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Magnetic resonance imaging (MRI) is the method of choice for noninvasive studies of micrometer-scale structures in biological tissues via their effects on the time- and frequency-dependent (restricted) and anisotropic self-diffusion of water. While new designs of time-dependent magnetic field gradient waveforms have enabled disambiguation between different aspects of translational motion that are convolved in traditional MRI methods relying on single pairs of field gradient pulses, data analysis for complex heterogeneous materials remains a challenge. Here, we propose and demonstrate nonparametric distributions of tensor-valued Lorentzian diffusion spectra, or "D(ω) distributions," as a general representation with sufficient flexibility to describe the MRI signal response from a wide range of model systems and biological tissues investigated with modulated gradient waveforms separating and correlating the effects of restricted and anisotropic diffusion. [ABSTRACT FROM AUTHOR]

Cyclic fluctuations in hypothalamic-pituitary-gonadal axis (HPG-axis) hormones exert powerful behavioral, structural, and functional effects through actions on the mammalian central nervous system. Yet, very little is known about how these fluctuations alter the structural nodes and information highways of the human brain. In a study of 30 naturally cycling women, we employed multidimensional diffusion and T1-weighted imaging during three estimated menstrual cycle phases (menses, ovulation, and mid-luteal) to investigate whether HPG-axis hormone concentrations co-fluctuate with alterations in white matter (WM) microstructure, cortical thickness (CT), and brain volume. Across the whole brain, 17β-estradiol and luteinizing hormone (LH) concentrations were directly proportional to diffusion anisotropy (μFA; 17β-estradiol: β1 = 0.145, highest density interval (HDI) = [0.211, 0.4]; LH: β1 = 0.111, HDI = [0.157, 0.364]), while follicle-stimulating hormone (FSH) was directly proportional to CT (β1 = 0 .162, HDI = [0.115, 0.678]). Within several individual regions, FSH and progesterone demonstrated opposing relationships with mean diffusivity (Diso) and CT. These regions mainly reside within the temporal and occipital lobes, with functional implications for the limbic and visual systems. Finally, progesterone was associated with increased tissue (β1 = 0.66, HDI = [0.607, 15.845]) and decreased cerebrospinal fluid (CSF; β1 = -0.749, HDI = [-11.604, -0.903]) volumes, with total brain volume remaining unchanged. These results are the first to report simultaneous brain-wide changes in human WM microstructure and CT coinciding with menstrual cycle-driven hormone rhythms. Effects were observed in both classically known HPG-axis receptor-dense regions (medial temporal lobe, prefrontal cortex) and in other regions located across frontal, occipital, temporal, and parietal lobes. Our results suggest that HPG-axis hormone fluctuations may have significant structural impacts across the entire brain.

Neurocognitive aging researchers are increasingly focused on the locus coeruleus, a neuromodulatory brainstem structure that degrades with age. With this rapid growth, the field will benefit from consensus regarding which magnetic resonance imaging (MRI) metrics of locus coeruleus structure are most sensitive to age and cognition. To address this need, the current study acquired magnetization transfer- and diffusion-weighted MRI images in younger and older adults who also completed a free recall memory task. Results revealed significantly larger differences between younger and older adults for maximum than average magnetization transfer-weighted contrast (MTC), axial than mean or radial single-tensor diffusivity (DTI), and free than restricted multi-compartment diffusion (NODDI) metrics in the locus coeruleus; with maximum MTC being the best predictor of age group. Age effects for all imaging modalities interacted with sex, with larger age group differences in males than females for MTC and NODDI metrics. Age group differences also varied across locus coeruleus subdivision for DTI and NODDI metrics, and across locus coeruleus hemispheres for MTC. Within older adults, however, there were no significant effects of age on MTC or DTI metrics, only an interaction between age and sex for free diffusion. Finally, independent of age and sex, higher restricted diffusion in the locus coeruleus was significantly related to better (lower) recall variability, but not mean recall. Whereas MTC has been widely used in the literature, our comparison between the average and maximum MTC metrics, inclusion of DTI and NODDI metrics, and breakdowns by locus coeruleus subdivision and hemisphere make important and novel contributions to our understanding of the aging of locus coeruleus structure.

The short association fibers or U-fibers travel in the superficial white matter (SWM) beneath the cortical layer. While the U-fibers play a crucial role in various brain disorders, there is a lack of effective tools to reconstruct their highly curved trajectory from diffusion MRI (dMRI). In this work, we propose a novel surface-based framework for the probabilistic tracking of fibers on the triangular mesh representation of the SWM. By deriving a closed-form solution to transform the spherical harmonics (SPHARM) coefficients of 3D fiber orientation distributions (FODs) to local coordinate systems on each triangle, we develop a novel approach to project the FODs onto the tangent space of the SWM. After that, we utilize parallel transport to realize the intrinsic propagation of streamlines on SWM following probabilistically sampled fiber directions. Our intrinsic and surface-based method eliminates the need to perform the necessary but challenging sharp turns in 3D compared with conventional volume-based tractography methods. Using data from the Human Connectome Project (HCP), we performed quantitative comparisons to demonstrate the proposed algorithm can more effectively reconstruct the U-fibers connecting the precentral and postcentral gyrus than previous methods. Quantitative validations were then performed on post-mortem MRIs to show the reconstructed U-fibers from our method more faithfully follow the SWM than volume-based tractography. Finally, we applied our algorithm to study the parietal U-fiber connectivity changes in autosomal dominant Alzheimers disease (ADAD) patients and successfully detected significant associations between U-fiber connectivity and disease severity.

BACKGROUND: Microvascular dysfunction is involved in the development of various cerebral disorders. It may contribute to these disorders by disrupting white matter tracts and altering brain connectivity, but evidence is scarce. We investigated the association between multiple biomarkers of microvascular function and whole-brain white matter connectivity. METHODS AND RESULTS: Cross-sectional data from The Maastricht Study, a Dutch population-based cohort (n=4326; age, 59.4±8.6 years; 49.7% women). Measures of microvascular function included urinary albumin excretion, central retinal arteriolar and venular calibers, composite scores of flicker light-induced retinal arteriolar and venular dilation, and plasma biomarkers of endothelial dysfunction (intercellular adhesion molecule-1, vascular cell adhesion molecule-1, E-selectin, and von Willebrand factor). White matter connectivity was calculated from 3T diffusion magnetic resonance imaging to quantify the number (average node degree) and organization (characteristic path length, global efficiency, clustering coefficient, and local efficiency) of white matter connections. A higher plasma biomarkers of endothelial dysfunction composite score was associated with a longer characteristic path length (β per SD, 0.066 [95% CI, 0.017-0.114]) after adjustment for sociodemographic, lifestyle, and cardiovascular factors but not with any of the other white matter connectivity measures. After multiple comparison correction, this association was nonsignificant. None of the other microvascular function measures were associated with any of the connectivity measures. CONCLUSIONS: These findings suggest that microvascular dysfunction as measured by indirect markers is not associated with whole-brain white matter connectivity.

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.

BACKGROUND: Sensory processing dysfunction (SPD) is linked to altered white matter (WM) microstructure in school-age children. Sensory over-responsivity (SOR), a form of SPD, affects at least 2.5% of all children and has substantial deleterious impact on learning and mental health. However, SOR has not been well studied using microstructural imaging such as diffusion MRI (dMRI). Since SOR involves hypersensitivity to external stimuli, we test the hypothesis that children with SOR require compensatory neuroplasticity in the form of superior WM microstructural integrity to protect against internalizing behavior, leaving those with impaired WM microstructure vulnerable to somatization and depression. METHODS: Children ages 8-12 years old with neurodevelopmental concerns were assessed for SOR using a comprehensive structured clinical evaluation, the Sensory Processing 3 Dimensions Assessment, and underwent 3 Tesla MRI with multishell multiband dMRI. Tract-based spatial statistics was used to measure diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics from global WM and nineteen selected WM tracts. Correlations of DTI and NODDI measures with measures of somatization and emotional disturbance from the Behavioral Assessment System for Children, 3rd edition (BASC-3), were computed in the SOR group and in matched children with neurodevelopmental concerns but not SOR. RESULTS: Global WM fractional anisotropy (FA) is negatively correlated with somatization and with emotional disturbance in the SOR group but not the non-SOR group. Also observed in children with SOR are positive correlations of radial diffusivity (RD) and free water fraction (FISO) with somatization and, in most cases, emotional disturbance. These effects are significant in boys with SOR, whereas the study is underpowered for girls. The most affected white matter are medial lemniscus and internal capsule sensory tracts, although effects of SOR are observed in many cerebral, cerebellar, and brainstem tracts. CONCLUSION: White matter microstructure is related to affective behavior in children with SOR.

BackgroundHigh b-value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). This study qualitatively and quantitatively compares synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa.MethodsOne hundred fifty-one consecutive patients who underwent prostate MRI and biopsy were included in the study. Axial DWI with b = 0, 500, 1000, and 2000 s/mm2 using a 3T clinical scanner using a 32-channel phased-array body coil were acquired. We retrospectively synthesized DWI for b = 2000 s/mm2 via extrapolation based on mono-exponential decay, using b = 0 and b = 500 s/mm2 (sDWI500) and b = 0, b = 500 s/mm2, and b = 1000 s/mm2 (sDWI1000). Differences in signal intensity between sDWI and aDWI were evaluated within different regions of interest (prostate alone, prostate plus 5 mm, 30 mm and 70 mm margin and full field of view). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC).ResultsWithin the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were -46 ± 35% for sDWI1000 and -67 ± 24% for sDWI500. AUC for aDWI, sDWI500, sDWI1000, and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively.ConclusionsDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.

Objective: The aim of this retrospective study was to analyze the predictive capabilities of preoperative mammography, dynamic contrast-enhancedmagnetic resonance imaging (DCE-MRI), and diffusion-weighted imaging (DWI) in determining hormone receptor (HRc) status for pure ductal carcinoma in situ (DCIS) lesions. Materials and Methods: The study included a total of 79 patients who underwent preoperative mammography (MG) and MRI between December 2018 and December 2023 and were subsequently diagnosed with pure DCIS after surgery. The correlation between MG, DCE-MRI, and DWI features and estrogen receptor (ER) and progesterone receptor (PR) status was examined. Results: Among the lesions, 44 were double HRc-positive (ER and PR-positive), 13 were single HRc-positive (ER-positive and PR-negative or ERnegative and PR-positive) and 22 were double HRc-negative (ER and PR-negative). The presence of symptom (p = 0.029), the presence of comedo necrosis (p = 0.005) and high histological grade (p<0.001) were found to be associated with ER and PR negativity. Amorphous microcalcifications were more commonly observed in the double HRc-negative group, while linear calcifications were more prevalent in both double and single HRc-positive groups (p = 0.020). Non-mass enhancement (NME) with a linear distribution was significantly more common in double HRc-negative lesions (38%), and NME with a segmental distribution in both double (43%) and single (50%) receptor-positive lesions (p = 0.042). Evaluation of DWI findings revealed that a higher lesion-to-normal breast parenchyma apparent diffusion coefficient (ADC) ratio statistically increased the probability of HRc positivity (p = 0.033). Conclusion: Certain clinicopathological, mammography, and MRI features, along with the lesion-to-normal breast parenchyma ADC ratio, can serve as predictors for HRc status in DCIS lesions. [ABSTRACT FROM AUTHOR]

Background: The objective is to analyze and review the clinical, laboratory, and neuroimaging characteristics of rheumatoid meningitis (RM) in six patients with known rheumatoid arthritis (RA). Methods: We performed a retrospective review of patients diagnosed with RM from August 2012 to June 2023. To identify the cases, we used medical term search engines and the hospital´s radiology case database. Clinical information and laboratory findings were gathered from the medical records. A neuroradiologist with five years of experience reviewed and analyzed the RM to determine the characteristics findings of RM. Results: Six patients with RM are included. Seizures along with headaches were among the clinical signs that were documented. All the patients had high levels of rheumatoid factor (RF) and anti-cyclic citrullinated peptides (ACPA) in the peripheral blood. Biopsy in two cases confirmed typical rheumatoid nodules. Leptomeningeal enhancement was found bilaterally in all cases and was predominantly found in the frontoparietal region. "Mismatch DWI/FLAIR" was found in five patients. Bilateral subdural collections could be found in two patients. Brain PET scan revealed increased metabolism in two cases. Conclusion: Rheumatoid meningitis is a rare complication of rheumatoid arthritis (RA) with challenging clinical diagnosis due to non-specific symptoms. This study highlights the importance of MR in detecting characteristic neuroimaging patterns, including "mismatch DWI/FLAIR", to aid in early diagnosis. Increased awareness of this condition may facilitate timely intervention and improve prognosis. These results still need to be verified by large studies. [ABSTRACT FROM AUTHOR]

Purpose: Pineal gland (PG) is a structure located in the midline of the brain, and is considered as a main part of the epithalamus. There are reports on the role of this area for brain function by hormone secretion, as well as few reports on its role in brain cognition. However, little knowledge is available on the PG, and in particular on the structural connectivity of this region with the other brain structures. Methods: Using diffusion-weighted images collected by a 3T MRI scanner, and using a sample of 61 (29 F) mentally and physically healthy young individuals in the age range of 20–30 years old, we tried to extract the white matter bundles connected to the PG. Based on prior knowledge, seven target bundles were suggested to be between the PG body and the PG roots, Pons, Periventricular region, thalamus, caudate, lentiform, suprachiasmatic nuclei, and the supercervical ganglia. Results: Nearly all the target bundles were successfully extracted, with the exception of the lentiform. Rate of identification of the tracts was different, with the bundle between the PG body and roots having the highest identification rate (97%); then it was with the Pons (70%), Periventricular region (57%), SCN (55%), left thalamus (52%), right thalamus (47%), left caudate (27%) and right caudate (22%). Conclusion: This study is an attempt to expand our knowledge on the neuroanatomy of the PG, which might help for identifying further roles for it in brain functionality, and also be a help for the treatment of some disorders in the future. [ABSTRACT FROM AUTHOR]

Objective: To investigate the associations between apparent diffusion coefficient (ADC) values extracted from three different region of interest (ROI) position approaches and programmed cell death ligand-1 (PD-L1) expression, and evaluate the performance of the nomogram established based on ADC values and clinicopathological parameters in predicting PD-L1 expression in cervical cancer (CC) patients. Methods: Through retrospective recruitment, a training cohort of 683 CC patients was created, and a validation cohort of 332 CC patients was prospectively recruited. ROIs were delineated using three different methods to measure the mean ADC (ADCmean), single-section ADC (ADCss), and the minimum ADC of tumors (ADCmin). Logistic regression was employed to identify independent factors related to PD-L1 expression. A nomogram was drawn based on ADC values combined with clinicopathological features, its discrimination and calibration performances were estimated using the area under the curve (AUC) of receiver operating characteristic and calibration curve. The clinical benefits were evaluated by decision curve analysis. Results: The ADCmin independently correlated with PD-L1 expression. The nomogram constructed with ADCmin and other independent clinicopathological-related factors: FIGO staging, pathological grade, parametrial invasion, and lymph node status demonstrated excellent diagnostic performance (AUC = 0.912 and 0.903, respectively), good calibration capacities, and greater net benefits compared to the clinicopathological model in both the training and validation cohorts. Conclusion: ADCmin independently correlated PD-L1 expression, and the nomogram established with ADCmin and clinicopathological independent prognostic factors had a strong predictive performance for PD-L1 expression, thereby serving as a promising tool for selecting cases eligible for immunotherapy. Clinical relevance statement: The minimum ADC can serve as a reliable imaging biomarker related to PD-L1 expression; the established nomogram combines the minimum ADC and clinicopathological factors that can assist clinical immunotherapy decisions. Key Points: Diffusion-weighted imaging quantitative parameters can characterize the internal characteristics of tumor tissue. The minimum ADC significantly correlated with programmed cell death ligand-1 expression. The proposed nomogram can assist clinicians with immunotherapy decision-making for patients with CC. [ABSTRACT FROM AUTHOR]

Objective: To identify brain edema in fetuses with Chiari II malformation using a multiparametric approach including structural T2-weighted, diffusion tensor imaging (DTI) metrics, and MRI-based radiomics. Methods: A single-center retrospective review of MRI scans obtained in fetuses with Chiari II was performed. Brain edema cases were radiologically identified using the following MR criteria: brain parenchymal T2 prolongation, blurring of lamination, and effacement of external CSF spaces. Fractional anisotropy (FA) values were calculated from regions of interest (ROI), including hemispheric parenchyma, internal capsule, and corticospinal tract, and compared group-wise. After 1:1 age matching and manual single-slice 2D segmentation of the fetal brain parenchyma using ITK-Snap, radiomics features were extracted using pyradiomics. Areas under the curve (AUCs) of the features regarding discriminating subgroups were calculated. Results: Ninety-one fetuses with Chiari II underwent a total of 101 MRI scans at a median gestational age of 24.4 weeks and were included. Fifty scans were visually classified as Chiari II with brain edema group and showed significantly reduced external CSF spaces compared to the nonedema group (9.8 vs. 18.3 mm, p < 0.001). FA values of all used ROIs were elevated in the edema group (p < 0.001 for all ROIs). The 10 most important radiomics features showed an AUC of 0.81 (95%CI: 0.71, 0.91) for discriminating between Chiari II fetuses with and without edema. Conclusions: Brain edema in fetuses with Chiari II is common and radiologically detectable on T2-weighted fetal MRI sequences, and DTI-based FA values and radiomics features provide further evidence of microstructure differences between subgroups with and without edema. Clinical relevance statement: A more severe phenotype of fetuses with Chiari II malformation is characterized by prenatal brain edema and more postnatal clinical morbidity and disability. Fetal brain edema is a promising prenatal MR imaging biomarker candidate for optimizing the risk-benefit evaluation of selection for fetal surgery. Key Points: Brain edema of fetuses prenatally diagnosed with Chiari II malformation is a common, so far unknown, association. DTI metrics and radiomics confirm microstructural differences between the brains of Chiari II fetuses with and without edema. Fetal brain edema may explain worse motor outcomes in this Chiari II subgroup, who may substantially benefit from fetal surgery. [ABSTRACT FROM AUTHOR]

Objectives: To distinguish isocitrate dehydrogenase (IDH) genotypes and tumor subtypes of adult-type diffuse gliomas based on the fifth edition of the World Health Organization classification of central nervous system tumors (WHO CNS5) in 2021 using standard, high, and ultra-high b-value diffusion-weighted imaging (DWI). Materials and methods: This prospective study enrolled 70 patients with adult-type diffuse gliomas who underwent multiple b-value DWI. Apparent diffusion coefficient (ADC) values including ADCb500/b1000, ADCb500/b2000, ADCb500/b3000, ADCb500/b4000, ADCb500/b6000, ADCb500/b8000, and ADCb500/b10000 in tumor parenchyma (TP) and contralateral normal-appearing white matter (NAWM) were calculated. The ADC ratios of TP/NAWM were assessed for correlations with IDH genotypes, tumor subtypes, and Ki-67 status; diagnostic performances were compared. Results: All ADCs were significantly higher in IDH mutant gliomas than in IDH wild-type gliomas (p < 0.01 for all); ADCb500/b8000 had the highest area under the curve (AUC) of 0.866. All ADCs were significantly lower in glioblastoma than in astrocytoma (p < 0.01 for all). ADCs other than ADCb500/b1000 were significantly lower in glioblastoma than in oligodendroglioma (p < 0.05 for all). ADCb500/b8000 and ADCb500/b10000 were significantly higher in oligodendroglioma than in astrocytoma (p = 0.034 and 0.023). The highest AUCs were 0.818 for ADCb500/b6000 when distinguishing glioblastoma from astrocytoma, 0.979 for ADCb500/b8000 and ADCb500/b10000 when distinguishing glioblastoma from oligodendroglioma, and 0.773 for ADCb500/b10000 when distinguishing astrocytoma from oligodendroglioma. Additionally, all ADCs were negatively correlated with Ki-67 status (p < 0.05 for all). Conclusion: Ultra-high b-value DWI can reliably separate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using WHO CNS5 criteria. Clinical relevance statement: Ultra-high b-value diffusion-weighted imaging can accurately distinguish isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas, which may facilitate personalized treatment and prognostic assessment for patients with glioma. Key Points: • Ultra-high b-value diffusion-weighted imaging can accurately distinguish subtle differences in water diffusion among biological tissues. • Ultra-high b-value diffusion-weighted imaging can reliably separate isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas. • Compared with standard b-value diffusion-weighted imaging, high and ultra-high b-value diffusion-weighted imaging demonstrate better diagnostic performances. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM). Materials and methods: In this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison. Results: The validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar. Conclusion: The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM. Clinical relevance statement: The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity. Key Points: • The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis. • Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors. • The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model. [ABSTRACT FROM AUTHOR]

Background: MRI diffusion-weighted imaging (DWI) is commonly used in MR enterography protocols for assessment of intestinal inflammation in patients with Crohn's disease. The intravoxel incoherent motion (IVIM) approach to DWI has been proposed as a more objective approach, providing quantitative parameters that reflect water diffusivity (D), blood flow (D*), and perfusion fraction (f). Purpose: We aimed to determine if DWI-IVIM metrics from the terminal ileum in patients with newly diagnosed Crohn's disease differ from healthy participants and change in response to biologic medical therapy. Methods: In this prospective case–control study, 20 consecutive pediatric patients (mean age = 14 years ± 2 [SD]; eight females) with newly diagnosed ileal Crohn's disease and 15 pediatric healthy participants (mean age = 18 years ± 4 [SD]; eight females) underwent research MRI examinations of the small bowel between 12/2018 and 10/2021. Participants with Crohn's disease underwent MR studies at baseline, 6 weeks, and 6 months following initiation of anti-TNF-alpha therapy, whereas control participants underwent one research MRI examination. The MRI protocol included a DWI-IVIM sequence with nine b-values and the IVIM parameters (D, D*, and f) were extracted. Unpaired t-tests and mixed-effects models were used for analyses. Results: Mean IVIM D (P < 0.001), D* (P = 0.004), and f (P = 0.001) metrics were lower for Crohn's patients at the time of diagnosis compared to healthy participants. Mean IVIM f value increased over time in response to medical therapy (mean f at baseline, 22% ± 6%; 6 weeks, 25% ± 7%; 6 months, 29% ± 10%; P = 0.016). Mean IVIM D* value increased over time in response to treatment (mean D* at baseline, 10.9 ± 3.0 × 10−3 mm2/s; 6 weeks, 11.8 ± 2.8 × 10−3 mm2/s; 6 months, 13.3 ± 3.3 × 10−3 mm2/s; P = 0.047), while there was no significant change in mean IVIM D value (P = 0.10). Conclusion: MRI DWI-IVIM metrics in patients with ileal Crohn's disease change over time in response to biological therapy and help discriminate these patients from healthy participants. [ABSTRACT FROM AUTHOR]

Ki-67 is a marker of cell proliferation and is increasingly being used as a primary outcome measure in preoperative window studies of endometrial cancer (EC). This study explored the feasibility of using apparent diffusion coefficient (ADC) values in noninvasive prediction of Ki-67 expression levels in EC patients before surgery, and constructs a nomogram by combining clinical data. This study retrospectively analyzed 280 EC patients who underwent preoperative magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in our hospital from January 2017 to February 2023. Evaluate the potential nonlinear relationship between ADC values and Ki-67 expression using the nomogram. The included patients were randomized into a training set (n = 186) and a validation set (n = 84). Using a combination of logistic regression and LASSO regression results, from which the four best predictors were identified for the construction of the nomogram. The accuracy and clinical applicability of the nomogram were assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). The results of this study showed a nonlinear correlation between ADCmin and Ki-67 expression (nonlinear P = 0.019), and the nonlinear correlation between ADCmean and Ki-67 expression (nonlinear P = 0.019). In addition, this study constructed the nomogram by incorporating ADCmax, International Federation of Gynecology and Obstetrics (FIGO), and chemotherapy. The area under the curve (AUC) values of the ROC for nomogram, ADCmax, FIGO, chemotherapy and grade in the training set were 0.783, 0.718, 0.579, 0.636, and 0.654, respectively. In the validation set, the AUC values for nomogram, ADCmax, FIGO, chemotherapy, and grade were 0.820, 0.746, 0.558, 0.542, and 0.738, respectively. In addition, the calibration curves and the DCA curves suggested a better predictive efficacy of the model. A nomogram prediction model constructed on the basis of ADCmax values combined with clinical data can be used as an effective method to noninvasively assess Ki-67 expression in EC patients before surgery. • The prognosis of patients with advanced endometrial cancer is poor. • Ki-67 directly reflects the proliferative activity. • ADC values were the independent factor for postoperative recurrence of EC. [ABSTRACT FROM AUTHOR]

MRI evaluation of the diabetic foot is still a challenge not only from an interpretative but also from a technical point of view. The incorporation of advanced sequences such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI into standard protocols for diabetic foot assessment could aid radiologists in differentiating between neuropathic osteoarthropathy (Charcot's foot) and osteomyelitis. This distinction is crucial as both conditions can coexist in diabetic patients, and they require markedly different clinical management and have distinct prognoses. Over the past decade, several studies have explored the effectiveness of DWI and dynamic contrast-enhanced MRI (DCE-MRI) in distinguishing between septic and reactive bone marrow, as well as soft tissue involvement in diabetic patients, yielding promising results. DWI, without the need for exogenous contrast, can provide insights into the cellularity of bone marrow and soft tissues. DCE-MRI allows for a more precise evaluation of soft tissue and bone marrow perfusion compared to conventional post-gadolinium imaging. The data obtained from these sequences will complement the traditional MRI approach in assessing the diabetic foot. The objective of this review is to familiarize readers with the fundamental concepts of DWI and DCE-MRI, including technical adjustments and practical tips for image interpretation in diabetic foot cases. [ABSTRACT FROM AUTHOR]

To investigate the association of quantitative parameter (apparent diffusion coefficient [ADC]) from diffusion-weighted imaging (DWI) and various quantitative and semiquantitative parameters from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with Ki-67 proliferation index (PI) in cervical carcinoma (CC). A total of 102 individuals with CC who received 3.0 T MRI examination (DWI and DCE MRI) between October 2016 and December 2022 were enrolled in our investigation. Two radiologists separately assessed the ADC parameter and various quantitative and semiquantitative parameters including (volume transfer constant [ K trans ], rate constant [ k ep ], extravascular extracellular space volume fraction [ v e ], volume fraction of plasma [ v p ], time to peak [TTP], maximum concentration [MaxCon], maximal slope [MaxSlope] and area under curve [AUC]) for each tumor. Their association with Ki-67 PI was analyzed by Spearman association analysis. The discrepancy between low-proliferation and high-proliferation groups was subsequently analyzed. The receiver operating characteristic (ROC) curve analysis utilized to identify optimal cut-off points for significant parameters. Both ADC (ρ = − 0.457, p < 0.001) and K trans (ρ = − 0.467, p < 0.001) indicated a strong negative association with Ki-67 PI. Ki-67 PI showed positive correlations with TTP, MaxCon, MaxSlope and AUC (ρ = 0.202, 0.231, 0.309, 0.235, respectively; all p values<0.05). Compared with the low-proliferation group, high-Ki-67 group presented a significantly lower ADC (0.869 ± 0.125 × 10−3 mm2/s vs. 1.149 ± 0.318 × 10−3 mm2/s; p < 0.001) and K trans (1.314 ± 1.162 min−1vs. 0.391 ± 0.390 min−1; p < 0.001), also significantly higher MaxCon values (0.756 ± 0.959 vs. 0.422 ± 0.341; p < 0.05) and AUC values (2.373 ± 3.012 vs. 1.273 ± 1.000; p < 0.05). The cut-offs of ADC, K trans , MaxCon and AUC for discrimating low- and high-Ki-67 groups were 0.920 × 10−3 mm2/s, 0.304 min−1, 0.209 and 1.918, respectively. ADC, K trans, TTP, MaxCon, MaxSlope and AUC are associated with Ki-67 PI. ADC and K trans exhibited high performance to discriminate low and high Ki-67 status of CC. • ADC and K trans were significantly and inversely correlated with Ki-67 proliferation index in cervical cancer. • In the high-Ki67 PI group, ADC and K trans were significantly lower than those of the low-Ki67 PI group. • DWI and DCE parameters could be used to predict the Ki-67 PI in cervical cancer. [ABSTRACT FROM AUTHOR]

To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features. We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves. The diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839–0.898) and 0.847 (95% CI: 0.787–0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration. This nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC. [ABSTRACT FROM AUTHOR]

This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches. • Modern MRI techniques are key in diagnosing and guiding glioma treatment. • Modern MRI techniques are key in diagnosing and guiding glioma treatment. • Standardizing MRI protocols remains challenging, needing more research and collaboration. • Differentiating tumor recurrence from pseudoprogression is crucial; advanced MRI often needs biopsy validation. [ABSTRACT FROM AUTHOR]

To compare and explore the characteristics of squamous cell carcinoma (SCC), adenocarcinoma (AC) and adenosquamous carcinoma (ASC), usual-type endocervical adenocarcinoma (UEA) and gastric adenocarcinoma (GAC) of cervix. A total of 728 cervical cancers (254 cases of AC, 252 cases of ASC, and 222 cases of SCC) confirmed by histopathology were retrospectively reviewed. Among AC, 119 UEA and 47 GAC were included. Clinical baseline data and tumor morphological features on MRI (including tumor location, shape, diameter and volume, margin, growth pattern, presence of fluid component or cyst, heterogenous and peritumoral enhancement) of all cases were collected and analyzed. The signal intensity (SI) of tumor and gluteus maximus muscle were measured and their ratios (SIR) were calculated based on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) and contrast-enhanced T1WI at arterial and delay phases (A/DCE-T1WI). These clinical and MRI features were compared between SCC, AC and ASC, UEA and GAC, and the specific ones of each subtype were identified. There was a significant difference in SCC-Ag, CA-199, CEA, ADC value, SIR-DWI, presence of intratumor cyst and peritumoral enhancement between AC and ASC; in patient age, menopausal status, International Federation of Gynecology and Obstetrics (FIGO) stage, SCC-Ag, CA-125, CA-199, CEA, tumor shape, growth pattern, margin, presence of intratumor fluid component and cyst, tumor diameter and volume, ADC value, SIR-T1WI, SIR-T2WI, and SIR-DWI between SCC and AC, as well as SCC and ASC. Also, there was a significant difference in deep stromal invasion (DSI), peritumoral and heterogenous enhancement between SCC and AC, and in SIR-ACE-T1WI between SCC and ASC. There was a significant difference in reproductive history, menopausal status, FIGO stage, CA-199, DSI, lymph node metastasis (LNM), parametrial invasion (PMI), tumor location, shape, margin, growth pattern, presence of fluid component and cyst, tumor diameter and volume, SIR-T1WI, SIR-DWI, and heterogenous enhancement between GAC and UEA. The clinical and MRI features with significant differences among SCC, AC and ASC, and between UEA and GAC, can help to identify each subtype of cervical cancer. • The accurate and preoperative identification of cervical cancer with different subtypes holds great significance. • Cervical AC and ASC carried a more similar pattern, while SCC carried a distinctive ones. • There were multifaceted differences of clinical and MRI features among SCC, AC and ASC, and between UEA and GAC. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the feasibility of diffusion tensor brain imaging at 0.55T with comparisons against 3T. Methods: Diffusion tensor imaging data with 2 mm isotropic resolution was acquired on a cohort of five healthy subjects using both 0.55T and 3T scanners. The signal‐to‐noise ratio (SNR) of the 0.55T data was improved using a previous SNR‐enhancing joint reconstruction method that jointly reconstructs the entire set of diffusion weighted images from k‐space using shared‐edge constraints. Quantitative diffusion tensor parameters were estimated and compared across field strengths. We also performed a test–retest assessment of repeatability at each field strength. Results: After applying SNR‐enhancing joint reconstruction, the diffusion tensor parameters obtained from 0.55T data were strongly correlated (R2≥0.70$$ {R}^2\ge 0.70 $$) with those obtained from 3T data. Test–retest analysis showed that SNR‐enhancing reconstruction improved the repeatability of the 0.55T diffusion tensor parameters. Conclusion: High‐resolution in vivo diffusion MRI of the human brain is feasible at 0.55T when appropriate noise‐mitigation strategies are applied. [ABSTRACT FROM AUTHOR]

Background: Physiologic MRI-based tumor habitat analysis has the potential to predict patient outcomes by identifying the spatiotemporal habitats of glioblastoma. This study aims to prospectively validate the cut-off for tumor progression obtained from tumor habitat analysis based on physiologic MRI in ascertaining time-to-progression (TTP) and the site of progression in glioblastoma patients following concurrent chemoradiotherapy (CCRT). Methods: In this prospective study (ClinicalTrials.gov ID: NCT02613988), we will recruit patients with IDH-wild type glioblastoma who underwent CCRT and obtained immediate post-operative and three serial post-CCRT MRI scans within a three-month interval, conducted using diffusion-weighted imaging and dynamic susceptibility contrast imaging. Voxels from cerebral blood volume and apparent diffusion coefficient maps will be grouped using k-means clustering into three spatial habitats (hypervascular cellular, hypovascular cellular, and nonviable tissue). The spatiotemporal habitats of the tumor will be evaluated by comparing changes in each habitat between the serial MRI scans (post-operative and post-CCRT #1, #2, and #3). Associations between spatiotemporal habitats and TTP will be analyzed using cox proportional hazard modeling. The site of progression will be matched with spatiotemporal habitats. Discussion: The perfusion- and diffusion-derived tumor habitat in glioblastoma is expected to stratify TTP and may serve as an early predictor for tumor progression in patients with IDH wild-type glioblastoma. Trial registration: ClinicalTrials.gov ID: NCT02613988. [ABSTRACT FROM AUTHOR]

Objectives: To develop radiomics models based on multi-sequence MRI from two centers for the preoperative prediction of the WHO/ISUP grade of Clear Cell Renal Cell Carcinoma (ccRCC). Methods: This retrospective study included 334 ccRCC patients from two centers. Significant clinical factors were identified through univariate and multivariate analyses. MRI sequences included Dynamic contrast-enhanced MRI, axial fat-suppressed T2-weighted imaging, diffusion-weighted imaging, and in-phase/out-of-phase images. Feature selection methods and logistic regression (LR) were used to construct clinical and radiomics models, and a combined model was developed using the Rad-score and significant clinical factors. Additionally, seven classifiers were used to construct the combined model and different folds LR was used to construct the combined model to evaluate its performance. Models were evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), and decision curve analysis (DCA). The Delong test compared ROC performance, with p < 0.050 considered significant. Results: Multivariate analysis identified intra-tumoral vessels as an independent predictor of high-grade ccRCC. In the external validation set, the radiomics model (AUC = 0.834) outperformed the clinical model (AUC = 0.762), with the combined model achieving the highest AUC (0.855) and significantly outperforming the clinical model (p = 0.003). DCA showed that the combined model had a higher net benefit within the 0.04–0.54 risk threshold range than clinical model. Additionally, the combined model constructed using logistic regression has a higher priority compared to other classifiers. Additionally, 10-fold cross-validation with LR for the combined model showed consistent AUC values (0.849–0.856) across different folds. Conclusion: The radiomics models based on multi-sequence MRI might be a noninvasive and effective tool, demonstrating good efficacy in preoperatively predicting the WHO/ISUP grade of ccRCC. [ABSTRACT FROM AUTHOR]

Rationale: Thromboembolism is a serious complication of endovascular treatment for ruptured cerebral aneurysms. The administration of antiplatelet agents before endovascular treatment for ruptured cerebral aneurysms may reduce the risk of thromboembolic complications. Aim: This study aimed to assess the safety and efficacy of preoperative aspirin administration in endovascular treatment for ruptured cerebral aneurysms. Sample size estimates: Assuming a 15% incidence rate of both intraoperative thromboembolic morbidity and symptomatic ischemic lesions on magnetic resonance imaging diffusion-weighted imaging scans assessed by an Independent Review Committee, a sample size of 484 will be required to detect a 10% improvement with aspirin administration with 90% power using the Pearson's chi-square test at a two-sided significance level of 2.5% for each primary outcome, after accounting for a 5% dropout rate. Methods and design: ASTOP is a multicenter, randomized, double-blind, placebo-controlled clinical trial. A total of 484 patients with ruptured cerebral aneurysms receiving coil embolization within 72 h of onset will be randomly assigned 1:1 to receive 200 mg of aspirin or placebo before the procedure. Study outcomes: The primary outcomes will be the incidence rates of intraoperative thromboembolic complications and symptomatic ischemic lesions on magnetic resonance imaging diffusion-weighted imaging scans evaluated by the Independent Review Committee. The secondary outcomes will be the incidence rate of cerebral ischemic events and all bleeding events within 14 days of enrollment and functional outcomes defined by the modified Rankin Scale score at 90 days. Discussion: This trial will provide valuable data on the role of antiplatelet agents during endovascular treatment for ruptured cerebral aneurysms. Trial registration: Registration: Japan Registry of Clinical Trials, Identifier: jRCTs031210421. [ABSTRACT FROM AUTHOR]

Backgrounds: Recovery for poststroke aphasia has a decelerating trajectory, with the greatest improvement is within weeks and the slope of change decreasing over time. Therefore, it is essential to predict the prognosis of aphasia at an early stage as it could provide useful data in specific plans for management strategies. The aim of this work was to assess the arcuate fasciculus in stroke patients with aphasia and its impact on predicting the outcome. A prospective study was performed including 25 patients with acute ischemic stroke and aphasia and 10 healthy control subjects with no history of neurologic or psychiatric disease. All patients underwent language assessment using an Arabic version of the Comprehensive Aphasia Test (Arabic CAT), with the resultant mean T-score aphasia quotient (AQ). Early assessment of stroke and delayed assessment at three months. All patients had diffusion-weighted magnetic resonance imaging (DWI-MRI) of the brain to localize the lesion and 3D diffusion tensor imaging (DTI) of the arcuate fasciculus (AF) within 30 days of stroke. Results: Patients in whom the AF could not be reconstructed had a poor score in early and late AQ and a poor prognosis compared to those in whom the AF could be reconstructed. Preservation of the left AF on DTI could mean the potential recovery of aphasia after stroke. Conclusion: The prognosis of aphasia in patients whose left AF could be reconstructed was better than those whose left AF could not be reconstructed, irrespective of the AF's integrity. That is why, we can assume that evaluation of the DTI of the left AF at early stages of stroke can help in predicting outcome of aphasia. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the value of multiparameter MRI of early cervical cancer (ECC) combined with pre-treatment serum squamous cell carcinoma antigen (SCC-Ag) in predicting its pelvic lymph node metastasis (PLNM). Material and methods: 115 patients with pathologically confirmed FIGO IB1~IIA2 cervical cancer were retrospectively included and divided into the PLNM group and the non-PLNM group according to pathological results. Quantitative parameters of the primary tumor include Ktrans, Kep, Ve from dynamic contrastenhanced magnetic resonance imaging (DCE-MRI), ADCmean, ADCmin, ADCmax, D, D* and f from intravoxel incoherent motion diffusion-weighted imaging (IVIMDWI) were measured. Pre-treatment serum SCC-Ag was obtained. The difference of the above parameters between the two groups were compared using the student t-test or Mann-Whitney U test. Multivariate Logistic regression analysis was performed to determine independent risk factors. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic efficacy of individual parameters and their combination in predicting PLNM from ECC. Results: The PLNM group presented higher SCC-Ag [14.25 (6.74,36.75) ng/ml vs.2.13 (1.32,6.00) ng/ml, P<0.001] and lower Ktrans (0.51 ± 0.20 min-1 vs.0.80 ± 0.33 min-1, P < 0.001), ADCmean (0.85 ± 0.09 mm/s² vs.1.06 ± 0.35 mm/s², P<0.001), ADCmin [0.67 (0.61,0.75) mm/s² vs. 0.75 (0.64,0.90) mm/s², P = 0.012] and f (0.91 ± 0.09 vs. 0.27 ± 0.14, P = 0.001) than the non-LNM group. Multivariate analysis showed that SCC-Ag (OR = 1.154, P = 0.007), Ktrans (OR=0.003, P < 0.001) and f (OR = 0.001, P=0.036) were independent risk factors of PLNM. The combination of SCC-Ag, Ktrans and f possessed the best predicting efficacy for PLNM with an area under curve (AUC) of 0.896, which is higher than any individual parameter: SCC-Ag (0.824), Ktrans (0.797), and f (0.703). The sensitivity and specificity of the combination were 79.1% and 94.0%, respectively. Conclusions: Quantitative parameters Ktrans and f derived from DCE-MRI and IVIM-DWI of primary tumor and SCC-Ag have great value in predicting PLNM. The diagnostic efficacy of their combination has been further improved. [ABSTRACT FROM AUTHOR]

Introduction: Magnetic Resonance Elastography (MRE) allows the non-invasive quantification of tumor biomechanical properties in vivo. With increasing incidence of brain metastases, there is a notable absence of appropriate preclinical models to investigate their biomechanical characteristics. Therefore, the purpose of this work was to assess the biomechanical characteristics of B16 melanoma brain metastases (MBM) and compare it to murine GL261 glioblastoma (GBM) model using multifrequency MRE with tomoelastography post processing. Methods: Intracranial B16 MBM (n = 6) and GL261 GBM (n = 7) mouse models were used. Magnetic Resonance Imaging (MRI) was performed at set intervals after tumor implantation: 5, 7, 12, 14 days for MBM and 13 and 22 days for GBM. The investigations were performed using a 7T preclinical MRI with 20 mm head coil. The protocol consisted of single-shot spin echo-planar multifrequency MRE with tomoelastography post processing, contrast-enhanced T1- and T2- weighted imaging and diffusion-weighted imaging (DWI) with quantification of apparent diffusion coefficient of water (ADC). Elastography quantified shear wave speed (SWS), magnitude of complex MR signal (T2/T2*) and loss angle (j). Immunohistological investigations were performed to assess vascularization, blood-brain-barrier integrity and extent of glucosaminoglucan coverage. Results: Volumetric analyses displayed rapid growth of both tumor entities and softer tissue properties than healthy brain (healthy: 5.17 ± 0.48, MBM: 3.83 ± 0.55, GBM: 3.7 ± 0.23, [m/s]). SWS of MBM remained unchanged throughout tumor progression with decreased T2/T2* intensity and increased ADC on days 12 and 14 (p<0.0001 for both). Conversely, GBM presented reduced j values on day 22 (p=0.0237), with no significant alterations in ADC. Histological analysis revealed substantial vascularization and elevated glycosaminoglycan content in both tumor types compared to healthy contralateral brain. Discussion: Our results indicate that while both, MBM and GBM, exhibited softer properties compared to healthy brain, imaging and histological analysis revealed different underlying microstructural causes: hemorrhages in MBM and increased vascularization and glycosaminoglycan content in GBM, further corroborated by DWI and T2/T2* contrast. These findings underscore the complementary nature of MRE and its potential to enhance our understanding of tumor characteristics when used alongside established techniques. This comprehensive approach could lead to improved clinical outcomes and a deeper understanding of brain tumor pathophysiology. [ABSTRACT FROM AUTHOR]

We present a novel neural network-based method for analyzing intra-voxel structures, addressing critical challenges in diffusion-weighted MRI analysis for brain connectivity and development studies. The network architecture, called the Local Neighborhood Neural Network, is designed to use the spatial correlations of neighboring voxels for an enhanced inference while reducing parameter overhead. Our model exploits these relationships to improve the analysis of complex structures and noisy data environments. We adopt a self-supervised approach to address the lack of ground truth data, generating signals of voxel neighborhoods to integrate the training set. This eliminates the need for manual annotations and facilitates training under realistic conditions. Comparative analyses show that our method outperforms the constrained spherical deconvolution (CSD)method in quantitative and qualitative validations. Using phantom images that mimic in vivo data, our approach improves angular error, volume fraction estimation accuracy, and success rate. Furthermore, a qualitative comparison of the results in actual data shows a better spatial consistency of the proposedmethod in areas of real brain images. This approach demonstrates enhanced intra-voxel structure analysis capabilities and holds promise for broader application in various imaging scenarios. [ABSTRACT FROM AUTHOR]

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development. [ABSTRACT FROM AUTHOR]

Background: To investigate the values of apparent diffusion coefficient (ADC) for the treatment response evaluation in pancreatic cancer (PC) patients receiving neoadjuvant therapy (NAT). Methods: This study included 103 NAT patients with histologically proven PC. ADC maps were generated using monoexponential diffusion-weighted imaging (b values: 50, 800 s/mm2). Tumors' minimum, maximum, and mean ADCs were measured and compared pre- and post-NAT. Variations in ADC values measured between pre- and post-NAT completion for NAT methods (chemotherapy, chemoradiotherapy), tumor locations (head/neck, body/tail), tumor regression grade (TRG) levels (0–2, 3), N stages (N0, N1/N2) and tumor resection margin status (R0, R1), were further analyzed. Results: The minimum, maximum, and mean ADC values all increased dramatically after NAT, rising from 23.4 to 25.4% (all p < 0.001): mean (average: 1.626 × 10− 3 mm2/s vs. 1.315 × 10− 3 mm2/s), minimum (median: 1.274 × 10− 3 mm2/s vs. 1.034 × 10− 3 mm2/s), and maximum (average: 1.981 × 10− 3 mm2/s vs. 1.580 × 10− 3 mm2/s). The ADCs between the subgroups of all the criteria under investigation did not differ significantly for the minimum, maximum, or mean values pre- or post-NAT (P = 0.08 to 1.00). In the patients with borderline resectable PC (n = 47), the rate of tumor size changes after NAT was correlated with the pre-NAT mean ADC values (Spearman's coefficient: 0.288, P = 0.049). Conclusions: The ADC values of PC increased significantly following NAT; however, the percentage increases failed to provide any predictive value for the resection margin status or TRG levels. [ABSTRACT FROM AUTHOR]

Background: Intracranial birth-related subdural hemorrhage frequently occurs in asymptomatic newborns and has no adverse long-term sequelae. It is medically and medicolegally important to differentiate birth-related subdural hemorrhage from other pathological causes of intracranial hemorrhage. There is limited literature available on the incidence of birth-related subdural hemorrhage, its imaging features, and evolution over time, mainly because asymptomatic infants do not routinely undergo cranial MRI. Objective: To establish the incidence and distribution of birth-related subdural hemorrhage and evaluate their association with various modes of delivery, identify associated features, and evaluate the sequential evolution of signal changes of the birth-related hemorrhages on MRI. Materials and methods: A total of 200 healthy term neonates and young infants were included in this retrospective review study. All infants underwent MRI of the brain and cervical spine at postnatal age of 0–2 months with acquisition of a 3D T1-weighted (T1W), 3D or 2D T2-weighted (T2W), and axial diffusion-weighted imaging (DWI) sequences. The scans were evaluated for the presence and distribution of subdural hemorrhages, other intracranial hemorrhages, and associated injuries. Prevalence of intracranial hemorrhage in various modes of delivery was analyzed. Relationship between the signal intensities of the bleeds on T1W, T2W, and DWI scans and the age of the infants was analyzed. Appropriate tests were applied to test for statistical significance of the data. Results: Out of 200 neonates, 66 (33%) had detectable intracranial hemorrhage on MRI with an age range of 11–25 days, including 31 (47%) males and 35 (53%) females. All of them had subdural hemorrhages, 54 (81.8%) of which were in the posterior fossa. Additional parenchymal hemorrhages were present in a few, but no subarachnoid hemorrhages, cervical spinal canal hemorrhages, cortical bridging vein injury, or cervical spinal ligamentous injury were identified within the limitations of the study. No detectable intracranial hemorrhage was found in subjects above 25 days of age. Overall incidence of subdural hemorrhage by mode of delivery was 8/68 (11.8%) in babies born by cesarean section and 58/132 (43.9%) in babies born by vaginal delivery. Among the vaginal deliveries, the highest incidence was observed in assisted vaginal delivery (19/30, 63.3%). Subjects with birth-related subdural hemorrhage were categorized into three age groups: <13 days, 13–21 days, and >21 days. All detected hemorrhages were T1W hyperintense. In the <13 days group, all bleeds were T2W hypointense. In the 13–21 days group, 73.1% were T2W hypointense, while 26.9% were T2W mixed. All bleeds in the >21 days group were T2W hypointense. All DWI hyperintense bleeds were found in the 13–21 days group. Conclusion: Birth-related subdural hemorrhage occurs in over a third of normal deliveries and has a characteristic distribution, predominantly in the posterior fossa. Associated cervical spinal subdural hemorrhages, cervical spinal ligamentous injury, or cortical bridging vein injury, which are concerning for traumatic etiology, were not identified. Birth-related subdural hemorrhages follow a characteristic pattern of signal changes on MRI. Although not completely reliable, this can help in differentiating them from traumatic intracranial hemorrhages which usually occur postnatally. No birth-related subdural hemorrhages were seen after 25 days of age in our cohort. [ABSTRACT FROM AUTHOR]

Military veterans with posttraumatic stress disorder (PTSD) commonly experience posttraumatic guilt. Guilt over commission or omission evolves when responsibility is assumed for an unfortunate outcome (e.g., the death of a fellow combatant). Survivor guilt is a state of intense emotional distress experienced by the weight of knowing that one survived while others did not. This study of the Translational Research Center for TBI and Stress Disorders (TRACTS) analyzed structural and diffusion-weighted magnetic resonance imaging data from 132 male Iraq/Afghanistan veterans with PTSD. The Clinician-Administered PTSD Scale for DSM-IV (CAPS-IV) was employed to classify guilt. Thirty (22.7 %) veterans experienced guilt over acts of commission or omission, 34 (25.8 %) experienced survivor guilt, and 68 (51.5 %) had no posttraumatic guilt. White matter microstructure (fractional anisotropy, FA), cortical thickness, and cortical volume were compared between veterans with guilt over acts of commission or omission, veterans with survivor guilt, and veterans without guilt. Veterans with survivor guilt had significantly lower white matter FA compared to veterans who did not experience guilt (p <.001), affecting several regions of major white matter fiber bundles. There were no significant differences in white matter FA, cortical thickness, or volumes between veterans with guilt over acts of commission or omission and veterans without guilt (p >.050). This cross-sectional study with exclusively male veterans precludes inferences of causality between the studied variables and generalizability to the larger veteran population that includes women. Survivor guilt may be a particularly impactful form of posttraumatic guilt that requires specific treatment efforts targeting brain health. • Posttraumatic guilt is highly common among veterans with PTSD. • Survivor guilt is linked to altered white matter in veterans, potentially impacting moral cognition. • Lower FA in survivor guilt in key brain fiber tracts suggests impaired tissue organization. • Findings highlight the need for comprehensive assessment and intervention for veteran survivor guilt. [ABSTRACT FROM AUTHOR]

The zona incerta (ZI) is a subcortical structure primarily investigated in rodents that is implicated in various behaviors, ranging from motor control to survival-associated activities, partly due to its integration in multiple neural circuits. In the current study, we used diffusion magnetic resonance imaging tractography to segment the ZI and gain insight into its connectivity in various circuits in humans. We performed probabilistic tractography in 7T diffusion MRI on 178 participants from the Human Connectome Project to validate the ZI's anatomical subdivisions and their respective tracts. K-means clustering segmented the ZI based on each voxel's connectivity profile. We further characterized the connections of each ZI subregion using probabilistic tractography with each subregion as a seed. We identified 2 dominant clusters that delineated the whole ZI into rostral and caudal subregions. The caudal ZI primarily connected with motor regions, while the rostral ZI received a topographic distribution of projections from prefrontal areas, notably the anterior cingulate and medial prefrontal cortices. We generated a probabilistic ZI atlas that was registered to a patient-participant's magnetic resonance imaging scan for placement of stereoencephalographic leads for electrophysiology-guided deep brain stimulation to treat their obsessive-compulsive disorder. Rostral ZI stimulation improved the patient's core symptoms (mean improvement 21%). We present a tractography-based atlas of the rostral and caudal ZI subregions constructed using high-resolution diffusion magnetic resonance imaging from 178 healthy participants. Our work provides an anatomical foundation to explore the rostral ZI as a novel target for deep brain stimulation to treat refractory obsessive-compulsive disorder and other disorders associated with dysfunctional reward circuitry. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-67 expression in glioma. Methods: The DWI, DCE and APTW images of 309 patients with glioma confirmed by pathology were retrospectively analyzed and divided into the IDH-1 group (IDH-1(+) group and IDH-1(-) group) and Ki-67 group (low expression group (Ki-67 ≤ 10%) and high expression group (Ki-67 > 10%)). All cases were divided into the training set, and validation set according to the ratio of 7:3. The training set was used to select features and establish machine learning models. The SVM model was established with the data after feature selection. Four single sequence models and one combined model were established in IDH-1 group and Ki-67 group. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. Validation set data was used for further validation. Results: Both in the IDH-1 group and Ki-67 group, the combined model had better predictive efficiency than single sequence model, although the single sequence model had a better predictive efficiency. In the Ki-67 group, the combined model was built from six selected radiomics features, and the AUC were 0.965 and 0.931 in the training and validation sets, respectively. In the IDH-1 group, the combined model was built from four selected radiomics features, and the AUC were 0.997 and 0.967 in the training and validation sets, respectively. Conclusion: The radiomics model established by DWI, DCE and APTW images could be used to detect IDH-1 mutation and Ki-67 expression in glioma patients before surgery. The prediction performance of the radiomics model based on the combination sequence was better than that of the single sequence model. [ABSTRACT FROM AUTHOR]

Introduction: Tobacco and alcohol are the two most common substances used during pregnancy, and both can disrupt neurodevelopment, resulting in cognitive and behavioral deficits including language difficulties. Previous studies show that children with prenatal substance exposure exhibit microstructural alterations in major white matter pathways, though few studies have investigated the impact of prenatal substance exposure on white matter microstructure and language skills during the toddler years. Methods: In this study, 93 children (34 exposed to alcohol and/or tobacco) aged 23 years from the Drakenstein Child Health Study, South Africa, completed Expressive and Receptive Communication assessments from the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) and underwent diffusion MRI scans. Diffusion images were preprocessed, and 11 major white matter tracts were isolated. Fractional anisotropy (FA) and mean diffusivity (MD) were extracted for each white matter tract. Linear regression was used to examine differences between the tobacco/alcohol exposed group and unexposed controls for FA, MD, and language scores, as well as relationships between brain metrics and language. There were no significant group differences in language scores or FA. Results: Children with alcohol or tobacco exposure had lower average MD in the splenium of the corpus callosum compared to unexposed controls. Significant interactions between prenatal substance exposure and language scores were seen in 7 tracts but did not survive multiple comparisons correction. Discussion: Our findings show that prenatal alcohol and/or tobacco exposure appear to alter the relationship between white matter microstructure and early language skills in this population of toddlers, potentially laying the basis of language deficits observed later in older children with prenatal substance exposure, which may have implications for learning and interventions. [ABSTRACT FROM AUTHOR]

Background: Cardiac pulsation propels blood through the cerebrovascular network to maintain cerebral homeostasis. The cerebrovascular network is uniquely surrounded by paravascular cerebrospinal fluid (pCSF), which plays a crucial role in waste removal, and its flow is suspected to be driven by arterial pulsations. Despite its importance, the relationship between vascular and paravascular fluid dynamics throughout the cardiac cycle remains poorly understood in humans. Methods: In this study, we developed a non-invasive neuroimaging approach to investigate the coupling between pulsatile vascular and pCSF dynamics within the subarachnoid space of the human brain. Resting-state functional MRI (fMRI) and dynamic diffusion-weighted imaging (dynDWI) were retrospectively cardiac-aligned to represent cerebral hemodynamics and pCSF motion, respectively. We measured the time between peaks (∆TTP) in d d ϕ f M R I and dynDWI waveforms and measured their coupling by calculating the waveforms correlation after peak alignment (correlation at aligned peaks). We compared the ∆TTP and correlation at aligned peaks between younger [mean age: 27.9 (3.3) years, n = 9] and older adults [mean age: 70.5 (6.6) years, n = 20], and assessed their reproducibility within subjects and across different imaging protocols. Results: Hemodynamic changes consistently precede pCSF motion. ∆TTP was significantly shorter in younger adults compared to older adults (−0.015 vs. −0.069, p < 0.05). The correlation at aligned peaks were high and did not differ between younger and older adults (0.833 vs. 0.776, p = 0.153). The ∆TTP and correlation at aligned peaks were robust across fMRI protocols (∆TTP: −0.15 vs. −0.053, p = 0.239; correlation at aligned peaks: 0.813 vs. 0.812, p = 0.985) and demonstrated good to excellent within-subject reproducibility (∆TTP: intraclass correlation coefficient = 0.36; correlation at aligned peaks: intraclass correlation coefficient = 0.89). Conclusion: This study proposes a non-invasive technique to evaluate vascular and paravascular fluid dynamics. Our findings reveal a consistent and robust cardiac pulsation-driven coupling between cerebral hemodynamics and pCSF dynamics in both younger and older adults. [ABSTRACT FROM AUTHOR]

This article discusses a study that used delta-radiomics of diffusion-weighted magnetic resonance imaging (DWI) to monitor prostate cancer after low-dose-rate hemigland brachytherapy. The study evaluated 30 patients who had undergone focal brachytherapy for localized prostate cancer. The researchers used radiomics analysis to extract texture features from DWI images and identified important features for predicting out-of-field cancer recurrence. The findings suggest that delta-radiomics of DWI may be a useful tool for detecting cases of out-of-field cancer recurrence after brachytherapy. However, the study had limitations, including a small sample size and the lack of multivariable analysis. The document also includes a list of image analysis features and their corresponding codes, as well as a figure demonstrating the importance of a specific feature in predicting cancer recurrence. The document references other studies related to texture feature analysis in urological conditions. [Extracted from the article]

Background: Health professionals need to be prepared for interdisciplinary research collaborations aimed at the development and implementation of medical technology. Expertise is highly domain-specific, and learned by being immersed in professional practice. Therefore, the approaches and results from one domain are not easily understood by experts from another domain. Interdisciplinary collaboration in medical research faces not only institutional, but also cognitive and epistemological barriers. This is one of the reasons why interdisciplinary and interprofessional research collaborations are so difficult. To explain the cognitive and epistemological barriers, we introduce the concept of disciplinary perspectives. Making explicit the disciplinary perspectives of experts participating in interdisciplinary collaborations helps to clarify the specific approach of each expert, thereby improving mutual understanding. Method: We developed a framework for making disciplinary perspectives of experts participating in an interdisciplinary research collaboration explicit. The applicability of the framework has been tested in an interdisciplinary medical research project aimed at the development and implementation of diffusion MRI for the diagnosis of kidney cancer, where the framework was applied to analyse and articulate the disciplinary perspectives of the experts involved. Results: We propose a general framework, in the form of a series of questions, based on new insights from the philosophy of science into the epistemology of interdisciplinary research. We explain these philosophical underpinnings in order to clarify the cognitive and epistemological barriers of interdisciplinary research collaborations. In addition, we present a detailed example of the use of the framework in a concrete interdisciplinary research project aimed at developing a diagnostic technology. This case study demonstrates the applicability of the framework in interdisciplinary research projects. Conclusion: Interdisciplinary research collaborations can be facilitated by a better understanding of how an expert's disciplinary perspectives enables and guides their specific approach to a problem. Implicit disciplinary perspectives can and should be made explicit in a systematic manner, for which we propose a framework that can be used by disciplinary experts participating in interdisciplinary research project. Furthermore, we suggest that educators can explore how the framework and philosophical underpinning can be implemented in HPE to support the development of students' interdisciplinary expertise. [ABSTRACT FROM AUTHOR]

Stroke, the second leading cause of mortality globally, predominantly results from ischemic conditions. Immediate attention and diagnosis, related to the characterization of brain lesions, play a crucial role in patient prognosis. Standard stroke protocols include an initial evaluation from a non-contrast CT to discriminate between hemorrhage and ischemia. However, non-contrast CTs lack sensitivity in detecting subtle ischemic changes in this phase. Alternatively, diffusion-weighted MRI studies provide enhanced capabilities, yet are constrained by limited availability and higher costs. Hence, we idealize new approaches that integrate ADC stroke lesion findings into CT, to enhance the analysis and accelerate stroke patient management. This study details a public challenge where scientists applied top computational strategies to delineate stroke lesions on CT scans, utilizing paired ADC information. Also, it constitutes the first effort to build a paired dataset with NCCT and ADC studies of acute ischemic stroke patients. Submitted algorithms were validated with respect to the references of two expert radiologists. The best achieved Dice score was 0.2 over a test study with 36 patient studies. Despite all the teams employing specialized deep learning tools, results reveal limitations of computational approaches to support the segmentation of small lesions with heterogeneous density. [ABSTRACT FROM AUTHOR]

This study focuses on predicting the prognosis of acute ischemic stroke patients with focal neurologic symptoms using a combination of diffusion-weighted magnetic resonance imaging (DWI) and clinical information. The primary outcome is a poor functional outcome defined by a modified Rankin Scale (mRS) score of 3–6 after 3 months of stroke. Employing nnUnet for DWI lesion segmentation, the study utilizes both multi-task and multi-modality methodologies, integrating DWI and clinical data for prognosis prediction. Integrating the two modalities was shown to improve performance by 0.04 compared to using DWI only. The model achieves notable performance metrics, with a dice score of 0.7375 for lesion segmentation and an area under the curve of 0.8080 for mRS prediction. These results surpass existing scoring systems, showing a 0.16 improvement over the Totaled Health Risks in Vascular Events score. The study further employs grad-class activation maps to identify critical brain regions influencing mRS scores. Analysis of the feature map reveals the efficacy of the multi-tasking nnUnet in predicting poor outcomes, providing insights into the interplay between DWI and clinical data. In conclusion, the integrated approach demonstrates significant advancements in prognosis prediction for cerebral infarction patients, offering a superior alternative to current scoring systems. [ABSTRACT FROM AUTHOR]

The given text is a list of names and initials, likely of authors or researchers, along with various page numbers and abbreviations. It does not provide any information or content that can be summarized. The purpose or context of this list is unclear, as there is no accompanying information or explanation provided. [Extracted from the article]

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a promising technique for assessing spinal cord injury (SCI) that has historically been challenged by the presence of metallic stabilization hardware. This study leverages recent advances in metal-artifact resistant multi-spectral DW-MRI to enable diffusion quantification throughout the spinal cord even after fusion stabilization. Twelve participants with cervical spinal cord injuries treated with fusion stabilization and 49 asymptomatic able-bodied control participants underwent multi-spectral DW-MRI evaluation. Apparent diffusion coefficient (ADC) values were calculated in axial cord sections. Statistical modeling assessed ADC differences across cohorts and within distinct cord regions of the SCI participants (at, above, or below injured level). Computed models accounted for subject demographics and injury characteristics. ADC was found to be elevated at injured levels compared with non-injured levels (z = 3.2, p = 0.001), with ADC at injured levels decreasing over time since injury (z = -9.2, p < 0.001). Below the injury level, ADC was reduced relative to controls (z = -4.4, p < 0.001), with greater reductions after more severe injuries that correlated with lower extremity motor scores (z = 2.56, p = 0.012). No statistically significant differences in ADC above the level of injury were identified. By enabling diffusion analysis near fusion hardware, the multi-spectral DW-MRI technique allowed intuitive quantification of cord diffusion changes after SCI both at and away from injured levels. This demonstrates the approach's potential for assessing post-surgical spinal cord integrity throughout stabilized regions. [ABSTRACT FROM AUTHOR]

• CF lung disease shows microstructural abnormalities via 129Xe diffusion MRI. • ADC and morphometric parameters correlate with PFTs in CF patients. • Basic statistical metrics (mean, SD) are not sensitive, obscuring disease regionality. • Age-adjusted linear binning better quantifies disease spatial heterogeneity. • Abnormally small and large alveolar microstructure was found to be prevalent in CF. Cystic Fibrosis (CF) progresses through recurrent infection and inflammation, causing permanent lung function loss and airway remodeling. CT scans reveal abnormally low-density lung parenchyma in CF, but its microstructural nature remains insufficiently explored due to clinical CT limitations. To this end, diffusion-weighted 129Xe MRI is a non-invasive and validated measure of lung microstructure. In this work, we investigate microstructural changes in people with CF (pwCF) relative to age-matched, healthy subjects using comprehensive imaging and analysis involving pulmonary-function tests (PFTs), and 129Xe MRI. 38 healthy subjects (age 6–40; 17.2 ± 9.5 years) and 39 pwCF (age 6–40; 15.6 ± 8.0 years) underwent 129Xe-diffusion MRI and PFTs. The distribution of diffusion measurements (i.e., apparent diffusion coefficients (ADC) and morphometric parameters) was assessed via linear binning (LB). The resulting volume percentages of bins were compared between controls and pwCF. Mean ADC and morphometric parameters were also correlated with PFTs. Mean whole-lung ADC correlated significantly with age (P < 0.001) for both controls and CF, and with PFTs (P < 0.05) specifically for pwCF. Although there was no significant difference in mean ADC between controls and pwCF (P = 0.334), age-adjusted LB indicated significant voxel-level diffusion (i.e., ADC and morphometric parameters) differences in pwCF compared to controls (P < 0.05). 129Xe diffusion MRI revealed microstructural abnormalities in CF lung disease. Smaller microstructural size may reflect compression from overall higher lung density due to interstitial inflammation, fibrosis, or other pathological changes. While elevated microstructural size may indicate emphysema-like remodeling due to chronic inflammation and infection. [Display omitted] [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) has emerged as a promising and appealing alternative to endoscopy in the objective assessment of patients with inflammatory bowel disease (IBD). Diffusion-weighted imaging (DWI) is a specialized imaging technique that enables the mapping of water molecule diffusion within biological tissues, eliminating the need for intravenous gadolinium contrast injection. It is expanding the capability of traditional MRI sequences in Ulcerative Colitis (UC). Recently, there has been growing interest in the application of intravoxel incoherent motion (IVIM) imaging in the field of IBD. This technique combines diffusion and perfusion information, making it a valuable tool for assessing IBD treatment response. Previous studies have extensively studied the use of DWI techniques for evaluating the severity of activity in IBD. However, the majority of these studies have primarily focused on Crohn's disease (CD), with only a limited number of reports specifically examining UC. Therefore, this review briefly introduces the basics of DWI and IVIM imaging and conducts a review of relevant studies that have investigated its application in UC to show whether these techniques are useful techniques for evaluating patients with UC in terms of detection, characterization, and quantification of disease activity. Through the extensive literature survey, most of these studies indicate that DWI proves valuable in the differential diagnosis of UC and could be used as an effective modality for staging UC. [ABSTRACT FROM AUTHOR]

Understanding the brain's mechanisms in individuals with obesity is important for managing body weight. Prior neuroimaging studies extensively investigated alterations in brain structure and function related to body mass index (BMI). However, how the network communication among the large‐scale brain networks differs across BMI is underinvestigated. This study used diffusion magnetic resonance imaging of 290 young adults to identify links between BMI and brain network mechanisms. Navigation efficiency, a measure of network routing, was calculated from the structural connectivity computed using diffusion tractography. The sensory and frontoparietal networks indicated positive associations between navigation efficiency and BMI. The neurotransmitter association analysis identified that serotonergic and dopaminergic receptors, as well as opioid and norepinephrine systems, were related to BMI‐related alterations in navigation efficiency. The transcriptomic analysis found that genes associated with network routing across BMI overlapped with genes enriched in excitatory and inhibitory neurons, specifically, gene enrichments related to synaptic transmission and neuron projection. Our findings suggest a valuable insight into understanding BMI‐related alterations in brain network routing mechanisms and the potential underlying cellular biology, which might be used as a foundation for BMI‐based weight management. [ABSTRACT FROM AUTHOR]

Pelizaeus‐Merzbacher disease (PMD) is a rare childhood hypomyelinating leukodystrophy. Quantification of the pronounced myelin deficit and delineation of subtle myelination processes are of high clinical interest. Quantitative magnetic resonance imaging (qMRI) techniques can provide in vivo insights into myelination status, its spatial distribution, and dynamics during brain maturation. They may serve as potential biomarkers to assess the efficacy of myelin‐modulating therapies. However, registration techniques for image quantification and statistical comparison of affected pediatric brains, especially those of low or deviant image tissue contrast, with healthy controls are not yet established. This study aimed first to develop and compare postprocessing pipelines for atlas‐based quantification of qMRI data in pediatric patients with PMD and evaluate their registration accuracy. Second, to apply an optimized pipeline to investigate spatial myelin deficiency using myelin water imaging (MWI) data from patients with PMD and healthy controls. This retrospective single‐center study included five patients with PMD (mean age, 6 years ± 3.8) who underwent conventional brain MRI and diffusion tensor imaging (DTI), with MWI data available for a subset of patients. Three methods of registering PMD images to a pediatric template were investigated. These were based on (a) T1‐weighted (T1w) images, (b) fractional anisotropy (FA) maps, and (c) a combination of T1w, T2‐weighted, and FA images in a multimodal approach. Registration accuracy was determined by visual inspection and calculated using the structural similarity index method (SSIM). SSIM values for the registration approaches were compared using a t test. Myelin water fraction (MWF) was quantified from MWI data as an assessment of relative myelination. Mean MWF was obtained from two PMDs (mean age, 3.1 years ± 0.3) within four major white matter (WM) pathways of a pediatric atlas and compared to seven healthy controls (mean age, 3 years ± 0.2) using a Mann–Whitney U test. Our results show that visual registration accuracy estimation and computed SSIM were highest for FA‐based registration, followed by multimodal, and T1w‐based registration (SSIMFA = 0.67 ± 0.04 vs. SSIMmultimodal = 0.60 ± 0.03 vs. SSIMT1 = 0.40 ± 0.14). Mean MWF of patients with PMD within the WM pathways was significantly lower than in healthy controls MWFPMD = 0.0267 ± 0.021 vs. MWFcontrols = 0.1299 ± 0.039. Specifically, MWF was measurable in brain structures known to be myelinated at birth (brainstem) or postnatally (projection fibers) but was scarcely detectable in other brain regions (commissural and association fibers). Taken together, our results indicate that registration accuracy was highest with an FA‐based registration pipeline, providing an alternative to conventional T1w‐based registration approaches in the case of hypomyelinating leukodystrophies missing normative intrinsic tissue contrasts. The applied atlas‐based analysis of MWF data revealed that the extent of spatial myelin deficiency in patients with PMD was most pronounced in commissural and association and to a lesser degree in brainstem and projection pathways. [ABSTRACT FROM AUTHOR]