Ischemia-reperfusion injury in a salvaged penumbra: Longitudinal high-tesla perfusion magnetic resonance imaging in a rat model.

INTRODUCTION: Although ischemia-reperfusion (I/R) injury varies between cortical and subcortical regions, its effects on specific regions remain unclear. In this study, we used various magnetic resonance imaging (MRI) techniques to examine the spatiotemporal dynamics of I/R injury within the salvaged ischemic penumbra (IP) and reperfused ischemic core (IC) of a rodent model, with the aim of enhancing therapeutic strategies by elucidating these dynamics. MATERIALS AND METHODS: A total of 17 Sprague-Dawley rats were subjected to 1 h of transient middle cerebral artery occlusion with a suture model. MRI, including diffusion tensor imaging (DTI), T2-weighted imaging, perfusion-weighted imaging, and T1 mapping, was conducted at multiple time points for up to 5 days during the I/R phases. The spatiotemporal dynamics of blood-brain barrier (BBB) modifications were characterized through changes in T1 within the IP and IC regions and compared with mean diffusivity (MD), T2, and cerebral blood flow. RESULTS: During the I/R phases, the MD of the IC initially decreased, normalized after recanalization, decreased again at 24 h, and peaked on day 5. By contrast, the IP remained relatively stable. Both the IP and IC exhibited hyperperfusion, with the IP reaching its peak at 24 h, followed by resolution, whereas hyperperfusion was maintained in the IC until day 5. Despite hyperperfusion, the IP maintained an intact BBB, whereas the IC experienced persistent BBB leakage. At 24 h, the IC exhibited an increase in the T2 signal, corresponding to regions exhibiting BBB disruption at 5 days. CONCLUSIONS: Hyperperfusion and BBB impairment have distinct patterns in the IP and IC. Quantitative T1 mapping may serve as a supplementary tool for the early detection of malignant hyperemia accompanied by BBB leakage, aiding in precise interventions after recanalization. These findings underscore the value of MRI markers in monitoring ischemia-specific regions and customizing therapeutic strategies to improve patient outcomes.

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework.

BACKGROUND: This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion-diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability. METHODS: Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland-Altman analysis. Feature importance was determined for explainability. RESULTS: In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6-157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1-144.9 mL). These PVs had a volume similarity of 0.88 (0.79-0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland-Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature. CONCLUSIONS: Our study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings. RELEVANCE STATEMENT: The proposed DTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting. KEY POINTS: • Penumbral volume can be estimated by DTI combined with stack-based ensemble ML. • Mean diffusivity was the most important feature used for predicting penumbral volume. • The proposed approach can be beneficial for patients with kidney dysfunction.

A prospective reappraisal of motor outcome prediction in patients with acute stroke by using atlas-based diffusion tensor imaging biomarkers.

BACKGROUND: Diffusion tensor imaging (DTI) biomarkers can be used to quantify microstructural changes in the cerebral white matter (WM) following injury. OBJECTIVES: This prospective single-center study aimed to evaluate whether atlas-based DTI-derived metrics obtained within 1 week after stroke can predict the motor outcome at 3 months. METHODS: Forty patients with small acute stroke (2-7 days after onset) involving the corticospinal tract were included. Each patient underwent magnetic resonance imaging (MRI) within 1 week and at 3 months after stroke, and the changes based on DTI-derived metrics were compared by performing WM tract atlas-based quantitative analysis. RESULTS: A total of 40 patients were included, with median age 63.5 years and a majority of males (72.5%). Patients were classified into good-prognosis group (mRS 0-2, n = 27) and poor-prognosis group (mRS 3-5, n = 13) by outcome. The median (25th-75th percentile) of MD (0.7 (0.6-0.7) vs. 0.7 (0.7-0.8); p = 0.049) and AD (0.6 (0.5, 0.7) vs. 0.7 (0.6, 0.8); p = 0.023) ratios within 1 week were significantly lower in the poor-prognosis group compared to the good-prognosis group. The ROC curve of the combined DTI-derived metrics model showed comparable Youden index (65.5% vs. 58.4%-65.4%) and higher specificity (96.3% vs. 69.2%-88.5%) compared to clinical indexes. The area under the ROC curve of the combined DTI-derived metrics model is comparable to those of the clinical indexes (all p > 0.1) and higher than those of the individual DTI-derived metrics parameters. CONCLUSIONS: Atlas-based DTI-derived metrics at acute stage provide objective information for prognosis prediction of patients with ischemic or lacunar stroke.

Thalamocortical coherence predicts persistent postconcussive symptoms.

The pathogenetic mechanism of persistent post-concussive symptoms (PCS) following concussion remains unclear. Thalamic damage is known to play a role in PCS prolongation while the evidence and biomarkers that trigger persistent PCS have never been elucidated. We collected longitudinal neuroimaging and behavior data from patients and rodents after concussion, complemented with rodents' histological staining data, to unravel the early biomarkers of persistent PCS. Diffusion tensor imaging (DTI) were acquired to investigated the thalamic damage, while quantitative thalamocortical coherence was derived through resting-state functional MRI for evaluating thalamocortical functioning and predicting long-term behavioral outcome. Patients with prolonged symptoms showed abnormal DTI-derived indices at the boundaries of bilateral thalami (peri-thalamic regions). Both patients and rats with persistent symptoms demonstrated enhanced thalamocortical coherence between different thalamocortical circuits, which disrupted thalamocortical multifunctionality. In rodents, the persistent DTI abnormalities were validated in thalamic reticular nucleus (TRN) through immunohistochemistry, and correlated with enhanced thalamocortical coherence. Strong predictive power of these coherence biomarkers for long-term PCS was also validated using another patient cohort. Postconcussive events may begin with persistent TRN injury, followed by disrupted thalamocortical coherence and prolonged PCS. Functional MRI-based coherence measures can be surrogate biomarkers for early prediction of long-term PCS.

Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography.

OBJECTIVE: This study developed a diagnostic tool combining machine learning (ML) segmentation and radiomic texture analysis (RTA) for bone density screening using chest low-dose computed tomography (LDCT). METHODS: A total of 197 patients who underwent LDCT followed by dual-energy X-ray absorptiometry were analyzed. First, an autosegmentation model was trained using LDCT to delineate the thoracic vertebral body (VB). Second, a two-level classifier was developed using radiomic features extracted from VBs for the hierarchical pairwise classification of each patient's bone status. All the patients were initially classified as either normal or abnormal, and all patients with abnormal bone density were then subdivided into an osteopenia group and an osteoporosis group. The performance of the classifier was evaluated through fivefold cross-validation. RESULTS: The model for automated VB segmentation achieved a Sorenson-Dice coefficient of 0.87 ± 0.01. Furthermore, the area under the receiver operating characteristic curve scores for the two-level classifier were 0.96 ± 0.01 for detecting abnormal bone density (accuracy = 0.91 ± 0.02; sensitivity = 0.93 ± 0.03; specificity = 0.89 ± 0.03) and 0.98 ± 0.01 for distinguishing osteoporosis (accuracy = 0.94 ± 0.02; sensitivity = 0.95 ± 0.03; specificity = 0.93 ± 0.03). The testing prediction accuracy levels for the first- and second-level classifiers were 0.92 ± 0.04 and 0.94 ± 0.05, respectively. The overall testing prediction accuracy of our method was 0.90 ± 0.05. CONCLUSION: The combination of ML segmentation and RTA for automated bone density prediction based on LDCT scans is a feasible approach that could be valuable for osteoporosis screening during lung cancer screening. KEY POINTS: • This study developed an automatic diagnostic tool combining machine learning-based segmentation and radiomic texture analysis for bone density screening using chest low-dose computed tomography. • The developed method enables opportunistic screening without quantitative computed tomography or a dedicated phantom. • The developed method could be integrated into the current clinical workflow and used as an adjunct for opportunistic screening or for patients who are ineligible for screening with dual-energy X-ray absorptiometry.

Personalized Prediction of Postconcussive Working Memory Decline: A Feasibility Study.

Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient's return to work. The predictors of long-term cognitive outcomes following mTBI remain unclear, because abnormality is often absent in structural imaging findings. Previous studies have demonstrated that WM functional activity estimated from functional magnetic resonance imaging (fMRI) has a high sensitivity to postconcussion WM deficits and may be used to not only evaluate but guide treatment strategies, especially targeting brain areas involved in postconcussion cognitive decline. The purpose of the study was to determine whether machine learning-based models using fMRI biomarkers and demographic or neuropsychological measures at the baseline could effectively predict the 1-year cognitive outcomes of concussion. We conducted a prospective, observational study of patients with mTBI who were compared with demographically matched healthy controls enrolled between September 2015 and August 2020. Baseline assessments were collected within the first week of injury, and follow-ups were conducted at 6 weeks, 3 months, 6 months, and 1 year. Potential demographic, neuropsychological, and fMRI features were selected according to their significance of correlation with the estimated changes in WM ability. The support vector machine classifier was trained using these potential features and estimated changes in WM between the predefined time periods. Patients demonstrated significant cognitive recovery at the third month, followed by worsened performance after 6 months, which persisted until 1 year after a concussion. Approximately half of the patients experienced prolonged cognitive impairment at the 1-year follow up. Satisfactory predictions were achieved for patients whose WM function did not recover at 3 months (accuracy = 87.5%), 6 months (accuracy = 83.3%), and 1 year (accuracy = 83.3%) and performed worse at the 1-year follow-up compared to the baseline assessment (accuracy = 83.3%). This study demonstrated the feasibility of personalized prediction for long-term postconcussive WM outcomes based on baseline fMRI and demographic features, opening a new avenue for early rehabilitation intervention in selected individuals with possible poor long-term cognitive outcomes.

Identification of Differentially Expressed Genes in Different Glioblastoma Regions and Their Association with Cancer Stem Cell Development and Temozolomide Response.

The molecular heterogeneity of gene expression profiles of glioblastoma multiforme (GBM) are the most important prognostic factors for tumor recurrence and drug resistance. Thus, the aim of this study was to identify potential target genes related to temozolomide (TMZ) resistance and GBM recurrence. The genomic data of patients with GBM from The Cancer Genome Atlas (TCGA; 154 primary and 13 recurrent tumors) and a local cohort (29 primary and 4 recurrent tumors), samples from different tumor regions from a local cohort (29 tumor and 25 peritumoral regions), and Gene Expression Omnibus data (GSE84465, single-cell RNA sequencing; 3589 cells) were included in this study. Critical gene signatures were identified based an analysis of differentially expressed genes (DEGs). DEGs were further used to evaluate gene enrichment levels among primary and recurrent GBMs and different tumor regions through gene set enrichment analysis. Protein-protein interactions (PPIs) were incorporated into gene regulatory networks to identify the affected metabolic pathways. The enrichment levels of 135 genes were identified in the peritumoral regions as being risk signatures for tumor recurrence. Fourteen genes (DVL1, PRKACB, ARRB1, APC, MAPK9, CAMK2A, PRKCB, CACNA1A, ERBB4, RASGRF1, NF1, RPS6KA2, MAPK8IP2, and PPM1A) derived from the PPI network of 135 genes were upregulated and involved in the regulation of cancer stem cell (CSC) development and relevant signaling pathways (Notch, Hedgehog, Wnt, and MAPK). The single-cell data analysis results indicated that 14 key genes were mainly expressed in oligodendrocyte progenitor cells, which could produce a CSC niche in the peritumoral region. The enrichment levels of 336 genes were identified as biomarkers for evaluating TMZ resistance in the solid tumor region. Eleven genes (ARID5A, CDC42EP3, CDKN1A, FLT3, JUNB, MAP2K3, MYBPC2, RGS14, RNASEK, TBC1D30, and TXNDC11) derived from the PPI network of 336 genes were upregulated and may be associated with a high risk of TMZ resistance; these genes were identified in both the TCGA and local cohorts. Furthermore, the expression patterns of ARID5A, CDKN1A, and MAP2K3 were identical to the gene signatures of TMZ-resistant cell lines. The identified enrichment levels of the two gene sets expressed in tumor and peritumoral regions are potentially helpful for evaluating TMZ resistance in GBM. Moreover, these key genes could be used as biomarkers, potentially providing new molecular strategies for GBM treatment.

Detection of PD-L1 Expression in Temozolomide-Resistant Glioblastoma by Using PD-L1 Antibodies Conjugated with Lipid­Coated Superparamagnetic Iron Oxide.

PURPOSE: Targeted superparamagnetic iron oxide (SPIO) nanoparticles are a promising tool for molecular magnetic resonance imaging (MRI) diagnosis. Lipid-coated SPIO nanoparticles have a nonfouling property that can reduce nonspecific binding to off-target cells and prevent agglomeration, making them suitable contrast agents for molecular MRI diagnosis. PD-L1 is a poor prognostic factor for patients with glioblastoma. Most recurrent glioblastomas are temozolomide resistant. Diagnostic probes targeting PD-L1 could facilitate early diagnosis and be used to predict responses to targeted PD-L1 immunotherapy in patients with primary or recurrent glioblastoma. We conjugated lipid-coated SPIO nanoparticles with PD-L1 antibodies to identify PD-L1 expression in glioblastoma or temozolomide-resistant glioblastoma by using MRI. METHODS: The synthesized PD-L1 antibody-conjugated SPIO (PDL1-SPIO) nanoparticles were characterized using dynamic light scattering, zeta potential assays, transmission electron microscopy images, Prussian blue assay, in vitro cell affinity assay, and animal MRI analysis. RESULTS: PDL1-SPIO exhibited a specific binding capacity to PD-L1 of the mouse glioblastoma cell line (GL261). The presence and quantity of PDL1-SPIO in temozolomide-resistant glioblastoma cells and tumor tissue were confirmed through Prussian blue staining and in vivo T2* map MRI, respectively. CONCLUSION: This is the first study to demonstrate that PDL1-SPIO can specifically target temozolomide-resistant glioblastoma with PD-L1 expression in the brain and can be quantified through MRI analysis, thus making it suitable for the diagnosis of PD-L1 expression in temozolomide-resistant glioblastoma in vivo.

Radiomic Immunophenotyping of GSEA-Assessed Immunophenotypes of Glioblastoma and Its Implications for Prognosis: A Feasibility Study.

Characterization of immunophenotypes in glioblastoma (GBM) is important for therapeutic stratification and helps predict treatment response and prognosis. Radiomics can be used to predict molecular subtypes and gene expression levels. However, whether radiomics aids immunophenotyping prediction is still unknown. In this study, to classify immunophenotypes in patients with GBM, we developed machine learning-based magnetic resonance (MR) radiomic models to evaluate the enrichment levels of four immune subsets: Cytotoxic T lymphocytes (CTLs), activated dendritic cells, regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSCs). Independent testing data and the leave-one-out cross-validation method were used to evaluate model effectiveness and model performance, respectively. We identified five immunophenotypes (G1 to G5) based on the enrichment level for the four immune subsets. G2 had the worst prognosis and comprised highly enriched MDSCs and lowly enriched CTLs. G3 had the best prognosis and comprised lowly enriched MDSCs and Tregs and highly enriched CTLs. The average accuracy of T1-weighted contrasted MR radiomics models of the enrichment level for the four immune subsets reached 79% and predicted G2, G3, and the "immune-cold" phenotype (G1) according to our radiomics models. Our radiomic immunophenotyping models feasibly characterize the immunophenotypes of GBM and can predict patient prognosis.

Machine learning-based segmentation of ischemic penumbra by using diffusion tensor metrics in a rat model.

BACKGROUND: Recent trials have shown promise in intra-arterial thrombectomy after the first 6-24 h of stroke onset. Quick and precise identification of the salvageable tissue is essential for successful stroke management. In this study, we examined the feasibility of machine learning (ML) approaches for differentiating the ischemic penumbra (IP) from the infarct core (IC) by using diffusion tensor imaging (DTI)-derived metrics. METHODS: Fourteen male rats subjected to permanent middle cerebral artery occlusion (pMCAO) were included in this study. Using a 7 T magnetic resonance imaging, DTI metrics such as fractional anisotropy, pure anisotropy, diffusion magnitude, mean diffusivity (MD), axial diffusivity, and radial diffusivity were derived. The MD and relative cerebral blood flow maps were coregistered to define the IP and IC at 0.5 h after pMCAO. A 2-level classifier was proposed based on DTI-derived metrics to classify stroke hemispheres into the IP, IC, and normal tissue (NT). The classification performance was evaluated using leave-one-out cross validation. RESULTS: The IC and non-IC can be accurately segmented by the proposed 2-level classifier with an area under the receiver operating characteristic curve (AUC) between 0.99 and 1.00, and with accuracies between 96.3 and 96.7%. For the training dataset, the non-IC can be further classified into the IP and NT with an AUC between 0.96 and 0.98, and with accuracies between 95.0 and 95.9%. For the testing dataset, the classification accuracy for IC and non-IC was 96.0 ± 2.3% whereas for IP and NT, it was 80.1 ± 8.0%. Overall, we achieved the accuracy of 88.1 ± 6.7% for classifying three tissue subtypes (IP, IC, and NT) in the stroke hemisphere and the estimated lesion volumes were not significantly different from those of the ground truth (p = .56, .94, and .78, respectively). CONCLUSIONS: Our method achieved comparable results to the conventional approach using perfusion-diffusion mismatch. We suggest that a single DTI sequence along with ML algorithms is capable of dichotomizing ischemic tissue into the IC and IP.

Diffusion Tensor-Derived Properties of Benign Oligemia, True "at Risk" Penumbra, and Infarct Core during the First Three Hours of Stroke Onset: A Rat Model.

Objective: The aim of this study was to investigate diffusion tensor (DT) imaging-derived properties of benign oligemia, true "at risk" penumbra (TP), and the infarct core (IC) during the first 3 hours of stroke onset. Materials and Methods: The study was approved by the local animal care and use committee. DT imaging data were obtained from 14 rats after permanent middle cerebral artery occlusion (pMCAO) using a 7T magnetic resonance scanner (Bruker) in room air. Relative cerebral blood flow and apparent diffusion coefficient (ADC) maps were generated to define oligemia, TP, IC, and normal tissue (NT) every 30 minutes up to 3 hours. Relative fractional anisotropy (rFA), pure anisotropy (rq), diffusion magnitude (rL), ADC (rADC), axial diffusivity (rAD), and radial diffusivity (rRD) values were derived by comparison with the contralateral normal brain. Results: The mean volume of oligemia was 24.7 ± 14.1 mm3, that of TP was 81.3 ± 62.6 mm3, and that of IC was 123.0 ± 85.2 mm3 at 30 minutes after pMCAO. rFA showed an initial paradoxical 10% increase in IC and TP, and declined afterward. The rq, rL, rADC, rAD, and rRD showed an initial discrepant decrease in IC (from -24% to -36%) as compared with TP (from -7% to -13%). Significant differences (p < 0.05) in metrics, except rFA, were found between tissue subtypes in the first 2.5 hours. The rq demonstrated the best overall performance in discriminating TP from IC (accuracy = 92.6%, area under curve = 0.93) and the optimal cutoff value was -33.90%. The metric values for oligemia and NT remained similar at all time points. Conclusion: Benign oligemia is small and remains microstructurally normal under pMCAO. TP and IC show a distinct evolution of DT-derived properties within the first 3 hours of stroke onset, and are thus potentially useful in predicting the fate of ischemic brain.

Impact of beverage consumption, age, and site dependency on dual energy X-ray absorptiometry (DEXA) measurements in perimenopausal women: a prospective study.

INTRODUCTION: The aim of this study was to determine the best site for bone mineral density (BMD) measurements based on T-scores, age, and beverage consumption. MATERIAL AND METHODS: In this prospective study, 271 women stratified by age (average age: 61.9 years) underwent dual energy X-ray absorptiometry (DEXA) scanning of their lumbar spine, hips, and forearms. Osteoporosis was defined as a BMD of 2.5 standard deviations or more below the mean peak bone mass based on a reference population of adult women (translated as a T-score ≤ -2.5), as measured by DEXA. Participants were also evaluated regarding alcohol and caffeine consumption by a semiquantitative questionnaire. RESULTS: A significant discrepancy was observed in the classification of osteoporosis at different locations, with hip and forearm showing the best correlation (Pearson's r = 0.627, p < 0.001). In addition, for participants over 50 years of age, hip and forearm showed the best correlation. Significant correlations were also noted between forearm T-scores and caffeine consumed and, to a lesser extent, the level of alcohol consumption. In the group ≤ 50 years of age, lumbar spine and forearm T-scores were only associated with alcohol consumption. In the group over 50 years of age, hip and forearm T-scores were only associated with caffeine consumption. CONCLUSIONS: Bone mineral density measurements at the hip and forearm correlated with caffeine consumption in elderly Taiwanese women. This is an important finding since age and caffeine consumption are known risk factors for osteoporosis.

Differentiation of the Infarct Core from Ischemic Penumbra within the First 4.5 Hours, Using Diffusion Tensor Imaging-Derived Metrics: A Rat Model.

OBJECTIVE: To investigate whether the diffusion tensor imaging-derived metrics are capable of differentiating the ischemic penumbra (IP) from the infarct core (IC), and determining stroke onset within the first 4.5 hours. MATERIALS AND METHODS: All procedures were approved by the local animal care committee. Eight of the eleven rats having permanent middle cerebral artery occlusion were included for analyses. Using a 7 tesla magnetic resonance system, the relative cerebral blood flow and apparent diffusion coefficient maps were generated to define IP and IC, half hour after surgery and then every hour, up to 6.5 hours. Relative fractional anisotropy, pure anisotropy (rq) and diffusion magnitude (rL) maps were obtained. One-way analysis of variance, receiver operating characteristic curve and nonlinear regression analyses were performed. RESULTS: The evolutions of tensor metrics were different in ischemic regions (IC and IP) and topographic subtypes (cortical, subcortical gray matter, and white matter). The rL had a significant drop of 40% at 0.5 hour, and remained stagnant up to 6.5 hours. Significant differences (p < 0.05) in rL values were found between IP, IC, and normal tissue for all topographic subtypes. Optimal rL threshold in discriminating IP from IC was about -29%. The evolution of rq showed an exponential decrease in cortical IC, from -26.9% to -47.6%; an rq reduction smaller than 44.6% can be used to predict an acute stroke onset in less than 4.5 hours. CONCLUSION: Diffusion tensor metrics may potentially help discriminate IP from IC and determine the acute stroke age within the therapeutic time window.

Early Idiopathic Normal Pressure Hydrocephalus Patients With Neuropsychological Impairment Are Associated With Increased Fractional Anisotropy in the Anterior Thalamic Nucleus.

In this study, we aimed to investigate the reactive changes in diffusion tensor imaging (DTI)-derived diffusion metrics of the anterior thalamic nucleus (AN), a relaying center for the Papez circuit, in early idiopathic normal pressure hydrocephalus (iNPH) patients with memory impairment, as well as its correlation with the patients' neuropsychological performances. In total, 28 probable iNPH patients with symptom onset within 1 year and 17 control subjects were prospectively recruited between 2010 and 2013 for this institutional review board-approved study. Imaging studies including DTI and a neuropsychological assessment battery were performed in all subjects. Diffusion metrics were measured from the region of the AN using tract-deterministic seeding method by reconstructing the mammillo-thalamo-cingulate connections within the Papez circuit. Differences in diffusion metrics and memory assessment scores between the patient and control group were examined via the Mann-Whitney U test. Spearman correlation analyses were performed to examine associations between diffusion metrics of AN and neuropsychological tests within the patient group. We discovered that early iNPH patients exhibited marked elevations in fractional anisotropy, pure diffusion anisotropy, and axial diffusivity (all P < 0.01), as well as lower neuropsychological test scores including verbal and nonverbal memory (all P < 0.05) compared with normal control. Spearman rank correlation analyses did not disclose significant correlations between AN diffusion metrics and neuropsychological test scores in the patient group, whereas ranked scatter plots clearly demonstrated a dichotic sample distribution between patient and control samples. In summary, our study highlighted the potential compensatory role of the AN by increasing thalamocortical connectivity within the Papez circuit because memory function declines in early iNPH when early shunt treatment may potentially reverse the memory deficits.

Quantitative diffusion-weighted magnetic resonance imaging for the diagnosis of partial-thickness rotator cuff tears.

BACKGROUND: This study investigated diffusion-weighted (DWI) magnetic resonance imaging (MRI) as an alternative to fat-suppressed T2-weighted imaging (FS-T2WI) for assessment of partial-thickness rotator cuff tears (RCTs). METHODS: Patients with arthroscopy proven partial-thickness RCTs who also received MRI (FS-T2WI and DWI) before surgery were prospectively included. Receiver operating characteristic curves were used to compare DWI vs. FS-T2WI using lesion-to-muscle signal intensity ratios. A cutoff point for predicting partial-thickness tears was determined using the Youden index. RESULTS: Included were 146 patients, with a mean age of 48.3 years (range, 19-86 years), of whom 43 had full-thickness RCTs, 67 had partial-thickness RCTs, and 36 had no tears. Areas under receiver operating characteristic curves for diagnosing partial-thickness tears were significantly higher for DWI (0.910) than for FS-T2WI (0.822, P = .016). Lesion-to-muscle signal intensity ratio cutoff values were 1.06 for DWI vs. 1.65 for FS-T2WI, respectively. The sensitivity and accuracy of DWI (89.1% [98 of 110] and 87.7% [128 of 146], respectively) for diagnosing partial-thickness and full-thickness tears were higher than for FS-T2WI (65.5% [72 of 110] and 72.6% [106 of 146], respectively). FS-T2WI, however, had higher specificity (94.4% [34 of 36]) than DWI (83.3% [30 of 36]). CONCLUSIONS: DWI is more accurate and sensitive than FS-T2WI for diagnosing partial-thickness RCTs.