Diagnostic value of electric properties tomography (EPT) for differentiating benign from malignant breast lesions: comparison with standard dynamic contrast-enhanced MRI

Fast bias-corrected conductivity mapping using stimulated echoes

OBJECTIVE

To demonstrate the potential of a double angle stimulated echo (DA-STE) method for fast and accurate "full" homogeneous Helmholtz-based electrical properties tomography using a simultaneous B 1 + magnitude and transceive phase measurement.

METHODS

The combination of a spin and stimulated echo can be used to yield an estimate of both B 1 + magnitude and transceive phase and thus provides the means for "full" EPT reconstruction. An interleaved 2D acquisition scheme is used for rapid acquisition. The method was validated in a saline phantom and compared to a double angle method based on two single gradient echo acquisitions (GRE-DAM). The method was evaluated in the brain of a healthy volunteer.

RESULTS

The B 1 + magnitude obtained with DA-STE showed excellent agreement with the GRE-DAM method. Conductivity values based on the "full" EPT reconstruction also agreed well with the expectations in the saline phantom. In the brain, the method delivered conductivity values close to literature values.

DISCUSSION

The method allows the use of the "full" Helmholtz-based EPT reconstruction without the need for additional measurements. As a result, quantitative conductivity values are improved compared to phase-based EPT reconstructions. DA-STE is a fast complex- B 1 + mapping technique that could render EPT clinically relevant at 3 T.

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.

Using the Probability Density Function-Based Channel-Combination Bloch-Siegert Method Realizes Permittivity Imaging at 3T

Magnetic resonance electrical properties tomography (MR EPT) can retrieve permittivity from the B1+ magnitude. However, the accuracy of the permittivity measurement using MR EPT is still not ideal due to the low signal-to-noise ratio (SNR) of B1+ magnitude. In this study, the probability density function (PDF)-based channel-combination Bloch-Siegert (BSS) method was firstly introduced to MR EPT for improving the accuracy of the permittivity measurement. MRI experiments were performed using a 3T scanner with an eight-channel receiver coil. The homogeneous water phantom was scanned for assessing the spatial distribution of B1+ magnitude obtained from the PDF-based channel-combination BSS method. Gadolinium (Gd) phantom and rats were scanned for assessing the feasibility of the PDF-based channel-combination BSS method in MR EPT. The Helmholtz-based EPT reconstruction algorithm was selected. For quantitative comparison, the permittivity measured by the open-ended coaxial probe method was considered as the ground-truth value. The accuracy of the permittivity measurement was estimated by the relative error between the reconstructed value and the ground-truth value. The reconstructed relative permittivity of Gd phantom was 52.413, while that of rat leg muscle was 54.053. The ground-truth values of relative permittivity of Gd phantom and rat leg muscle were 78.86 and 49.04, respectively. The relative error of average permittivity was 33.53% for Gd and 10.22% for rat leg muscle. The results indicated the high accuracy of the permittivity measurement using the PDF-based channel-combination BSS method in MR EPT. This improvement may promote the clinical application of MR EPT technology, such as in the early diagnosis of cancers.

Application of Texture and Volume Model Analysis to Dedicated Axillary High-resolution 3D T2-weighted MR Imaging: A Novel Method for Diagnosing Lymph Node Metastasis in Patients with Clinically Node-negative Breast Cancer

PURPOSE

To evaluate the effectiveness of the texture analysis of axillary high-resolution 3D T2-weighted imaging (T2WI) in distinguishing positive and negative lymph node (LN) metastasis in patients with clinically node-negative breast cancer.

METHODS

Between December 2017 and May 2021, 242 consecutive patients underwent high-resolution 3D T2WI and were classified into the training (n = 160) and validation cohorts (n = 82). We performed manual 3D segmentation of all visible LNs in axillary level I to extract the texture features. As the additional parameters, the number of the LNs and the total volume of all LNs for each case were calculated. The least absolute shrinkage and selection operator algorithm and Random Forest were used to construct the models. We constructed the texture model using the features from the LN with the largest least axis length in the training cohort. Furthermore, we constructed the 3 models combining the selected texture features of the LN with the largest least axis length, the number of LNs, and the total volume of all LNs: texture-number model, texture-volume model, and texture-number-volume model. As a conventional method, we manually measured the largest cortical diameter. Moreover, we performed the receiver operating curve analysis in the validation cohort and compared area under the curves (AUCs) of the models.

RESULTS

The AUCs of the texture model, texture-number model, texture-volume model, texture-number-volume model, and conventional method in the validation cohort were 0.7677, 0.7403, 0.8129, 0.7448, and 0.6851, respectively. The AUC of the texture-volume model was higher than those of other models and conventional method. The sensitivity, specificity, positive predictive value, and negative predictive value of the texture-volume model were 90%, 69%, 49%, and 96%, respectively.

CONCLUSION

The texture-volume model of high-resolution 3D T2WI effectively distinguished positive and negative LN metastasis for patients with clinically node-negative breast cancer.

Problem Solving MRI to Reduce False-Positive Biopsy Related to Breast US: Conductivity vs. DWI vs. Abbreviated Contrast-Enhanced MRI

BACKGROUND

While breast ultrasound (US) is a useful tool for diagnosing breast masses, it can entail false-positive biopsy results because of some overlapping features between benign and malignant breast masses and subjective interpretation.

PURPOSE

To evaluate the performance of conductivity imaging for reducing false-positive biopsy results related to breast US, as compared to diffusion-weighted imaging (DWI) and abbreviated MRI consisting of one pre- and one post-contrast T1-weighted imaging.

STUDY TYPE

Prospective.

SUBJECTS

Seventy-nine women (median age, 44 years) with 86 Breast Imaging Reporting and Data System (BI-RADS) category 4 masses as detected by breast US.

FIELD STRENGTH/SEQUENCE

3-T, T2-weighted turbo spin echo sequence, DWI, and abbreviated contrast-enhanced MRI (T1-weighted gradient echo sequence).

ASSESSMENT

US-guided biopsy (reference standard) was obtained on the same day as MRI. The maximum and mean conductivity parameters from whole and single regions of interest (ROIs) were measured. Apparent diffusion coefficient (ADC) values were obtained from an area with the lowest signal within a lesion on the ADC map. The performance of conductivity, ADC, and abbreviated MRI for reducing false-positive biopsies was evaluated using the following criteria: lowest conductivity and highest ADC values among malignant breast lesions and BI-RADS categories 2 or 3 on abbreviated MRI.

STATISTICAL TESTS

One conductivity parameter with the maximum area under the curve (AUC) from receiver operating characteristics was selected. A P-value <0.05 was considered statistically significant.

RESULTS

US-guided biopsy revealed 65 benign lesions and 21 malignant lesions. The mean conductivity parameter of the single ROI method was selected (AUC = 0.74). Considering conductivity (≤0.10 S/m), ADC (≥1.60 × 10-3  mm2 /sec), and BI-RADS categories 2 or 3 reduced false-positive biopsies by 23% (15 of 65), 38% (25 of 65), and 43% (28 of 65), respectively, without missing malignant lesions.

DATA CONCLUSION

Conductivity imaging may show lower performance than DWI and abbreviated MRI in reducing unnecessary biopsies.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Data-driven electrical conductivity brain imaging using 3 T MRI

Magnetic resonance electrical properties tomography (MR-EPT) is a non-invasive measurement technique that derives the electrical properties (EPs, e.g., conductivity or permittivity) of tissues in the radiofrequency range (64 MHz for 1.5 T and 128 MHz for 3 T MR systems). Clinical studies have shown the potential of tissue conductivity as a biomarker. To date, model-based conductivity reconstructions rely on numerical assumptions and approximations, leading to inaccuracies in the reconstructed maps. To address such limitations, we propose an artificial neural network (ANN)-based non-linear conductivity estimator trained on simulated data for conductivity brain imaging. Network training was performed on 201 synthesized T2-weighted spin-echo (SE) data obtained from the finite-difference time-domain (FDTD) electromagnetic (EM) simulation. The dataset was composed of an approximated T2-w SE magnitude and transceive phase information. The proposed method was tested three in-silico and in-vivo on two volunteers and three patients' data. For comparison purposes, various conventional phase-based EPT reconstruction methods were used that ignoreB 1 + magnitude information, such as Savitzky-Golay kernel combined with Gaussian filter (S-G Kernel), phase-based convection-reaction EPT (cr-EPT), magnitude-weighted polynomial-fitting phase-based EPT (Poly-Fit), and integral-based phase-based EPT (Integral-based). From the in-silico experiments, quantitative analysis showed that the proposed method provides more accurate and improved quality (e.g., high structural preservation) conductivity maps compared to conventional reconstruction methods. Representatively, in the healthy brain in-silico phantom experiment, the proposed method yielded mean conductivity values of 1.97 ± 0.20 S/m for CSF, 0.33 ± 0.04 S/m for WM, and 0.52 ± 0.08 S/m for GM, which were closer to the ground-truth conductivity (2.00, 0.30, 0.50 S/m) than the integral-based method (2.56 ± 2.31, 0.39 ± 0.12, 0.68 ± 0.33 S/m). In-vivo ANN-based conductivity reconstructions were also of improved quality compared to conventional reconstructions and demonstrated network generalizability and robustness to in-vivo data and pathologies. The reported in-vivo brain conductivity values were in agreement with literatures. In addition, the proposed method was observed for various SNR levels (SNR levels = 10, 20, 40, and 58) and repeatability conditions (the eight acquisitions with the number of signal averages = 1). The preliminary investigations on brain tumor patient datasets suggest that the network trained on simulated dataset can generalize to unforeseen in-vivo pathologies, thus demonstrating its potential for clinical applications.

REC-NN: A reconstruction error compensation neural network for Magnetic Resonance Electrical Property Tomography (MREPT)

Electrical properties (EPs) are expected as biomarkers for early cancer detection. Magnetic resonance electrical properties tomography (MREPT) is a technique to non-invasively estimate the EPs of tissues from MRI measurements. While noise sensitivity and artifact problems of MREPT are being solved progressively through recent efforts, the loss of tissue contrast emerges as an obstacle to the clinical applications of MREPT. To solve the problem, we propose a reconstruction error compensation neural network scheme (REC-NN) for a typical analytic MREPT method, Stab-EPT. Two NN structures: one with only ResNet blocks, and the other hybridizing ResNet blocks with an encoder-decoder structure. Results of experiments with digital brain phantoms show that, compared with Stab-EPT, and conventional NN based reconstruction, REC-NN improves both reconstruction accuracy and tissue contrast. It is found that, the encoder-decoder structure could improve the compensation accuracy of EPs in homogeneous region but showed worse reconstruction than only ResNet structure for tumorous tissues unseen in the training samples. Future research is required to address overcompensation problems, optimization of NN structure and application to clinical data.

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.

Technical note: Multi-receiver combination method for phase-based electrical property tomography of the breast

BACKGROUND

Phase-based electrical property tomography (EPT) is a technique that allows conductivity reconstruction with only phase of the B₁ field under the assumption that the magnitude of the B₁ fields are homogeneous. The more this assumption is violated, the less accurate the reconstructed conductivity. Thus, a method that ensures homogeneity of | B 1 - | $| {{\rm{B}}_1^ - } |$ field is important for breast image using multi-receiver coil.

PURPOSE

To develop a method for multi-receiver combination for phase-based EPT usable for breast EPT imaging in the clinic.

METHODS

Theory of the proposed method is presented. To validate the proposed method, the phantom and in-vivo experiments were conducted. Conductivity images were reconstructed using the transceive phase of the combined image and results were compared with another combination method.

RESULTS

The proposed method's conductivity results were more stable than those of the previous method when | B 1 + | $| {{\rm{B}}_1^ + } |$ was not homogeneous and when the homogeneous contrast region was small. The phantom and in-vivo results indicate that the proposed method produces improved conductivity images than the previous method. The proposed combination method also increased the conductivity contrast between benign and cancerous tissues.

CONCLUSION

The proposed method produced more stable multi-receiver combination for phase-based EPT of the breast in a clinical environment.

3D Extrusion Printing of Biphasic Anthropomorphic Brain Phantoms Mimicking MR Relaxation Times Based on Alginate-Agarose-Carrageenan Blends

The availability of adapted phantoms mimicking different body parts is fundamental to establishing the stability and reliability of magnetic resonance imaging (MRI) methods. The primary purpose of such phantoms is the mimicking of physiologically relevant, contrast-creating relaxation times T1 and T2. For the head, frequently examined by MRI, an anthropomorphic design of brain phantoms would imply the discrimination of gray matter and white matter (WM) within defined, spatially distributed compartments. Multichannel extrusion printing allows the layer-by-layer fabrication of multiple pastelike materials in a spatially defined manner with a predefined shape. In this study, the advantages of this method are used to fabricate biphasic brain phantoms mimicking MR relaxation times and anthropomorphic geometry. The printable ink was based on purely naturally derived polymers: alginate as a calcium-cross-linkable gelling agent, agarose, ι-carrageenan, and GdCl3 in different concentrations (0-280 μmol kg-1) as the paramagnetic component. The suggested inks (e.g., 3Alg-1Agar-6Car) fulfilled the requirements of viscoelastic behavior and printability of large constructs (>150 mL). The microstructure and distribution of GdCl3 were assessed by scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDX). In closely monitored steps of technological development and characterization, from monophasic and biphasic samples as printable inks and cross-linked gels, we describe the construction of large-scale phantom models whose relaxation times were characterized and checked for stability over time.

Physics Informed Neural Networks (PINN) for Low Snr Magnetic Resonance Electrical Properties Tomography (MREPT)

Electrical properties (EPs) of tissues facilitate early detection of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is a technique to non-invasively probe the EPs of tissues from MRI measurements. Most MREPT methods rely on numerical differentiation (ND) to solve partial differential Equations (PDEs) to reconstruct the EPs. However, they are not practical for clinical data because ND is noise sensitive and the MRI measurements for MREPT are noisy in nature. Recently, Physics informed neural networks (PINNs) have been introduced to solve PDEs by substituting ND with automatic differentiation (AD). To the best of our knowledge, it has not been applied to MREPT due to the challenges in using PINN on MREPT as (i) a PINN requires part of ground-truth EPs as collocation points to optimize the network's AD, (ii) the noisy input data disrupts the optimization of PINNs despite the noise-filtering nature of NNs and additional denoising processes. In this work, we propose a PINN-MREPT model based on a canonical analytic MREPT model. A reference padding layer with known EPs was added to surround the region of interest for providing additive collocation points. Moreover, an optimizable diffusion coefficient was embedded in the analytic MREPT model used in the PINN-MREPT. The noise robustness of the proposed PINN-MREPT for single-sample reconstruction was tested by using numerical phantoms of human brain with extra tumor-like tissues at different noise levels. The results of numerical experiments show that PINN-MREPT outperforms two typical numerical MREPT methods in terms of reconstruction accuracy, sensitivity to the extra tissues, and the correlations of line profiles in the regions of interest. The advantage of the PINN-MREPT is shown by the results of an experiment on phantom measurement, too. Moreover, it is found that the diffusion term plays an important role to achieve a noise-robust PINN-MREPT. This is an important step moving forward to a clinical application of MREPT.

Sigmoid model analysis of breast dynamic contrast-enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction

Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed to distinguish between benign and malignant lesions by evaluating the changes in signal intensity of the acquired image (kinetic curve). This study aimed to verify whether the existing breast DCE‐MRI analyzed by the sigmoid model can accurately distinguish between benign and invasive ductal carcinoma (IDC) and predict the subtype.

A total of 154 patients who underwent breast MRI for detailed breast mass examinations were included in this study (38 with benign masses and 116 with IDC. The sigmoid model involved the acquisition of images at seven timepoints in 1‐min intervals to determine the change in signal intensity before and after contrast injection. From this curve, the magnitude of the increase in signal intensity in the early phase, the time to reach the maximum increase, and the slopes in the early and late phases were calculated. The Mann–Whitney U‐test was used for the statistical analysis.

The IDC group exhibited a significantly larger and faster signal increase in the early phase and a significantly smaller rate of increase in the late phase than the benign group (P < 0.001). The luminal A‐like group demonstrated a significantly longer time to reach the maximum signal increase rate than other IDC subtypes (P < 0.05).

The sigmoid model analysis of breast DCE‐MRI can distinguish between benign lesions and IDC and may also help in predicting luminal A‐like breast cancer.

In vivo electrical conductivity measurement of muscle, cartilage, and peripheral nerve around knee joint using MR-electrical properties tomography

This study aimed to investigate whether in vivo MR-electrical properties tomography (MR-EPT) is feasible in musculoskeletal tissues by evaluating the conductivity of muscle, cartilage, and peripheral nerve around the knee joint, and to explore whether these measurements change after exercise. This prospective study was approved by the institutional review board. On February 2020, ten healthy volunteers provided written informed consent and underwent MRI of the right knee using a three-dimensional balanced steady-state free precession (bSSFP) sequence. To test the effect of loading, the subjects performed 60 squatting exercises after baseline MRI, immediately followed by post-exercise MRI with the same sequences. After reconstruction of conductivity map based on the bSSFP sequence, conductivity of muscles, cartilages, and nerves were measured. Measurements between the baseline and post-exercise MRI were compared using the paired t-test. Test–retest reliability for baseline conductivity was evaluated using the intraclass correlation coefficient. The baseline and post-exercise conductivity values (mean ± standard deviation) [S/m] of muscles, cartilages, and nerves were 1.73 ± 0.40 and 1.82 ± 0.50 (p = 0.048), 2.29 ± 0.47 and 2.51 ± 0.37 (p = 0.006), and 2.35 ± 0.57 and 2.36 ± 0.57 (p = 0.927), respectively. Intraclass correlation coefficient for the baseline conductivity of muscles, cartilages, and nerves were 0.89, 0.67, and 0.89, respectively. In conclusion, in vivo conductivity measurement of musculoskeletal tissues is feasible using MR-EPT. Conductivity of muscles and cartilages significantly changed with an overall increase after exercise.

Development of MRI-based axillary numerical models and estimation of axillary lymph node dielectric properties for microwave imaging

PURPOSE

Microwave imaging (MWI) has been studied as a complementary imaging modality to improve sensitivity and specificity of diagnosis of axillary lymph nodes (ALNs), which can be metastasized by breast cancer. The feasibility of such a system is based on the dielectric contrast between healthy and metastasized ALNs. However, reliable information such as anatomically realistic numerical models and matching dielectric properties of the axillary region and ALNs, which are crucial to develop MWI systems, are still limited in the literature. The purpose of this work is to develop a methodology to infer dielectric properties of structures from magnetic resonance imaging (MRI), in particular, ALNs. We further use this methodology, which is tailored for structures farther away from MR coils, to create MRI-based numerical models of the axillary region and share them with the scientific community, through an open-access repository.

METHODS

We use a dataset of breast MRI scans of 40 patients, 15 of them with metastasized ALNs. We apply image processing techniques to minimize the artifacts in MR images and segment the tissues of interest. The background, lung cavity, and skin are segmented using thresholding techniques and the remaining tissues are segmented using a K-means clustering algorithm. The ALNs are segmented combining the clustering results of two MRI sequences. The performance of this methodology was evaluated using qualitative criteria. We then apply a piecewise linear interpolation between voxel signal intensities and known dielectric properties, which allow us to create dielectric property maps within an MRI and consequently infer ALN properties. Finally, we compare healthy and metastasized ALN dielectric properties within and between patients, and we create an open-access repository of numerical axillary region numerical models which can be used for electromagnetic simulations.

RESULTS

The proposed methodology allowed creating anatomically realistic models of the axillary region, segmenting 80 ALNs and analyzing the corresponding dielectric properties. The estimated relative permittivity of those ALNs ranged from 16.6 to 49.3 at 5 GHz. We observe there is a high variability of dielectric properties of ALNs, which can be mainly related to the ALN size and, consequently, its composition. We verified an average dielectric contrast of 29% between healthy and metastasized ALNs. Our repository comprises 10 numerical models of the axillary region, from five patients, with variable number of metastasized ALNs and body mass index.

CONCLUSIONS

The observed contrast between healthy and metastasized ALNs is a good indicator for the feasibility of a MWI system aiming to diagnose ALNs. This paper presents new contributions regarding anatomical modeling and dielectric properties' characterization, in particular for axillary region applications.

High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network

Magnetic resonance electrical properties tomography (MREPT) aims to visualize the internal high-frequency conductivity distribution at Larmor frequency using the B1 transceive phase data. From the magnetic field perturbation by the electrical field associated with the radiofrequency (RF) magnetic field, the high-frequency conductivity and permittivity distributions inside the human brain have been reconstructed based on the Maxwell’s equation. Starting from the Maxwell’s equation, the complex permittivity can be described as a second order elliptic partial differential equation. The established reconstruction algorithms have focused on simplifying and/or regularizing the elliptic partial differential equation to reduce the noise artifact. Using the nonlinear relationship between the Maxwell’s equation, measured magnetic field, and conductivity distribution, we design a deep learning model to visualize the high-frequency conductivity in the brain, directly derived from measured magnetic flux density. The designed moving local window multi-layer perceptron (MLW-MLP) neural network by sliding local window consisting of neighboring voxels around each voxel predicts the high-frequency conductivity distribution in each local window. The designed MLW-MLP uses a family of multiple groups, consisting of the gradients and Laplacian of measured B1 phase data, as the input layer in a local window. The output layer of MLW-MLP returns the conductivity values in each local window. By taking a non-local mean filtering approach in the local window, we reconstruct a noise suppressed conductivity image while maintaining spatial resolution. To verify the proposed method, we used B1 phase datasets acquired from eight human subjects (five subjects for training procedure and three subjects for predicting the conductivity in the brain).

Improving phase-based conductivity reconstruction by means of deep learning-based denoising of B 1 + phase data for 3T MRI

PURPOSE

To denoise B 1 + phase using a deep learning method for phase-based in vivo electrical conductivity reconstruction in a 3T MR system.

METHODS

For B 1 + phase deep-learning denoising, a convolutional neural network (U-net) was chosen. Training was performed on data sets from 10 healthy volunteers. Input data were the real and imaginary components of single averaged spin-echo data (SNR = 45), which was used to approximate the B 1 + phase. For label data, multiple signal-averaged spin-echo data (SNR = 128) were used. Testing was performed on in silico and in vivo data. Reconstructed conductivity maps were derived using phase-based conductivity reconstructions. Additionally, we investigated the usability of the network to various SNR levels, imaging contrasts, and anatomical sites (ie, T₁ , T₂ , and proton density-weighted brain images and proton density-weighted breast images. In addition, conductivity reconstructions from deep learning-based denoised data were compared with conventional image filters, which were used for data denoising in electrical properties tomography (ie, the Gaussian filtering and the Savitzky-Golay filtering).

RESULTS

The proposed deep learning-based denoising approach showed improvement for B 1 + phase for both in silico and in vivo experiments with reduced quantitative error measures compared with other methods. Subsequently, this resulted in an improvement of reconstructed conductivity maps from the denoised B 1 + phase with deep learning.

CONCLUSION

The results suggest that the proposed approach can be used as an alternative preprocessing method to denoise B 1 + maps for phase-based conductivity reconstruction without relying on image filters or signal averaging.

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.

Low conductivity on electrical properties tomography demonstrates unique tumor habitats indicating progression in glioblastoma

OBJECTIVES

Tissue conductivity measurements made with electrical properties tomography (EPT) can be used to define temporal changes in tissue habitats on longitudinal multiparametric MRI. We aimed to demonstrate the added insights for identifying tumor habitats obtained by including EPT with diffusion- and perfusion-weighted MRI, and to evaluate the use of these tumor habitats for determining tumor treatment response in post-treatment glioblastoma.

METHODS

Tumor habitats were developed from EPT, diffusion-weighted, and perfusion-weighted MRI in 60 patients with glioblastoma who underwent concurrent chemoradiotherapy. Voxels from EPT, apparent diffusion coefficient (ADC), and cerebral blood volume (CBV) maps were clustered into habitats, and each habitat was serially examined to assess its temporal change. The usefulness of temporal changes in tumor habitats for diagnosing tumor progression and treatment-related change was investigated using logistic regression. The performance of significant predictors was measured using the area under the curve (AUC) from receiver-operating-characteristics analysis with 1000-fold bootstrapping.

RESULTS

Five tumor habitats were identified, and of these, the hypervascular cellular habitat (odds ratio [OR] 5.45; 95% CI, 1.75-31.42; p = .02), hypovascular low conductivity habitat (OR 2.00; 95% CI, 1.45-3.05; p < .001), and hypovascular intermediate habitat (OR 1.57; 95% CI, 1.18-2.30; p = .006) were predictive of tumor progression. Low EPT and low CBV reflected a unique hypovascular low conductivity habitat that showed the highest diagnostic performance (AUC 0.86; 95% CI, 0.76-0.96). The combined habitats showed high performance (AUC 0.90; 95% CI, 0.82-0.98) in the differentiation of tumor progression from treatment-related change.

CONCLUSION

EPT reveals low conductivity habitats that can improve the diagnosis of tumor progression in post-treatment glioblastoma.

KEY POINTS

• Electrical properties tomography (EPT) demonstrated lower conductivity in tumor progression than in treatment-related change. • EPT allowed identification of a unique hypovascular low conductivity habitat when combined with cerebral blood volume mapping. • Tumor habitats with a hypovascular low conductivity habitat, hypervascular cellular habitat, and hypovascular intermediate habitat yielded high diagnostic performance for diagnosing tumor progression.

Decomposition of high-frequency electrical conductivity into extracellular and intracellular compartments based on two-compartment model using low-to-high multi-b diffusion MRI

Background

As an object’s electrical passive property, the electrical conductivity is proportional to the mobility and concentration of charged carriers that reflect the brain micro-structures. The measured multi-b diffusion-weighted imaging (Mb-DWI) data by controlling the degree of applied diffusion weights can quantify the apparent mobility of water molecules within biological tissues. Without any external electrical stimulation, magnetic resonance electrical properties tomography (MREPT) techniques have successfully recovered the conductivity distribution at a Larmor-frequency.

Methods

This work provides a non-invasive method to decompose the high-frequency conductivity into the extracellular medium conductivity based on a two-compartment model using Mb-DWI. To separate the intra- and extracellular micro-structures from the recovered high-frequency conductivity, we include higher b-values DWI and apply the random decision forests to stably determine the micro-structural diffusion parameters.

Results

To demonstrate the proposed method, we conducted phantom and human experiments by comparing the results of reconstructed conductivity of extracellular medium and the conductivity in the intra-neurite and intra-cell body. The phantom and human experiments verify that the proposed method can recover the extracellular electrical properties from the high-frequency conductivity using a routine protocol sequence of MRI scan.

Conclusion

We have proposed a method to decompose the electrical properties in the extracellular, intra-neurite, and soma compartments from the high-frequency conductivity map, reconstructed by solving the electro-magnetic equation with measured B1 phase signals.

Staging MRI of uterine malignant mixed Müllerian tumors versus endometrial carcinomas with emphasis on dynamic enhancement characteristics

PURPOSE

To determine whether staging pelvic magnetic resonance imaging (MRI) can distinguish malignant mixed Müllerian tumor (MMMT) from EC.

METHODS

Thirty-seven treatment-naïve patients with histologically proven uterine MMMT and 42 treatment-naïve patients with EC, treated at our institution, were included in our retrospective study. Staging pelvic MRI scans were reviewed for tumor size, prolapse through cervical os, and other features. Time-intensity curves for tumor and surrounding myometrium regions of interest were generated, and positive enhancement integral (PEI), maximum slope of increase (MSI), and signal enhancement ratio (SER) were measured. The Fisher's exact test or Wilcoxon rank-sum test was used to compare characteristics between disease groups. Multivariate and univariate logistic regression models were used to distinguish MMMT from EC. Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate prediction ability.

RESULTS

MMMTs were larger than ECs with higher rate of tumor prolapse and more heterogeneous tumor enhancement compared to ECs. During the late phase of contrast enhancement, 100% of ECs, but only 84% of MMMTs, had lower signal intensity than the myometrium. Threshold PEI ratio ≥ 0.67 predict MMMT with 76% sensitivity, 84%, specificity and 0.83 AUC. Threshold SER ≤ 125 predict MMMT with 90% sensitivity, 50% specificity, and 0.72 AUC.

CONCLUSION

MMMTs may show more frequent tumor prolapse, more heterogeneous enhancement, delayed iso- or hyper-enhancement, higher PEI ratios, and lower tumor SERs compared with EC. MRI can be used as a biomarker to distinguish MMMT from EC based on the enhancement pattern.

Transceive phase mapping using the PLANET method and its application for conductivity mapping in the brain

Purpose

To demonstrate feasibility of transceive phase mapping with the PLANET method and its application for conductivity reconstruction in the brain.

Methods

Accuracy and precision of transceive phase (ϕ^±) estimation with PLANET, an ellipse fitting approach to phase‐cycled balanced steady state free precession (bSSFP) data, were assessed with simulations and measurements and compared to standard bSSFP. Measurements were conducted on a homogeneous phantom and in the brain of healthy volunteers at 3 tesla. Conductivity maps were reconstructed with Helmholtz‐based electrical properties tomography. In measurements, PLANET was also compared to a reference technique for transceive phase mapping, i.e., spin echo.

Results

Accuracy and precision of ϕ^± estimated with PLANET depended on the chosen flip angle and TR. PLANET‐based ϕ^± was less sensitive to perturbations induced by off‐resonance effects and partial volume (e.g., white matter + myelin) than bSSFP‐based ϕ^±. For flip angle = 25° and TR = 4.6 ms, PLANET showed an accuracy comparable to that of reference spin echo but a higher precision than bSSFP and spin echo (factor of 2 and 3, respectively). The acquisition time for PLANET was ~5 min; 2 min faster than spin echo and 8 times slower than bSSFP. However, PLANET simultaneously reconstructed T₁, T₂, B₀ maps besides mapping ϕ^±. In the phantom, PLANET‐based conductivity matched the true value and had the smallest spread of the three methods. In vivo, PLANET‐based conductivity was similar to spin echo‐based conductivity.

Conclusion

Provided that appropriate sequence parameters are used, PLANET delivers accurate and precise ϕ^± maps, which can be used to reconstruct brain tissue conductivity while simultaneously recovering T₁, T₂, and B₀ maps.