participating in the Adolescent Brain Cognitive Development (ABCD) Study (release 3.0) at baseline (9-10 y) and at a Year 2 follow-up (11-12 y). Latent change score modeling (LCSM) was used to disentangle contributions of baseline measures to two-year changes in WC percentile and NAcc cellularity. In addition, the role of NAcc cellularity in mediating the relationship between diet and WC percentile was assessed using dietary intake data collected at Year 2.

RESULTS

LCSM indicates that baseline WC percentile influences change in NAcc cellularity and that baseline NAcc cell density influences change in WC percentile. NAcc cellularity was significantly associated with WC percentile at Year 2 and mediated the relationship between dietary fat consumption and WC percentile.

CONCLUSIONS

These results implicate a vicious cycle whereby NAcc cell density biases longitudinal changes in WC percentile and vice versa. Moreover, NAcc cell density may mediate the relationship between diet and weight gain in youth. These findings suggest that diet-induced inflammation of reward circuitry may lead to behavioral changes that further contribute to weight gain.

Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain

The molecular determinants of tissue composition of the human brain remain largely unknown. Recent genome-wide association studies (GWAS) on this topic have had limited success due to methodological constraints. Here, we apply advanced whole-brain analyses on multi-shell diffusion imaging data and multivariate GWAS to two large scale imaging genetic datasets (UK Biobank and the Adolescent Brain Cognitive Development study) to identify and validate genetic association signals. We discover 503 unique genetic loci that have impact on multiple regions of human brain. Among them, more than 79% are validated in either of two large-scale independent imaging datasets. Key molecular pathways involved in axonal growth, astrocyte-mediated neuroinflammation, and synaptogenesis during development are found to significantly impact the measured variations in tissue-specific imaging features. Our results shed new light on the biological determinants of brain tissue composition and their potential overlap with the genetic basis of neuropsychiatric disorders.

Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding

Background

Tumor-related hyperintensities in high b-value diffusion-weighted imaging (DWI) are radiologically important in the workup of gliomas. However, the white matter may also appear as hyperintense, which may conflate interpretation.

Purpose

To investigate whether DWI with spherical b-tensor encoding (STE) can be used to suppress white matter and enhance the conspicuity of glioma hyperintensities unrelated to white matter.

Materials and Methods

Twenty-five patients with a glioma tumor and at least one pathology-related hyperintensity on DWI underwent conventional MRI at 3 T. The DWI was performed both with linear and spherical tensor encoding (LTE-DWI and STE-DWI). The LTE-DWI here refers to the DWI obtained with conventional diffusion encoding and averaged across diffusion-encoding directions. Retrospectively, the differences in contrast between LTE-DWI and STE-DWI, obtained at a b-value of 2,000 s/mm², were evaluated by comparing hyperintensities and contralateral normal-appearing white matter (NAWM) both visually and quantitatively in terms of the signal intensity ratio (SIR) and contrast-to-noise ratio efficiency (CNR_eff).

Results

The spherical tensor encoding DWI was more effective than LTE-DWI at suppressing signals from white matter and improved conspicuity of pathology-related hyperintensities. The median SIR improved in all cases and on average by 28%. The median (interquartile range) SIR was 1.9 (1.6 – 2.1) for STE and 1.4 (1.3 – 1.7) for LTE, with a significant difference of 0.4 (0.3 –0.5) (p < 10^–4, paired U-test). In 40% of the patients, the SIR was above 2 for STE-DWI, but with LTE-DWI, the SIR was below 2 for all patients. The CNR_eff of STE-DWI was significantly higher than of LTE-DWI: 2.5 (2 – 3.5) vs. 2.3 (1.7 – 3.1), with a significant difference of 0.4 (−0.1 –0.6) (p < 10^–3, paired U-test). The STE improved CNR_eff in 70% of the cases. We illustrate the benefits of STE-DWI in three patients, where STE-DWI may facilitate an improved radiological description of tumor-related hyperintensity, including one case that could have been missed out if only LTE-DWI was inspected.

Conclusion

The contrast mechanism of high b-value STE-DWI results in a stronger suppression of white matter than conventional LTE-DWI, and may, therefore, be more sensitive and specific for assessment of glioma tumors and DWI-hyperintensities.

Effects of Multi-Shell Free Water Correction on Glioma Characterization

Diffusion MRI is a useful tool to investigate the microstructure of brain tumors. However, the presence of fast diffusing isotropic signals originating from non-restricted edematous fluids, within and surrounding tumors, may obscure estimation of the underlying tissue characteristics, complicating the radiological interpretation and quantitative evaluation of diffusion MRI. A multi-shell regularized free water (FW) elimination model was therefore applied to separate free water from tissue-related diffusion components from the diffusion MRI of 26 treatment-naïve glioma patients. We then investigated the diagnostic value of the derived measures of FW maps as well as FW-corrected tensor-derived maps of fractional anisotropy (FA). Presumed necrotic tumor regions display greater mean and variance of FW content than other parts of the tumor. On average, the area under the receiver operating characteristic (ROC) for the classification of necrotic and enhancing tumor volumes increased by 5% in corrected data compared to non-corrected data. FW elimination shifts the FA distribution in non-enhancing tumor parts toward higher values and significantly increases its entropy (p ≤ 0.003), whereas skewness is decreased (p ≤ 0.004). Kurtosis is significantly decreased (p < 0.001) in high-grade tumors. In conclusion, eliminating FW contributions improved quantitative estimations of FA, which helps to disentangle the cancer heterogeneity.

Characterization of the diffusion signal of breast tissues using multi-exponential models

PURPOSE

Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues.

METHODS

The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging.

RESULTS

A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D_1,3 = 0, D_2,3 = 1.5 × 10^-3 , and D_3,3 = 10.8 × 10^-3 mm² /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent.

CONCLUSION

Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.

Advanced Imaging Techniques for Differentiating Pseudoprogression and Tumor Recurrence After Immunotherapy for Glioblastoma

Glioblastoma (GBM) is the most common malignant tumor of the central nervous system with poor prognosis. Although the field of immunotherapy in glioma is developing rapidly, glioblastoma is still prone to recurrence under strong immune intervention. The major challenges in the process of immunotherapy are evaluating the curative effect, accurately distinguishing between treatment-related reactions and tumor recurrence, and providing guidance for clinical decision-making. Since the conventional magnetic resonance imaging (MRI) is usually difficult to distinguish between pseudoprogression and the true tumor progression, many studies have used various advanced imaging techniques to evaluate treatment-related responses. Meanwhile, criteria for efficacy evaluation of immunotherapy are constantly updated and improved. A standard imaging scheme to evaluate immunotherapeutic response will benefit patients finally. This review mainly summarizes the application status and future trend of several advanced imaging techniques in evaluating the efficacy of GBM immunotherapy.

Microstructural development from 9 to 14 years: Evidence from the ABCD Study

During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.

Association of Outdoor Ambient Fine Particulate Matter With Intracellular White Matter Microstructural Properties Among Children

Importance

Outdoor particulate matter 2.5 μm or less in diameter (PM2.5) is a ubiquitous environmental neurotoxicant that may affect the developing brain. Little is known about associations between PM2.5 and white matter connectivity.

Objectives

To assess associations between annual residential PM2.5 exposure and white matter microstructure health in a US sample of children 9 to 10 years of age and to examine whether associations are specific to certain white matter pathways or vary across neuroimaging diffusion markers reflective of intracellular and extracellular microstructural processes.

Design, Setting, and Participants

This cross-sectional study, the Adolescent Brain and Cognitive Development (ABCD) Study, was composed of 21 study sites across the US and used baseline data collected from children 9 to 10 years of age from September 1, 2016, to October 15, 2018. Data analysis was performed from September 15, 2020, to June 30, 2021.

Exposures

Annual mean PM2.5 exposure estimated by ensemble-based models and assigned to the primary residential addresses at baseline.

Main Outcomes and Measures

Diffusion-weighted imaging (DWI) and tractography were used to delineate white matter tracts. The biophysical modeling technique of restriction spectrum imaging (RSI) was implemented to examine total hindered diffusion and restricted isotropic and anisotropic intracellular diffusion in each tract. Hierarchical mixed-effects models with natural splines were used to analyze the associations between PM2.5 exposure and DWI.

Results

In a study population of 7602 children (mean [SD] age, 119.1 [7.42] months; 3955 [52.0%] female; 160 [ 21.%] Asian, 1025 [13.5%] Black, 1616 [21.3%] Hispanic, 4025 [52.9%] White, and 774 [10.2%] other [identified by parents as American Indian/Native American or Alaska Native; Native Hawaiian, Guamanian, Samoan, other Pacific Islander; Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, or other Asian; or other race]), associations were seen between annual ambient PM2.5 and hemispheric differences in white matter microstructure. Hemisphere-stratified models revealed significant associations between PM2.5 exposure and restricted isotropic intracellular diffusion in the left cingulum, in the left superior longitudinal fasciculus, and bilaterally in the fornix and uncinate fasciculus. In tracts with strong positive associations, a PM2.5 increase from 8 to 12 μg/m3 was associated with increases of 2.16% (95% CI, 0.49%-3.84%) in the left cingulum, 1.95% (95% CI, 0.43%-3.47%) in the left uncinate, and 1.68% (95% CI, 0.01%-3.34%) in the right uncinate. Widespread negative associations were observed between PM2.5 and mean diffusivity.

Conclusions and Relevance

The findings of this cross-sectional study suggest that annual mean PM2.5 exposure during childhood is associated with increased restricted isotropic diffusion and decreased mean diffusivity of specific white matter tracts, potentially reflecting differences in the composition of white matter microarchitecture.

Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges

Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.

Survival Prediction Analysis in Glioblastoma With Diffusion Kurtosis Imaging

SIMPLE SUMMARY

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Diffusion kurtosis imaging (DKI) has characterized non-Gaussian diffusion behaviors in brain normal tissue and gliomas, but there are very limited efforts in investigating treatment responses of kurtosis in GBM. This study aimed to investigate whether any parameter derived from the DKI is a significant predictor of overall survival (OS). We found that the large mean, 80 and 90 percentile kurtosis values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1-weighted images pre-RT were significantly associated with reduced OS. In the multivariate Cox model, the mean kurtosis Gd-GTV pre-RT after considering effects of age, extent of surgery, and methylation were significant predictors of OS. In addition, the 80 and 90 percentile kurtosis values in Gd-GTV post RT were significantly associated with progression free survival (PFS). The DKI model demonstrates the potential to predict outcomes in the patients with GBM.

PURPOSE

Non-Gaussian diffusion behaviors in gliomas have been characterized by diffusion kurtosis imaging (DKI). But there are very limited efforts in investigating the kurtosis in glioblastoma (GBM) and its prognostic and predictive values. This study aimed to investigate whether any of the diffusion kurtosis parameters derived from DKI is a significant predictor of overall survival.

METHODS AND MATERIALS

Thirty-three patients with GBM had pre-radiation therapy (RT) and mid-RT diffusion weighted (DW) images. Kurtosis and diffusion coefficient (DC) values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1 weighted images pre-RT and mid-RT were calculated. Univariate and multivariate Cox models were used to evaluate the DKI parameters and clinical factors for prediction of OS and PFS.

RESULTS

The large mean kurtosis values in the Gd-GTV pre-RT were significantly associated with reduced OS (p = 0.02), but the values at mid-RT were not (p > 0.8). In the multivariate Cox model, the mean kurtosis in the Gd-GTV pre-RT (p = 0.009) was still a significant predictor of OS after adjusting effects of age, O6-Methylguanine-DNA Methyl transferase (MGMT) methylation and extent of resection. In Gd-GTV post-RT, 80 and 90 percentile kurtosis values were significant predictors (p ≤ 0.05) for progression free survival (PFS).

CONCLUSION

The DKI model demonstrates the potential to predict OS and PFS in the patients with GBM. Further development and histopathological validation of the DKI model will warrant its role in clinical management of GBM.

Altered hippocampal microstructure and function in children who experienced Hurricane Irma

Hurricane Irma was the most powerful Atlantic hurricane in recorded history, displacing 6 million and killing over 120 people in the state of Florida alone. Unpredictable disasters like Irma are associated with poor cognitive and health outcomes that can disproportionately impact children. This study examined the effects of Hurricane Irma on the hippocampus and memory processes previously related to unpredictable stress. We used an innovative application of an advanced diffusion-weighted imaging technique, restriction spectrum imaging (RSI), to characterize hippocampal microstructure (i.e., cell density) in 9- to 10-year-old children who were exposed to Hurricane Irma relative to a non-exposed control group (i.e., assessed the year before Hurricane Irma). We tested the hypotheses that the experience of Hurricane Irma would be associated with decreases in: (a) hippocampal cellularity (e.g., neurogenesis), based on known associations between unpredictable stress and hippocampal alterations; and (b) hippocampal-related memory function as indexed by delayed recall. We show an association between decreased hippocampal cellularity and delayed recall memory in children who experienced Hurricane Irma relative to those who did not. These findings suggest an important role of RSI for assessing subtle microstructural changes related to functionally significant changes in the developing brain in response to environmental events.

Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma

Apparent diffusion coefficient has limits to differentiate solid tumor from normal tissue or edema in glioblastoma (GBM). This study investigated a microstructure model (MSM) in GBM using a clinically available diffusion imaging technique. The MSM was modified to integrate with bi-polar diffusion gradient waveforms, and applied to 30 patients with newly diagnosed GBM. Diffusion-weighted (DW) images acquired on a 3 T scanner with b-values from 0 to 2500 s/mm² were fitted in volumes of interest (VOIs) of solid tumor to obtain the apparent restriction size of intracellular water (ARS), the fractional volume of intracellular water (V_in), and extracellular (D_ex) water diffusivity. The parameters in solid tumor were compared with those of other tissue types by Students’ t test. For comparison, DW images were fitted by conventional mono-exponential and bi-exponential models. ARS, D_ex, and V_in from the MSM in tumor VOIs were significantly greater than those in WM, GM, and edema (P values of .01–.001). ARS values in solid tumors (from 21.6 to 34.5 um) had absolutely no overlap with those in all other tissue types (from 0.9 to 3.5 um). V_in values showed a descending order from solid tumor (from 0.32 to 0.52) to WM, GM, and edema (from 0.05 to 0.25), consisting with the descending cellularity in these tissue types. The parameters from mono-exponential and bi-exponential models could not significantly differentiate solid tumor from all other tissue types, particularly from edema. Further development and histopathological validation of the MSM will warrant its role in clinical management of GBM.

Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model

PURPOSE

Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model.

EXPERIMENTAL DESIGN

Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C 1 and C 2 and their product, C 1 C 2, and signal fractions F 1, F 2, and F 1 F 2 were compared with the image defined on maximum b-value (DWI max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC.

RESULTS

Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for C 1 C 2, 0.136 (95% CI, 0.092-0.180) for C 1, 0.068 (95% CI, 0.049-0.087) for C 2, 0.462 (95% CI, 0.425-0.499) for F 1 F 2, 0.832 (95% CI, 0.797-0.868) for F 1, 0.176 (95% CI, 0.150-0.203) for F 2, 0.159 (95% CI, 0.114-0.204) for DWI max, 0.731 (95% CI, 0.692-0.770) for ADC, and 0.684 (95% CI, 0.660-0.709) for K app. Mean ROC AUC for C 1 C 2 was 0.984 (95% CI, 0.977-0.991).

CONCLUSIONS

The C 1 C 2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.

Application of multiparametric MR imaging to predict the diversification of renal function in miR29a-mediated diabetic nephropathy

Diabetic nephropathy (DN) is one of the major leading cause of kidney failure. To identify the progression of chronic kidney disease (CKD), renal function/fibrosis is playing a crucial role. Unfortunately, lack of sensitivities/specificities of available clinical biomarkers are key major issues for practical healthcare applications to identify the renal functions/fibrosis in the early stage of DN. Thus, there is an emerging approach such as therapeutic or diagnostic are highly desired to conquer the CKD at earlier stages. Herein, we applied and examined the application of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI) to identify the progression of fibrosis between wild type (WT) and miR29a transgenic (Tg) mice during streptozotocin (STZ)-induced diabetes. Further, we also validate the potential renoprotective role of miR29a to maintain the renal perfusion, volume, and function. In addition, Ktrans values of DCE-MRI and apparent diffusion coefficient (ADC) of DWI could significantly reflect the level of fibrosis between WT and Tg mice at identical conditions. As a result, we strongly believed that the present non-invasive MR imaging platforms have potential to serveas an important tool in research and clinical imaging for renal fibrosis in diabetes, and that microenvironmental changes could be identified by MR imaging acquisition prior to histological biopsy and diabetic podocyte dysfunction.

Age and Sex Differences in the Associations of Pulse Pressure With White Matter and Subcortical Microstructure

Midlife vascular disease increases risk for dementia and effects of vascular dysfunction on brain health differ between men and women. Elevated pulse pressure, a surrogate for arterial stiffness, contributes to cerebrovascular pathology and white matter damage that may advance cognitive aging; however, it remains unclear how associations between pulse pressure and neural integrity differ by sex and age. This study used restriction spectrum imaging to examine associations between pulse pressure and brain microstructure in community-dwelling women (N=88) and men (N=55), aged 56 to 97 (mean, 76.3) years. Restricted isotropic (presumed intracellular), hindered isotropic (presumed extracellular), neurite density, and free water diffusion were computed in white matter tracts and subcortical regions. After adjustment for age and sex, higher pulse pressure correlated with lower restricted isotropic diffusion in global white matter, with more pronounced associations in parahippocampal cingulum, as well as in thalamus and hippocampus. Subgroup analyses demonstrated stronger correlations between pulse pressure and restricted isotropic diffusion in association fibers for participants ≤75 years than for older participants, with stronger effects for women than men of this age group. Microstructure in parahippocampal cingulum and thalamus differed by pulse pressure level regardless of antihypertensive treatment. Increased pulse pressure may lead to widespread injury to white matter and subcortical structures, with greatest vulnerability for women in late middle to early older age. Restriction spectrum imaging could be useful for monitoring microstructural changes indicative of neuronal loss or shrinkage, demyelination, or inflammation that accompany age-related cerebrovascular dysfunction.

Multiparametric MRI in the Diagnosis of Prostate Cancer: Physical Foundations, Limitations, and Prospective Advances of Diffusion-Weighted MRI

BACKGROUND

 Diffusion-weighted imaging (DWI) is an essential component of the multiparametric MRI exam for the diagnosis and assessment of prostate cancer (PCa). Over the last two decades, various models have been developed to quantitatively correlate the DWI signal with microstructural characteristics of prostate tissue. The simplest approach (ADC: apparent diffusion coefficient) - currently established as the clinical standard - describes monoexponential decay of the DWI signal. While numerous studies have shown an inverse correlation of ADC values with the Gleason score, the ADC model lacks specificity and is based on water diffusion dynamics that are not true in human tissue. This article aims to explain the biophysical limitations of the standard DWI model and to discuss the potential of more complex, advanced DWI models.

METHODS

 This article is a review based on a selective literature review.

RESULTS

 Four phenomenological DWI models are introduced: diffusion tensor imaging, intravoxel incoherent motion, biexponential model, and diffusion kurtosis imaging. Their parameters may potentially improve PCa diagnostics but show varying degrees of statistical significance with respect to the detection and characterization of PCa in current studies. Phenomenological model parameters lack specificity, which has motivated the development of more descriptive tissue models that directly relate microstructural features to the DWI signal. Finally, we present two of such structural models, i. e. the VERDICT (Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors) and RSI (Restriction Spectrum Imaging) model. Both have shown promising results in initial studies regarding the characterization and prognosis of PCa.

CONCLUSION

 Recent developments in DWI techniques promise increasing accuracy and more specific statements about microstructural changes of PCa. However, further studies are necessary to establish a standardized DWI protocol for the diagnosis of PCa.

KEY POINTS

  · DWI is paramount to the mpMRI exam for the diagnosis of PCa.. · Though of clinical value, the ADC model lacks specificity and oversimplifies tissue complexities.. · Advanced phenomenological and structural models have been developed to describe the DWI signal.. · Phenomenological models may improve diagnostics but show inconsistent results regarding PCa assessment.. · Structural models have demonstrated promising results in initial studies regarding PCa characterization..

CITATION FORMAT

· Wichtmann BD, Zöllner FG, Attenberger UI et al. Multiparametric MRI in the Diagnosis of Prostate Cancer: Physical Foundations, Limitations, and Prospective Advances of Diffusion-Weighted MRI. Fortschr Röntgenstr 2021; 193: 399 - 409.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

BACKGROUND

Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.

PURPOSE

To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.

FIELD STRENGTH/SEQUENCE

3T multishell diffusion-weighted sequence.

ASSESSMENT

Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps.

STATISTICAL TESTS

Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.

RESULTS

The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm² /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order.

DATA CONCLUSION

The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Diagnostic utility of Restriction Spectrum Imaging in the characterization of the peritumoral brain zone in glioblastoma: Analysis of overall and progression-free survival

PURPOSE

We studied the ability of Restriction Spectrum Imaging (RSI), a novel advanced diffusion imaging technique, to estimate levels of cellularity in different glioblastoma regions, evaluated their prognostic value compared with established clinical diffusion metrics such as fractional anisotropy (FA) and mean diffusivity (MD).

METHODS

Forty-two patients with untreated glioblastoma, IDH-wildtype, were examined with an advanced MRI tumor protocol. The region of interest (ROI) was obtained from the contrast-enhancing part of tumor and the peritumoral brain zones and then co-registered with RSI-cellularity index, FA and MD maps. Histogram parameters of diffusion metrics were assessed for all ROI locations and compared to MGMT promoter methylation status and survival. The ability of RSI-cellularity index, FA, and MD to stratify survival and were assessed by Cox proportional hazard regression, adjusted for significant clinical predictors.

RESULTS

The highest RSI-cellularity index was measured in contrast-enhancing tumor core with a negative gradient from tumor core to the periphery of peritumoral zone with predictive accuracy 81 % (P < 0.001). Shorter overall survival was significant associated with higher RSI-cellularity index (hazard ratio (HR) 3.6, 95 % confidence interval (CI) 1.3-9.5, P = 0.002) with synchronal decrease in MD (HR 0.31, 95 %CI 0.1-0.8, P = 0.008) in the contrast-enhanced tumor core. This association was also consistent for RSI-cellularity index value measured in the peri-enhancing zone (HR 3.6, 95 % CI 1.0-12.3, P = 0.041). No statistically significant differences were noted between RSI-cellularity index, FA, nor MD and MGMT promoter methylation.

CONCLUSION

RSI-cellularity index may be used as prognostic biomarker to improve risk stratification in patients with glioblastoma.

Nucleus accumbens cytoarchitecture predicts weight gain in children

The prevalence of obesity in children and adolescents worldwide has quadrupled since 1975 and is a key predictor of obesity later in life. Previous work has consistently observed relationships between macroscale measures of reward-related brain regions (e.g., the nucleus accumbens [NAcc]) and unhealthy eating behaviors and outcomes; however, the mechanisms underlying these associations remain unclear. Recent work has highlighted a potential role of neuroinflammation in the NAcc in animal models of diet-induced obesity. Here, we leverage a diffusion MRI technique, restriction spectrum imaging, to probe the microstructure (cellular density) of subcortical brain regions. More specifically, we test the hypothesis that the cell density of reward-related regions is associated with obesity-related metrics and early weight gain. In a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain Cognitive Development (ABCD) study, we demonstrate that cellular density in the NAcc is related to individual differences in waist circumference at baseline and is predictive of increases in waist circumference after 1 y. These findings suggest a neurobiological mechanism for pediatric obesity consistent with rodent work showing that high saturated fat diets increase gliosis and neuroinflammation in reward-related brain regions, which in turn lead to further unhealthy eating and obesity.

Emerging Techniques in Imaging of Glioma Microenvironment

Magnetic resonance imaging (MRI) has been the cornerstone of imaging of brain tumors in the past 4 decades. Conventional MRI remains the workhorse for neuro-oncologic imaging, not only for basic information such as location, extent, and navigation but also able to provide information regarding proliferation and infiltration, angiogenesis, hemorrhage, and more. More sophisticated MRI sequences have extended the ability to assess and quantify these features; for example, permeability and perfusion acquisitions can assess blood-brain barrier disruption and angiogenesis, diffusion techniques can assess cellularity and infiltration, and spectroscopy can address metabolism. Techniques such as fMRI and diffusion fiber tracking can be helpful in diagnostic planning for resection and radiation therapy, and more sophisticated iterations of these techniques can extend our understanding of neurocognitive effects of these tumors and associated treatment responses and effects. More recently, MRI has been used to go beyond such morphological, physiological, and functional characteristics to assess the tumor microenvironment. The current review highlights multiple recent and emerging approaches in MRI to characterize the tumor microenvironment.

Associations between age and brain microstructure in older community-dwelling men and women: the Rancho Bernardo Study

Cytoarchitectural brain changes during normal aging remain poorly characterized, and it is unclear whether patterns of brain aging differ by sex. This study used restriction spectrum imaging to examine associations between age and brain microstructure in 147 community-dwelling participants (aged 56-99 years). Widespread associations with age in multiple diffusion compartments, including increased free water, decreased restricted and hindered diffusion, and reduced neurite complexity, were observed in the cortical gray matter, the white matter tracts, and the hippocampus. Age differences in cortical microstructure were largely independent of atrophy. Associations were mostly global, although foci of stronger effects emerged in the fornix, anterior thalamic radiation and commissural fibers, and the medial temporal, orbitofrontal, and occipital cortices. Age differences were stronger and more widespread for women than men, even after adjustment for education, hypertension, and body mass index. Restriction spectrum imaging may be a convenient, noninvasive tool for monitoring changes in diffusion properties that are thought to reflect reduced cellular fractions and neurite density or complexity, which occur with typical aging, and for detecting sex differences in patterns of brain aging.

Apparent diffusion coefficient estimates based on 24 hours tracer movement support glymphatic transport in human cerebral cortex

The recently proposed glymphatic system suggests that bulk flow is important for clearing waste from the brain, and as such may underlie the development of e.g. Alzheimer’s disease. The glymphatic hypothesis is still controversial and several biomechanical modeling studies at the micro-level have questioned the system and its assumptions. In contrast, at the macro-level, there are many experimental findings in support of bulk flow. Here, we will investigate to what extent the CSF tracer distributions seen in novel magnetic resonance imaging (MRI) investigations over hours and days are suggestive of bulk flow as an additional component to diffusion. In order to include the complex geometry of the brain, the heterogeneous CSF flow around the brain, and the transport over the time-scale of days, we employed the methods of partial differential constrained optimization to identify the apparent diffusion coefficient (ADC) that would correspond best to the MRI findings. We found that the computed ADC in the cortical grey matter was 5–26% larger than the ADC estimated with DTI, which suggests that diffusion may not be the only mechanism governing transport.

The role of diffusion tractography in refining glial tumor resection

Primary brain tumors are notoriously hard to resect surgically. Due to their infiltrative nature, finding the optimal resection boundary without damaging healthy tissue can be challenging. One potential tool to help make this decision is diffusion-weighted magnetic resonance imaging (dMRI) tractography. dMRI exploits the diffusion of water molecule along axons to generate a 3D modelization of the white matter bundles in the brain. This feature is particularly useful to visualize how a tumor affects its surrounding white matter and plan a surgical path. This paper reviews the different ways in which dMRI can be used to improve brain tumor resection, its benefits and also its limitations. We expose surgical tools that can be paired with dMRI to improve its impact on surgical outcome, such as loading the 3D tractography in the neuronavigation system and direct electrical stimulation to validate the position of the white matter bundles of interest. We also review articles validating dMRI findings using other anatomical investigation techniques, such as postmortem dissections, manganese-enhanced MRI, electrophysiological stimulations, and phantom studies with known ground truth. We will be discussing the areas of the brain where dMRI performs well and where the future challenges are. We will conclude this review with suggestions and take home messages for neurosurgeons, tractographers, and vendors for advancing the field and on how to benefit from tractography's use in clinical practice.

Restriction Spectrum Imaging Differentiates True Tumor Progression From Immune-Mediated Pseudoprogression: Case Report of a Patient With Glioblastoma

Immunotherapy is increasingly used in the treatment of glioblastoma (GBM), with immune checkpoint therapy gaining in popularity given favorable outcomes achieved for other tumors. However, immune-mediated (IM)-pseudoprogression is common, remains poorly characterized, and renders conventional imaging of little utility when evaluating for treatment response. We present the case of a 64-year-old man with GBM who developed pathologically proven IM-pseudoprogression after initiation of a checkpoint inhibitor, and who subsequently developed true tumor progression at a distant location. Based on both qualitative and quantitative analysis, we demonstrate that an advanced diffusion-weighted imaging (DWI) technique called restriction spectrum imaging (RSI) can differentiate IM-pseudoprogression from true progression even when conventional imaging, including standard DWI/apparent diffusion coefficient (ADC), is not informative. These data complement existing literature supporting the ability of RSI to estimate tumor cellularity, which may help to resolve complex diagnostic challenges such as the identification of IM-pseudoprogression.

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation

Objectives

Primary brain tumors are composed of tumor cells, neural/glial tissues, edema, and vasculature tissue. Conventional MRI has a limited ability to evaluate heterogeneous tumor pathologies. We developed a novel diffusion MRI-based method—Heterogeneity Diffusion Imaging (HDI)—to simultaneously detect and characterize multiple tumor pathologies and capillary blood perfusion using a single diffusion MRI scan.

Methods

Seven adult patients with primary brain tumors underwent standard-of-care MRI protocols and HDI protocol before planned surgical resection and/or stereotactic biopsy. Twelve tumor sampling sites were identified using a neuronavigational system and recorded for imaging data quantification. Metrics from both protocols were compared between World Health Organization (WHO) II and III tumor groups. Cerebral blood volume (CBV) derived from dynamic susceptibility contrast (DSC) perfusion imaging was also compared with the HDI-derived perfusion fraction.

Results

The conventional apparent diffusion coefficient did not identify differences between WHO II and III tumor groups. HDI-derived slow hindered diffusion fraction was significantly elevated in the WHO III group as compared with the WHO II group. There was a non-significantly increasing trend of HDI-derived tumor cellularity fraction in the WHO III group, and both HDI-derived perfusion fraction and DSC-derived CBV were found to be significantly higher in the WHO III group. Both HDI-derived perfusion fraction and slow hindered diffusion fraction strongly correlated with DSC-derived CBV. Neither HDI-derived cellularity fraction nor HDI-derived fast hindered diffusion fraction correlated with DSC-derived CBV.

Conclusions

Conventional apparent diffusion coefficient, which measures averaged pathology properties of brain tumors, has compromised accuracy and specificity. HDI holds great promise to accurately separate and quantify the tumor cell fraction, the tumor cell packing density, edema, and capillary blood perfusion, thereby leading to an improved microenvironment characterization of primary brain tumors. Larger studies will further establish HDI’s clinical value and use for facilitating biopsy planning, treatment evaluation, and noninvasive tumor grading.

Inflammation appears as high Prostate Imaging-Reporting and Data System scores on prostate magnetic resonance imaging (MRI) leading to false positive MRI fusion biopsy

Purpose

To investigate if inflammation as a potential cause of false-positive lesions from recent UroNav magnetic resonance imaging (MRI) fusion prostate biopsy patients.

Materials and Methods

We retrospectively identified 43 men with 61 MRI lesions noted on prostate MRI before MRI ultrasound-guided fusion prostate biopsy. Men underwent MRI with 3T Siemens TIM Trio MRI system (Siemens AG, Germany), and lesions were identified and marked in DynaCAD system (Invivo Corporation, USA) with subsequent biopsy with MRI fusion with UroNav. We obtained targeted and standard 12-core needle biopsies. We retrospectively reviewed pathology reports for inflammation.

Results

We noted a total of 43 (70.5%) false-positive lesions with 28 having no cancer on any cores, and 15 lesions with cancer noted on systematic biopsy but not in the target region. Of the men with cancer, 6 of the false positive lesions had inflammation in the location of the targeted region of interest (40.0%, 6/15). However, when we examine the 21/28 lesions with an identified lesion on MRI with no cancer in all cores, 54.5% had inflammation on prostate biopsy pathology (12/22, p=0.024). We noted the highest proportion of inflammation.

Conclusions

Inflammation can confound the interpretation of MRI by mimicking prostate cancer. We suggested focused efforts to differentiate inflammation and cancer on prostate MRI.

Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status

INTRODUCTION

Few studies have applied diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) for the comprehensive assessment of gliomas [tumour grade, isocitrate dehydrogenase-1 (IDH-1) mutation status and tumour proliferation rate (Ki-67)]. This study describes the efficacy of DKI and DTI to comprehensively evaluate gliomas, compares their results.

METHODS

Fifty-two patients (18 females; median age, 47.5 years) with pathologically proved gliomas were prospectively included. All cases underwent DKI examination. DKI (mean kurtosis: MK, axial kurtosis: Ka, radial kurtosis: Kr) and DTI (mean diffusivity: MD, fractional anisotropy: FA) maps of each metric was derived. Three ROIs were manually drawn.

RESULTS

MK, Ka, Kr and FA were significantly higher in HGGs than in LGGs, whereas MD was significantly lower in HGGs than in LGGs (P < 0.01). ROC analysis demonstrated that MK (specificity: 100% sensitivity: 79%) and Ka (specificity: 96% sensitivity: 82%) had the same and highest (AUC: 0.93) diagnostic value. Moreover, MK, Ka, and Kr were significantly higher in grade III than II gliomas (P ≦ 0.01). Further, DKI and DTI can significantly identify IDH-1 mutation status (P ≦ 0.03). Ka (sensitivity: 74%, specificity: 75%, AUC: 0.72) showed the highest diagnostic value. In addition, DKI metrics and MD showed significant correlations with Ki-67 (P ≦ 0.01) and Ka had the highest correlation coefficient (r_s = 0.72).

CONCLUSIONS

Compared with DTI, DKI has great advantages for the comprehensive assessment of gliomas. Ka might serve as a promising imaging index in predicting glioma grading, tumour cell proliferation rate and IDH-1 gene mutation status.

Differentiation of Edematous, Tumoral and Normal Areas of Brain Using Diffusion Tensor and Neurite Orientation Dispersion and Density Imaging

BACKGROUND

Presurigical planning for glioma tumor resection and radiotherapy treatment require proper delineation of tumoral and peritumoral areas of brain. Diffusion tensor imaging (DTI) is the most common mathematical model applied for diffusion weighted MRI data. Neurite orientation dispersion and density imaging (NODDI) is another mathematical model for DWI data modeling.

OBJECTIVE

We studied whether extracted parameters of DTI, and NODDI models can be used to differentiate between edematous, tumoral, and normal areas in brain white matter (WM).

MATERIAL AND METHODS

12 patients with peritumoral edema underwent 3T multi-shell diffusion imaging with b-values of 1000 and 2000 smm-2 in 30 and 64 gradient directions, respectively. We fitted DTI and NODDI to data in manually drawn regions of interest and used their derived parameters to characterize edematous, tumoral and normal brain areas.

RESULTS

We found that DTI parameters fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) all significantly differentiated edematous from contralateral normal brain WM (p<0.005). However, only FA was found to distinguish between edematous WM fibers and tumor invaded fibers (p = 0.001). Among NODDI parameters, the intracellular volume fraction (ficvf) had the best distinguishing power with (p = 0.001) compared with the isotropic volume fraction (fiso), the orientation dispersion index (odi), and the concentration parameter of Watson distribution (κ), while comparing fibers inside normal, tumoral, and edematous areas.

CONCLUSION

The combination of two diffusion based methods, i.e. DTI and NODDI parameters can distinguish and characterize WM fibers involved in edematus, tumoral, and normal brain areas with reasonable confidence. Further studies will be required to improve the detectability of WM fibers inside the solid tumor if they hypothetically exist in tumoral parenchyma.

Imaging biomarkers guided anti-angiogenic therapy for malignant gliomas

Antiangiogenic therapy is a universal approach to the treatment of malignant gliomas but fails to prolong the overall survival of newly diagnosed or recurrent glioblastoma patients. Imaging biomarkers are quantitative imaging parameters capable of objectively describing biological processes, pathological changes and treatment responses in some situations and have been utilized for outcome predictions of malignant gliomas in anti-angiogenic therapy. Advanced magnetic resonance imaging techniques (including perfusion-weighted imaging and diffusion-weighted imaging), positron emission computed tomography and magnetic resonance spectroscopy are imaging techniques that can be used to acquire imaging biomarkers, including the relative cerebral blood volume (rCBV), K^trans, and the apparent diffusion coefficient (ADC). Imaging indicators for a better prognosis when treating malignant gliomas with antiangiogenic therapy include the following: a lower pre- or post-treatment rCBV, less change in rCBV during treatment, a lower pre-treatment K^trans, a higher vascular normalization index during treatment, less change in arterio-venous overlap during treatment, lower pre-treatment ADC values for the lower peak, smaller ADC volume changes during treatment, and metabolic changes in glucose and phenylalanine. The investigation and utilization of these imaging markers may confront challenges, but may also promote further development of anti-angiogenic therapy. Despite considerable evidence, future prospective studies are critically needed to consolidate the current data and identify novel biomarkers.

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Anti-angiogenic therapy only benefits specific populations of glioma patients.

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Advanced imaging techniques can produce quantitative imaging biomarkers.

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Physiological and metabolic parameter can predict outcome for anti-angiogenic therapy.

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Larger prospective studies are needed to provide further evidence.

Quantitative analysis of neurite orientation dispersion and density imaging in grading gliomas and detecting IDH-1 gene mutation status

Background and purpose

Neurite orientation dispersion and density imaging (NODDI) is a new diffusion MRI technique that has rarely been applied for glioma grading. The purpose of this study was to quantitatively evaluate the diagnostic efficiency of NODDI in tumour parenchyma (TP) and peritumoural area (PT) for grading gliomas and detecting isocitrate dehydrogenase-1 (IDH-1) mutation status.

Methods

Forty-two patients (male: 23, female: 19, mean age: 44.5 y) were recruited and underwent whole brain NODDI examination. Intracellular volume fraction (icvf) and orientation dispersion index (ODI) maps were derived. Three ROIs were manually placed on TP and PT regions for each case. The corresponding average values of icvf and ODI were calculated, and their diagnostic efficiency was assessed.

Results

Tumours with high icvf_TP (≥0.306) and low icvf_PT (≤0.331) were more likely to be high-grade gliomas (HGGs), while lesions with low icvf_TP (<0.306) and high icvf_PT (>0.331) were prone to be low-grade gliomas (LGGs) (P < 0.001). A multivariate logistic regression model including patient age and icvf values in TP and PT regions most accurately predicted glioma grade (AUC = 0.92, P < 0.001), with a sensitivity and specificity of 92% and 89%, respectively. However, no significant differences were found in NODDI metrics for differentiating IDH-1 mutation status.

Conclusions

The quantitative NODDI metrics in the TP and PT regions are highly valuable for glioma grading. A multivariate logistic regression model using the patient age and the icvf values in TP and PT regions showed very high predictive power. However, the utility of NODDI metrics for detecting IDH-1 mutation status has not been fully explored, as a larger sample size may be necessary to uncover benefits.

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Neurite orientation dispersion and density imaging (NODDI) is a new diffusion MRI technique

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Quantitative NOODI metrics in TP and PT area could help grading gliomas

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Age, icvf in TP and PT area were significantly associated with glioma grading

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The utility of NODDI in detecting IDH-1 mutation status has not been fully explored

Restriction spectrum imaging of white matter and its relation to neurological disability in multiple sclerosis

BACKGROUND

Restriction spectrum imaging (RSI) is a recently introduced magnetic resonance imaging diffusion technique. The utility of RSI in multiple sclerosis (MS) is unknown.

OBJECTIVE

To investigate the association between RSI-derived parameters and neurological disability in MS.

METHODS

Seventy-seven relapsing-remitting MS patients were scanned with RSI on a 3-T scanner. RSI-derived parameters: fast and slow apparent diffusion coefficient (sADC), fractional anisotropy, restricted fractional anisotropy, neurite density (ND), cellularity, extracellular water fraction, and free water fraction, were obtained in white matter lesions (WML) and normal appearing white matter (NAWM). Patients were divided into three groups according to their expanded disability status scale (EDSS): with minimal, low, and substantial disability (<2.5, 2.5-3, and >3, respectively). Group comparisons and correlation analyses were performed.

RESULTS

All tested RSI-derived parameters differed between WML and NAWM ( p < 0.001 for all pairwise comparisons). The sADC in WML showed largest difference across disability subgroups (analysis of variance (ANOVA): F = 5.1, η² = 0.12, p = 0.008). ND in NAWM showed strongest correlation with disability (ϱ = -0.39, p < 0.001).

CONCLUSION

The strongest correlation with EDSS of ND obtained in NAWM indicates that processes outside lesions are important for disability in MS. Our study suggests that RSI-derived parameters may help understand the "clinico-radiological paradox" and improve disease monitoring in MS.

Historadiological correlations in high-grade glioma with the histone 3.3 G34R mutation

BACKGROUND AND PURPOSE

Molecular alterations were recently added to the World Health Organization (WHO) 2016 classification of central nervous system (CNS) tumors. We correlated the histological and radiological features of G34R mutant high-grade gliomas, a recently described hemispheric and supratentorial glioma of children and young adults.

MATERIALS AND METHODS

We performed a retrospective multicenter study on the histopathological and MRI results of 12 patients.

RESULTS

All tumors were supratentorial. Several radiological aspects were observed. Height over 12 were bulky and well delineated tumors, without visible peritumoral infiltration on MRI and pathologically characterized by highly cellular tissue associated with a moderate peritumoral infiltrative component. Two tumors were ill-defined and hyperintense on T2 sequences and pathologically characterized by diffuse tumoral infiltration. Two tumors were bulky and well delineated with an infiltrative component, both radiologically and histopathologically.

CONCLUSIONS

These different patterns may correspond to different pathological mechanisms and a potential link with prognosis should be assessed in further studies.