Comparative analysis of brain age prediction using structural and diffusion MRIs in neonates.

Using machine learning techniques to predict brain age from multimodal data has become a crucial biomarker for assessing brain development. Among various types of brain imaging data, structural magnetic resonance imaging (sMRI) and diffusion magnetic resonance imaging (dMRI) are the most commonly used modalities. sMRI focuses on depicting macrostructural features of the brain, while dMRI reveals the orientation of major white matter fibers and changes in tissue microstructure. However, their differential capabilities in reflecting newborn age and clinical implications have not been systematically studied. This study aims to explore the impact of sMRI and dMRI on brain age prediction. Comparing predictions based on T2-weighted(T2w) and fractional anisotropy (FA) images, we found their mean absolute errors (MAE) in predicting infant age to be similar. Exploratory analysis revealed for T2w images, areas such as the cerebral cortex and ventricles contribute most significantly to age prediction, whereas FA images highlight the cerebral cortex and regions of the main white matter tracts. Despite both modalities focusing on the cerebral cortex, they exhibit significant region-wise differences, reflecting developmental disparities in macro- and microstructural aspects of the cortex. Additionally, we examined the effects of prematurity, gender, and hemispherical asymmetry of the brain on age prediction for both modalities. Results showed significant differences (p<0.05) in age prediction biases based on FA images across gender and hemispherical asymmetry, whereas no significant differences were observed with T2w images. This study underscores the differences between T2w and FA images in predicting infant brain age, offering new perspectives for studying infant brain development and aiding more effective assessment and tracking of infant development.

Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks.

Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine. We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures. We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data. In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies.

Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique.

Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t-test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three-tier prostate cancer risk were evaluated with Spearman's correlation coefficient (rS ). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R-CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (rS = -0.21, p = 0.001), Decipher (rS = -0.193, p = 0.003), and three-tier risk (rS = -0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple-reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times.

T1/T2-weighted ratio: A feasible MRI biomarker in multiple sclerosis.

T1/T2-weighted ratio is a novel magnetic resonance imaging (MRI) biomarker based on conventional sequences, related to microstructural integrity and with increasing use in multiple sclerosis (MS) research. Different from other advanced MRI techniques, this method has the advantage of being based on routinely acquired MRI sequences, a feature that enables analysis of retrospective cohorts with considerable clinical value. This article provides an overview of this method, describing the previous cross-sectional and longitudinal findings in the main MS clinical phenotypes and in different brain tissues: focal white matter (WM) lesions, normal-appearing white matter (NAWM), cortical gray matter (GM), and deep normal-appearing gray matter (NAGM). We also discuss the clinical associations, possible reasons for conflicting results, correlations with other MRI-based measures, and histopathological associations. We highlight the limitations of the biomarker itself and the methodology of each study. Finally, we update the reader on its potential use as an imaging biomarker in research.

Static and dynamic responses to hyperoxia of normal placenta across gestation with T2*-weighted MRI sequences.

OBJECTIVES: T2*-weighted magnetic resonance imaging (MRI) sequences have been identified as non-invasive tools with which to study placental oxygenation in vivo. This study aimed to use these to investigate both static and dynamic responses to hyperoxia of the normal placenta across gestation. METHODS: We conducted a single-center prospective study including 52 uncomplicated pregnancies. Two T2*-weighted sequences (T2* relaxometry) were performed, one before and one after maternal hyperoxia. The distribution of placental T2* values was modeled by fitting a gamma probability density function (T2* ~ Γ α ß ), describing the structure of the histogram using the mean T2* value, the shape parameter (α) and the rate (ß). A dynamic acquisition (blood-oxygen-level-dependent (BOLD) MRI) was also performed before and during maternal oxygen supply, until placental oxygen saturation had been achieved. The signal change over time was modeled using a sigmoid function, to determine the intensity of enhancement (ΔBOLD (% with respect to baseline)), a temporal variation coefficient (λ (min-1), controlling the slope of the curve) and the maximum steepness (Vmax (% of placental enhancement/min)). RESULTS: The histogram analysis of the T2* values in normoxia showed a whole-placenta variation, with a decreasing linear trend in the mean T2* value (Pearson's correlation coefficient (R) = -0.83 (95% CI, -0.9 to -0.71), P < 0.001), along with an increasingly peaked and narrower distribution of T2* values with advancing gestation. After maternal hyperoxia, the mean T2* ratios (mean T2*hyperoxia/mean T2*baseline) were positively correlated with gestational age, while the other histogram parameters remained stable, suggesting a translation of the histogram towards higher values with a similar appearance after maternal hyperoxia. ΔBOLD showed a non-linear increase across gestation. Conversely, λ showed an inverted trend across gestation, with a weaker correlation (R = -0.33 (95% CI, -0.58 to -0.02), P = 0.04, R2 = 0.1). As a combination of ΔBOLD and λ, the changes in Vmax throughout gestation were influenced mainly by the changes in ΔBOLD and showed a positive non-linear correlation with gestational age. CONCLUSIONS: Our results suggest that the decrease in the T2* placental signal as gestation progresses does not reflect placental dysfunction. The BOLD dynamic signal change is representative of a free-diffusion model of oxygenation and highlights the increasing differences in oxygen saturation between mother and fetus as gestation progresses (ΔBOLD) and in the placental permeability to oxygen (λ). © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.

Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near or above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16 + InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16 + InceptionV3 models using SPECT DaTscan dataset.

Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance.

BACKGROUND: The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition. PURPOSE: To compare the image quality and diagnostic performance of conventional turbo spin-echo (TSE) T2-weighted (T2W) imaging with DL-accelerated sagittal T2W imaging in the female pelvic cavity. METHODS: This study evaluated 149 consecutive female pelvic MRI examinations, including conventional T2W imaging with TSE (acquisition time = 2:59) and DL-accelerated T2W imaging with breath hold (DL-BH) (1:05 [0:14 × 3 breath-holds]) in the sagittal plane. In 294 randomly ordered sagittal T2W images, two radiologists independently assessed image quality (sharpness, subjective noise, artifacts, and overall image quality), made a diagnosis for uterine leiomyomas, and scored diagnostic confidence. For the uterus and piriformis muscle, quantitative imaging analysis was also performed. Wilcoxon signed rank tests were used to compare the two sets of T2W images. RESULTS: In the qualitative analysis, DL-BH showed similar or significantly higher scores for all features than conventional T2W imaging (P <0.05). In the quantitative analysis, the noise in the uterus was lower in DL-BH, but the noise in the muscle was lower in conventional T2W imaging. In the uterus and muscle, the signal-to-noise ratio was significantly lower in DL-BH than in conventional T2W imaging (P <0.001). The diagnostic performance of the two sets of T2W images was not different for uterine leiomyoma. CONCLUSIONS: DL-accelerated sagittal T2W imaging obtained with three breath-holds demonstrated superior or comparable image quality to conventional T2W imaging with no significant difference in diagnostic performance for uterine leiomyomas.

Value of radiomics-based automatic grading of muscle edema in polymyositis/dermatomyositis based on MRI fat-suppressed T2-weighted images.

BACKGROUND: The precise and objective assessment of thigh muscle edema is pivotal in diagnosing and monitoring the treatment of dermatomyositis (DM) and polymyositis (PM). PURPOSE: Radiomic features are extracted from fat-suppressed (FS) T2-weighted (T2W) magnetic resonance imaging (MRI) of thigh muscles to enable automatic grading of muscle edema in cases of polymyositis and dermatomyositis. MATERIAL AND METHODS: A total of 241 MR images were analyzed and classified into five levels using the Stramare criteria. The correlation between muscle edema grading and T2-mapping values was assessed using Spearman's correlation. The dataset was divided into a 7:3 ratio of training (168 samples) and testing (73 samples). Thigh muscle boundaries in FS T2W images were manually delineated with 3D-Slicer. Radiomics features were extracted using Python 3.7, applying Z-score normalization, Pearson correlation analysis, and recursive feature elimination for reduction. A Naive Bayes classifier was trained, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curves and comparing sensitivity and specificity with senior doctors. RESULTS: A total of 1198 radiomics parameters were extracted and reduced to 18 features for Naive Bayes modeling. In the testing set, the model achieved an area under the ROC curve of 0.97, sensitivity of 0.85, specificity of 0.98, and accuracy of 0.91. The Naive Bayes classifier demonstrated grading performance comparable to senior doctors. A significant correlation (r = 0.82, P <0.05) was observed between Stramare edema grading and T2-mapping values. CONCLUSION: The Naive Bayes model, utilizing radiomics features extracted from thigh FS T2W images, accurately assesses the severity of muscle edema in cases of PM/DM.

MRI study on the influence of lumbosacral vertebral body and disc factors on lumbar lordosis in children.

PURPOSE: To evaluate the influence of vertebral and disc wedging on the contribution of lumbar lordosis and the change of disc thickness before and after walking based on MRI. METHODS: Cross-sectional study. A total of 96 normally developing children, aged 5.7 ± 3.0 years old, 55 boys and 41 girls. They were divided into 3 groups: Pre-walking group, Walking group, and Post-walking group. PARAMETERS: lumbar lordosis Angle (LLA), the sum of the lumbar disc wedge Angle (∑D), the sum of the lumbar vertebral body wedge Angle (∑B), disc height (DH). RESULTS: (1) LLA, ∑D, ∑B, and DHL1-S1 were 33.2 ± 8.7°, 14.1 ± 8.6°, 11.9 ± 8.6°, and 6.9 ± 1.2 mm, 7.6 ± 1.4 mm, 8.2 ± 1.6 mm, 8.9 ± 1.7 mm, 8.5 ± 1.8 mm. (2) The difference in LLA values between the Pre-walking and the Post-walking group was statistically significant. DH were significantly different among the three groups. (3) In the Post-walking group, LLA value of girls was significantly higher than that of boys, and DHL3 - 4 and DHL4 - 5 values of girls were significantly lower than that of boys. (4) Age had a low positive correlation with LLA and ∑D and a moderate to strong positive correlation with DH; LLA showed a moderate positive correlation with ∑D, and a low positive correlation with ∑B and DH. CONCLUSION: Age and walking activity are the influencing factors of lumbar lordosis and disc thickening. Walking activity can significantly increase lumbar lordosis, and age is the main factor promoting lumbar disc thickening. DHL4-5 was the thickest lumbar intervertebral disc with the fastest intergroup thickening. Disc wedging contributes more to lumbar lordosis than vertebral wedging.

Enhancement of the lymphatic system following intravenous administration of ferumoxytol.

BACKGROUND: Lymphatic imaging is becoming increasingly important in the management of patients with congenital heart disease. However, the influence of the intravenous contrast agent ferumoxytol on lymphatic imaging is not well understood. OBJECTIVE: To evaluate the impact of intravenous ferumoxytol on T1-weighted and T2-weighted lymphatic imaging in patients with congenital heart disease. MATERIALS AND METHODS: We included consecutive patients receiving ferumoxytol-enhanced 3D angiography for congenital heart disease evaluation. The visibility of the thoracic duct was reviewed on the T1-weighted 3D inversion recovery balanced-steady-state free precession (SSFP) with respiratory navigator gating sequence which is routinely used for angiography and the heavily T2-weighted turbo spin echo sequence which is employed for lymphatic imaging. Data on demographics and time interval between contrast administration and imaging were collected. Statistical analyses were performed using t-tests for continuous variables and chi-squared tests for categorical variables. RESULTS: One hundred nineteen consecutive patients with a mean age of 12.46 years±7.7 years were included. Of these, 45 cases underwent both T1-weighted and T2-weighted imaging; the other 74 underwent only T1-weighted imaging. Of the 45 patients, 20 had thoracic duct enhancement on T1-weighted imaging; among the 26 sedated, only 2 showed enhancement, while 18 of 19 non-sedated patients showed enhancement (P<0.001), indicating a strong association between sedation and reduced thoracic duct visibility. If T2-weighted imaging was performed after contrast administration, the thoracic duct was not visible on those images. For all 45 cases of visible thoracic duct in the entire cohort, the time from contrast administration to imaging ranged from 8 min up to 75 min. CONCLUSION: The enhancement of the thoracic lymphatic duct on T1-weighted imaging, coupled with degradation observed on T2-weighted imaging, suggests that intravenously administered ferumoxytol rapidly enters the lymphatic fluid. To prevent T2 shortening from degrading the imaging results, T2-weighted imaging for lymphatic evaluation should be performed prior to the administration of ferumoxytol. Sedation and, by inference, fasting may influence this property and warrant further investigation in future studies.

Expanding Ventricular Diverticulum Overlying the Cerebral Hemisphere through an Open-Lip Schizencephalic Cleft: A Report of Two Pediatric Cases.

INTRODUCTION: Open-lip-type schizencephaly is characterized by trans-cerebral clefts filled with cerebrospinal fluid (CSF) between the subarachnoid space at the hemisphere surface and the lateral ventricles. Disorders related to CSF retention, including hydrocephalus and arachnoid cysts, have reportedly been associated with open-lip schizencephaly and have induced intracranial hypertension in some cases. However, detailed neuroimaging and surgical treatment findings have rarely been described. CASE PRESENTATION: We report 2 cases of open-lip schizencephaly with an expanding CSF-filled cavity overlying the ipsilateral cerebral hemisphere that manifested as signs of intracranial hypertension. Detailed three-dimensional heavily T2-weighted imaging revealed thin borders between the CSF-filled cavity and the subarachnoid space, but no separating structures between the cavity and the lateral ventricle, suggesting that the cavity was directly connected to the lateral ventricle through the schizencephalic cleft but not to the subarachnoid space. Neuroendoscopic observation in case 1 confirmed this finding. Endoscopic fenestration of the cavity to the prepontine cistern was ineffective in case 1. Shunting between the lateral ventricle (case 1) or CSF-filled cavity (case 2) and the peritoneal cavity slightly decreased the size of the CSF-filled cavity. DISCUSSION: We speculate that the thin borders along the margin of the CSF-filled cavity are membranes that previously covered the schizencephalic cleft and are now pushed peripherally. In addition, we believe that the cavity is a ventricular diverticulum protruding through the cleft and that shunting operation is effective against such expanding cavity. Detailed magnetic resonance imaging can be useful for evaluating patients with schizencephaly associated with CSF retention disorders.

Prediction of microvascular invasion in hepatocellular carcinoma patients with MRI radiomics based on susceptibility weighted imaging and T2-weighted imaging.

BACKGROUND: The accurate identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance. PURPOSE: To develop a radiomics nomogram based on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) for predicting MVI in early-stage (Barcelona Clinic Liver Cancer stages 0 and A) HCC patients. MATERIALS AND METHODS: A prospective cohort of 189 participants with HCC was included for model training and testing, and an additional 34 participants were enrolled for external validation. ITK-SNAP was used to manually segment the tumour, and PyRadiomics was used to extract radiomic features from the SWI and T2W images. Variance filtering, student's t test, least absolute shrinkage and selection operator regression and random forest (RF) were applied to select meaningful features. Four machine learning classifiers, including K-nearest neighbour, RF, logistic regression and support vector machine-based models, were established. Independent clinical and radiological risk factors were also determined to establish a clinical model. The best radiomics and clinical models were further evaluated in the validation set. In addition, a nomogram was constructed from the radiomic model and independent clinical factors. Diagnostic efficacy was evaluated by receiver operating characteristic curve analysis with fivefold cross-validation. RESULTS: AFP levels greater than 400 ng/mL [odds ratio (OR) 2.50; 95% confidence interval (CI) 1.239-5.047], tumour diameter greater than 5 cm (OR 2.39; 95% CI 1.178-4.839), and absence of pseudocapsule (OR 2.053; 95% CI 1.007-4.202) were found to be independent risk factors for MVI. The areas under the curve (AUCs) of the best radiomic model were 1.000 and 0.882 in the training and testing cohorts, respectively, while those of the clinical model were 0.688 and 0.6691. In the validation set, the radiomic model achieved better diagnostic performance (AUC = 0.888) than the clinical model (AUC = 0.602). The combination of clinical factors and the radiomic model yielded a nomogram with the best diagnostic performance (AUC = 0.948). CONCLUSION: SWI and T2WI-derived radiomic features are valuable for noninvasively and accurately identifying MVI in early-stage HCC. Furthermore, the integration of radiomics and clinical factors yielded a predictive nomogram with satisfactory diagnostic performance and potential clinical benefits.

Cerebral microbleeds is a predictor of recurrent small vessel cerebrovascular disease: Evaluation based on the recurrent stroke pattern.

OBJECTIVES: An increased number of cerebral microbleeds (CMBs) is considered a predictive factor for recurrent small vessel cerebrovascular diseases, including lacunar infarction and non-lobar intracerebral hemorrhage (ICH). However, it is unclear which recurrent stroke pattern is mainly reflected in the number of CMBs. MATERIALS AND METHODS: This study enrolled 217 patients with their first stroke (148 deep lacunar infarctions and 69 non-lobar ICHs), between January 2009 and March 2015. The numbers of baseline and newly appearing CMBs in patients with recurrent stroke were compared with those in patients with non-recurrent stroke, and the dynamics of the number of CMBs was evaluated according to recurrent stroke patterns. RESULTS: Fifty-nine patients with recurrent stroke were included in this study. A larger number of baseline and newly appearing CMBs was significantly associated with recurrent stroke (p = 0.04, p < 0.001, respectively). Recurrent stroke patterns were divided into four types: deep lacunar infarction/deep lacunar infarction (37 patients), deep lacunar infarction/non-lobar ICH (eight patients), non-lobar ICH/deep lacunar infarction (eight patients), and non-lobar ICH/non-lobar ICH (six patients). The number of newly appearing CMBs was significantly higher in patients with deep lacunar infarction/non-lobar ICH than in those with other recurrent stroke patterns (p = 0.04). CONCLUSIONS: The number of CMBs is associated with recurrent stroke, including deep lacunar infarction and non-lobar ICH, and differs depending on the recurrent stroke patterns. The increase in the number of CMBs was strongly correlated with the deep lacunar infarction/non-lobar ICH recurrence pattern.

Brush Sign on pre-treatment imaging is associated with good functional outcome in stroke patients treated with mechanical thrombectomy: A prospective monocentric study.

BACKGROUND: The Brush Sign (BrS) is a radiological biomarker (MRI) showing signal decrease of subependymal and deep medullary veins on paramagnetic-sensitive magnetic resonance sequences. Previous studies have shown controversial results regarding the prognostic value of BrS. We aimed to assess whether BrS on T2*-weighted sequences could predict functional prognosis in patients treated with mechanical thrombectomy (MT). METHODS: We included all consecutive patients with large artery occlusion related stroke in anterior circulation treated with MT between February 2020 and August 2022 at Reims University Hospital. Multivariable logistic regression models were used to investigate factors associated with BrS and its impact on outcomes. RESULTS: Of the 327 included patients, 124 (37,9%) had a BrS on baseline MRI. Mean age was 72 ± 16 years and 184 (56,2 %) were female. In univariate analysis, BrS was associated with a younger age (67 vs 74; p<0.001), a higher NIHSS score (16(10-20) vs 13(8-19); p = 0.047) history of diabetes (15.3% vs 26.1 %; p = 0.022) and a shorter onset to MRI time (145.5 (111.3-188.5) vs 162 (126-220) p = 0.008). In multivariate analyses, patients with a BrS were younger (OR:0.970 (0.951 - 0.989)), tend to have a higher NIHSS score at baseline (OR:1.046 (1.000 - 1.094) and were less likely to have diabetes (OR: 0.433; 0.214-0.879). The presence of BrS was independently associated with functional independence (OR: 2.234(1.158-4,505) at 3 months but not with mortality nor with symptomatic intracerebral hemorrhage. CONCLUSION: BrS on pre-treatment imaging could be considered as a biomarker of physiological adaptation to cerebral ischemia, allowing prolonged viability of brain tissue and might participate in the therapeutic decision.

Effect of motion, cortical orientation and spatial resolution on quantitative imaging of cortical R2* and magnetic susceptibility at 0.3 mm in-plane resolution at 7 T.

MR images of the effective relaxation rate R2* and magnetic susceptibility χ derived from multi-echo T2*-weighted (T2*w) MRI can provide insight into iron and myelin distributions in the brain, with the potential of providing biomarkers for neurological disorders. Quantification of R2* and χ at submillimeter resolution in the cortex in vivo has been difficult because of challenges such as head motion, limited signal to noise ratio, long scan time, and motion related magnetic field fluctuations. This work aimed to improve the robustness for quantifying intracortical R2* and χ and analyze the effects from motion, spatial resolution, and cortical orientation. T2*w data was acquired with a spatial resolution of 0.3 × 0.3 × 0.4 mm3 at 7 T and downsampled to various lower resolutions. A combined correction for motion and B0 changes was deployed using volumetric navigators. Such correction improved the T2*w image quality rated by experienced image readers and test-retest reliability of R2* and χ quantification with reduced median inter-scan differences up to 10 s-1 and 5 ppb, respectively. R2* and χ near the line of Gennari, a cortical layer high in iron and myelin, were as much as 10 s-1 and 10 ppb higher than the region at adjacent cortical depth. In addition, a significant effect due to the cortical orientation relative to the static field (B0) was observed in χ with a peak-to-peak amplitude of about 17 ppb. In retrospectively downsampled data, the capability to distinguish different cortical depth regions based on R2* or χ contrast remained up to isotropic 0.5 mm resolution. This study highlights the unique characteristics of R2* and χ along the cortical depth at submillimeter resolution and the need for motion and B0 corrections for their robust quantification in vivo.

High spatial overlap but diverging age-related trajectories of cortical magnetic resonance imaging markers aiming to represent intracortical myelin and microstructure.

Statistical effects of cortical metrics derived from standard T1- and T2-weighted magnetic resonance imaging (MRI) images, such as gray-white matter contrast (GWC), boundary sharpness coefficient (BSC), T1-weighted/T2-weighted ratio (T1w/T2w), and cortical thickness (CT), are often interpreted as representing or being influenced by intracortical myelin content with little empirical evidence to justify these interpretations. We first examined spatial correspondence with more biologically specific microstructural measures, and second compared between-marker age-related trends with the underlying hypothesis that different measures primarily driven by similar changes in myelo- and microstructural underpinnings should be highly related. Cortical MRI markers were derived from MRI images of 127 healthy subjects, aged 18-81, using cortical surfaces that were generated with the CIVET 2.1.0 pipeline. Their gross spatial distributions were compared with gene expression-derived cell-type densities, histology-derived cytoarchitecture, and quantitative R1 maps acquired on a subset of participants. We then compared between-marker age-related trends in their shape, direction, and spatial distribution of the linear age effect. The gross anatomical distributions of cortical MRI markers were, in general, more related to myelin and glial cells than neuronal indicators. Comparing MRI markers, our results revealed generally high overlap in spatial distribution (i.e., group means), but mostly divergent age trajectories in the shape, direction, and spatial distribution of the linear age effect. We conclude that the microstructural properties at the source of spatial distributions of MRI cortical markers can be different from microstructural changes that affect these markers in aging.

Intracellular Construction of Cathepsin B-Guided Gadolinium Nanoparticles for Enhanced T2 -Weighted MR Tumor Imaging.

Magnetic resonance imaging (MRI) is a superior and noninvasive imaging technique with unlimited tissue penetration depth and superb spatiotemporal resolution, however, using intracellular self-assembly of Gd-containing nanoparticles to enhance the T2 -weighted MR contrast of cancer cells in vivo for precise tumor MRI is rarely reported. The lysosomal cysteine protease cathepsin B (CTSB) is regarded as an attractive biomarker for the early diagnosis of cancers and metastasis. Herein, taking advantage of a biocompatible condensation reaction, a "smart" Gd-based CTSB-responsive small molecular contrast agent VC-Gd-CBT is developed, which can self-assemble into large intracellular Gd-containing nanoparticles by glutathione reduction and CTSB cleavage to enhance the T2 -weighted MR contrast of CTSB-overexpressing MDA-MB-231 cells at 9.4 T. In vivo T2 -weighted MRI studies using MDA-MB-231 murine xenografts show that the T2 -weighted MR contrast change of tumors in VC-Gd-CBT-injected mice is distinctly larger than the mice injected with the commercial agent gadopentetate dimeglumine, or co-injected with CTSB inhibitor and VC-Gd-CBT, indicating that the accumulation of self-assembled Gd-containing nanoparticles at tumor sites effectively enhances the T2 -weighted MR tumor imaging. Hence, this CTSB-targeted small molecule VC-Gd-CBT has the potential to be employed as a T2 contrast agent for the clinical diagnosis of cancers at an early stage.

Heart failure-induced brain myelin changes and differences between sexes.

Heart failure (HF) leads to brain injury in autonomic, respiratory, mood, and cognitive control sites, revealed as tissue volume loss, altered metabolites, and impaired diffusion tissue properties. The extent of myelin changes in HF and variations within sexes are unclear. Our aim was to examine regional brain subcortical and white matter myelin integrity in HF patients over control subjects, as well as differences between sexes using T1- and T2-weighted clinical images. We acquired T1- and T2-weighted images from 63 HF patients and 129 controls using a 3.0-Tesla MRI scanner. Using T1- and T2-weighted images, ratio maps were computed, normalized to a common space, smoothed, and compared between groups (ANCOVA; covariates: age and sex; SPM12, false discovery rate, p < .010), as well as between male versus female HF (ANCOVA; covariate: age; SPM12, uncorrected p < .005). Multiple brain areas in HF showed decreased myelin integrity, including the amygdala, hippocampus, cingulate, insula, cerebellum, prefrontal cortices, and multiple white matter areas, compared to controls. Female HF patients showed more brain injuries in the parietal, prefrontal and frontal, hippocampus, amygdala, pons, cerebellar, insula, and corpus callosum compared to male HF patients. HF subjects showed compromised subcortical and white matter myelin integrity, especially in sites regulating autonomic, respiratory, mood, and cognition, with more changes in females over males. These findings provide a structural basis for the enhanced symptoms identified in female over male HF patients with similar disease severity.

Volumetric T2 -weighted spin echo imaging with improved SNR using localized quadratic encoding and a spiral readout trajectory.

PURPOSE: To demonstrate T2 -weighted (single-echo) spin-echo (SE) imaging with near-optimal acquisition efficiency by applying SNR-efficient RF slice encoding and spiral readout. METHODS: A quadratic-phase (frequency swept) excitation RF pulse replaced the conventional excitation in T2 -weighted SE sequence to excite a thick slab that is internally spatially encoded by a variable phase along the slice direction. Highly overlapping slabs centered at every desired slice location were acquired in multiple passes, such that the entire imaging volume was excited by contiguous slabs in any given pass. Following 90° excitation, each slab was refocused with a conventional 180° RF to produce a SE signal, followed by a spiral in-out readout. A noise-insensitive reconstruction removed the quadratic phase in the spatial frequency domain, yielding desired slice resolution and improved SNR. RESULTS: Increasing the RF frequency sweep (hence, excitation width) allowed more frequent encoding of each slice over the multiple passes, improving final image SNR, until crosstalk ensued at excessive slab widths compared to their center-to-center spacing. With an optimized slab width, the proposed technique used all passes to acquire every prescribed slice, with substantially improved SNR over conventional SE or 2D-turbo-spin-echo (TSE) scans. Quantitative SNR measurements indicated similar SNR as 3D-TSE, but radiologist scoring favored 3D-TSE, mainly because of spiral-related artifacts and possibly because of regularized reconstructions in 3D-TSE. CONCLUSION: Using SNR-efficient slice excitation scheme and spiral readout helped eliminate SNR and temporal inefficiencies in conventional T2 -weighted imaging, yielding SNR independent of TR or number of passes.

Model-based image reconstruction with wavelet sparsity regularization for through-plane resolution restoration in T2 -weighted spin-echo prostate MRI.

PURPOSE: The purpose is to develop a model-based image-reconstruction method using wavelet sparsity regularization for maintaining restoration of through-plane resolution but with improved retention of SNR versus linear reconstruction using Tikhonov (TK) regularization in high through-plane resolution (1 mm) T2 -weighted spin-echo (T2SE) images of the prostate. METHODS: A wavelet sparsity (WS)-regularized image reconstruction was developed that takes as input a set of ≈80 overlapped 3-mm-thick slices acquired using a T2SE multislice scan and typically 30 coil elements. After testing in contrast and resolution phantoms and calibration in 6 subjects, the WS reconstruction was evaluated in 16 consecutive prostate T2SE MRI exams. Results reconstructed with nominal 1-mm thickness were compared with those from the TK reconstruction with the same raw data. Results were evaluated radiologically. The ratio of magnitude of prostate signal to periprostatic muscle signal was used to assess the presence of noise reduction. Technical performance was also compared with a commercial 3D-T2SE sequence. RESULTS: The new WS reconstruction was assessed as superior statistically to TK for overall SNR, contrast, and multiple evaluation criteria related to sharpness while retaining the high (1 mm) through-plane resolution. Wavelet sparsity tended to provide improved overall diagnostic quality versus TK, but not significantly so. In all 16 studies, the prostate-to-muscle signal ratio increased. CONCLUSIONS: Model-based WS-regularized reconstruction consistently provides improved SNR in high (1 mm) through-plane resolution images of prostate T2SE MRI versus linear reconstruction using TK regularization.