Correlation of quantitative conductivity mapping and total tissue sodium concentration at 3T/4T

Radiosurgery-induced early changes in peritumoral tissue sodium concentration of brain metastases

BACKGROUND

Stereotactic radiosurgery (SRS) is an effective therapy for brain metastases. Response is assessed with serial 1H magnetic resonance imaging (MRI). Early markers for response are desirable to allow for individualized treatment adaption. Previous studies indicated that radiotherapy might have impact on tissue sodium concentration. Thus, 23Na MRI could provide early quantification of response to SRS.

PURPOSE

We investigated whether longitudinal detection of tissue sodium concentration alteration within brain metastases and their peritumoral tissue after SRS with 23Na MRI was feasible.

STUDY TYPE

Prospective.

POPULATION

Twelve patients with a total of 14 brain metastases from various primary tumors.

ASSESSMENT

23Na MRI scans were acquired from patients 2 days before, 5 days after, and 40 days after SRS. Gross tumor volume (GTV) and healthy-appearing regions were manually segmented on the MPRAGE obtained 2 days before SRS, onto which all 23Na MR images were coregistered. Radiation isodose areas within the peritumoral tissue were calculated with the radiation planning system. Tissue sodium concentration before and after SRS within GTV, peritumoral tissue, and healthy-appearing regions as well as the routine follow-up with serial MRI were evaluated.

STATISTICAL TESTS

Results were compared using Student's t-test and correlation was evaluated with Pearson's correlation coefficient.

RESULTS

We found a positive correlation between the tissue sodium concentration within the peritumoral tissue and radiation dosage. Two patients showed local progression and a differing tissue sodium concentration evolution within GTV and the peritumoral tissue compared to mean tissue sodium concentration of the other patients. No significant tissue sodium concentration changes were observed within healthy-appearing regions.

CONCLUSION

Tissue sodium concentration assessment within brain metastases and peritumoral tissue after SRS with 23Na MRI is feasible and might be able to quantify tissue response to radiation.

Changes of in vivo electrical conductivity in the brain and torso related to age, fat fraction and sex using MRI

This work was inspired by the observation that a majority of MR-electrical properties tomography studies are based on direct comparisons with ex vivo measurements carried out on post-mortem samples in the 90's. As a result, the in vivo conductivity values obtained from MRI in the megahertz range in different types of tissues (brain, liver, tumors, muscles, etc.) found in the literature may not correspond to their ex vivo equivalent, which still serves as a reference for electromagnetic modelling. This study aims to pave the way for improving current databases since the definition of personalized electromagnetic models (e.g. for Specific Absorption Rate estimation) would benefit from better estimation. Seventeen healthy volunteers underwent MRI of both brain and thorax/abdomen using a three-dimensional ultrashort echo-time (UTE) sequence. We estimated conductivity (S/m) in several classes of macroscopic tissue using a customized reconstruction method from complex UTE images, and give general statistics for each of these regions (mean-median-standard deviation). These values are used to find possible correlations with biological parameters such as age, sex, body mass index and/or fat volume fraction, using linear regression analysis. In short, the collected in vivo values show significant deviations from the ex vivo values in conventional databases, and we show significant relationships with the latter parameters in certain organs for the first time, e.g. a decrease in brain conductivity with age.

Repeatability of brain phase-based magnetic resonance electric properties tomography methods and effect of compressed SENSE and RF shimming

Magnetic resonance electrical properties tomography (MREPT) is an emerging imaging modality to noninvasively measure tissue conductivity and permittivity. Implementation of MREPT in the clinic requires repeatable measurements at a short scan time and an appropriate protocol. The aim of this study was to investigate the repeatability of conductivity measurements using phase-based MREPT and the effects of compressed SENSE (CS), and RF shimming on the precision of conductivity measurements. Conductivity measurements using turbo spin echo (TSE) and three-dimensional balanced fast field echo (bFFE) with CS factors were repeatable. Conductivity measurement using bFFE phase showed smaller mean and variance that those measured by TSE. The conductivity measurements using bFFE showed minimal deviation with CS factors up to 8, with deviation increasing at CS factors > 8. Subcortical structures produced less consistent measurements than cortical parcellations at higher CS factors. RF shimming using full slice coverage 2D dual refocusing echo acquisition mode (DREAM) and full coverage 3D dual TR approaches further improved measurement precision. BFFE is a more optimal sequence than TSE for phase-based MREPT in brain. Depending on the area of the brain being measured, the scan can be safely accelerated with compressed SENSE without sacrifice of precision, offering the potential to employ MREPT in clinical research and applications. RF shimming with better field mapping further improves precision of the conductivity measures.

Variability by region and method in human brain sodium concentrations estimated by 23Na magnetic resonance imaging: a meta-analysis

Sodium imaging (23Na-MRI) is of interest in neurological conditions given potential sensitivity to the physiological and metabolic status of tissues. Benchmarks have so far been restricted to parenchyma or grey/white matter (GM/WM). We investigate (1) the availability of evidence, (2) regional pooled estimates and (3) variability attributable to region/methodology. MEDLINE literature search for tissue sodium concentration (TSC) measured in specified 'healthy' brain regions returned 127 reports, plus 278 retrieved from bibliographies. 28 studies met inclusion criteria, including 400 individuals. Reporting variability led to nested data structure, so we used multilevel meta-analysis and a random effects model to pool effect sizes. The pooled mean from 141 TSC estimates was 40.51 mM (95% CI 37.59-43.44; p < 0.001, I2Total=99.4%). Tissue as a moderator was significant (F214 = 65.34, p-val < .01). Six sub-regional pooled means with requisite statistical power were derived. We were unable to consider most methodological and demographic factors sought because of non-reporting, but each factor included beyond tissue improved model fit. Significant residual heterogeneity remained. The current estimates provide an empirical point of departure for better understanding in 23Na-MRI. Improving on current estimates supports: (1) larger, more representative data collection/sharing, including (2) regional data, and (3) agreement on full reporting standards.

Neural stochastic differential equations network as uncertainty quantification method for EEG source localization

EEG source localization remains a challenging problem given the uncertain conductivity values of the volume conductor models (VCMs). As uncertain conductivities vary across people, they may considerably impact the forward and inverse solutions of the EEG, leading to an increase in localization mistakes and misdiagnoses of brain disorders. Calibration of conductivity values using uncertainty quantification (UQ) techniques is a promising approach to reduce localization errors. The widely-known UQ methods involve Bayesian approaches, which utilize prior conductivity values to derive their posterior inference and estimate their optimal calibration. However, these approaches have two significant drawbacks: solving for posterior inference is intractable, and choosing inappropriate priors may lead to increased localization mistakes. This study used the Neural Stochastic Differential equations Network (SDE-Net), a combination of dynamical systems and deep learning techniques that utilizes the Wiener process to minimize conductivity uncertainties in the VCM and improve the inverse problem. Results revealed that SDE-Net generated a lower localization error rate in the inverse problem compared to Bayesian techniques. Future studies may employ new stochastic dynamical systems-based techniques as a UQ technique to address further uncertainties in the EEG Source Localization problem. Our code can be found here:https://github.com/rrwabina/SDENet-UQ-ESL.

Natural Zeolite for The Purification of Saline Groundwater and Irrigation Potential Analysis

Groundwater is one of the main sources of water for irrigation used worldwide. However, the application of the resource is threatened by the possibility of high saline levels, especially in low-lying coastal regions. Furthermore, the lack of readily accessible materials for successful treatment procedures makes the purification of such water a constant challenge. Based on the fact that natural zeolite is one of the easily accessible and relatively cheap filter materials, this study examined the potential use of high-salinity groundwater filtered by natural zeolite for irrigation. Zeolite-filled filters at two different depths (0.5 m and 1 m) were studied. The samples were collected from the low-lying areas of Dar es Salaam City, Tanzania. The study observed that when the raw groundwater samples were exposed to the 0.5 m column depth, sodium (Na⁺) had the lowest removal efficiency at 40.2% and calcium (Ca²⁺) had the highest removal efficiency at 98.9%. On the other hand, magnesium (Mg²⁺) had the lowest removal efficiency, at about 61.2%, whereas potassium (K⁺) had up to about 99.7% removal efficiency from the 1 m column depth treatment system. Additionally, from the salinity hazard potential analysis, most of the samples fell within C4 (based on the electrical conductivity), which is a "very high salinity" class, and based on the quality it means the water cannot be directly applied for irrigation purposes. From the 0.5 m column depth, most of the samples fell within C3 (the "high salinity" class), and from the 1 m column depth most of the samples fell within C1 ("low salinity" class). The findings of this study offer some valuable insight into the prospective use of natural zeolite for the filtration of saline groundwater before its application for irrigation.

Combining sodium MRI, proton MR spectroscopic imaging, and intracerebral EEG in epilepsy

Whole brain ionic and metabolic imaging has potential as a powerful tool for the characterization of brain diseases. We combined sodium MRI (²³ Na MRI) and ¹ H-MR Spectroscopic Imaging (¹ H-MRSI), assessing changes within epileptogenic networks in comparison with electrophysiologically normal networks as defined by stereotactic EEG (SEEG) recordings analysis. We applied a multi-echo density adapted 3D projection reconstruction pulse sequence at 7 T (²³ Na-MRI) and a 3D echo-planar spectroscopic imaging sequence at 3 T (¹ H-MRSI) in 19 patients suffering from drug-resistant focal epilepsy who underwent presurgical SEEG. We investigated ²³ Na MRI parameters including total sodium concentration (TSC) and the sodium signal fraction associated with the short component of T₂ * decay (f), alongside the level of metabolites N-acetyl aspartate (NAA), choline compounds (Cho), and total creatine (tCr). All measures were extracted from spherical regions of interest (ROIs) centered between two adjacent SEEG electrode contacts and z-scored against the same ROI in controls. Group comparison showed a significant increase in f only in the epileptogenic zone (EZ) compared to controls and compared to patients' propagation zone (PZ) and non-involved zone (NIZ). TSC was significantly increased in all patients' regions compared to controls. Conversely, NAA levels were significantly lower in patients compared to controls, and lower in the EZ compared to PZ and NIZ. Multiple regression analyzing the relationship between sodium and metabolites levels revealed significant relations in PZ and in NIZ but not in EZ. Our results are in agreement with the energetic failure hypothesis in epileptic regions associated with widespread tissue reorganization.

Comprehensive Account of Sodium Imaging and Spectroscopy for Brain Research

Sodium (²³Na) is a vital component of neuronal cells and plays a key role in various signal transmission processes. Hence, information on sodium distribution in the brain using magnetic resonance imaging (MRI) provides useful information on neuronal health. ²³Na MRI and MR spectroscopy (MRS) improve the diagnosis, prognosis, and clinical monitoring of neurological diseases but confront some inherent limitations that lead to low signal-to-noise ratio, longer scan time, and diminished partial volume effects. Recent advancements in multinuclear MR technology have helped in further exploration in this domain. We aim to provide a comprehensive description of ²³Na MRI and MRS for brain research including the following aspects: (a) theoretical background for understanding ²³Na MRI and MRS fundamentals; (b) technological advancements of ²³Na MRI with respect to pulse sequences, RF coils, and sodium compartmentalization; (c) applications of ²³Na MRI in the early diagnosis and prognosis of various neurological disorders; (d) structural-chronological evolution of sodium spectroscopy in terms of its numerous applications in human studies; (e) the data-processing tools utilized in the quantitation of sodium in the respective anatomical regions.

Sodium MR Neuroimaging

Sodium MR imaging has the potential to complement routine proton MR imaging examinations with the goal of improving diagnosis, disease characterization, and clinical monitoring in neurologic diseases. In the past, the utility and exploration of sodium MR imaging as a valuable clinical tool have been limited due to the extremely low MR signal, but with recent improvements in imaging techniques and hardware, sodium MR imaging is on the verge of becoming clinically realistic for conditions that include brain tumors, ischemic stroke, and epilepsy. In this review, we briefly describe the fundamental physics of sodium MR imaging tailored to the neuroradiologist, focusing on the basics necessary to understand factors that play into making sodium MR imaging feasible for clinical settings and describing current controversies in the field. We will also discuss the current state of the field and the potential future clinical uses of sodium MR imaging in the diagnosis, phenotyping, and therapeutic monitoring in neurologic diseases.

High-frequency electrical properties tomography at 9.4T as a novel contrast mechanism for brain tumors

PURPOSE

To establish high-frequency magnetic resonance electrical properties tomography (MREPT) as a novel contrast mechanism for the assessment of glioblastomas using a rat brain tumor model.

METHODS

Six F98 intracranial tumor bearing rats were imaged longitudinally 8, 11 and 14 days after tumor cell inoculation. Conductivity and mean diffusivity maps were generated using MREPT and Diffusion Tensor Imaging. These maps were co-registered with T2 -weighted images and volumes of interests (VOIs) were segmented from the normal brain, ventricles, edema, viable tumor, tumor rim, and tumor core regions. Longitudinal changes in conductivity and mean diffusivity (MD) values were compared in these regions. A correlation analysis was also performed between conductivity and mean diffusivity values.

RESULTS

The conductivity of ventricles, edematous area and tumor regions (tumor rim, viable tumor, tumor core) was significantly higher (P < .01) compared to the contralateral cortex. The conductivity of the tumor increased over time while MD from the tumor did not change. A marginal positive correlation was noted between conductivity and MD values for tumor rim and viable tumor, whereas this correlation was negative for the tumor core.

CONCLUSION

We demonstrate a novel contrast mechanism based on ionic concentration and mobility, which may aid in providing complementary information to water diffusion in probing the microenvironment of brain tumors.

Noncontrast-Enhanced MR-Based Conductivity Imaging for Breast Cancer Detection and Lesion Differentiation

BACKGROUND

There is increasing interest in noncontrast-enhanced MRI due to safety concerns for gadolinium contrast agents.

PURPOSE

To investigate the clinical feasibility of MR-based conductivity imaging for breast cancer detection and lesion differentiation.

STUDY TYPE

Prospective.

SUBJECTS

One hundred and ten women, with 112 known cancers and 17 benign lesions (biopsy-proven), scheduled for preoperative MRI.

FIELD STRENGTH/SEQUENCE

Non-fat-suppressed T2-weighted turbo spin-echo sequence (T2WI), dynamic contrast-enhanced MRI and diffusion-weighted imaging (DWI) at 3T.

ASSESSMENT

Cancer detectability on each imaging modality was qualitatively evaluated on a per-breast basis: the conductivity maps derived from T2WI were independently reviewed by three radiologists (R1-R3). T2WI, DWI, and pre-operative digital mammography were independently reviewed by three other radiologists (R4-R6). Conductivity and apparent diffusion coefficient (ADC) measurements (mean, minimum, and maximum) were performed for 112 cancers and 17 benign lesions independently by two radiologists (R1 and R2). Tumor size was measured from surgical specimens.

STATISTICAL TESTS

Cancer detection rates were compared using generalized estimating equations. Multivariable logistic regression analysis was performed to identify factors associated with cancer detectability. Discriminating ability of conductivity and ADC was evaluated by using the areas under the receiver operating characteristic curve (AUC).

RESULTS

Conductivity imaging showed lower cancer detection rates (20%-32%) compared to T2WI (62%-71%), DWI (85%-90%), and mammography (79%-88%) (all P < 0.05). Fatty breast on MRI (odds ratio = 11.8, P < 0.05) and invasive tumor size (odds ratio = 1.7, P < 0.05) were associated with cancer detectability of conductivity imaging. The maximum conductivity showed comparable ability to the mean ADC in discriminating between cancers and benign lesions (AUC = 0.67 [95% CI: 0.59, 0.75] vs. 0.84 [0.76, 0.90], P = 0.06 (R1); 0.65 [0.56, 0.73] vs. 0.82 [0.74, 0.88], P = 0.07 (R2)).

DATA CONCLUSION

Although conductivity imaging showed suboptimal performance in breast cancer detection, the quantitative measurement of conductivity showed the potential for lesion differentiation.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Deep Learning-Based Development of Personalized Human Head Model With Non-Uniform Conductivity for Brain Stimulation

Electromagnetic stimulation of the human brain is a key tool for neurophysiological characterization and the diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is a commonly used clinical procedure. However, personalized TMS requires a pipeline for individual head model generation to provide target-specific stimulation. This process includes intensive segmentation of several head tissues based on magnetic resonance imaging (MRI), which has significant potential for segmentation error, especially for low-contrast tissues. Additionally, a uniform electrical conductivity is assigned to each tissue in the model, which is an unrealistic assumption based on conventional volume conductor modeling. This study proposes a novel approach for fast and automatic estimation of the electric conductivity in the human head for volume conductor models without anatomical segmentation. A convolutional neural network is designed to estimate personalized electrical conductivity values based on anatomical information obtained from T1- and T2-weighted MRI scans. This approach can avoid the time-consuming process of tissue segmentation and maximize the advantages of position-dependent conductivity assignment based on the water content values estimated from MRI intensity values. The computational results of the proposed approach provide similar but smoother electric field distributions of the brain than that provided by conventional approaches.

Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry

Radio-frequency dosimetry is an important process in assessments for human exposure safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially to consider the inter-subject variability. However, a common procedure to develop personalized models is time consuming because it involves excessive segmentation of several components that represent different biological tissues, which is a major obstacle in the inter-subject variability assessment of radiation safety. Deep learning methods have been shown to be a powerful approach for pattern recognition and signal analysis. Convolutional neural networks with deep architecture are proven robust for feature extraction and image mapping in several biomedical applications. In this study, we develop a learning-based approach for fast and accurate estimation of the dielectric properties and density of tissues directly from magnetic resonance images in a single shot. The smooth distribution of the dielectric properties in head models, which is realized using a process without tissue segmentation, improves the smoothness of the specific absorption rate (SAR) distribution compared with that in the commonly used procedure. The estimated SAR distributions, as well as that averaged over 10 g of tissue in a cubic shape, are found to be highly consistent with those computed using the conventional methods that employ segmentation.