Synthesis and Characterization of Dextran coated Iron Oxide Nanoparticles as a T1 Contrast Agent

This is a second part of iron oxide nanoparticles synthesis by co-precipitation method with various PH values. This study includes stabilizing iron oxide nanoparticles with dextran of molecular weight 10000 Da by aqueous route, then study its characteristics with AFM, FTIR and VSM also using the stabilized material as a contrast agent in T1-weighted image then compare the contrast enhancement with gadolinium based commercially available contrast agent (MagnevistTM). Ph values were (7,11,14), all samples were injected to mice bodies then imaged with MRI best result of T1 contrast enhancement was obtained from sample with Ph 14 compared with gadolinium-based T1 contrast agent with no toxic effects.

Fusion of quantitative susceptibility maps and T1-weighted images improve brain tissue contrast in primates

Recent progress in quantitative susceptibility mapping (QSM) has enabled the accurate delineation of submillimeter scale subcortical brain structures in humans. QSM reflects the magnetic susceptibility arising from the spatial distribution of iron, myelin, and calcium in the brain. The simultaneous visualization of cortical, subcortical, and white matter structure remains, however, challenging, utilizing QSM data solely. Here we present TQ-SILiCON, a fusion method that enhances the contrast of cortical and subcortical structures and provides an excellent white matter delineation by combining QSM and conventional T1-weighted (T1w) images. In this study, we first established QSM in the macaque monkey to map iron-rich subcortical structures. Implementing the same QSM acquisition and analyses methods allowed a similar accurate delineation of subcortical structures in humans. Moreover, applying automatic brain tissue segmentation to TQ-SILiCON images of the macaque improved the classification of the brain tissue types as compared to the single T1 contrast. Furthermore, we validate our dual-contrast fusion approach in humans and similarly demonstrate improvements in automated segmentation of cortical and subcortical structures. We believe the proposed contrast will facilitate translational studies in non-human primates to investigate the pathophysiology of neurodegenerative diseases that affect the subcortical structures of the basal ganglia in humans.HighlightsThe subcortical gray matter areas of macaque monkeys are reliably mapped by QSM, much as they are in humans.Combining T1w and QSM images improves the visualization and segmentation of white matter, cortical and subcortical structures in the macaque monkey.The proposed dual contrast TQ-SILiCON provides a similar image quality also in humans.TQ-SILiCON facilitates comparative and translational neuroscience studies investigating subcortical structures.

IDH–wild-type glioblastoma cell density and infiltration distribution influence on supramarginal resection and its impact on overall survival: a mathematical model

OBJECTIVE Recent studies have proposed resection of the T2 FLAIR hyperintensity beyond the T1 contrast enhancement (supramarginal resection [SMR]) for IDH–wild-type glioblastoma (GBM) to further improve patients’ overall survival (OS). GBMs have significant variability in tumor cell density, distribution, and infiltration. Advanced mathematical models based on patient-specific radiographic features have provided new insights into GBM growth kinetics on two important parameters of tumor aggressiveness: proliferation rate (ρ) and diffusion rate (D). The aim of this study was to investigate OS of patients with IDH–wild-type GBM who underwent SMR based on a mathematical model of cell distribution and infiltration profile (tumor invasiveness profile). METHODS Volumetric measurements were obtained from the selected regions of interest from pre- and postoperative MRI studies of included patients. The tumor invasiveness profile (proliferation/diffusion [ρ/D] ratio) was calculated using the following formula: ρ/D ratio = (4π/3)2/3 × (6.106/[VT21/1 − VT11/1])2, where VT2 and VT1 are the preoperative FLAIR and contrast-enhancing volumes, respectively. Patients were split into subgroups based on their tumor invasiveness profiles. In this analysis, tumors were classified as nodular, moderately diffuse, or highly diffuse. RESULTS A total of 101 patients were included. Tumors were classified as nodular (n = 34), moderately diffuse (n = 34), and highly diffuse (n = 33). On multivariate analysis, increasing SMR had a significant positive correlation with OS for moderately and highly diffuse tumors (HR 0.99, 95% CI 0.98–0.99; p = 0.02; and HR 0.98, 95% CI 0.96–0.99; p = 0.04, respectively). On threshold analysis, OS benefit was seen with SMR from 10% to 29%, 10% to 59%, and 30% to 90%, for nodular, moderately diffuse, and highly diffuse, respectively. CONCLUSIONS The impact of SMR on OS for patients with IDH–wild-type GBM is influenced by the degree of tumor invasiveness. The authors’ results show that increasing SMR is associated with increased OS in patients with moderate and highly diffuse IDH–wild-type GBMs. When grouping SMR into 10% intervals, this benefit was seen for all tumor subgroups, although for nodular tumors, the maximum beneficial SMR percentage was considerably lower than in moderate and highly diffuse tumors.

An MRI-Based Machine Learning Prediction Framework to Lateralize Hippocampal Sclerosis in Patients With Temporal Lobe Epilepsy

Objective.MRI fails to reveal hippocampal pathology in 30-50% of temporal lobe epilepsy (TLE) surgical candidates. To address this clinical challenge, we developed an automated MRI-based classifier that lateralizes the side of covert hippocampal pathology in TLE.Methods.We trained a surface-based linear discriminant classifier that uses T1-weighted (morphology) and T2-weighted as well as FLAIR/T1 (intensity) features. The classifier was trained on 60 TLE patients (mean age: 35.6; 58% female) with histologically-verified hippocampal sclerosis (HS). Images were deemed as MRI-negative in 42% of cases based on neuroradiological reading (40% based on hippocampal volumetry). The predictive model automatically labelled patients as left or right TLE. Lateralization accuracy was compared to electro-clinical data, including side of surgery. Accuracy of the classifier was further assessed in two independent TLE cohorts with similar demographics and electro-clinical characteristics (n=57; 58% MRI-negative).Results.The overall lateralization accuracy was 93% (95%; CI 92% - 94%), regardless of HS visibility. In MRI-negative TLE, the combination of T2 and FLAIR/T1 intensities provided the highest accuracy both in the training (84%, area-under-the-curve (AUC): 0.95±0.02) and the validation cohorts (Cohort 1: 90%, AUC: 0.99; Cohort 2: 76%, AUC: 0.94).Conclusion.This prediction model for TLE lateralization operates on readily available conventional MRI contrasts and offers gain in accuracy over visual radiological assessment. The combined contribution of decreased T1- and increased T2-weighted intensities makes the synthetic FLAIR/T1 contrast particularly effective in MRI-negative HS, setting the basis for broad clinical translation.

Lhermitte–Duclos Disease Mimicking Cerebellopontine Angle Tumor: A Case Report

Lhermitte-Duclos disease (LDD) is dysplastic gangliocytoma of the cerebellum, which is rare, tends to grow slowly, usually has good prognosis, and wherein the cerebellar cortex becomes thick and the central white matter is not observed. On MRI of the brain, it has hyperintense and hypointense presentations on T2- and T1-weighted scans, respectively. It has nonhomogenous contrasting pattern on T1-contrast scans. LDD can be seen unilaterally or bilaterally in the cerebellum. When the disease is located at the cerebellopontine angle, it can be mistaken for the tumors located in this region. Recurrence can occur postoperatively, which is rare. The present case is a rare one because the tumor was located at the cerebellopontine angle and radiologically mimicked cerebellopontine angle tumors.

Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics

Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.

An Activatable T1-Weighted MR Contrast Agent: A Noninvasive Tool for Tracking the Vicinal Thiol Motif of Thioredoxin in Live Cells

We have synthesized new magnetic resonance imaging (MRI) T1 contrast agents (CA1 and CA2) that permit the activatable recognition of the cellular vicinal thiol motifs of the protein thioredoxin. The contrast agents showed MR relaxivities typical of gadolinium complexes with a single water molecule coordinated to a Gd3+ center (i.e., ~4.54 mM−1s−1) for both CA1 and CA2 at 60 MHz. The contrast agent CA1 showed a ~140% relaxivity enhancement in the presence of thioredoxin, a finding attributed to a reduction in the flexibility of the molecule after binding to thioredoxin. Support for this rationale, as opposed to one based on preferential binding, came from 1H-15N-HSQC NMR spectral studies; these revealed that the binding affinities toward thioredoxin were almost the same for both CA1 and CA2. In the case of CA1, T1-weighted phantom images of cancer cells (MCF-7, A549) could be generated based on the expression of thioredoxin. We further confirmed thioredoxin expression-dependent changes in the T1-weighted contrast via knockdown of the expression of the thioredoxin using siRNA-transfected MCF-7 cells. The nontoxic nature of CA1, coupled with its relaxivity features, leads us to suggest that it constitutes a first-in-class MRI T1 contrast agent that allows for the facile and noninvasive monitoring of vicinal thiol protein motif expression in live cells.