A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla.

Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.

Psychophysical Evaluation of Visual vs. Computer-Aided Detection of Brain Lesions on Magnetic Resonance Images.

BACKGROUND: Magnetic resonance imaging (MRI) diagnosis is usually performed by analyzing contrast-weighted images, where pathology is detected once it reached a certain visual threshold. Computer-aided diagnosis (CAD) has been proposed as a way for achieving higher sensitivity to early pathology. PURPOSE: To compare conventional (i.e., visual) MRI assessment of artificially generated multiple sclerosis (MS) lesions in the brain's white matter to CAD based on a deep neural network. STUDY TYPE: Prospective. POPULATION: A total of 25 neuroradiologists (15 males, age 39 ± 9, 9 ± 9.8 years of experience) independently assessed all synthetic lesions. FIELD STRENGTH/SEQUENCE: A 3.0 T, T2 -weighted multi-echo spin-echo (MESE) sequence. ASSESSMENT: MS lesions of varying severity levels were artificially generated in healthy volunteer MRI scans by manipulating T2 values. Radiologists and a neural network were tasked with detecting these lesions in a series of 48 MR images. Sixteen images presented healthy anatomy and the rest contained a single lesion at eight increasing severity levels (6%, 9%, 12%, 15%, 18%, 21%, 25%, and 30% elevation in T2 ). True positive (TP) rates, false positive (FP) rates, and odds ratios (ORs) were compared between radiological diagnosis and CAD across the range lesion severity levels. STATISTICAL TESTS: Diagnostic performance of the two approaches was compared using z-tests on TP rates, FP rates, and the logarithm of ORs across severity levels. A P-value <0.05 was considered statistically significant. RESULTS: ORs of identifying pathology were significantly higher for CAD vis-à-vis visual inspection for all lesions' severity levels. For a 6% change in T2 value (lowest severity), radiologists' TP and FP rates were not significantly different (P = 0.12), while the corresponding CAD results remained statistically significant. DATA CONCLUSION: CAD is capable of detecting the presence or absence of more subtle lesions with greater precision than the representative group of 25 radiologists chosen in this study. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

Mapping of magnetic resonance imaging's transverse relaxation time at low signal-to-noise ratio using Bloch simulations and principal component analysis image denoising.

High-resolution mapping of magnetic resonance imaging (MRI)'s transverse relaxation time (T2 ) can benefit many clinical applications by offering improved anatomic details, enhancing the ability to probe tissues' microarchitecture, and facilitating the identification of early pathology. Increasing spatial resolutions, however, decreases data's signal-to-noise ratio (SNR), particularly at clinical scan times. This impairs imaging quality, and the accuracy of subsequent radiological interpretation. Recently, principal component analysis (PCA) was employed for denoising diffusion-weighted MR images and was shown to be effective for improving parameter estimation in multiexponential relaxometry. This study combines the Marchenko-Pastur PCA (MP-PCA) signal model with the echo modulation curve (EMC) algorithm for denoising multiecho spin-echo (MESE) MRI data and improving the precision of EMC-generated single T2 relaxation maps. The denoising technique was validated on simulations, phantom scans, and in vivo brain and knee data. MESE scans were performed on a 3-T Siemens scanner. The acquired images were denoised using the MP-PCA algorithm and were then provided as input for the EMC T2 -fitting algorithm. Quantitative analysis of the denoising quality included comparing the standard deviation and coefficient of variation of T2 values, along with gold standard SNR estimation of the phantom scans. The presented denoising technique shows an increase in T2 maps' precision and SNR, while successfully preserving the morphological features of the tissue. Employing MP-PCA denoising as a preprocessing step decreases the noise-related variability of T2 maps produced by the EMC algorithm and thus increases their precision. The proposed method can be useful for a wide range of clinical applications by facilitating earlier detection of pathologies and improving the accuracy of patients' follow-up.

Data-driven algorithm for myelin water imaging: Probing subvoxel compartmentation based on identification of spatially global tissue features.

PURPOSE: Multicomponent analysis of MRI T2 relaxation time (mcT2 ) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T2 values. This voxel-based approach is challenging due to the large ambiguity in the multi-T2 space and the low SNR of MRI signals. Herein, we present a data-driven mcT2 analysis, which utilizes the statistical strength of identifying spatially global mcT2 motifs in white matter segments before deconvolving the local signal at each voxel. METHODS: Deconvolution is done using a tailored optimization scheme, which incorporates the global mcT2 motifs without additional prior assumptions regarding the number of microscopic components. The end results of this process are voxel-wise myelin water fraction maps. RESULTS: Validations are shown for computer-generated signals, uniquely designed subvoxel mcT2 phantoms, and in vivo human brain. Results demonstrated excellent fitting accuracy, both for the numerical and the physical mcT2 phantoms, exhibiting excellent agreement between calculated myelin water fraction and ground truth. Proof-of-concept in vivo validation is done by calculating myelin water fraction maps for white matter segments of the human brain. Interscan stability of myelin water fraction values was also estimated, showing good correlation between scans. CONCLUSION: We conclude that studying global tissue motifs prior to performing voxel-wise mcT2 analysis stabilizes the optimization scheme and efficiently overcomes the ambiguity in the T2 space. This new approach can improve myelin water imaging and the investigation of microstructural compartmentation in general.

Post-Run T2 Mapping Changes in Knees of Adolescent Basketball Players.

OBJECTIVE: While articular cartilage defects are common incidental findings among adult athletes, the effect of running on the cartilage of adolescent athletes have rarely been assessed. This study aims to assess the variations in the articular cartilage of the knees in healthy adolescent basketball players using quantitative T2 MRI (magnetic resonance imaging). DESIGN: Fifteen adolescent basketball players were recruited (13.8 ± 0.5 years old). Girls were excluded to avoid potential gender-related confounding effects. Players underwent a pre-run MRI scan of both knees. All participants performed a 30-minute run on a treadmill. Within 15 minutes after completion of their run, players underwent a second, post-run MRI scan. Quantitative T2 maps were generated using the echo modulation curve (EMC) algorithm. Pre-run scans and post-run scans were compared using paired t test. RESULTS: Participants finished their 30-minute run with a mean running distance of 5.77 ± 0.42 km. Pre-run scans analysis found statistically significant (P < 0.05) changes in 3 regions of the knee lateral compartment representing the cartilaginous tissue. No differences were found in the knee medial compartment. Post-run analysis showed lower T2 values in the medial compartment compared to the pre-run scans in several weight-bearing regions: femoral condyle central (pre/post mean values of 33.9/32.2 ms, P = 0.020); femoral condyle posterior (38.1/36.8 ms, P = 0.038); and tibial plateau posterior (34.1/31.0 ms, P < 0.001). The lateral regions did not show any significant changes. CONCLUSIONS: Running leads to microstructural changes in the articular cartilage in several weight-bearing areas of the medial compartment, both in the femoral and the tibial cartilage.

Quantitative platform for accurate and reproducible assessment of transverse (T2 ) relaxation time.

MRI's transverse relaxation time (T2 ) is sensitive to tissues' composition and pathological state. While variations in T2 values can be used as clinical biomarkers, it is challenging to quantify this parameter in vivo due to the complexity of the MRI signal model, differences in protocol implementations, and hardware imperfections. Herein, we provide a detailed analysis of the echo modulation curve (EMC) platform, offering accurate and reproducible mapping of T2 values, from 2D multi-slice multi-echo spin-echo (MESE) protocols. Computer simulations of the full Bloch equations are used to generate an advanced signal model, which accounts for stimulated echoes and transmit field (B1+ ) inhomogeneities. In addition to quantifying T2 values, the EMC platform also provides proton density (PD) maps, and fat-water fraction maps. The algorithm's accuracy, reproducibility, and insensitivity to T1 values are validated on a phantom constructed by the National Institute of Standards and Technology and on in vivo human brains. EMC-derived T2 maps show excellent agreement with ground truth values for both in vitro and in vivo models. Quantitative values are accurate and stable across scan settings and for the physiological range of T2 values, while showing robustness to main field (B0 ) inhomogeneities, to variations in T1 relaxation time, and to magnetization transfer. Extension of the algorithm to two-component fitting yields accurate fat and water T2 maps along with their relative fractions, similar to a reference three-point Dixon technique. Overall, the EMC platform allows to generate accurate and stable T2 maps, with a full brain coverage using a standard MESE protocol and at feasible scan times. The utility of EMC-based T2 maps was demonstrated on several clinical applications, showing robustness to variations in other magnetic properties. The algorithm is available online as a full stand-alone package, including an intuitive graphical user interface.

Classification of node-positive melanomas into prognostic subgroups using keratin, immune, and melanogenesis expression patterns.

Cutaneous melanoma tumors are heterogeneous and show diverse responses to treatment. Identification of robust molecular biomarkers for classifying melanoma tumors into clinically distinct and homogenous subtypes is crucial for improving the diagnosis and treatment of the disease. In this study, we present a classification of melanoma tumors into four subtypes with different survival profiles based on three distinct gene expression signatures: keratin, immune, and melanogenesis. The melanogenesis expression pattern includes several genes that are characteristic of the melanosome organelle and correlates with worse survival, suggesting the involvement of melanosomes in melanoma aggression. We experimentally validated the secretion of melanosomes into surrounding tissues by melanoma tumors, which potentially affects the lethality of metastasis. We propose a simple molecular decision tree classifier for predicting a tumor's subtype based on representative genes from the three identified signatures. Key predictor genes were experimentally validated on melanoma samples taken from patients with varying survival outcomes. Our three-pattern approach for classifying melanoma tumors can contribute to advancing the understanding of melanoma variability and promote accurate diagnosis, prognostication, and treatment.

PROMO: an interactive tool for analyzing clinically-labeled multi-omic cancer datasets.

BACKGROUND: Analysis of large genomic datasets along with their accompanying clinical information has shown great promise in cancer research over the last decade. Such datasets typically include thousands of samples, each measured by one or several high-throughput technologies ('omics') and annotated with extensive clinical information. While instrumental for fulfilling the promise of personalized medicine, the analysis and visualization of such large datasets is challenging and necessitates programming skills and familiarity with a large array of software tools to be used for the various steps of the analysis. RESULTS: We developed PROMO (Profiler of Multi-Omic data), a friendly, fully interactive stand-alone software for analyzing large genomic cancer datasets together with their associated clinical information. The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing, and survival analysis that can be performed on a single or multi-omic dataset. The tool can be used for quick exploration and stratification of tumor samples taken from patients into clinically significant molecular subtypes. Identification of prognostic biomarkers and generation of simple subtype classifiers are additional important features. We review PROMO's main features and demonstrate its analysis capabilities on a breast cancer cohort from TCGA. CONCLUSIONS: PROMO provides a single integrated solution for swiftly performing a complete analysis of cancer genomic data for subtype discovery and biomarker identification without writing a single line of code, and can, therefore, make the analysis of these data much easier for cancer biologists and biomedical researchers. PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo/.