Diffusion Magnetic Resonance Imaging Research Articles

Ratio of Incidentally Detected Brain Tumors in Diffusion Magnetic Resonance Imaging Scans Performed in the Emergency Department on the Suspicion of Acute Stroke

Ratio of Incidentally Detected Brain Tumors in Diffusion Magnetic Resonance Imaging Scans Performed in the Emergency Department on the Suspicion of Acute Stroke

Corticolimbic structural connectivity encapsulates real-world emotional reactivity andhappiness.

Emotional reactivity to everyday events predicts happiness, but the neural circuits underlying this relationship remain incompletely understood. Here, we combined experience sampling methods and diffusion magnetic resonance imaging to examine the association among corticolimbic structural connectivity, real-world emotional reactivity and daily experiences of happiness from 79 young adults (35 females). Participants recorded momentary assessments of emotional and happiness experiences five times a day for a week, approximately 2weeks after brain scanning. Model-based emotional reactivity scores, which index the degree to which moment-to-moment affective state varies with the occurrence of positive or negative events, were computed. Results showed that stronger microstructural integrity of the uncinate fasciculus and the external capsule was associated with both greater positive and negative emotional reactivity scores. The relationship between these fiber tracts and experienced happiness was explained by emotional reactivity. Importantly, this indirect effect was observed for emotional reactivity to positive but not negative real-world events. Our findings suggest that the corticolimbic circuits supporting socioemotional functions are associated with emotional reactivity and happiness in the real world.

Multimodel habitats constructed by perfusion and/or diffusion MRI predict isocitrate dehydrogenase mutation status and prognosis in high-grade gliomas

To determine whether tumour vascular and cellular heterogeneity of high-grade glioma (HGG) is predictive of isocitrate dehydrogenase (IDH) mutation status and overall survival (OS) by using tumour habitat-based analysis constructed by perfusion and/or diffusion magnetic resonance imaging (MRI). Seventy-eight HGG patients that met the 2021 World Health Organization WHO Classification of Tumors of the Central Nervous System, 5th edition (WHO CNS5), were enrolled to predict IDH mutation status, of which 32 grade 4 patients with unmethylated O6-methylguanine-DNA methyltransferase (MGMT) promoter were enrolled for prognostic analysis. The deep-learning-based model nnU-Net and K-means clustering algorithm were applied to construct the Traditional Habitat, Vascular Habitat (VH), Cellular Density Habitat (DH), and their Combined Habitat (CH). Quantitative parameters were extracted and compared between IDH-mutant and IDH-wild-type patients, respectively, and the prediction potential was evaluated by receiver operating characteristic (ROC) curve analysis. OS was analysed using Kaplan-Meier survival analysis and the log-rank test. Compared with IDH-mutants, median relative cerebral blood volume (rCBVmedian) values in the whole enhancing tumour (WET), VH1, VH3, CH1-4 habitats were significantly increased in IDH-wild-type HGGs (all p<0.05). Additionally, the accuracy of rCBVmedian values in CH1 outperformed other habitats in identifying IDH mutation status (p<0.001) at a cut-off value of 4.83 with AUC of 0.815. Kaplan-Meier survival analysis highlighted significant differences in OS between the populations dichotomised by the median of rCBVmedian in WET, VH1, CH1-3 habitats (all p<0.05). The habitat imaging technique may improve the accuracy of predicting IDH mutation status and prognosis, and even provide a new direction for subsequent personalised precision treatment.

MRI features for predicting the pathological grade of HCC in patients undergoing liver transplantation

BackgroundContrast-enhanced magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of hepatocellular carcinoma (HCC). This study aims to assess the performance of MRI features for evaluating hepatocellular carcinoma (HCC) aggressiveness in living liver transplantation in patients.Material and methodsThis retrospective study included patients who underwent liver transplantation in our hospital between 2015 and 2020. Abdominal contrast-enhanced MRIs of these patients were reviewed, and clinical, radiological, and histopathological findings of HCCs were recorded. The prognostic features of HCCs as determined by MRI were compared with Edmondson-Steiner (E-S) grades. Liver parenchyma fibrosis based on an apparent diffusion coefficient (ADC) map was correlated with histological subclassification of cirrhosis using the Laennec staging system.ResultsThe study subjects included 37 men and 8 women with a mean age of 59.56 ± 7.81 (range: 25–72). The mean tumour size was 37.33 ± 22.27 mm (range: 10–118 mm), and nine tumours (23.1%) involved portal vein tumour thrombosis. There was a significant correlation between tumour grade and size (p = 0.007) and intratumoral fat (p = 0.014) even though no significant correlations between grade and mean ADC value, capsule appearance, presence of satellite lesions, smooth margin, imaging of the tumour feeding artery, and corona enhancement of HCC (p > 0.05) were found. There was a statistically significant correlation between mild (stage 4A) and moderate (stage 4B) fibrosis of non-tumorous liver parenchyma and ADC value (p < 0.001).ConclusionOur study found that ADC values can be used to distinguish mild cirrhotic livers from moderate cirrhotic livers. Diffusion MRI might be used to diagnose the degree of liver fibrosis without histopathological analysis. According to our results, only intralesional fat and tumour size correlated with tumour grade, and as such, these parameters could be used as prognostic MRI biomarkers for HCC.

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods.

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.

Simulation of Diffusion Magnetic Resonance Based on Chain Method

Magnetic resonance diffusion imaging is an important tool for pathology research, and the cost of instrument development is extremely high. The construction of simulation models can optimize the parameters of key components of the instrument on the numerical platform, which can greatly reduce the R&D cost and improve the success rate. In this paper, a chain magnetic resonance simulation calculation method is obtained based on equation Bloch-Torrey to simulate the signal change law caused by gradient coding and diffusion characteristics, and MATLAB is used to construct a numerical imitation model and write a simulation program. Based on this calculation method, the diffusion-weighted imaging, diffusion coefficient imaging and diffusion tensor imaging simulation experiments were carried out by using the spin-echo (SE) diffusion-weighted sequence, and the images and results were reconstructed by combining the anti-Fourier transform. Experiments show that the simulated images can accurately reflect the set simulation model, reconstruct the diffusion coefficient and tensor characteristics, and the chain magnetic resonance simulation calculation method deduced in this paper.

Multi-Connectivity Representation Learning Network for Major Depressive Disorder Diagnosis.

The pathophysiology of major depressive disorder (MDD) has been demonstrated to be highly associated with the dysfunctional integration of brain activity. Existing studies only fuse multi-connectivity information in a one-shot approach and ignore the temporal property of functional connectivity. A desired model should utilize the rich information in multiple connectivities to help improve the performance. In this study, we develop a multi-connectivity representation learning framework to integrate multi-connectivity topological representation from structural connectivity, functional connectivity and dynamic functional connectivities for automatic diagnosis of MDD. Briefly, structural graph, static functional graph and dynamic functional graphs are first computed from the diffusion magnetic resonance imaging (dMRI) and resting state functional magnetic resonance imaging (rsfMRI). Secondly, a novel Multi-Connectivity Representation Learning Network (MCRLN) approach is developed to integrate the multiple graphs with modules of structural-functional fusion and static-dynamic fusion. We innovatively design a Structural-Functional Fusion (SFF) module, which decouples graph convolution to capture modality-specific features and modality-shared features separately for an accurate brain region representation. To further integrate the static graphs and dynamic functional graphs, a novel Static-Dynamic Fusion (SDF) module is developed to pass the important connections from static graphs to dynamic graphs via attention values. Finally, the performance of the proposed approach is comprehensively examined with large cohorts of clinical data, which demonstrates its effectiveness in classifying MDD patients. The sound performance suggests the potential of the MCRLN approach for the clinical use in diagnosis. The code is available at https://github.com/LIST-KONG/MultiConnectivity-master.

A simulation-driven supervised learning framework to estimate brain microstructure using diffusion MRI

We propose a framework to train supervised learning models on synthetic data to estimate brain microstructure parameters using diffusion magnetic resonance imaging (dMRI). Although further validation is necessary, the proposed framework aims to seamlessly incorporate realistic simulations into dMRI microstructure estimation.Synthetic data were generated from over 1,000 neuron meshes converted from digital neuronal reconstructions and linked to their neuroanatomical parameters (such as soma volume and neurite length) using an optimized diffusion MRI simulator that produces intracellular dMRI signals from the solution of the Bloch–Torrey partial differential equation. By combining random subsets of simulated neuron signals with a free diffusion compartment signal, we constructed a synthetic dataset containing dMRI signals and 40 tissue microstructure parameters of 1.45 million artificial brain voxels.To implement supervised learning models we chose multilayer perceptrons (MLPs) and trained them on a subset of the synthetic dataset to estimate some microstructure parameters, namely, the volume fractions of soma, neurites, and the free diffusion compartment, as well as the area fractions of soma and neurites. The trained MLPs perform satisfactorily on the synthetic test sets and give promising in-vivo parameter maps on the MGH Connectome Diffusion Microstructure Dataset (CDMD). Most importantly, the estimated volume fractions showed low dependence on the diffusion time, the diffusion time independence of the estimated parameters being a desired property of quantitative microstructure imaging.The synthetic dataset we generated will be valuable for the validation of models that map between the dMRI signals and microstructure parameters. The surface meshes and microstructures parameters of the aforementioned neurons have been made publicly available.

Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord.

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is widely used for MS diagnosis and clinical follow-up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball-and-Stick models. We analyzed spinal cord data acquired from multiple hospitals and extracted average diffusion MRI metrics per vertebral level using a collection of image processing methods and an atlas-based approach. We then performed a statistical analysis to evaluate the feasibility of these metrics for detecting lesions, exploring the usefulness of combining different metrics to improve accuracy. Our study demonstrates the sensitivity of each metric to underlying microstructure changes in MS patients. We show that selecting a specific subset of metrics, which provide complementary information, significantly improves the prediction score of lesion presence in the cervical spinal cord. Furthermore, the Ball-and-Stick model has the potential to provide novel information about the microstructure of damaged tissue. Our results suggest that diffusion measures, particularly combined measures, are sensitive in discriminating abnormal from healthy cervical vertebral levels in patients. This information could aid in improving MS diagnosis and clinical follow-up. Our study highlights the potential of the Ball-and-Stick model in providing additional insights into the microstructure of the damaged tissue.

The Challenge of Diffusion Magnetic Resonance Imaging in Cerebral Palsy: A Proposed Method to Identify White Matter Pathways.

Cerebral palsy (CP), a neuromotor disorder characterized by prenatal brain lesions, leads to white matter alterations and sensorimotor deficits. However, the CP-related diffusion neuroimaging literature lacks rigorous and consensual methodology for preprocessing and analyzing data due to methodological challenges caused by the lesion extent. Advanced methods are available to reconstruct diffusion signals and can update current advances in CP. Our study demonstrates the feasibility of analyzing diffusion CP data using a standardized and open-source pipeline. Eight children with CP (8-12 years old) underwent a single diffusion magnetic resonance imaging (MRI) session on a 3T scanner (Achieva 3.0T (TX), Philips Healthcare Medical Systems, Best, The Netherlands). Exclusion criteria were contraindication to MRI and claustrophobia. Anatomical and diffusion images were acquired. Data were corrected and analyzed using Tractoflow 2.3.0 version, an open-source and robust tool. The tracts were extracted with customized procedures based on existing atlases and freely accessed standardized libraries (ANTs, Scilpy). DTI, CSD, and NODDI metrics were computed for each tract. Despite lesion heterogeneity and size, we successfully reconstructed major pathways, except for a participant with a larger lesion. Our results highlight the feasibility of identifying and quantifying subtle white matter pathways. Ultimately, this will increase our understanding of the clinical symptoms to provide precision medicine and optimize rehabilitation.

A Phase II Trial Evaluating Rapid Mid-Treatment Nodal Shrinkage to Select for Adaptive Deescalation in p16+ Oropharyngeal Cancer Patients Undergoing Definitive Chemoradiation

The purpose of this study is to determine if rapid mid-treatment nodal shrinkage (RMNS) can identify patients with p16+ oropharyngeal cancer (OPC) who can be safely deescalated with reduced dose chemoradiation therapy (CRT). The primary endpoint was 2-year progression free survival (PFS). Inclusion criteria were as follows: T1-3, N1, M0 (AJCC 8th edition) p16+ OPC with <10 pack-year smoking history. All patients were initially planned for standard dose CRT (70 Gy) and weekly cisplatin. Patients were evaluated with a CT scan at week 4 for RMNS, defined as >40% nodal volumetric reduction from baseline. If RMNS was achieved, they proceeded to deescalated CRT (60 Gy). If not, they received standard CRT. Biomarker correlates were collected at baseline and week 4 of CRT including plasma TTMV (tumor tissue modified viral) HPV DNA and MRI diffusion weighted imaging (DWI). Univariate logistic regression analyses (UVA) were performed to evaluate predictors of RMNS. Odds ratios with 95% CI are reported, using a p<0.05 for statistical significance with a two-sided test. Wilcoxon rank sum tests were used to evaluate differences between the two groups using p < 0.05, 2-sided) for statistical significance. All statistical procedures were performed using R () with no adjustments for multiple testing. Thirty-six patients were enrolled: median age: 60 years; 81% male; primary site: 36% base of tongue, 53% tonsil, 11% both; T-stage: 39% T1, 50% T2, 11% T3; N-stage: 100% N1; any smoking history: 58% yes, 42% no; 67% (n = 24) had RMNS and received deescalated CRT while the remaining proceeded to standard CRT. At a median follow-up of 32.4 months, 2-year PFS between the standard and deescalated groups were 91.7% vs 90.9%, respectively (p = 0.97). All patients with recurrence underwent successful salvage treatment with 2-year OS 100% for all patients. On UVA, rapid TTMV HPV DNA clearance (baseline to week 4) (OR 12.0 [1.65-250], p = 0.034), lower MRI diffusivity (ADC) at baseline (OR 0.79 [0.61-0.97], p = 0.042) and week 4 (OR 0.76 [0.60-0.91], p = 0.009), and higher MRI diffusional kurtosis at baseline (OR 1.09 [1.01-1.21], p = 0.051) and week 4 (OR 1.24 [1.09-1.52], p = 0.009) were significantly associated with RMNS. When comparing the deescalated and standard cohorts, the mean baseline and week 4 MRI ADC were significantly lower and week 4 MRI diffusional kurtosis was significantly higher in the deescalated group. In this phase II study, rapid mid-treatment nodal shrinkage appeared to select favorable risk p16+ oropharynx cancer patients for treatment de-escalation. Rapid clearance of TTMV HPV DNA at week 4 as well as MRI DWI biomarkers of low ADC and high diffusional kurtosis values were correlated with RMNS. A larger study is planned to incorporate RMNS and biomarkers for further treatment de-escalation. Additional trial information is available at ClinicalTrials.gov (Identifier: NCT03215719).

Multimodal magnetic resonance imaging in the diagnosis of cervical cancer and its correlation with the differentiation process of cervical cancer

PurposeThis study seeks to evaluate the value of MRI (Magnetic resonance imaging) diffusion weighted images (DWI), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in the diagnosis of cervical carcinoma.MethodsSeventy-nine cases of cervical cancer (CC group) (39 cases of squamous carcinoma (SCC group) and 40 cases of adenocarcinoma (ACC group)) and 30 cases of healthy controls (HC group) were included in this study. All the subjects were informed of the purpose of this study. The study was approved by the Ethics Committee of Beihua University Hospital, Jinlin, China. In this study, images were acquired based on a 3T MR scanner (Ingenia; Philips, Best, the Netherlands) and measured the imaging parameters by DWI, IVIM and DKI techniques. The parameters were obtained by Philips post-processing workstation, DKE and IVIM. These ROIs (region of interest) were manually drawn on each parameter mapping image by MRI physicians. Finally, SPSS 23.0 statistical software was used for data analysis.ResultsThe ADC (apparent diffusion coefficient) value of M group was lower than that of N group, and the difference was statistically significant (P < 0.05). The D (true diffusion coefficient) value, D*(pseudo diffusion coefficient) value, f (perfusion fraction) value, MD (mean diffusivity) value, and ADC value in the SCC group were lower than those in the ACC group with statistically significant differences (P < 0.05). The MK (mean kurtosis) value was higher than that of the ACC group, and the difference was statistically significant (P < 0.05). Compared with the HC group, the ADC values, D values, MD values of group CC group were lower, and the D* values, f values, MK values were higher; all the parameters were statistically significant (P < 0.05). The higher the differentiation degree of cervical cancer, the higher ADC values, D values, MD values, and the smaller D* values, f values, MK values. The difference of ADC values, D values and MK values was statistically significant (P < 0.05). MK value had the best diagnostic efficiency in the differential diagnosis of cervical cancer with low and medium differentiation, high and low differentiation (P < 0.05). There was no significant difference in the f value between high and low differentiation cervical cancer (P > 0.05). There was no significant difference in the MD value between low and high differentiation cervical cancer (P > 0.05). The strongest correlation between MK values (r = 0.796) and the degree of pathological differentiation of cervical cancer is positively correlated. The D values, MD values, and ADC values are negatively correlated with the degree of pathological differentiation of cervical cancer.ConclusionThe ADC value of DWI parameters has important diagnostic value for different menstrual states of cervical cancer. The parameter values of DWI, IVIM, and DKI can be used to differentiate cervical cancer from normal cervical tissue, and thus have important diagnostic value for differentiating pathological types of cervical cancer. This means that these parameter values may have great significance in the differential diagnosis of cervical cancer with different degrees of pathological differentiation. The pathological differentiation degree of cervical cancer is significantly positively correlated with the MK value in the parameter values of DWI, IVIM, and DKI, while negatively correlated with the D value, MD value, and ADC value.

Water diffusion and T2 quantification in transient-state MRI: the effect of RF pulse sequence.

In quantitative measurement of the value of tissues, the diffusion of water molecules has been recognized as a confounder. This is most notably so for transient-state quantitative mapping techniques, which allow simultaneous estimation of and . In prior work, apparently conflicting conclusions are presented on the level of diffusion-induced bias on the T2 estimate. So far there is a lack of studies on the effect of the RF pulse angle sequence on the level of diffusion-induced bias. In this work, we show that the specific transient-state RF pulse sequence has a large effect on this level of bias. In particular, the bias level is strongly influenced by the mean value of the RF pulse angles. Also, for realistic values of the spoiling gradient area, we infer that the diffusion-induced bias is negligible for non-liquid human tissues; yet, for phantoms, the effect can be substantial (15% of the true value) for some RF pulse sequences. This should be taken into account in validation procedures.

Microstructural Neurodegeneration of the Entorhinal-Hippocampus Pathway along the Alzheimer's Disease Continuum.

Conventional neuroimaging biomarkers for the neurodegeneration of Alzheimer's disease (AD) are not sensitive enough to detect neurodegenerative alterations during the preclinical stage of AD individuals. We examined whether neurodegeneration of the entorhinal-hippocampal pathway could be detected along the AD continuum using ultra-high-field diffusion tensor imaging and tractography for ex vivo brain tissues. Postmortem brain specimens from a cognitively unimpaired individual without AD pathological changes (non-AD), a cognitively unimpaired individual with AD pathological changes (preclinical AD), and a demented individual with AD pathological changes (AD dementia) were scanned with an 11.7T diffusion magnetic resonance imaging. Fractional anisotropy (FA) values of the entorhinal layer II and number of perforant path fibers counted by tractography were compared among the AD continuum. Following the imaging analyses, the status of myelinated fibers and neuronal cells were verified by subsequent serial histological examinations. At 250μm (zipped to 125μm) isotropic resolution, the entorhinal layer II islands and the perforant path fibers could be identified in non-AD and preclinical AD, but not in AD dementia, followed by histological verification. The FA value of the entorhinal layer II was the highest among the entorhinal laminae in non-AD and preclinical AD, whereas the FA values in the entorhinal laminae were homogeneously low in AD dementia. The FA values and number of perforant path fibers decreased along the AD continuum (non-AD>preclinical AD > AD dementia). We successfully detected neurodegenerative alterations of the entorhinal-hippocampal pathway at the preclinical stage of the AD continuum.

Differentiation of glioblastoma and primary central nervous system lymphomas using multiparametric diffusion and perfusion magnetic resonance imaging.

The present study aimed to determine whether combining diffusion-weighted (DWI) and dynamic susceptibility contrast-enhanced perfusion-weighted (DSC-PWI) magnetic resonance imaging (MRI) could differentiate between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). The present retrospective study evaluated 45 patients with histologically confirmed brain tumors, of which 18 had PCNSLs and 27 had GBMs. All patients underwent conventional, DWI, and DSC-PWI MRIs before the surgical removal of the lesion or stereotactic biopsy. The solid tumor component, peritumoral edema, and abnormal white matter were measured in three regions of interest to evaluate relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC) and DWI. In conventional MRI, there were significant differences in tumor numbers, tumor enhancement type, tumor necrosis, hemorrhage and open-ring sign between GBM and PCNSL. Solid tumor ADC and rCBV values (ADCt and rCBVt, respectively) and their ratios with abnormal white matter amounts were significantly higher in GBM cases than in PCNSL cases (P<0.05). The rCBV value for peritumoral edema (rCBVe) and its ratio with abnormal white matter amount (rCBVe/n) were significantly higher in GBM cases than in PCNSL cases (P<0.05). However, ADC values did not differ significantly for peritumoral edema. DWI values did not differ significantly. Combining rCBVt and rCBVe/n provided a perfect area under the receiver operating characteristic curve of 1.00, with 100% sensitivity and 100% specificity for distinguishing GBM from PCNSL. In the results of the present study, the major criterion in the decision-making process distinguishing PCNSL from GBM was the combined rCBVt and rCBVe/n parameter. A minor criterion was the ADCt value of the lesion.

Can Neutrophil-Lymphocyte and Platelet-Lymphocyte Ratios Predict the Risk of Developing New Ischemic Lesions After Carotid Stenting?

Aim: It has been reported that the neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) are associated with carotid artery stenosis rate, risk of restenosis after stenting, and clinical outcome after an ischemic stroke, and are also predictive markers. The objective of this study is to evaluate whether NLR and PLR values and the associated temporal changes are indicators of the risk for newly developing ischemic lesions. Material and Methods: Patients who underwent stenting in our clinic between November 2019 and January 2022 and who had a complete blood count and a diffusion magnetic resonance imaging scan before and after the procedure, were included in the study and evaluated in two groups; patients with and without newly developing ischemic lesions. Results: Newly developing ischemic lesions were detected in 27 of the 50 patients included in the study. There was no difference in baseline and 48th-hour NLR and PLR rates and the temporal variation of these rates between patients with and without newly developing ischemic lesions. Erythrocyte distribution width (RDW) and hemoglobin (HGB) values were higher in the without newly developing ischemic lesions group at 48 hours, but there was only a significant difference between the RDW temporal change between the two groups. In the correlation analysis, no significant correlation was found between NLR, PLR, and their temporal changes, ipsilateral and contralateral stenosis rates, age, and residual stenosis rates. Conclusion: There was no significant relationship between the development of newly developing ischemic lesions and NLR and PLR values and the associated temporal changes.

Anesthesia-related brain microstructure modulations detected by diffusion magnetic resonance imaging.

Recent studies have shown significant changes to brain microstructure during sleep and anesthesia. In vivo optical microscopy and magnetic resonance imaging (MRI) studies have attributed these changes to anesthesia and sleep-related modulation of the brain's extracellular space (ECS). Isoflurane anesthesia is widely used in preclinical diffusion MRI (dMRI) and it is therefore important to investigate if the brain's microstructure is affected by anesthesia to an extent detectable with dMRI. Here, we employ diffusion kurtosis imaging (DKI) to assess brain microstructure in the awake and anesthetized mouse brain (n = 22). We find both mean diffusivity (MD) and mean kurtosis (MK) to be significantly decreased in the anesthetized mouse brain compared with the awake state (p < 0.001 for both). This effect is observed in both gray matter and white matter. To further investigate the time course of these changes we introduce a method for time-resolved fast DKI. With this, we show the time course of the microstructural alterations in mice (n = 5) as they transition between states in an awake-anesthesia-awake paradigm. We find that the decrease in MD and MK occurs rapidly after delivery of gas isoflurane anesthesia and that values normalize only slowly when the animals return to the awake state. Finally, time-resolved fast DKI is employed in an experimental mouse model of brain edema (n = 4), where cell swelling causes the ECS volume to decrease. Our results show that isoflurane affects DKI parameters and metrics of brain microstructure and point to isoflurane causing a reduction in the ECS volume. The demonstrated DKI methods are suitable for in-bore perturbation studies, for example, for investigating microstructural modulations related to sleep/wake-dependent functions of the glymphatic system. Importantly, our study shows an effect of isoflurane anesthesia on rodent brain microstructure that has broad relevance to preclinical dMRI.

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

Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy. Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint. Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC. The automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.

A Rare Case of Isolated Infarcts of the Pons with Sudden Vertigo: Clinical Features and Imaging Findings

Isolated infarcts of the pons (IIP) are a rare subtype of ischemic stroke, accounting for less than 1% of all strokes. It refers to a specific type of ischemic stroke that occurs within the pons region of the brain. IIP can be associated with vertebrobasilar insufficiency in certain cases. This case report describes a 64-year-old female patient who presented with acute vertigo and spontaneous nystagmus to the lesion side, ultimately diagnosed with IIP in the posterior right side of pons. Tegmental pontine infarcts typically manifest as oblique small (lacunar) infarctions that are localized in the mediolateral tegmental area of the middle to upper pons. The patient’s symptoms resolved spontaneously, but a 2-mm infarct was confirmed by brain diffusion magnetic resonance imaging. This case highlights the distinctive symptoms associated with IIP and emphasizes the importance of careful neurological examination and advanced neuroimaging techniques for accurate diagnosis.

Assessment of spinal cord injury using ultrasound elastography in a rabbit model in vivo

The effect of the mechanical micro-environment on spinal cord injury (SCI) and treatment effectiveness remains unclear. Currently, there are limited imaging methods that can directly assess the localized mechanical behavior of spinal cords in vivo. In this study, we apply new ultrasound elastography (USE) techniques to assess SCI in vivo at the site of the injury and at the time of one week post injury, in a rabbit animal model. Eleven rabbits underwent laminectomy procedures. Among them, spinal cords of five rabbits were injured during the procedure. The other six rabbits were used as control. Two neurological statuses were achieved: non-paralysis and paralysis. Ultrasound data were collected one week post-surgery and processed to compute strain ratios. Histologic analysis, mechanical testing, magnetic resonance imaging (MRI), computerized tomography and MRI diffusion tensor imaging (DTI) were performed to validate USE results. Strain ratios computed via USE were found to be significantly different in paralyzed versus non-paralyzed rabbits. The myelomalacia histologic score and spinal cord Young’s modulus evaluated in selected animals were in good qualitative agreement with USE assessment. It is feasible to use USE to assess changes in the spinal cord of the presented animal model. In the future, with more experimental data available, USE may provide new quantitative tools for improving SCI diagnosis and prognosis.