Susceptibility-Weighted MRI for Predicting NF-2 Mutations and S100 Protein Expression in Meningiomas.

S100 protein expression levels and neurofibromatosis type 2 (NF-2) mutations result in different disease courses in meningiomas. This study aimed to investigate non-invasive biomarkers of NF-2 copy number loss and S100 protein expression in meningiomas using morphological, radiomics, and deep learning-based features of susceptibility-weighted MRI (SWI). This retrospective study included 99 patients with S100 protein expression data and 92 patients with NF-2 copy number loss information. Preoperative cranial MRI was conducted using a 3T clinical MR scanner. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and subsequent registration of FLAIR to high-resolution SWI was performed. First-order textural features of SWI were extracted and assessed using Pyradiomics. Morphological features, including the tumor growth pattern, peritumoral edema, sinus invasion, hyperostosis, bone destruction, and intratumoral calcification, were semi-quantitatively assessed. Mann-Whitney U tests were utilized to assess the differences in the SWI features of meningiomas with and without S100 protein expression or NF-2 copy number loss. A logistic regression analysis was used to examine the relationship between these features and the respective subgroups. Additionally, a convolutional neural network (CNN) was used to extract hierarchical features of SWI, which were subsequently employed in a light gradient boosting machine classifier to predict the NF-2 copy number loss and S100 protein expression. NF-2 copy number loss was associated with a higher risk of developing high-grade tumors. Additionally, elevated signal intensity and a decrease in entropy within the tumoral region on SWI were observed in meningiomas with S100 protein expression. On the other hand, NF-2 copy number loss was associated with lower SWI signal intensity, a growth pattern described as "en plaque", and the presence of calcification within the tumor. The logistic regression model achieved an accuracy of 0.59 for predicting NF-2 copy number loss and an accuracy of 0.70 for identifying S100 protein expression. Deep learning features demonstrated a strong predictive capability for S100 protein expression (AUC = 0.85 ± 0.06) and had reasonable success in identifying NF-2 copy number loss (AUC = 0.74 ± 0.05). In conclusion, SWI showed promise in identifying NF-2 copy number loss and S100 protein expression by revealing neovascularization and microcalcification characteristics in meningiomas.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 2.

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1.

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

The effect of tumor shape irregularity on Gamma Knife treatment plan quality and treatment outcome: an analysis of 234 vestibular schwannomas.

The primary aim of Gamma Knife (GK) radiosurgery is to deliver high-dose radiation precisely to a target while conforming to the target shape. In this study, the effects of tumor shape irregularity (TSI) on GK dose-plan quality and treatment outcomes were analyzed in 234 vestibular schwannomas. TSI was quantified using seven different metrics including volumetric index of sphericity (VioS). GK treatment plans were created on a single GK-Perfexion/ICON platform. The plan quality was measured using selectivity index (SI), gradient index (GI), Paddick's conformity index (PCI), and efficiency index (EI). Correlation and linear regression analyses were conducted between shape irregularity features and dose plan indices. Machine learning was employed to identify the shape feature that predicted dose plan quality most effectively. The treatment outcome analysis including tumor growth control and serviceable hearing preservation at 2 years, were conducted using Cox regression analyses. All TSI features correlated significantly with the dose plan indices (P < 0.0012). With increasing tumor volume, vestibular schwannomas became more spherical (P < 0.05) and the dose plan indices varied significantly between tumor volume subgroups (P < 0.001 and P < 0.01). VioS was the most effective predictor of GK indices (P < 0.001) and we obtained 89.36% accuracy (79.17% sensitivity and 100% specificity) for predicting PCI. Our results indicated that TSI had significant effects on the plan quality however did not adversely affect treatment outcomes.

Identification of IDH and TERTp mutation status using 1 H-MRS in 112 hemispheric diffuse gliomas.

BACKGROUND: There is a growing interest in noninvasively defining molecular subsets of hemispheric diffuse gliomas based on the isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutation status, which correspond to distinct tumor entities, and differ in demographics, natural history, treatment response, recurrence, and survival patterns. PURPOSE: To investigate whether metabolite levels detected with short echo time (TE) proton MR spectroscopy (1 H-MRS) at 3T can be used for noninvasive molecular classification of IDH and TERTp mutation-based subsets of gliomas. STUDY TYPE: Retrospective. SUBJECTS: In all, 112 hemispheric diffuse gliomas (70 males/42 females, mean age: 42.1 ± 13.9 years). FIELD STRENGTH/SEQUENCE: Short-TE 1 H-MRS (repetition time (TR) = 2000 msec, TE = 30 msec, number of signal averages = 192) and routine clinical brain tumor MR protocols were acquired at 3T. ASSESSMENT: 1 H-MRS data were quantified using LCModel software. TERTp and IDH1 or IDH2 (IDH1/2) mutations in the tissue were determined by either minisequencing or Sanger sequencing. STATISTICAL TESTS: Metabolic differences between IDH mutant and IDH wildtype gliomas were assessed by a Mann-Whitney U-test. A Kruskal-Wallis test followed by a Tukey-Kramer test was used to analyze metabolic differences between IDH and TERTp mutational molecular subsets of gliomas. A Spearman rank correlation coefficient was used to assess the correlations of metabolite intensities with the Ki-67 index. Furthermore, machine learning was employed to classify the IDH and TERTp mutational status of gliomas, and the accuracy, sensitivity, and specificity values were estimated. RESULTS: Short-TE 1 H-MRS classified the presence of an IDH mutation with 88.39% accuracy, 76.92% sensitivity, and 94.52% specificity, and a TERTp mutation within primary IDH wildtype gliomas with 92.59% accuracy, 83.33% sensitivity, and 95.24% specificity. DATA CONCLUSION: Short-TE 1 H-MRS could be used to identify molecular subsets of hemispheric diffuse gliomas corresponding to IDH and TERTp mutations. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1799-1809.

On using signal magnitude in diffusion magnetic resonance measurements of restricted motion.

Tissue microstructure has significance as a biomarker, however its accurate inference with diffusion magnetic resonance (MR) is still an open problem. With few exceptions, diffusion weighted (DW) MR models either process diffusion MR data using signal magnitude, whereby microstructural information is forcefully confined to symmetry due to Fourier transform properties, or directly use symmetric basis expansions. Herein, information loss from magnitude utilization is demonstrated by numerically simulating particles undergoing diffusion near a fully reflective infinite wall and an orthogonal corner. Simulation results show that the loss of the Hermitian property when using signal magnitude impedes DW-MR from accurately inferring microstructural information in both of the geometries.

A Platform Integrating Acquisition, Reconstruction, Visualization, and Manipulator Control Modules for MRI-Guided Interventions.

This work presents a platform that integrates a customized MRI data acquisition scheme with reconstruction and three-dimensional (3D) visualization modules along with a module for controlling an MRI-compatible robotic device to facilitate the performance of robot-assisted, MRI-guided interventional procedures. Using dynamically-acquired MRI data, the computational framework of the platform generates and updates a 3D model representing the area of the procedure (AoP). To image structures of interest in the AoP that do not reside inside the same or parallel slices, the MRI acquisition scheme was modified to collect a multi-slice set of intraoblique to each other slices; which are termed composing slices. Moreover, this approach interleaves the collection of the composing slices so the same k-space segments of all slices are collected during similar time instances. This time matching of the k-space segments results in spatial matching of the imaged objects in the individual composing slices. The composing slices were used to generate and update the 3D model of the AoP. The MRI acquisition scheme was evaluated with computer simulations and experimental studies. Computer simulations demonstrated that k-space segmentation and time-matched interleaved acquisition of these segments provide spatial matching of the structures imaged with composing slices. Experimental studies used the platform to image the maneuvering of an MRI-compatible manipulator that carried tubing filled with MRI contrast agent. In vivo experimental studies to image the abdomen and contrast enhanced heart on free-breathing subjects without cardiac triggering demonstrated spatial matching of imaged anatomies in the composing planes. The described interventional MRI framework could assist in performing real-time MRI-guided interventions.

Interactive Feature Space Explorer© for multi-modal magnetic resonance imaging.

Wider information content of multi-modal biomedical imaging is advantageous for detection, diagnosis and prognosis of various pathologies. However, the necessity to evaluate a large number images might hinder these advantages and reduce the efficiency. Herein, a new computer aided approach based on the utilization of feature space (FS) with reduced reliance on multiple image evaluations is proposed for research and routine clinical use. The method introduces the physician experience into the discovery process of FS biomarkers for addressing biological complexity, e.g., disease heterogeneity. This, in turn, elucidates relevant biophysical information which would not be available when automated algorithms are utilized. Accordingly, the prototype platform was designed and built for interactively investigating the features and their corresponding anatomic loci in order to identify pathologic FS regions. While the platform might be potentially beneficial in decision support generally and specifically for evaluating outlier cases, it is also potentially suitable for accurate ground truth determination in FS for algorithm development. Initial assessments conducted on two different pathologies from two different institutions provided valuable biophysical perspective. Investigations of the prostate magnetic resonance imaging data resulted in locating a potential aggressiveness biomarker in prostate cancer. Preliminary findings on renal cell carcinoma imaging data demonstrated potential for characterization of disease subtypes in the FS.

Complete fourier direct magnetic resonance imaging (CFD-MRI) for diffusion MRI.

The foundation for an accurate and unifying Fourier-based theory of diffusion weighted magnetic resonance imaging (DW-MRI) is constructed by carefully re-examining the first principles of DW-MRI signal formation and deriving its mathematical model from scratch. The derivations are specifically obtained for DW-MRI signal by including all of its elements (e.g., imaging gradients) using complex values. Particle methods are utilized in contrast to conventional partial differential equations approach. The signal is shown to be the Fourier transform of the joint distribution of number of the magnetic moments (at a given location at the initial time) and magnetic moment displacement integrals. In effect, the k-space is augmented by three more dimensions, corresponding to the frequency variables dual to displacement integral vectors. The joint distribution function is recovered by applying the Fourier transform to the complete high-dimensional data set. In the process, to obtain a physically meaningful real valued distribution function, phase corrections are applied for the re-establishment of Hermitian symmetry in the signal. Consequently, the method is fully unconstrained and directly presents the distribution of displacement integrals without any assumptions such as symmetry or Markovian property. The joint distribution function is visualized with isosurfaces, which describe the displacement integrals, overlaid on the distribution map of the number of magnetic moments with low mobility. The model provides an accurate description of the molecular motion measurements via DW-MRI. The improvement of the characterization of tissue microstructure leads to a better localization, detection and assessment of biological properties such as white matter integrity. The results are demonstrated on the experimental data obtained from an ex vivo baboon brain.

Quantifying the CDK inhibitor VMY-1-103's activity and tissue levels in an in vivo tumor model by LC-MS/MS and by MRI.

The development of new small molecule-based therapeutic drugs requires accurate quantification of drug bioavailability, biological activity and treatment efficacy. Rapidly measuring these endpoints is often hampered by the lack of efficient assay platforms with high sensitivity and specificity. Using an in vivo model system, we report a simple and sensitive liquid chromatography-tandem mass spectrometry assay to quantify the bioavailability of a recently developed novel cyclin-dependent kinase inhibitor VMY-1-103, a purvalanol B-based analog whose biological activity is enhanced via dansylation. We developed a rapid organic phase extraction technique and validated wide and functional VMY-1-103 distribution in various mouse tissues, consistent with its enhanced potency previously observed in a variety of human cancer cell lines. More importantly, in vivo MRI and single voxel proton MR-Spectroscopy further established that VMY-1-103 inhibited disease progression and affected key metabolites in a mouse model of hedgehog-driven medulloblastoma.

A system for endoscopic mechanically scanned localized proton MR and light-induced fluorescence emission spectroscopies.

Molecular and near-cellular modalities offer new opportunities in assessing living tissue in situ, and multimodality approaches, which offer complementary information, may lead to improved characterization of tissue pathophysiology benefiting diagnosis and focal therapy. However, many such modalities are limited by their low penetration through tissue, which has led to minimally invasive trans-cannula approaches to place the corresponding sensors locally at the area of interest. This work presents a system for performing localized fluorescence emission and proton magnetic resonance (MR) spectroscopies via endoscopic access. The in-house developed side-firing 1.9-mm wide dual-sensor integrates a three-fiber optical sensor for fluorescence emission optical spectroscopy and a 1-mm circular radiofrequency (RF) coil for localized MR proton spectroscopy. An MR-compatible manipulator was developed for carrying and mechanically translating the dual-sensor along a linear access channel. The hardware and software control of the system allows reconfigurable synchronization of the manipulator-assisted translation of the sensor, and MR and optical data collection. The manipulator serves as the mechanical link for the three modalities and MR images, MR spectra and optical spectra are inherently co-registered to the MR scanner coordinate system. These spectra were then used to generate spatio-spectral maps of the fluorophores and proton MR-signal sources in three-compartment phantoms with optically- and MR-visible, and distinguishable, materials. These data demonstrate a good spatial match between MR images, MR spectra and optical spectra along the scanned path. In addition to basic research, such a system may have clinical applications for assessing and characterizing cancer in situ, as well as guiding focal therapies.

Background and Mathematical Analysis of Diffusion MRI Methods.

The addition of a pair of magnetic field gradient pulses had initially provided the measurement of spin motion with nuclear magnetic resonance (NMR) techniques. In the adaptation of DW-NMR techniques to magnetic resonance imaging (MRI), the taxonomy of mathematical models is divided in two categories: model matching and spectral methods. In this review, the methods are summarized starting from early diffusion weighted (DW) NMR models followed up with their adaptation to DW MRI. Finally, a newly introduced Fourier analysis based unifying theory, so-called Complete Fourier Direct MRI, is included to explain the mechanisms of existing methods.

COMPARISON OF THE COMPLETE FOURIER DIRECT MRI WITH EXISTING DIFFUSION WEIGHTED MRI METHODS.

The Complete Fourier Direct (CFD) MRI method introduced in earlier work for modeling the diffusion weighted MRI signal is compared with the existing methods. The preservation of Hermitian symmetry in the diffusion weighted MRI signal without affecting its energy is the key point that differentiates CFD-MRI from the existing methods. By keeping the correct Fourier relationship intact, the joint distribution function is represented 'as it is', without any constraints, e.g. being symmetric. The necessity to model or assume models for spin motion and try to fit the model to the samples of the Fourier transform as in case of model matching methods is not required because the Discrete Fourier Transform applied to correctly processed signal in CFD-MRI gives more accurate results.

Minimization of imaging gradient effects in diffusion tensor imaging.

A new, sample independent optimization criterion for minimizing the effect of the imaging gradients, including the directional awareness they create, is defined for diffusion tensor imaging (DTI) experiments. The DTI linear algebraic framework is expanded to a normed space to design optimal diffusion gradient schemes (DGS) in an integral fashion without separating the magnitude and direction of the DGS vectors. The feasible space of DGS vectors, for which the estimation equations are determinate, thus a hard constraint for the optimization, is parametrized. Newly generated optimal DGSs demonstrate on an isotropic sample and an ex-vivo baboon brain that the optimization goals are reached as well as a significant increase in estimation performance.

Simulations and experimental demonstration of coupling molecular and macroscopic level modalities with a robotic manipulator.

Established and emerging molecular and cellular modalities, such as optical imaging and spectroscopy, offer new opportunities for assessing tissue pathophysiology in situ. A challenge with such applications is their limited tissue penetration and low sensitivity that can be addressed with trans-needle or trans-catheter access. In this work, we describe the use of an actuated manipulator to physically manipulate such sensors to scan an area of interest generating 1-D scans while registering them to a guiding modality. Simulations were performed for a miniature RF coil to determine the voxel size, and experimental studies were conducted using a miniature RF coil manipulated by the MR-compatible device. The experimental results on phantom studies show that potential diagnostic information can be collected by using this methodology. This system was pursued to address a critical limitation of emerging molecular and near-cellular modalities; the limited tissue penetration.

The validation of complete Fourier direct MR method for diffusion MRI via biological and numerical phantoms.

The equations of the Complete Fourier Direct (CFD) MR model are explicitly derived for diffusion weighted NMR experiments. The CFD-MR theory is validated by comparing a biological phantom constructed from nerve bundles and agar gel with its numerical implementation. The displacement integral distribution function estimated from the experimental data is in high agreement with the numerical phantom. CFD-MR's ability to estimate accurately and fully spin diffusion properties demonstrated here, provides the experimental validation of the theoretical CFD-MR model.

A new model for diffusion weighted MRI: complete Fourier direct MRI.

The diffusion weighted MR signal is modeled using particle methods. The model shows that the signal is the Fourier transform of the distribution function of the number of spins at the initial time at a given position with a given displacement integral value. The distribution function is computed via Fourier transform steps that keep the Hermitian property of the signal in order to guarantee that the distribution function is real valued. The function, which depicts the tissue microstructure 'as it is' without being tied to any expansions, transformations and assumptions such as Markovian property or symmetry about the spin motions, is displayed using isosurfaces overlayed on the spin density map for a fixed baboon brain sample.

Characterization of imaging gradients in diffusion tensor imaging.

For obtaining a complete model the diffusion tensor imaging (DTI) method is derived in a new linear algebraic framework in order to include the effect of all of the magnetic field gradients on the MRI signal. In the framework, the coefficient matrix of the estimation equations consists of the sum of three matrices corresponding to diffusion gradients, imaging gradients and the cross-terms between them. The derivations demonstrate that there exists modeling incongruities originating from the choice of phase-encoding gradient magnitude and the read-out gradient affecting the entirety of the signal sample points. These reflect on the cross-terms and the imaging gradient coefficient matrix, revealing the DTI's inadequacy for the inclusion of imaging gradients. The linear algebraic framework mitigates the inadequacy by the utilization of center-symmetric gradient schemes. The observations are verified by the experimental results obtained from an isotropic phantom using several existing diffusion gradient schemes.

Noise and nonlinear estimation with optimal schemes in DTI.

In general, the estimation of the diffusion properties for diffusion tensor experiments (DTI) is accomplished via least squares estimation (LSE). The technique requires applying the logarithm to the measurements, which causes bad propagation of errors. Moreover, the way noise is injected to the equations invalidates the least squares estimate as the best linear unbiased estimate. Nonlinear estimation (NE), despite its longer computation time, does not possess any of these problems. However, all of the conditions and optimization methods developed in the past are based on the coefficient matrix obtained in a LSE setup. In this article, NE for DTI is analyzed to demonstrate that any result obtained relatively easily in a linear algebra setup about the coefficient matrix can be applied to the more complicated NE framework. The data, obtained using non-optimal and optimized diffusion gradient schemes, are processed with NE. In comparison with LSE, the results show significant improvements, especially for the optimization criterion. However, NE does not resolve the existing conflicts and ambiguities displayed with LSE methods.

Theoretical and experimental analysis of imaging gradients in DTI.

A comprehensive, analytical framework for MR- DTI is constructed in a linear algebra setup. The expressions describing the effects of imaging gradients show formulation ambiguities. Center-symmetric gradient schemes use no cross terms (NoCroT) in the calculations resulting, at least in theory, in the alleviation of the issue. When three estimation methods, all gradients, NoCroT and diffusion gradients only are compared based on experimental results it is observed that the full inclusion of imaging gradients can be detrimental and there is slight improvement with NoCroT over diffusion gradients only. It is concluded that design of new diffusion gradient schemes via optimization is necessary to decouple to the maximum extent the effects of imaging gradients.