Conventional MRI Research Articles

Roadmap on nanoscale magnetic resonance imaging.

The field of nanoscale magnetic resonance imaging (NanoMRI) was started 30 years ago. It was motivated by the desire to image single molecules and molecular assemblies, such as proteins and virus particles, with near-atomic spatial resolution and on a length scale of 100 nm. Over the years, the NanoMRI field has also expanded to include the goal of useful high-resolution nuclear magnetic resonance (NMR) spectroscopy of molecules under ambient conditions, including samples up to the micron-scale. The realization of these goals requires the development of spin detection techniques that are many orders of magnitude more sensitive than conventional NMR and MRI, capable of detecting and controlling nanoscale ensembles of spins. Over the years, a number of different technical approaches to NanoMRI have emerged, each possessing a distinct set of capabilities for basic and applied areas of science. The goal of this roadmap article is to report the current state of the art in NanoMRI technologies, outline the areas where they are poised to have impact, identify the challenges that lie ahead, and propose methods to meet these challenges. This roadmap also shows how developments in NanoMRI techniques can lead to breakthroughs in emerging quantum science and technology applications.&#xD.

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study

BackgroundAccurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade.MethodsA total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice.ResultsThe intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort.ConclusionThis study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

Longitudinal Evaluation of DCE-MRI as an Early Indicator of Progression after Standard Therapy in Glioblastoma

Background and Purpose: Distinguishing treatment-induced imaging changes from progressive disease has important implications for avoiding inappropriate discontinuation of a treatment. Our goal in this study is to evaluate the utility of dynamic contrast-enhanced (DCE) perfusion MRI as a biomarker for the early detection of progression. We hypothesize that DCE-MRI may have the potential as an early predictor for the progression of disease in GBM patients when compared to the current standard of conventional MRI. Methods: We identified 26 patients from 2011 to 2023 with newly diagnosed primary glioblastoma by histopathology and gross or subtotal resection of the tumor. Then, we classified them into two groups: patients with progression of disease (POD) confirmed by pathology or change in chemotherapy and patients with stable disease without evidence of progression or need for therapy change. Finally, at least three DCE-MRI scans were performed prior to POD for the progression cohort, and three consecutive DCE-MRI scans were performed for those with stable disease. The volume of interest (VOI) was delineated by a neuroradiologist to measure the maximum values for Ktrans and plasma volume (Vp). A Friedman test was conducted to evaluate the statistical significance of the parameter changes between scans. Results: The mean interval between subsequent scans was 57.94 days, with POD-1 representing the first scan prior to POD and POD-3 representing the third scan. The normalized maximum Vp values for POD-3, POD-2, and POD-1 are 1.40, 1.86, and 3.24, respectively (FS = 18.00, p = 0.0001). It demonstrates that Vp max values are progressively increasing in the three scans prior to POD when measured by routine MRI scans. The normalized maximum Ktrans values for POD-1, POD-2, and POD-3 are 0.51, 0.09, and 0.51, respectively (FS = 1.13, p < 0.57). Conclusions: Our analysis of the longitudinal scans leading up to POD significantly correlated with increasing plasma volume (Vp). A longitudinal study for tumor perfusion change demonstrated that DCE perfusion could be utilized as an early predictor of tumor progression.

Incremental Prognostic Value of Left Atrial Strain in Patients With Suspected Myocarditis and Preserved Left Ventricular Ejection Fraction.

Analysis of left atrial (LA) strain and left atrioventricular coupling index (LACI) have prognostic value in cardiovascular diseases. However, the prognostic value of LA strain and LACI in patients with suspected myocarditis and preserved left ventricular ejection fraction (LVEF) is unclear. To investigate the prognostic value of LA strain and LACI in patients with suspected myocarditis and preserved LVEF in comparison with conventional MRI outcome predictors. Retrospective. One hundred sixty-five patients with clinically suspected myocarditis and preserved LVEF with available follow-up data. Steady-state free precession cine and phase-sensitive inversion recovery segmented gradient echo late gadolinium enhancement sequences at 3.0 T. Left ventricular (LV) and LA strain were evaluated using feature tracking. LACI was calculated as the ratio of LA and LV volumes at LV end-diastole. Patients were followed-up with the primary endpoint being major adverse cardiovascular events (MACE). Independent-samples t-test and Mann-Whitney U test to compare patients with and without MACE, receiver operating characteristic (ROC) curve analysis to define high/low risk groups, Kaplan-Meier survival analysis and Cox proportional hazards regression to assess prognosis. A P value of <0.05 was considered statistically significant. The associations of LV strain parameters (including global radial, circumferential, and longitudinal strain) and LACI with MACE were not significant (P = 0.511, 0.108, 0.148, and 0.847, respectively). An optimal LA conduit strain (Ԑe) cutoff value of 10.4% was identified to best classify patients into low- and high-risk groups. Only Ԑe was significantly associated with MACE in both univariable (hazards ratio [HR] 0.936, 95% confidence interval [CI] 0.884-0.991) and multivariable Cox survival analyses (HR 0.937, 95% CI 0.884-0.994). LA conduit strain has prognostic value in patients with suspected myocarditis and preserved LVEF, incremental to conventional MRI outcome predictors, whereas LACI was not associated with MACE occurrence. 3 TECHNICAL EFFICACY: Stage 2.

3D inversion recovery ultrashort echo time MRI can detect demyelination in cuprizone-treated mice

PurposeTo test the ability of inversion-recovery ultrashort echo time (IR-UTE) MRI to directly detect demyelination in mice using a standard cuprizone mouse model.MethodsNon-aqueous myelin protons have ultrashort T2s and are “invisible” with conventional MRI sequences but can be detected with UTE sequences. The IR-UTE sequence uses an adiabatic inversion-recovery preparation to suppress the long T2 water signal so that the remaining signal is from the ultrashort T2 myelin component. In this study, eight 8-week-old C57BL/6 mice were fed cuprizone (n = 4) or control chow (n = 4) for 5 weeks and then imaged by 3D IR-UTE MRI. The differences in IR-UTE signal were compared in the major white matter tracts in the brain and correlated with the Luxol Fast Blue histochemical marker of myelin.ResultsIR-UTE signal decreased in cuprizone-treated mice in white matter known to be sensitive to demyelination in this model, such as the corpus callosum, but not in white matter known to be resistant to demyelination, such as the internal capsule. These findings correlated with histochemical staining of myelin content.Conclusions3D IR-UTE MRI was sensitive to cuprizone-induced demyelination in the mouse brain, and is a promising noninvasive method for measuring brain myelin content.

AI-Accelerated prostate MRI: a systematic review.

Prostate cancer ranks among the most prevalent cancers affecting men globally. While conventional MRI serves as a diagnostic tool, its extended acquisition time, associated costs, and strain on healthcare systems, underscore the necessity for more efficient methods. The emergence of AI-acceleration in prostate MRI offers promise to mitigate these challenges. A systematic review of studies looking at AI-accelerated prostate MRI was conducted, with a focus on acquisition time along with various qualitative and quantitative measurements. Two primary findings were observed. Firstly, all studies indicated that AI-acceleration in MRI achieved notable reductions in acquisition times without compromising image quality. This efficiency offers potential clinical advantages, including reduced scan durations, improved scheduling, diminished patient discomfort, and economic benefits. Secondly, AI demonstrated a beneficial effect in reducing or maintaining artifact levels in T2-weighted images despite this accelerated acquisition time. Inconsistent results were found in all other domains, which were likely influenced by factors such as heterogeneity in methodologies, variability in AI models, and diverse radiologist profiles. These variances underscore the need for larger, more robust studies, standardisation, and diverse training datasets for AI models. The integration of AI-acceleration in prostate MRI thus far shows some promising results for efficient and enhanced scanning. These advancements may fill current gaps in early detection and prognosis. However, careful navigation and collaborative efforts are essential to overcome challenges and maximize the potential of this innovative and evolving field. This paper reveals overall significant reductions in acquisition time without compromised image quality in AI-accelerated prostate MRI, highlighting potential clinical and diagnostic advantages.

Automatic geometry-based estimation of the locus coeruleus region on T1-weighted magnetic resonance images

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. For out-of-sample segmentation, we compare the results with atlas-based segmentation, as well as test the hypothesis that using the phase image as input can improve the robustness. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report provides an evaluation of computational methods estimating neural structure.

Imaging in temporomandibular joint disorders

The temporomandibular joint is a unique and complex joint. Various imaging techniques have been developed to properly visualize this complex joint, such as conventional radiology, orthopantomography, CBCT and MRI. Imaging can contribute to the differential diagnosis of temporomandibular joint disorders. Common joint disorders are arthritis and internal derangement. Osseous changes of the temporomandibular joint can be clearly visualized with CBCT. MRI is superior for imaging the internal anatomy of the temporomandibular joint and is preferred in the context of internal derangement.

Dynamic Regularized Adaptive Cluster Optimization (DRACO) for Quantitative Cardiac Cine MRI in Complex Arrhythmias.

Irregular cardiac motion can render conventional segmented cine MRI nondiagnostic. Clustering has been proposed for cardiac motion binning and may be optimized for complex arrhythmias. To develop an adaptive cluster optimization method for irregular cardiac motion, and to generate the corresponding time-resolved cine images. Prospective. Thirteen with atrial fibrillation, four with premature ventricular contractions, and one patient in sinus rhythm. Free-running balanced steady state free precession (bSSFP) with sorted golden-step, reference real-time sequence. Each subject underwent both the sorted golden-step bSSFP and the reference Cartesian real-time imaging. Golden-step bSSFP images were reconstructed using the dynamic regularized adaptive cluster optimization (DRACO) method and k-means clustering. Image quality (4-point Likert scale), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge sharpness, and ventricular function were assessed. Paired t-tests, Friedman test, regression analysis, Fleiss' Kappa, Bland-Altman analysis. Significance level P < 0.05. The DRACO method had the highest percent of images with scores ≥3 (96% for diastolic frame, 93% for systolic frame, and 93% for multiphase cine) and the percentages were significantly higher compared with both the k-means and real-time methods. Image quality scores, SNR, and CNR were significantly different between DRACO vs. k-means and between DRACO vs. real-time. Cardiac function analysis showed no significant differences between DRACO vs. the reference real-time. DRACO with time-resolved reconstruction generated high quality images and has early promise for quantitative cine cardiac MRI in patients with complex arrhythmias including atrial fibrillation. Stage 2.

Vulnerability of Thalamic Nuclei at CSF Interface During the Entire Course of Multiple Sclerosis.

Thalamic atrophy can be used as a proxy for neurodegeneration in multiple sclerosis (MS). Some data point toward thalamic nuclei that could be affected more than others. However, the dynamic of their changes during MS evolution and the mechanisms driving their differential alterations are still uncertain. We paired a large cohort of 1,123 patients with MS with the same number of healthy controls, all scanned with conventional 3D-T1 MRI. To highlight the main atrophic regions at the thalamic nuclei level, we validated a segmentation strategy consisting of deep learning-based synthesis of sequences, which were used for automatic multiatlas segmentation. Then, through a lifespan-based approach, we could model the dynamics of the 4 main thalamic nuclei groups. All analyses converged toward a higher rate of atrophy for the posterior and medial groups compared with the anterior and lateral groups. We also demonstrated that focal MS white matter lesions were associated with atrophy of groups of nuclei when specifically located within the associated thalamocortical projections. The volumes of the most affected posterior group, but also of the anterior group, were better associated with clinical disability than the volume of the whole thalamus. These findings point toward the thalamic nuclei adjacent to the third ventricle as more susceptible to neurodegeneration during the entire course of MS through potentiation of disconnection effects by regional factors. Because this information can be obtained even from standard T1-weighted MRI, this paves the way toward such an approach for future monitoring of patients with MS.

Role of Diffusion Weighted Imaging in Localization of Non-palpable Undescended Testes Taking Laparoscopic Finding as Gold Standard

Objective: To ascertain the accuracy of diffusion-weighted imaging (DWI) in the localization of undescended testes. Material &amp; Methods: This prospective cross-sectional study was conducted in the radiology department of a tertiary care hospital from 01-06-2022 to 15-12-2023. Total of 150 patients were included in the study. Two experienced radiologists independently examined, one observing conventional MRI alone and the other observing DWI-MRI. Following that, all of the patients underwent laparoscopic examination. The data was entered into a proforma. The findings of conventional MRI alone and DWI-MRI were correlated with laparoscopic findings. A 2 x 2 table was used to calculate the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of DWI in comparison to laparoscopy. Results. The average age was 10.37 +/- 5.663. Conventional MRI imaging localized 50.6 % of the cases in our study, 55% of cases via DWI-MRI and 60% by laparoscopy. Sensitivity, specificity, PPV, NPV, and accuracy of conventional MRI were 72 %, 83 %, 86 %, 66 % and 76 % respectively, while for DWI-MRI was 82 %, 86 %, 93 %, 89 %, and 78 % respectively. Conclusion: The detection of undescended testes is improved by DWI-MRI. DWI-MRI is a recommended imaging tool for improving MRI's preoperative diagnostic accuracy in localization and identification of nonpalpable undescended testes.

Diagnostic utility of 3D MRI sequences in the assessment of central, recess and foraminal stenoses of the spine: a systematic review.

To perform a systematic literature review on the diagnostic utility of 3D MRI sequences in the assessment of central canal, recess and foraminal stenosis in the spine. The databases PubMed, MEDLINE (via OVID) and The Cochrane Central Register of Controlled Trials, were searched for studies that investigated the diagnostic use of 3D MRI to evaluate stenoses in various parts of the spine in humans. Three reviewers examined the literature and conducted systematic review according to PRISMA 2020 guidelines. Thirty studies were retrieved from 2 595 publications for this systematic review. The overall diagnostic performance of 3D MRI outperformed the conventional 2D MRI with reported sensitivities ranging from 79 to 100% and specificities ranging from 86 to 100% regarding the evaluation of central, recess and foraminal stenoses. In general, high level of agreement (both intra- and interrater) regarding visibility and pathology on 3D sequences was reported. Studies show that well-optimized 3D sequences allow the use of higher spatial resolution, similar scan time and increased SNR and CNR when compared to corresponding 2D sequences. However, the benefit of 3D sequences is in the additional information provided by them and in the possibility to save total protocol scan times. The literature on the spine 3D MRI assessment of stenoses is heterogeneous with varying MRI protocols and diagnostic results. However, the 3D sequences offer similar or superior detection of stenoses with high reliability. Especially, the advantage of 3D MRI seems to be the better evaluation of recess stenoses.

Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: A multicenter study

BackgroundWithin the domain of multiple sclerosis (MS), the precise discrimination between active and inactive lesions bears immense significance. Active lesions are enhanced on T1-weighted MRI images after administration of gadolinium-based contrast agents, which brings about associated complexities. This study investigates the potential of deep learning to differentiate between active and inactive lesions in MS using non-contrast FLAIR-type MRI data, presenting a non-invasive alternative to conventional gadolinium-based MRI methods. MethodsThe dataset encompasses 9097 lesion images collected from 130 MS patients across four distinct imaging centers, with post-contrast T1-weighted images as the benchmark reference. We initially identified and labeled the lesions and carefully selected corresponding regions of interest (ROIs). These ROIs were employed as inputs for a convolutional neural network (CNN) to predict lesion status. Also, transfer learning was utilized, incorporating 12 pre-trained CNN models. Subsequently, an ensemble technique was applied to 3 of best models, followed by a systematic comparison of the results. ResultsThrough a 5-fold cross-validation, our custom designed network exhibited an average accuracy of 85 %, a sensitivity of 95 %, a specificity of 75 %, and an AUC value of 0.90. Among the pre-trained models, ResNet50 emerged as the most effective, achieving a specificity of 58 %, an accuracy of 75 %, a sensitivity of 91 %, and an AUC value of 0.81. Our comprehensive evaluations encompassed the receiver operating characteristic curve, precision-recall curve, and confusion matrix analyses. ConclusionThe findings underscore the efficacy of the proposed CNN, trained on FLAIR MRI data ROIs, in accurately discerning active and inactive lesions without reliance on contrast agents. Our multicenter study of 130 patients with diverse imaging devices outperforms the other single-center studies, achieving superior sensitivity and specificity. Unlike studies using multiple modalities, our exclusive use of FLAIR images streamlines the process, and our streamlined approach, excluding conventional pre-processing, demonstrates efficiency. The external validation conducted on diverse datasets, coupled with the analysis of dilated masks, underscores the adaptability and efficacy of our custom CNN model in discerning between active and inactive lesions.

The role of [18F]FDOPA PET as an adjunct to conventional MRI in the diagnosis of aggressive glial lesions.

Amino acid PET is recommended for the initial diagnosis of brain lesions, but its value for identifying aggressive lesions remains to be established. The current study therefore evaluates the added-value of dynamic [18F]FDOPA PET as an adjunct to conventional MRI for determining the aggressiveness of presumed glial lesions at diagnosis. Consecutive patients, with a minimal 1 year-follow-up, underwent contrast-enhanced MRI (CE MRI) and dynamic [18F]FDOPA PET to characterize their suspected glial lesion. Lesions were classified semi-automatically by their CE MRI (MRI-/+), and PET parameters (static tumor-to-background ratio, TBR; dynamic time-to-peak ratio, TTPratio). Diagnostic accuracies of MRI and PET parameters for the differentiation of tumor aggressiveness were evaluated by chi-square test or receiver operating characteristic analyses. Aggressive lesions were either defined as lesions with dismal molecular characteristics based on the WHO 2021 classification of brain tumors or with compatible clinico-radiological profiles. Time-to-treatment failure (TTF) and overall survival (OS) were evaluated. Of the 109 patients included, 46 had aggressive lesions (45 confirmed by histo-molecular analyses). CE MRI identified aggressive lesions with an accuracy of 73%. TBRmax (threshold of 3.2), and TTPratio (threshold of 5.4min) respectively identified aggressive lesions with an accuracy of 83% and 76% and were independent of CE MRI and clinical factors in the multivariate analysis. Among the MRI-lesions, 11/56 (20%) were aggressive and respectively 55% and 50% of these aggressive lesions showed high TBRmax and short TTPratio in PET. High TBRmax and short TTPratio in PET were significantly associated to poorer survivals (p ≤ 0.009). Dynamic [18F]FDOPA PET provides a similar diagnostic accuracy as contrast enhancement in MRI to identify the aggressiveness of suspected glial lesions at diagnosis. Both methods, however, are complementary and [18F]FDOPA PET may be a useful additional tool in equivocal cases.

Parameters That Can Be Used to Quantify Reduction Accuracy in Talar Neck Fractures and Malunions: A PRISMA-Compliant Scoping Review and Meta-Analysis.

Understanding the three-dimensional anatomy of the talar neck is essential in assessing the accuracy of reduction in talar neck fractures as well as for planning surgical correction for talar malunions. However, the geometrical parameters that describe this anatomy are sparsely reported in the orthopedics literature. We aimed to identify from the existing literature, geometrical parameters that describe the anatomy of the talar neck, determine how these are measured, and their normative values. A scoping literature review was conducted in accordance with thePreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)extension for scoping reviews (PRISMA-ScR) guidelines. The primary searches were conducted on the PubMed, Embase, and Scopus databases. Any original research study looking at the human talus neck geometry was included. Parameters that described the anatomy of the talar neck were identified, andpooled estimates were determined by the random-effects meta-analysis model. Heterogeneity was assessed by the I2 test and leave-one-out meta-analysis. Subgroup analysis was done to compare the values of parameters between the Asian and Non-Asian populations. The risk of bias was assessed by the National Institutes of Health (NIH) Case Series Tool. The combined searches yielded 6326 results, of which 21 studies were included in the review and 15 in six different sets of metanalysis. The majority of the studies (n=19, 90.5%) evaluated adult tali, and only two (9.5%) evaluated pediatric tali.In most of the studies (n=13, 61.9%), talus neck geometry was evaluated on dry bones or anatomical specimens; evaluation by imaging techniques (radiographs, CT, MRI, and radiostereometric analysis) was used in eight studies, (39.1%). A total of eight different geometrical parameters (neck length, height, width, declination angle, inclination angle, torsion angle, circumference, and cross-sectional area) were identified. Except for talar torsion, variability was noted in methods of measurement of all other parameters. Subgroup analysis revealed that Asians had a higher neck height as compared to non-Asians; other parameters were not significantly different. Although the literature reports geometrical parameters to assess the talar geometry, the methods of measurement of these parameters are variable. Most of the available literature describes measurement techniques on cadaveric tali, and there is no literature on how these parameters should be measured on conventional CT or MRI slices. Further research needs to focus on the standardization of measurement techniques for these parameters on conventional CT and/or MRI scans.

Prognostic value of radiological T category using conventional MRI in patients with oral tongue cancer: comparison with pathological T category.

This study aimed to compare the radiological tumor (T)-category using multiparametric MRI with the pathological T category in patients with oral tongue squamous cell carcinoma (OTSCC) and to examine which is a better predictor of prognosis. This retrospective study included 110 consecutive patients with surgically resected primary OTSCC who underwent preoperative contrast-enhanced MRI. T categories determined by maximum diameter and depth of invasion were retrospectively assessed based on the pathological specimen and multiparametric MRI. The MRI assessment included the axial and coronal T1-weighted image (T1WI), axial T2-weighted image (T2WI), coronal fat-suppressed T2WI, and axial and coronal fat-suppressed contrast-enhanced T1WI (CET1WI). Axial and coronal CET1WI measurements were divided into two groups: measurements excluding peritumoral enhancement (MEP) and measurements including peritumoral enhancement. The prognostic values for recurrence and disease-specific survival after radiological and pathological T categorization of cases into T1/T2 and T3/T4 groups were compared. The T category of MEP on coronal CET1WI was the most relevant prognostic factor for recurrence [hazard ratio (HR) = 3.30, p = 0.001] and the HR was higher than the HR for pathological assessment (HR = 2.26, p = 0.026). The T category determined by MEP on coronal CET1WI was also the most relevant prognostic factor for disease-specific survival (HR = 3.12, p = 0.03), and the HR was higher than the HR for pathological assessment (HR = 2.02, p = 0.20). The T category determined by MEP on the coronal CET1WI was the best prognostic factor among all radiological and pathological T category measurements.

Alterations in white matter integrity in Egyptian youth with smartphone dependence: does DTI have a role?

BackgroundSmartphones provide various functions that facilitate our communication, organization, and entertainment in different situations. Diffusion tensor imaging (DTI) is a method measuring tissue microstructure as well as white matter integrity of the brain and detecting early changes. Several research studies recently aim to utilize conventional MRI for assessing brain structural alterations among smartphone users, but our study was aimed at identifying the DTI value while assessing white matter alterations in Egyptian youth with smartphone dependence.ResultsOur prospective case–control study involved fifty-three individuals with smart phone dependence (SPD group) as well as twenty-five volunteers who represented the control group. SPD individuals and controls were right-handed. The SPD group mean age exhibited 20.54 ± 1.56 years, while controls exhibited 26.8 ± 15.1 years. When utilizing smart phone addiction scale-short version, SPD group median total score exhibited 33. The diagnostic performance of fornix (fractional anisotropy) FA and external capsule fractional anisotropy (EC FA) regarding area under curve (AUC) exhibited significant increase as opposed to all other tested regions, with a sensitivity of 90.6% as well as a specificity of 96%. While regarding the mean diffusivity (MD), the greatest (AUC) was for EC (0.927, p < 0.001), in which the MD value = 0.825 was the cutoff value and able to diagnose the smart phone dependency with a sensitivity of 92.5% as well as a specificity of 76%.ConclusionsQuantitative DTI parameters (FA, MD) in different white matter regions can diagnose and detect white matter changes in excessive smartphone users even when conventional MRI data are normal. This study demonstrates the recent noninvasive MRI technique value while revealing covered brain white matter alterations in Egyptian youth due to smartphone overuse.

Predicting cytogenetic risk in multiple myeloma using conventional whole-body MRI, spinal dynamic contrast-enhanced MRI, and spinal diffusion-weighted imaging

ObjectivesCytogenetic abnormalities are predictors of poor prognosis in multiple myeloma (MM). This paper aims to build and validate a multiparametric conventional and functional whole-body MRI-based prediction model for cytogenetic risk classification in newly diagnosed MM.MethodsPatients with newly diagnosed MM who underwent multiparametric conventional whole-body MRI, spinal dynamic contrast-enhanced (DCE-)MRI, spinal diffusion-weighted MRI (DWI) and had genetic analysis were retrospectively included (2011–2020/Ghent University Hospital/Belgium). Patients were stratified into standard versus intermediate/high cytogenetic risk groups. After segmentation, 303 MRI features were extracted. Univariate and model-based methods were evaluated for feature and model selection. Testing was performed using receiver operating characteristic (ROC) and precision-recall curves. Models comparing the performance for genetic risk classification of the entire MRI protocol and of all MRI sequences separately were evaluated, including all features. Four final models, including only the top three most predictive features, were evaluated.ResultsThirty-one patients were enrolled (mean age 66 ± 7 years, 15 men, 13 intermediate-/high-risk genetics). None of the univariate models and none of the models with all features included achieved good performance. The best performing model with only the three most predictive features and including all MRI sequences reached a ROC-area-under-the-curve of 0.80 and precision-recall-area-under-the-curve of 0.79. The highest statistical performance was reached when all three MRI sequences were combined (conventional whole-body MRI + DCE-MRI + DWI). Conventional MRI always outperformed the other sequences. DCE-MRI always outperformed DWI, except for specificity.ConclusionsA multiparametric MRI-based model has a better performance in the noninvasive prediction of high-risk cytogenetics in newly diagnosed MM than conventional MRI alone.Critical relevance statementAn elaborate multiparametric MRI-based model performs better than conventional MRI alone for the noninvasive prediction of high-risk cytogenetics in newly diagnosed multiple myeloma; this opens opportunities to assess genetic heterogeneity thus overcoming sampling bias.Key points• Standard genetic techniques in multiple myeloma patients suffer from sampling bias due to tumoral heterogeneity.• Multiparametric MRI noninvasively predicts genetic risk in multiple myeloma.• Combined conventional anatomical MRI, DCE-MRI, and DWI had the highest statistical performance to predict genetic risk.• Conventional MRI alone always outperformed DCE-MRI and DWI separately to predict genetic risk. DCE-MRI alone always outperformed DWI separately, except for the parameter specificity to predict genetic risk.• This multiparametric MRI-based genetic risk prediction model opens opportunities to noninvasively assess genetic heterogeneity thereby overcoming sampling bias in predicting genetic risk in multiple myeloma.Graphical

Advances in imaging modalities for spinal tumors

Abstract The spinal cord occupies a narrow region, which is tightly surrounded by vertebrae as well as ligaments, and spinal tumors can damage this structure and deprive patients of their ability to independently perform activities of daily living. Hence, imaging is vital for the prompt detection and accurate diagnosis of spinal tumors, as well as determining the optimal treatment and follow up plan. However, many clinicians might not be familiar with the imaging characteristics of spinal tumors due to their rarity. In addition, spinal surgeons might not fully utilize imaging for the surgical planning and management of spinal tumors because of the complex heterogeneity of these lesions. In the present review, we focus on conventional and advanced spinal tumor imaging techniques. These imaging modalities include CT, PET, digital subtraction angiography, conventional and microstructural MRI and high-resolution ultrasound. We discuss advantages and disadvantages of conventional and emerging imaging modalities, followed by an examination of cutting-edge medical technology to complement current needs in the field of spinal tumors. Moreover, machine learning and artificial intelligence are anticipated to impact the application of spinal imaging techniques. Through this review, we discuss the importance of conventional and advanced spinal tumor imaging, and the opportunity to combine advanced technologies with conventional modalities to better manage patients with these lesions.

Leverage of applying diffusion tensor imaging (DTI) indices in assessment of cervical spondylotic myelopathy

BackgroundCervical spondylotic myelopathy (CSM) is the most prevalent form of dysfunction in the cervical cord. For best results, CSM must be identified and treated quickly, before spinal cord injury develops. We aimed at determining the diagnostic value of quantitative and qualitative diffusion tensor imaging (DTI) indices in the assessment of CSM. Thirty patients were included in this prospective study with clinically suspected CSM of both sexes. This study aimed at determining the diagnostic value of quantitative and qualitative DTI indices in early assessment of CSM and subsequently early and proper management decision rendering better clinical outcome.ResultsThis prospective study included 30 patients: with clinically suspected CSM with a mean age of 51.88 ± 10.28 years. Patients with CSM were graded to 3 grades, mild (No. = 17), moderate (No. = 13) and severe (No. = 0) according to the modified Japanese orthopedic association (mJOA) grading system. Correlation test was performed between mJOA grades of severity with fractional anisotropy (FA), apparent diffusion coefficient (ADC) and T2 cord signal. We found a negative correlation between ADC and FA with Spearman’s rho value of − 0.612 and “P value 0.000” (P value < 0.05), a positive correlation between FA with mJOA clinical score with Spearman’s rho value of − 0.504 & “P value 0.036” (P value < 0.05) and a negative correlation between ADC and mJOA clinical score with Spearman’s rho value of 0.385 and P value 0.005 (P value < 0.05), and no significant correlation was found between mJOA clinical score and T2 hyperintense signal with Spearman’s rho value of − 0.304 and “P value 0.102” (P value < 0.05). Qualitative maps grading by 3D tractography images were done, and 18 patients in the study (60%) showed homogenous intact fiber tracts (grade I), 9 patients (30%) showed reduction or alteration of anisotropy or mixed colors intensity (grade II), and 3 patients (10%) showed fiber tract disruption or displaced cord (grade III). Three DTI parameters (other than FA and ADC) were measured, and two of them show significant difference between their measures in the stenotic and non-stenotic portions of the spinal cord—RA (P value = 0.00) and RD (P value = 0.00).ConclusionsWe concluded that DTI is a crucial tool for early diagnosis and grading of CSM (cervical spondylosis myelopathy)—quantitatively and qualitatively—hence, it should be routinely integrated with conventional cervical spine MRI in case of clinically or radiologically suspected cervical cord compression, as the FA parameter together with the clinical assessment formulates the management plan decision for the CSM whether surgical or non-surgical and depicts the need for early surgical decision rendering better clinical outcome compared to that based on T2 hyperintense cord signal.