1
|
Katscher U. Editorial for "Effect of Ethanol on Brain Electrical Tissue Conductivity in Social Drinkers". J Magn Reson Imaging 2024. [PMID: 39166881 DOI: 10.1002/jmri.29549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Accepted: 06/20/2024] [Indexed: 08/23/2024] Open
|
2
|
Iyyakkunnel S, Weigel M, Bieri O. Fast bias-corrected conductivity mapping using stimulated echoes. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01194-3. [PMID: 39105952 DOI: 10.1007/s10334-024-01194-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 07/15/2024] [Accepted: 07/22/2024] [Indexed: 08/07/2024]
Abstract
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.
Collapse
Affiliation(s)
- Santhosh Iyyakkunnel
- Division of Radiological Physics, Department of Radiology , University Hospital Basel, Basel, Switzerland.
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Matthias Weigel
- Division of Radiological Physics, Department of Radiology , University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Oliver Bieri
- Division of Radiological Physics, Department of Radiology , University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| |
Collapse
|
3
|
He Z, Soullié P, Lefebvre P, Ambarki K, Felblinger J, Odille F. Changes of in vivo electrical conductivity in the brain and torso related to age, fat fraction and sex using MRI. Sci Rep 2024; 14:16109. [PMID: 38997324 PMCID: PMC11245625 DOI: 10.1038/s41598-024-67014-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 07/08/2024] [Indexed: 07/14/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Zhongzheng He
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
| | - Paul Soullié
- IADI U1254, INSERM and Université de Lorraine, Nancy, France.
| | | | | | - Jacques Felblinger
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
- CIC-IT 1433, INSERM, Université de Lorraine and CHRU Nancy, Nancy, France
| | - Freddy Odille
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
- CIC-IT 1433, INSERM, Université de Lorraine and CHRU Nancy, Nancy, France
| |
Collapse
|
4
|
Çan MK, Ider YZ. Bias correction for phase-based cr-MREPT using low resolution B1+ magnitude. Phys Med Biol 2024; 69:125020. [PMID: 38830364 DOI: 10.1088/1361-6560/ad53a1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 06/03/2024] [Indexed: 06/05/2024]
Abstract
ObjectiveFull-form Magnetic Resonance Electrical Properties Tomography (MREPT) requires bothB1+magnitude and phase information. SinceB1+phase can be obtained faster and with higher SNR compared toB1+magnitude, several phase-based methods have been developed for conductivity imaging. However, phase-based methods suffer from a concave bias due to the assumption that∇|B1+|is negligible in the ROI.ApproachIn this paper, we re-derive the central equation of phase-based cr-MREPT without assuming that∇|B1+|is negligible and thus propose a correction method directly integrated into the equation system.Main resultsProposed method successfully corrects the concave bias on both simulated and experimental data and significantly increases image quality.SignificanceThe proposed correction method depends on a very low-resolution|B1+|map, and therefore the imaging time does not increase significantly for obtainingB1+magnitude. Moreover, correction can be achieved using simulatedB1+magnitude, hence completely removing the additional imaging requirement.
Collapse
Affiliation(s)
- Mustafa Kaan Çan
- Department of Electrical and Electronics Engineering, Bilkent University, 06800 Ankara, Turkey
| | - Yusuf Ziya Ider
- Department of Biomedical Engineering, Başkent University, 06790 Ankara, Turkey
| |
Collapse
|
5
|
Zumbo S, Mandija S, Meliadò EF, Stijnman P, Meerbothe TG, van den Berg CA, Isernia T, Bevacqua MT. Unrolled Optimization via Physics-Assisted Convolutional Neural Network for MR-Based Electrical Properties Tomography: A Numerical Investigation. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:505-513. [PMID: 39050972 PMCID: PMC11268945 DOI: 10.1109/ojemb.2024.3402998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/14/2024] [Accepted: 05/14/2024] [Indexed: 07/27/2024] Open
Abstract
Magnetic Resonance imaging based Electrical Properties Tomography (MR-EPT) is a non-invasive technique that measures the electrical properties (EPs) of biological tissues. In this work, we present and numerically investigate the performance of an unrolled, physics-assisted method for 2D MR-EPT reconstructions, where a cascade of Convolutional Neural Networks is used to compute the contrast update. Each network takes in input the EPs and the gradient descent direction (encoding the physics underlying the adopted scattering model) and returns as output the updated contrast function. The network is trained and tested in silico using 2D slices of realistic brain models at 128 MHz. Results show the capability of the proposed procedure to reconstruct EPs maps with quality comparable to that of the popular Contrast Source Inversion-EPT, while significantly reducing the computational time.
Collapse
Affiliation(s)
- Sabrina Zumbo
- Department DIIESUniversità Mediterranea di Reggio Calabria89124Reggio CalabriaItaly
| | - Stefano Mandija
- Department of Radiotherapy, Division of Imaging & OncologyUniversity Medical Center Utrecht3584 CXUtrechtThe Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image SciencesUtrecht University3584 CSUtrechtThe Netherlands
| | - Ettore F. Meliadò
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image SciencesUtrecht University3584 CSUtrechtThe Netherlands
| | - Peter Stijnman
- Department of Radiotherapy, Division of Imaging & OncologyUniversity Medical Center Utrecht3584 CXUtrechtThe Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image SciencesUtrecht University3584 CSUtrechtThe Netherlands
| | - Thierry G. Meerbothe
- Department of Radiotherapy, Division of Imaging & OncologyUniversity Medical Center Utrecht3584 CXUtrechtThe Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image SciencesUtrecht University3584 CSUtrechtThe Netherlands
| | - Cornelis A.T. van den Berg
- Department of Radiotherapy, Division of Imaging & OncologyUniversity Medical Center Utrecht3584 CXUtrechtThe Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image SciencesUtrecht University3584 CSUtrechtThe Netherlands
| | - Tommaso Isernia
- Department DIIESUniversità Mediterranea di Reggio Calabria89124Reggio CalabriaItaly
| | - Martina T. Bevacqua
- Department DIIESUniversità Mediterranea di Reggio Calabria89124Reggio CalabriaItaly
| |
Collapse
|
6
|
Özdemir S, Ilicak E, Zapp J, Schad LR, Zöllner FG. Feasibility of undersampled spiral trajectories in MREPT for fast conductivity imaging. Magn Reson Med 2024; 91:1567-1575. [PMID: 38044757 DOI: 10.1002/mrm.29952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/09/2023] [Accepted: 11/13/2023] [Indexed: 12/05/2023]
Abstract
PURPOSE To investigate spiral-based imaging including trajectories with undersampling as a fast and robust alternative for phase-based magnetic resonance electrical properties tomography (MREPT) techniques. METHODS Spiral trajectories with various undersampling ratios were prescribed to acquire images from an experimental phantom and a healthy volunteer at 3T. The non-Cartesian acquisitions were reconstructed using SPIRiT, and conductivity maps were derived using phase-based cr-MREPT. The resulting maps were compared between different sampling trajectories. Additionally, a conductivity map was obtained using a Cartesian balanced SSFP acquisition from the volunteer to comparatively demonstrate the robustness of the proposed method. RESULTS The phantom and volunteer results illustrate the benefits of the spiral acquisitions. Specifically, undersampled spiral acquisitions display improved robustness against field inhomogeneity artifacts and lowered SD values with shortened readout times. Furthermore, average of conductivity values measured for the cerebrospinal fluid with the spiral acquisitions were 1.703 S/m, indicating a close agreement with the theoretical values of 1.794 S/m. CONCLUSION A spiral-based acquisition framework for conductivity imaging with and without undersampling is presented. Overall, spiral-based acquisitions improved robustness against field inhomogeneity artifacts, while achieving whole head coverage with multiple averages in less than a minute.
Collapse
Affiliation(s)
- Safa Özdemir
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Efe Ilicak
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jascha Zapp
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lothar R Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| |
Collapse
|
7
|
Tha KK. One size does not fit all: qualitative vs. quantitative arterial spin labelling MRI assessment. Eur Radiol 2023; 33:8002-8004. [PMID: 37812298 DOI: 10.1007/s00330-023-10280-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/10/2023] [Accepted: 09/21/2023] [Indexed: 10/10/2023]
Affiliation(s)
- Khin Khin Tha
- Global Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, N15 W7, Kita-Ku, Sapporo, 060-8638, Japan.
| |
Collapse
|
8
|
Jung K, Mandija S, Cui C, Kim J, Al‐masni MA, Meerbothe TG, Park M, van den Berg CAT, Kim D. Data-driven electrical conductivity brain imaging using 3 T MRI. Hum Brain Mapp 2023; 44:4986-5001. [PMID: 37466309 PMCID: PMC10502651 DOI: 10.1002/hbm.26421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 06/14/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Kyu‐Jin Jung
- Department of Electrical and Electronic EngineeringYonsei UniversitySeoulRepublic of Korea
| | - Stefano Mandija
- Computational Imaging Group for MR Therapy and DiagnosticsUniversity Medical Center UtrechtUtrechtThe Netherlands
- Department of RadiotherapyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Chuanjiang Cui
- Department of Electrical and Electronic EngineeringYonsei UniversitySeoulRepublic of Korea
| | - Jun‐Hyeong Kim
- Department of Electrical and Electronic EngineeringYonsei UniversitySeoulRepublic of Korea
| | - Mohammed A. Al‐masni
- Department of Artificial IntelligenceCollege of Software & Convergence Technology, Daeyang AI Center, Sejong UniversitySeoulRepublic of Korea
| | - Thierry G. Meerbothe
- Computational Imaging Group for MR Therapy and DiagnosticsUniversity Medical Center UtrechtUtrechtThe Netherlands
- Department of RadiotherapyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Mina Park
- Department of Radiology, Gangnam Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Cornelis A. T. van den Berg
- Computational Imaging Group for MR Therapy and DiagnosticsUniversity Medical Center UtrechtUtrechtThe Netherlands
- Department of RadiotherapyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Dong‐Hyun Kim
- Department of Electrical and Electronic EngineeringYonsei UniversitySeoulRepublic of Korea
| |
Collapse
|
9
|
He Z, Chen B, Lefebvre PM, Odille F. An Adaptative Savitzky-Golay Kernel for Laplacian Estimation in Magnetic Resonance Electrical Property Tomography . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083553 DOI: 10.1109/embc40787.2023.10341200] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Magnetic Resonance electrical property tomography (MR-EPT) is a non-invasive imaging modality that reconstructs the living biological tissue's conductivity σ and εr permittivity using spatial derivatives of the measured RF field, also termed B1 data, in a magnetic resonance imaging system. The spatial derivative operator, particularly the Laplacian, amplifies the noise in the reconstructed electrical property (EP) maps, hence decreasing accuracy and increasing boundary artifacts. We propose a novel adaptative convolution kernel for generating numerical derivatives based on 3D Savitzky-Golay (SG) filters and local segmentation in a magnitude image. In comparison to typical SG kernel, the proposed kernel allows arbitrary shapes and sizes to vary with local tissue. It provides an automatic trade-off between noise and resolution, thereby significantly enhancing reconstruction accuracy and eliminating boundary artifacts.
Collapse
|
10
|
Qin R, Garcia Inda AJ, Zhou Z, Enomoto Y, Yang T, Imamoglu N, Gomez-Tames J, Huang S, Yu W. REC-NN: A reconstruction error compensation neural network for Magnetic Resonance Electrical Property Tomography (MREPT). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082583 DOI: 10.1109/embc40787.2023.10340423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
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.
Collapse
|
11
|
Hamaguchi H, Kitagawa M, Sakamoto D, Katscher U, Sudo H, Yamada K, Kudo K, Tha KK. Quantitative Assessment of Intervertebral Disc Composition by MRI: Sensitivity to Diurnal Variation. Tomography 2023; 9:1029-1040. [PMID: 37218944 DOI: 10.3390/tomography9030084] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/24/2023] Open
Abstract
Whether diurnal variation exists in quantitative MRI indices such as the T1rho relaxation time (T1ρ) of the intervertebral disc (IVD) is yet to be explored. This prospective study aimed to evaluate the diurnal variation in T1ρ, apparent diffusion coefficient (ADC), and electrical conductivity (σ) of lumbar IVD and its relationship with other MRI or clinical indices. Lumbar spine MRI, including T1ρ imaging, diffusion-weighted imaging (DWI), and electric properties tomography (EPT), was conducted on 17 sedentary workers twice (morning and evening) on the same day. The T1ρ, ADC, and σ of IVD were compared between the time points. Their diurnal variation, if any, was tested for correlation with age, body mass index (BMI), IVD level, Pfirrmann grade, scan interval, and diurnal variation in IVD height index. The results showed a significant decrease in T1ρ and ADC and a significant increase in the σ of IVD in the evening. T1ρ variation had a weak correlation with age and scan interval, and ADC variation with scan interval. Diurnal variation exists for the T1ρ, ADC, and σ of lumbar IVD, which should be accounted for in image interpretation. This variation is thought to be due to diurnal variations in intradiscal water, proteoglycan, and sodium ion concentration.
Collapse
Affiliation(s)
- Hiroyuki Hamaguchi
- Laboratory for Biomarker Imaging Science, Graduate School of Biomedical Science and Engineering, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, Japan
| | - Maho Kitagawa
- Laboratory for Biomarker Imaging Science, Graduate School of Biomedical Science and Engineering, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, Japan
| | - Daiki Sakamoto
- Laboratory for Biomarker Imaging Science, Graduate School of Biomedical Science and Engineering, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, Japan
| | - Ulrich Katscher
- Philips Research Laboratories, Roentgenstrasse 24-26, 22335 Hamburg, Germany
| | - Hideki Sudo
- Department of Orthopaedic Surgery, Hokkaido University Hospital, N14 W5, Kita-ku, Sapporo 060-8648, Japan
| | - Katsuhisa Yamada
- Department of Orthopaedic Surgery, Hokkaido University Hospital, N14 W5, Kita-ku, Sapporo 060-8648, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Hokkaido University Hospital, N14 W5, Kita-ku, Sapporo 060-8648, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, Japan
| | - Khin Khin Tha
- Laboratory for Biomarker Imaging Science, Graduate School of Biomedical Science and Engineering, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, Japan
| |
Collapse
|
12
|
Hernandez D, Kim KN. Use of machine learning to improve the estimation of conductivity and permittivity based on longitudinal relaxation time T1 in magnetic resonance at 7 T. Sci Rep 2023; 13:7837. [PMID: 37188769 DOI: 10.1038/s41598-023-35104-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/12/2023] [Indexed: 05/17/2023] Open
Abstract
Electrical property tomography (EPT) is a noninvasive method that uses magnetic resonance imaging (MRI) to estimate the conductivity and permittivity of tissues, and hence, can be used as a biomarker. One branch of EPT is based on the correlation of water and relaxation time T1 with the conductivity and permittivity of tissues. This correlation was applied to a curve-fitting function to estimate electrical properties, it was found to have a high correlation between permittivity and T1 however the computation of conductivity based on T1 requires to estimate the water content. In this study, we developed multiple phantoms with several ingredients that modify the conductivity and permittivity and explored the use of machine learning algorithms to have a direct estimation of conductivity and permittivity based on MR images and the relaxation time T1. To train the algorithms, each phantom was measured using a dielectric measurement device to acquire the true conductivity and permittivity. MR images were taken for each phantom, and the T1 values were measured. Then, the acquired data were tested using curve fitting, regression learning, and neural fit models to estimate the conductivity and permittivity values based on the T1 values. In particular, the regression learning algorithm based on Gaussian process regression showed high accuracy with a coefficient of determination R2 of 0.96 and 0.99 for permittivity and conductivity, respectively. The estimation of permittivity using regression learning demonstrated a lower mean error of 0.66% compared to the curve fitting method, which resulted in a mean error of 3.6%. The estimation of conductivity also showed that the regression learning approach had a lower mean error of 0.49%, whereas the curve fitting method resulted in a mean error of 6%. The findings suggest that utilizing regression learning models, specifically Gaussian process regression, can result in more accurate estimations for both permittivity and conductivity compared to other methods.
Collapse
Affiliation(s)
- Daniel Hernandez
- Neuroscience Research Institute, Gachon University, Incheon, 21988, Korea
| | - Kyoung-Nam Kim
- Department of Biomedical Engineering, Gachon University, Seongnam, 13120, Korea.
| |
Collapse
|
13
|
Kim JH, Shin J, Jung KJ, Cui C, Kim SY, Lee JH, Kim DH. Technical note: Multi-receiver combination method for phase-based electrical property tomography of the breast. Med Phys 2023; 50:1660-1669. [PMID: 36585806 DOI: 10.1002/mp.16195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 11/15/2022] [Accepted: 12/11/2022] [Indexed: 01/01/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Jun-Hyeong Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Jaewook Shin
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Kyu-Jin Jung
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Chuanjiang Cui
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Soo-Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jae-Hun Lee
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| |
Collapse
|
14
|
High frequency conductivity decomposition by solving physically constraint underdetermined inverse problem in human brain. Sci Rep 2023; 13:3273. [PMID: 36841894 PMCID: PMC9968322 DOI: 10.1038/s41598-023-30344-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 02/21/2023] [Indexed: 02/27/2023] Open
Abstract
The developed magnetic resonance electrical properties tomography (MREPT) can visualize the internal conductivity distribution at Larmor frequency by measuring the B1 transceive phase data from magnetic resonance imaging (MRI). The recovered high-frequency conductivity (HFC) value is highly complex and heterogeneous in a macroscopic imaging voxel. Using high and low b-value diffusion weighted imaging (DWI) data, the multi-compartment spherical mean technique (MC-SMT) characterizes the water molecule movement within and between intra- and extra-neurite compartments by analyzing the microstructures and underlying architectural organization of brain tissues. The proposed method decomposes the recovered HFC into the conductivity values in the intra- and extra-neurite compartments via the recovered intra-neurite volume fraction (IVF) and the diffusion patterns using DWI data. As a form of decomposition of intra- and extra-neurite compartments, the problem to determine the intra- and extra-neurite conductivity values from the HFC is still an underdetermined inverse problem. To solve the underdetermined problem, we use the compartmentalized IVF as a criterion to decompose the electrical properties because the ion-concentration and mobility have different characteristics in the intra- and extra-neurite compartments. The proposed method determines a representative apparent intra- and extra-neurite conductivity values by changing the underdetermined equation for a voxel into an over-determined minimization problem over a local window consisting of surrounding voxels. To suppress the noise amplification and estimate a feasible conductivity, we define a diffusion pattern distance to weight the over-determined system in the local window. To quantify the proposed method, we conducted a simulation experiment. The simulation experiments show the relationships between the noise reduction and the spatial resolution depending on the designed local window sizes and diffusion pattern distance. Human brain experiments (five young healthy volunteers and a patient with brain tumor) were conducted to evaluate and validate the reliability of the proposed method. To quantitatively compare the results with previously developed methods, we analyzed the errors for reconstructed extra-neurite conductivity using existing methods and indirectly verified the feasibility of the proposed method.
Collapse
|
15
|
Arduino A, Pennecchi F, Katscher U, Cox M, Zilberti L. Repeatability and Reproducibility Uncertainty in Magnetic Resonance-Based Electric Properties Tomography of a Homogeneous Phantom. Tomography 2023; 9:420-435. [PMID: 36828386 PMCID: PMC9961522 DOI: 10.3390/tomography9010034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023] Open
Abstract
Uncertainty assessment is a fundamental step in quantitative magnetic resonance imaging because it makes comparable, in a strict metrological sense, the results of different scans, for example during a longitudinal study. Magnetic resonance-based electric properties tomography (EPT) is a quantitative imaging technique that retrieves, non-invasively, a map of the electric properties inside a human body. Although EPT has been used in some early clinical studies, a rigorous experimental assessment of the associated uncertainty has not yet been performed. This paper aims at evaluating the repeatability and reproducibility uncertainties in phase-based Helmholtz-EPT applied on homogeneous phantom data acquired with a clinical 3 T scanner. The law of propagation of uncertainty is used to evaluate the uncertainty in the estimated conductivity values starting from the uncertainty in the acquired scans, which is quantified through a robust James-Stein shrinkage estimator to deal with the dimensionality of the problem. Repeatable errors are detected in the estimated conductivity maps and are quantified for various values of the tunable parameters of the EPT implementation. The spatial dispersion of the estimated electric conductivity maps is found to be a good approximation of the reproducibility uncertainty, evaluated by changing the position of the phantom after each scan. The results underpin the use of the average conductivity (calculated by weighting the local conductivity values by their uncertainty and taking into account the spatial correlation) as an estimate of the conductivity of the homogeneous phantom.
Collapse
Affiliation(s)
- Alessandro Arduino
- Istituto Nazionale di Ricerca Metrologica, Strada delle Cacce 91, 10135 Torino, Italy
- Correspondence:
| | - Francesca Pennecchi
- Istituto Nazionale di Ricerca Metrologica, Strada delle Cacce 91, 10135 Torino, Italy
| | | | - Maurice Cox
- National Physical Laboratory, Teddington TW11 0LW, UK
| | - Luca Zilberti
- Istituto Nazionale di Ricerca Metrologica, Strada delle Cacce 91, 10135 Torino, Italy
| |
Collapse
|
16
|
Park JA, Kim Y, Yang J, Choi BK, Katoch N, Park S, Hur YH, Kim JW, Kim HJ, Kim HC. Effects of Irradiation on Brain Tumors Using MR-Based Electrical Conductivity Imaging. Cancers (Basel) 2022; 15:cancers15010022. [PMID: 36612018 PMCID: PMC9817812 DOI: 10.3390/cancers15010022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/04/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
Ionizing radiation delivers sufficient energy inside the human body to create ions, which kills cancerous tissues either by damaging the DNA directly or by creating charged particles that can damage the DNA. Recent magnetic resonance (MR)-based conductivity imaging shows higher sensitivity than other MR techniques for evaluating the responses of normal tissues immediately after irradiation. However, it is still necessary to verify the responses of cancer tissues to irradiation by conductivity imaging for it to become a reliable tool in evaluating therapeutic effects in clinical practice. In this study, we applied MR-based conductivity imaging to mouse brain tumors to evaluate the responses in irradiated and non-irradiated tissues during the peri-irradiation period. Absolute conductivities of brain tissues were measured to quantify the irradiation effects, and the percentage changes were determined to estimate the degree of response. The conductivity of brain tissues with irradiation was higher than that without irradiation for all tissue types. The percentage changes of tumor tissues with irradiation were clearly different than those without irradiation. The measured conductivity and percentage changes between tumor rims and cores to irradiation were clearly distinguished. The contrast of the conductivity images following irradiation may reflect the response to the changes in cellularity and the amounts of electrolytes in tumor tissues.
Collapse
Affiliation(s)
- Ji Ae Park
- Division of Applied RI, Korea Institute of Radiological and Medical Science, Seoul 01812, Republic of Korea
| | - Youngsung Kim
- Office of Strategic R&D Planning (MOTIE), Seoul 06152, Republic of Korea
| | - Jiung Yang
- Division of Applied RI, Korea Institute of Radiological and Medical Science, Seoul 01812, Republic of Korea
| | - Bup Kyung Choi
- Medical Science Research Institute, Kyung Hee University Hospital, Seoul 02447, Republic of Korea
| | - Nitish Katoch
- Medical Science Research Institute, Kyung Hee University Hospital, Seoul 02447, Republic of Korea
| | - Seungwoo Park
- Comprehensive Radiation Irradiation Center, Korea Institute of Radiological and Medical Science, Seoul 01812, Republic of Korea
| | - Young Hoe Hur
- Department of Hepato-Biliary-Pancreas Surgery, Chonnam National University Medical School, Gwangju 61469, Republic of Korea
| | - Jin Woong Kim
- Department of Radiology, Chosun University Hospital, Gwangju 61453, Republic of Korea
| | - Hyung Joong Kim
- Medical Science Research Institute, Kyung Hee University Hospital, Seoul 02447, Republic of Korea
| | - Hyun Chul Kim
- Department of Radiology, Chosun University Hospital, Gwangju 61453, Republic of Korea
- Correspondence:
| |
Collapse
|
17
|
Matković A, Šarolić A. The Effect of Freezing and Thawing on Complex Permittivity of Bovine Tissues. SENSORS (BASEL, SWITZERLAND) 2022; 22:9806. [PMID: 36560174 PMCID: PMC9788127 DOI: 10.3390/s22249806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/29/2022] [Accepted: 12/10/2022] [Indexed: 06/17/2023]
Abstract
The aim of this study was to investigate how the freezing and thawing of biological tissues affect their complex permittivity in the microwave frequency range from 0.5 MHz to 18 GHz. We measured the complex permittivity of ex vivo bovine tissues, including brain white and grey matter, liver, and muscle, using an open-ended coaxial probe. Bovine tissues were chosen for their availability and similarity to human tissue permittivity. The samples were measured at 25 °C, before they were frozen either in a commercial freezer below -18 °C or in liquid nitrogen, nominally at -196 °C. The measured permittivity before freezing was compared to the permittivity measured after freezing and thawing the tissues back to 25 °C. Statistical analysis of the results showed a statistically significant change in permittivity after freezing and thawing by both methods for all the measured tissues, at least in some parts of the measured frequency range. The largest difference was observed for the white matter, while the liver had the smallest percent change.
Collapse
|
18
|
Inda AJG, Huang SY, İmamoğlu N, Qin R, Yang T, Chen T, Yuan Z, Yu W. Physics Informed Neural Networks (PINN) for Low Snr Magnetic Resonance Electrical Properties Tomography (MREPT). Diagnostics (Basel) 2022; 12:2627. [PMID: 36359471 PMCID: PMC9689361 DOI: 10.3390/diagnostics12112627] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 12/26/2023] Open
Abstract
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.
Collapse
Affiliation(s)
| | - Shao Ying Huang
- Department of Surgery, National University of Singapore, Singapore 119077, Singapore
- Engineering Product Development Department, Singapore University of Technology and Design, Singapore 487372, Singapore
| | - Nevrez İmamoğlu
- Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan
| | - Ruian Qin
- Department of Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Tianyi Yang
- Department of Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Tiao Chen
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, China
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, China
| | - Wenwei Yu
- Department of Medical Engineering, Chiba University, Chiba 263-8522, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| |
Collapse
|
19
|
Direct investigations of the electrical conductivity of normal and cancer breast cells by conductive atomic force microscopy. Ultramicroscopy 2022; 237:113531. [DOI: 10.1016/j.ultramic.2022.113531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 01/21/2022] [Accepted: 04/10/2022] [Indexed: 12/24/2022]
|
20
|
Park S, Jung SM, Lee MB, Rhee HY, Ryu CW, Cho AR, Kwon OI, Jahng GH. Application of High-Frequency Conductivity Map Using MRI to Evaluate It in the Brain of Alzheimer's Disease Patients. Front Neurol 2022; 13:872878. [PMID: 35651350 PMCID: PMC9150564 DOI: 10.3389/fneur.2022.872878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Background The previous studies reported increased concentrations of metallic ions, imbalanced Na+ and K+ ions, and the increased mobility of protons by microstructural disruptions in Alzheimer's disease (AD). Purpose (1) to apply a high-frequency conductivity (HFC) mapping technique using a clinical 3T MRI system, (2) compare HFC values in the brains of participants with AD, amnestic mild cognitive impairment (MCI), and cognitively normal (CN) elderly people, (3) evaluate the relationship between HFC values and cognitive decline, and (4) explore usefulness of HFC values as an imaging biomarker to evaluate the differentiation of AD from CN. Materials and Methods This prospective study included 74 participants (23 AD patients, 27 amnestic MCI patients, and 24 CN elderly people) to explore the clinical application of HFC mapping in the brain from March 2019 to August 2021. We performed statistical analyses to compare HFC maps between the three participant groups, evaluate the association of HFC maps with Mini-Mental State Examination (MMSE) scores, and to evaluate the differentiation between the participant groups for HFC values for some brain areas. Results We obtained a good HFC map non-invasively. The HFC value was higher in the AD group than in the CN and MCI groups. MMSE scores were negatively associated with HFC values. Age was positively associated with HFC values. The HFC value in the insula has a high area under the receiver operating characteristic (ROC) curve (AUC) value to differentiate AD patients from the CN participants (Sensitivity [SE] = 82, Specificity [SP] =97, AUC = 0.902, p < 0.0001), better than gray matter volume (GMV) in hippocampus (SE = 79, SP = 83, AUC = 0.880, p < 0.0001). The classification for differentiating AD from CN was highest by adding the hippocampal GMV to the insular HFC value (SE = 87, SP = 87, AUC = 0.928, p < 0.0001). Conclusion High-frequency conductivity values were significantly increased in the AD group compared to the CN group and increased with age and disease severity. HFC values of the insula along with the GMV of the hippocampus can be used as an imaging biomarker to improve the differentiation of AD from CN.
Collapse
Affiliation(s)
- Soonchan Park
- Department of Radiology, College of Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, South Korea
| | - Sue Min Jung
- Department of Biomedical Engineering, Undergraduate School, College of Electronics and Information, Kyung Hee University, Yongin-si, South Korea
| | - Mun Bae Lee
- Department of Mathematics, College of Basic Science, Konkuk University, Seoul, South Korea
| | - Hak Young Rhee
- Department of Neurology, College of Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, South Korea
| | - Chang-Woo Ryu
- Department of Radiology, College of Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, South Korea
| | - Ah Rang Cho
- Department of Psychiatry, College of Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, South Korea
| | - Oh In Kwon
- Department of Mathematics, College of Basic Science, Konkuk University, Seoul, South Korea
| | - Geon-Ho Jahng
- Department of Radiology, College of Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, South Korea
| |
Collapse
|
21
|
Garcia Inda AJ, Huang SY, Imamoglu N, Yu W. Physics-Coupled Neural Network Magnetic Resonance Electrical Property Tomography (MREPT) for Conductivity Reconstruction. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2022; 31:3463-3478. [PMID: 35533164 DOI: 10.1109/tip.2022.3172220] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The electrical property (EP) of human tissues is a quantitative biomarker that facilitates early diagnosis of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is an imaging modality that reconstructs EPs by the radio-frequency field in an MRI system. MREPT reconstructs EPs by solving analytic models numerically based on Maxwell's equations. Most MREPT methods suffer from artifacts caused by inaccuracy of the hypotheses behind the models, and/or numerical errors. These artifacts can be mitigated by adding coefficients to stabilize the models, however, the selection of such coefficient has been empirical, which limit its medical application. Alternatively, end-to-end Neural networks-based MREPT (NN-MREPT) learns to reconstruct the EPs from training samples, circumventing Maxwell's equations. However, due to its pattern-matching nature, it is difficult for NN-MREPT to produce accurate reconstructions for new samples. In this work, we proposed a physics-coupled NN for MREPT (PCNN-MREPT), in which an analytic model, cr-MREPT, works with diffusion and convection coefficients, learned by NNs from the difference between the reconstructed and ground-truth EPs to reduce artifacts. With two simulated datasets, three generalization experiments in which test samples deviate gradually from the training samples, and one noise-robustness experiment were conducted. The results show that the proposed PCNN-MREPT achieves higher accuracy than two representative analytic methods. Moreover, compared with an end-to-end NN-MREPT, the proposed method attained higher accuracy in two critical generalization tests. This is an important step to practical MREPT medical diagnoses.
Collapse
|
22
|
Lee JH, Yoon YC, Kim HS, Lee J, Kim E, Findeklee C, Katscher U. In vivo electrical conductivity measurement of muscle, cartilage, and peripheral nerve around knee joint using MR-electrical properties tomography. Sci Rep 2022; 12:73. [PMID: 34996978 PMCID: PMC8741940 DOI: 10.1038/s41598-021-03928-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 12/10/2021] [Indexed: 11/20/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Ji Hyun Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, Korea
| | - Young Cheol Yoon
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, Korea.
| | - Hyun Su Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, Korea
| | - Jiyeong Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, Korea
| | | | | | | |
Collapse
|
23
|
Sadleir R, Gabriel C, Minhas AS. Electromagnetic Properties and the Basis for CDI, MREIT, and EPT. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1380:1-16. [DOI: 10.1007/978-3-031-03873-0_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
24
|
Brain Tissue Conductivity in Focal Cerebral Ischemia. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1395:23-27. [PMID: 36527608 DOI: 10.1007/978-3-031-14190-4_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Cerebral ischemia leads to oxygen depletion with rapid breakdown of transmembrane transporters and subsequent impaired electrolyte haemostasis. Electric properties tomography (EPT) is a new contrast in MRI which delivers information on tissue electrical conductivity. In the clinical realm it has been mostly used for tumour mapping. Ischemic cerebral stroke is another promising but neglected application. It might deliver additional information on tissue viability and possible response to therapy. AIM The aim of this study was to demonstrate tissue conductivity in a rodent model of stroke. Further, we aimed to compare electric conductivity in ischemic and non-ischemic cerebral tissue. MATERIALS AND METHODS Two male Wistar rats were used in this study and were subjected to permanent MCAO. The animals were scanned in a 3 Tesla system (Philips Achieva/Best, the Netherlands) using a dedicated solenoid animal coil (Philips/Hamburg, Germany). In addition to diffusion weighted imaging (DWI), EPT was performed using a steady-state free-precession (SSFP) sequence (repetition time/echo time = 4.5/2.3 ms, measured voxel size = 0.6 × 0.6 × 1.2 mm3, flip angle = 38°, number of excitations = 4). From the transceive phase ϕ of these SSFP scans, conductivity σ was estimated by the equation σ = Δϕ/(2μ0ω) with Δ the Laplacian operator, μ0 the magnetic permeability, and ω the Larmor frequency. Subsequently, a median filter was applied, which was locally restricted to voxels with comparable signal magnitude. RESULTS The animals exhibited an infarct as demonstrated on DWI. Conductivity within the infarcted region was 60-70 % of the conductivity of not affected contralateral tissue (0.39 ± 0.07 S/m and 0.31 ± 0.14 S/m vs. 0.64 ± 0.15 S/m and 0.66 ± 0.16 S/m, respectively). DISCUSSION Infarcted tissue exhibited decreased conductivity. Further in-vivo experiments with examination of the influence of reperfusion status and temporal evolution of the infarcted areas should be conducted. Depiction of the ischemic penumbra and possibly subclassification of the DWI lesion still seems to be a fruitful target for further studies.
Collapse
|
25
|
Katscher U, Minhas AS, Katoch N. Magnetic Resonance Electrical Properties Tomography (MREPT). ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1380:185-202. [DOI: 10.1007/978-3-031-03873-0_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
26
|
Katscher U, Weiss S. Mapping electric bulk conductivity in the human heart. Magn Reson Med 2021; 87:1500-1506. [PMID: 34739149 DOI: 10.1002/mrm.29067] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/04/2021] [Accepted: 10/13/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE To explore the technical feasibility of mapping the electric bulk conductivity in the human heart, and to determine quantitative conductivity values of myocardium and blood from a small group of volunteers. METHODS Using a 3T MR system, 6 healthy male volunteers were measured. For all volunteers, a time-resolved 2D sequence over the cardiac cycle was applied (electrocardiogram [ECG]-triggered SSFP acquired in breath-hold). From these data, a dedicated, so-called "2D conductivity" has been derived in the framework of electrical properties tomography (EPT). To validate the concept of 2D conductivity, a static 3D sequence (ECG-triggered and respiratory-gated SSFP 3D whole heart acquisition, allowing the full 3D reconstruction of conductivity) as well as a Q-flow sequence (for investigating the relation between flow and reconstruction errors of the conductivity) have been applied for one of the volunteers. RESULTS For both, blood and myocardium, quantitative values of obtained 2D conductivity were approximately two-thirds of the obtained 3D conductivity, as expected from Maxwell's equations. Furthermore, the quantitative conductivity values agreed with corresponding literature values. Conductivity of left-ventricular blood volume showed characteristic over- and under-shooting at specific time points during the cardiac cycle for all volunteers investigated. This over- and under-shooting correlated with the phase pattern caused by blood flow into/out of the ventricle. CONCLUSION The study demonstrated the technical feasibility of cardiac conductivity measurements using standard MR systems and standard MR sequences, and therefore, may open new options for MR-based cardiac diagnosis.
Collapse
|
27
|
Katoch N, Choi BK, Park JA, Ko IO, Kim HJ. Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI. Molecules 2021; 26:5499. [PMID: 34576970 PMCID: PMC8467711 DOI: 10.3390/molecules26185499] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/30/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022] Open
Abstract
Imaging of the electrical conductivity distribution inside the human body has been investigated for numerous clinical applications. The conductivity tensors of biological tissue have been obtained from water diffusion tensors by applying several models, which may not cover the entire phenomenon. Recently, a new conductivity tensor imaging (CTI) method was developed through a combination of B1 mapping, and multi-b diffusion weighted imaging. In this study, we compared the most recent CTI method with the four existing models of conductivity tensors reconstruction. Two conductivity phantoms were designed to evaluate the accuracy of the models. Applied to five human brains, the conductivity tensors using the four existing models and CTI were imaged and compared with the values from the literature. The conductivity image of the phantoms by the CTI method showed relative errors between 1.10% and 5.26%. The images by the four models using DTI could not measure the effects of different ion concentrations subsequently due to prior information of the mean conductivity values. The conductivity tensor images obtained from five human brains through the CTI method were comparable to previously reported literature values. The images by the four methods using DTI were highly correlated with the diffusion tensor images, showing a coefficient of determination (R2) value of 0.65 to 1.00. However, the images by the CTI method were less correlated with the diffusion tensor images and exhibited an averaged R2 value of 0.51. The CTI method could handle the effects of different ion concentrations as well as mobilities and extracellular volume fractions by collecting and processing additional B1 map data. It is necessary to select an application-specific model taking into account the pros and cons of each model. Future studies are essential to confirm the usefulness of these conductivity tensor imaging methods in clinical applications, such as tumor characterization, EEG source imaging, and treatment planning for electrical stimulation.
Collapse
Affiliation(s)
- Nitish Katoch
- Department of Biomedical Engineering, Kyung Hee University, Seoul 02447, Korea; (N.K.); (B.-K.C.)
| | - Bup-Kyung Choi
- Department of Biomedical Engineering, Kyung Hee University, Seoul 02447, Korea; (N.K.); (B.-K.C.)
| | - Ji-Ae Park
- Division of Applied RI, Korea Institute of Radiological and Medical Science, Seoul 01812, Korea;
| | - In-Ok Ko
- Division of Applied RI, Korea Institute of Radiological and Medical Science, Seoul 01812, Korea;
| | - Hyung-Joong Kim
- Department of Biomedical Engineering, Kyung Hee University, Seoul 02447, Korea; (N.K.); (B.-K.C.)
| |
Collapse
|
28
|
Lesbats C, Katoch N, Minhas AS, Taylor A, Kim HJ, Woo EJ, Poptani H. High-frequency electrical properties tomography at 9.4T as a novel contrast mechanism for brain tumors. Magn Reson Med 2021; 86:382-392. [PMID: 33533114 PMCID: PMC8603929 DOI: 10.1002/mrm.28685] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 12/03/2020] [Accepted: 12/24/2020] [Indexed: 11/11/2022]
Abstract
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.
Collapse
Affiliation(s)
- Clémentine Lesbats
- Centre for Preclinical ImagingDepartment of Molecular and Clinical Cancer MedicineUniversity of LiverpoolLiverpoolUK
| | - Nitish Katoch
- Department of Biomedical EngineeringKyung Hee UniversitySeoulSouth Korea
| | - Atul Singh Minhas
- Centre for Preclinical ImagingDepartment of Molecular and Clinical Cancer MedicineUniversity of LiverpoolLiverpoolUK
- School of EngineeringMacquarie UniversitySydneyNSWAustralia
| | - Arthur Taylor
- Centre for Preclinical ImagingDepartment of Molecular and Clinical Cancer MedicineUniversity of LiverpoolLiverpoolUK
| | - Hyung Joong Kim
- Department of Biomedical EngineeringKyung Hee UniversitySeoulSouth Korea
| | - Eung Je Woo
- Department of Biomedical EngineeringKyung Hee UniversitySeoulSouth Korea
| | - Harish Poptani
- Centre for Preclinical ImagingDepartment of Molecular and Clinical Cancer MedicineUniversity of LiverpoolLiverpoolUK
| |
Collapse
|
29
|
Lee MB, Jahng GH, Kim HJ, Kwon OI. High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network. PLoS One 2021; 16:e0251417. [PMID: 34014939 PMCID: PMC8136747 DOI: 10.1371/journal.pone.0251417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 04/26/2021] [Indexed: 11/18/2022] Open
Abstract
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).
Collapse
Affiliation(s)
- Mun Bae Lee
- Department of Mathematics, Konkuk University, Seoul, Korea
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Korea
| | - Hyung Joong Kim
- Department of Biomedical Engineering, Kyung Hee University, Seoul, Korea
| | - Oh-In Kwon
- Department of Mathematics, Konkuk University, Seoul, Korea
- * E-mail:
| |
Collapse
|
30
|
Tha KK, Kikuchi Y, Ishizaka K, Kamiyama T, Yoneyama M, Katscher U. Higher Electrical Conductivity of Liver Parenchyma in Fibrotic Patients: Noninvasive Assessment by Electric Properties Tomography. J Magn Reson Imaging 2021; 54:1689-1691. [PMID: 33998080 DOI: 10.1002/jmri.27701] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 12/25/2022] Open
Affiliation(s)
- Khin Khin Tha
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan.,Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Yasuka Kikuchi
- Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Kinya Ishizaka
- Department of Radiological Technology, Hokkaido University Hospital, Sapporo, Japan
| | - Toshiya Kamiyama
- Department of Gastroenterological Surgery, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | | | | |
Collapse
|
31
|
Jung KJ, Mandija S, Kim JH, Ryu K, Jung S, Cui C, Kim SY, Park M, van den Berg CAT, Kim DH. Improving phase-based conductivity reconstruction by means of deep learning-based denoising of B 1 + phase data for 3T MRI. Magn Reson Med 2021; 86:2084-2094. [PMID: 33949721 DOI: 10.1002/mrm.28826] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 03/28/2021] [Accepted: 04/13/2021] [Indexed: 12/24/2022]
Abstract
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.
Collapse
Affiliation(s)
- Kyu-Jin Jung
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Stefano Mandija
- Computational Imaging Group for MR Diagnostic & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands.,Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jun-Hyeong Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Kanghyun Ryu
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Soozy Jung
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Chuanjiang Cui
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Soo-Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Cornelis A T van den Berg
- Computational Imaging Group for MR Diagnostic & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands.,Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| |
Collapse
|
32
|
Stijnman PRS, Stefano Mandija, Fuchs PS, van den Berg CAT, Remis RF. Transceive phase corrected 2D contrast source inversion-electrical properties tomography. Magn Reson Med 2021; 85:2856-2868. [PMID: 33280166 PMCID: PMC7898605 DOI: 10.1002/mrm.28619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/08/2020] [Accepted: 11/05/2020] [Indexed: 11/11/2022]
Abstract
PURPOSE To remove the necessity of the tranceive phase assumption for CSI-EPT and show electrical properties maps reconstructed from measured data obtained using a standard 3T birdcage body coil setup. METHODS The existing CSI-EPT algorithm is reformulated to use the transceive phase rather than relying on the transceive phase assumption. Furthermore, the radio frequency (RF)-shield is numerically implemented to accurately model the RF fields inside the MRI scanner. We verify that the reformulated two-dimensional (2D) CSI-EPT algorithm can reconstruct electrical properties maps given 2D electromagnetic simulations. Afterward, the algorithm is tested with three-dimensional (3D) FDTD simulations to investigate if the 2D CSI-EPT can retrieve the electrical properties for 3D RF fields. Finally, an MR experiment at 3T with a phantom is performed. RESULTS From the results of the 2D simulations, it is seen that CSI-EPT can reconstruct the electrical properties using MRI accessible quantities. For 3D simulations, it is observed that the electrical properties are underestimated, nonetheless, CSI-EPT has a lower standard deviation than the standard Helmholtz based methods. Finally, the first CSI-EPT reconstructions based on measured data are presented showing comparable accuracy and precision to reconstructions based on simulated data, and demonstrating the feasibility of CSI-EPT. CONCLUSIONS The CSI-EPT algorithm was rewritten to use MRI accessible quantities. This allows for CSI-EPT to fully exploit the benefits of the higher static magnetic field strengths with a standard quadrature birdcage coil setup.
Collapse
Affiliation(s)
- Peter R. S. Stijnman
- Computational Imaging Group for MRI Diagnostics and TherapyCentre for Image Sciences UMC UtrechtUtrechtThe Netherlands
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
| | - Stefano Mandija
- Computational Imaging Group for MRI Diagnostics and TherapyCentre for Image Sciences UMC UtrechtUtrechtThe Netherlands
| | - Patrick S. Fuchs
- Circuit & Systems Group of the Electrical EngineeringDelft University of TechnologyDelftThe Netherlands
| | - Cornelis A. T. van den Berg
- Computational Imaging Group for MRI Diagnostics and TherapyCentre for Image Sciences UMC UtrechtUtrechtThe Netherlands
| | - Rob F. Remis
- Circuit & Systems Group of the Electrical EngineeringDelft University of TechnologyDelftThe Netherlands
| |
Collapse
|
33
|
Park JE, Kim HS, Kim N, Kim YH, Kim JH, Kim E, Hwang J, Katscher U. Low conductivity on electrical properties tomography demonstrates unique tumor habitats indicating progression in glioblastoma. Eur Radiol 2021; 31:6655-6665. [PMID: 33880619 DOI: 10.1007/s00330-021-07976-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 02/24/2021] [Accepted: 04/01/2021] [Indexed: 12/22/2022]
Abstract
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.
Collapse
Affiliation(s)
- Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, South Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, South Korea.
| | | | - Young-Hoon Kim
- Deparment of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, 05505, South Korea
| | - Jeong Hoon Kim
- Deparment of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, 05505, South Korea
| | - Eunju Kim
- Philips Healthcare, Seoul, South Korea
| | | | - Ulrich Katscher
- Department of Tomographic Imaging, Philips Research Laboratories, Hamburg, Germany
| |
Collapse
|
34
|
Lee MB, Kim HJ, Kwon OI. Decomposition of high-frequency electrical conductivity into extracellular and intracellular compartments based on two-compartment model using low-to-high multi-b diffusion MRI. Biomed Eng Online 2021; 20:29. [PMID: 33766044 PMCID: PMC7993544 DOI: 10.1186/s12938-021-00869-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 03/16/2021] [Indexed: 01/21/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Mun Bae Lee
- Department of Mathematics, Konkuk University, 05029, Seoul, South Korea
| | - Hyung Joong Kim
- Department of Biomedical Engineering, Kyung Hee University, 02447, Seoul, South Korea
| | - Oh In Kwon
- Department of Mathematics, Konkuk University, 05029, Seoul, South Korea.
| |
Collapse
|
35
|
Leijsen R, Brink W, van den Berg C, Webb A, Remis R. Electrical Properties Tomography: A Methodological Review. Diagnostics (Basel) 2021; 11:176. [PMID: 33530587 PMCID: PMC7910937 DOI: 10.3390/diagnostics11020176] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 01/18/2021] [Accepted: 01/22/2021] [Indexed: 11/25/2022] Open
Abstract
Electrical properties tomography (EPT) is an imaging method that uses a magnetic resonance (MR) system to non-invasively determine the spatial distribution of the conductivity and permittivity of the imaged object. This manuscript starts by providing clear definitions about the data required for, and acquired in, EPT, followed by comprehensively formulating the physical equations underlying a large number of analytical EPT techniques. This thorough mathematical overview of EPT harmonizes several EPT techniques in a single type of formulation and gives insight into how they act on the data and what their data requirements are. Furthermore, the review describes machine learning-based algorithms. Matlab code of several differential and iterative integral methods is available upon request.
Collapse
Affiliation(s)
- Reijer Leijsen
- Department of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, 2333ZA Leiden, The Netherlands; (R.L.); (W.B.); (A.W.)
| | - Wyger Brink
- Department of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, 2333ZA Leiden, The Netherlands; (R.L.); (W.B.); (A.W.)
| | - Cornelis van den Berg
- Computational Imaging Group for MRI Diagnostics and Therapy, Centre for Image Sciences, University Medical Centre Utrecht, 3508GA Utrecht, The Netherlands;
| | - Andrew Webb
- Department of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, 2333ZA Leiden, The Netherlands; (R.L.); (W.B.); (A.W.)
| | - Rob Remis
- Circuits and Systems Group, Faculty of Electrical Engineering, Mathematics and Computes Science, Delft University of Technology, 2628CD Delft, The Netherlands
| |
Collapse
|
36
|
Mandija S, Petrov PI, Vink JJT, Neggers SFW, van den Berg CAT. Brain Tissue Conductivity Measurements with MR-Electrical Properties Tomography: An In Vivo Study. Brain Topogr 2021; 34:56-63. [PMID: 33289858 PMCID: PMC7803705 DOI: 10.1007/s10548-020-00813-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 11/28/2020] [Indexed: 01/19/2023]
Abstract
First in vivo brain conductivity reconstructions using Helmholtz MR-Electrical Properties Tomography (MR-EPT) have been published. However, a large variation in the reconstructed conductivity values is reported and these values differ from ex vivo conductivity measurements. Given this lack of agreement, we performed an in vivo study on eight healthy subjects to provide reference in vivo brain conductivity values. MR-EPT reconstructions were performed at 3 T for eight healthy subjects. Mean conductivity and standard deviation values in the white matter, gray matter and cerebrospinal fluid (σWM, σGM, and σCSF) were computed for each subject before and after erosion of regions at tissue boundaries, which are affected by typical MR-EPT reconstruction errors. The obtained values were compared to the reported ex vivo literature values. To benchmark the accuracy of in vivo conductivity reconstructions, the same pipeline was applied to simulated data, which allow knowledge of ground truth conductivity. Provided sufficient boundary erosion, the in vivo σWM and σGM values obtained in this study agree for the first time with literature values measured ex vivo. This could not be verified for the CSF due to its limited spatial extension. Conductivity reconstructions from simulated data verified conductivity reconstructions from in vivo data and demonstrated the importance of discarding voxels at tissue boundaries. The presented σWM and σGM values can therefore be used for comparison in future studies employing different MR-EPT techniques.
Collapse
Affiliation(s)
- Stefano Mandija
- Computational Imaging Group for MR Diagnostic & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
- Division of Imaging & Oncology, Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
| | - Petar I Petrov
- Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Jord J T Vink
- Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Sebastian F W Neggers
- Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Cornelis A T van den Berg
- Computational Imaging Group for MR Diagnostic & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
- Division of Imaging & Oncology, Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| |
Collapse
|
37
|
Lang ST, Gan LS, McLennan C, Monchi O, Kelly JJP. Impact of Peritumoral Edema During Tumor Treatment Field Therapy: A Computational Modelling Study. IEEE Trans Biomed Eng 2020; 67:3327-3338. [PMID: 32286953 DOI: 10.1109/tbme.2020.2983653] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Tumor treatment fields (TTFie-lds) are an approved adjuvant therapy for glioblastoma (GBM). The magnitude of applied electrical field has been shown to be related to the anti-tumoral response. However, peritumoral edema may result in shunting of electrical current around the tumor, thereby reducing the intra-tumoral electric field. In this study, we systematically address this issue with computational simulations. METHODS Finite element models are created of a human head with varying amounts of peritumoral edema surrounding a virtual tumor. The electric field distribution was simulated using the standard TTFields electrode montage. Electric field magnitude was extracted from the tumor and related to edema thickness. Two patient specific models were created to confirm these results. RESULTS The inclusion of peritumoral edema decreased the average magnitude of the electric field within the tumor. In the model considering a frontal tumor and an anterior-posterior electrode configuration, ≥6 mm of peritumoral edema decreased the electric field by 52%. In the patient specific models, peritumoral edema decreased the electric field magnitude within the tumor by an average of 26%. The effect of peritumoral edema on the electric field distribution was spatially heterogenous, being most significant at the tissue interface between edema and tumor. CONCLUSIONS The inclusion of peritumoral edema during TTFields modelling may have a dramatic effect on the predicted electric field magnitude within the tumor. Given the importance of electric field magnitude for the anti-tumoral effects of TTFields, the presence of edema should be considered both in future modelling studies and when planning TTField therapy.
Collapse
|
38
|
Jahng GH, Lee MB, Kim HJ, Je Woo E, Kwon OI. Low-frequency dominant electrical conductivity imaging of in vivo human brain using high-frequency conductivity at Larmor-frequency and spherical mean diffusivity without external injection current. Neuroimage 2020; 225:117466. [PMID: 33075557 DOI: 10.1016/j.neuroimage.2020.117466] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 08/24/2020] [Accepted: 10/12/2020] [Indexed: 10/23/2022] Open
Abstract
Diffusion weighted imaging based on random Brownian motion of water molecules within a voxel provides information on the micro-structure of biological tissues through water molecule diffusivity. As the electrical conductivity is primarily determined by the concentration and mobility of ionic charge carriers, the macroscopic electrical conductivity of biological tissues is also related to the diffusion of electrical ions. This paper aims to investigate the low-frequency electrical conductivity by relying on a pre-defined biological model that separates the brain into the intracellular (restricted) and extracellular (hindered) compartments. The proposed method uses B1 mapping technique, which provides a high-frequency conductivity distribution at Larmor frequency, and the spherical mean technique, which directly estimates the microscopic tissue structure based on the water molecule diffusivity and neurite orientation distribution. The total extracellular ion concentration, which is separated from the high-frequency conductivity, is recovered using the estimated diffusivity parameters and volume fraction in each compartment. We propose a method to reconstruct the low-frequency dominant conductivity tensor by taking into consideration the extracted extracellular diffusion tensor map and the reconstructed electrical parameters. To demonstrate the reliability of the proposed method, we conducted two phantom experiments. The first one used a cylindrical acrylic cage filled with an agar in the background region and four anomalies for the effect of ion concentration on the electrical conductivity. The other experiment, in which the effect of ion mobility on the conductivity was verified, used cell-like materials with thin insulating membranes suspended in an electrolyte. Animal and human brain experiments were conducted to visualize the low-frequency dominant conductivity tensor images. The proposed method using a conventional MRI scanner can predict the internal current density map in the brain without directly injected external currents.
Collapse
Affiliation(s)
- Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Dongnam-ro, Gangdong-gu, Seoul 05278, Republic of Korea
| | - Mun Bae Lee
- Department of Mathematics, Konkuk University, Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
| | - Hyung Joong Kim
- Department of Biomedical Engineering, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Eung Je Woo
- Department of Biomedical Engineering, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Oh-In Kwon
- Department of Mathematics, Konkuk University, Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.
| |
Collapse
|
39
|
Rashed EA, Gomez-Tames J, Hirata A. Deep Learning-Based Development of Personalized Human Head Model With Non-Uniform Conductivity for Brain Stimulation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2351-2362. [PMID: 31995479 DOI: 10.1109/tmi.2020.2969682] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
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.
Collapse
|
40
|
Hampe N, Katscher U, van den Berg CAT, Tha KK, Mandija S. Investigating the challenges and generalizability of deep learning brain conductivity mapping. ACTA ACUST UNITED AC 2020; 65:135001. [DOI: 10.1088/1361-6560/ab9356] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
41
|
Gavazzi S, van den Berg CAT, Savenije MHF, Kok HP, de Boer P, Stalpers LJA, Lagendijk JJW, Crezee H, van Lier ALHMW. Deep learning-based reconstruction of in vivo pelvis conductivity with a 3D patch-based convolutional neural network trained on simulated MR data. Magn Reson Med 2020; 84:2772-2787. [PMID: 32314825 PMCID: PMC7402024 DOI: 10.1002/mrm.28285] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 03/25/2020] [Accepted: 03/26/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE To demonstrate that mapping pelvis conductivity at 3T with deep learning (DL) is feasible. METHODS 210 dielectric pelvic models were generated based on CT scans of 42 cervical cancer patients. For all dielectric models, electromagnetic and MR simulations with realistic accuracy and precision were performed to obtain B 1 + and transceive phase (ϕ± ). Simulated B 1 + and ϕ± served as input to a 3D patch-based convolutional neural network, which was trained in a supervised fashion to retrieve the conductivity. The same network architecture was retrained using only ϕ± in input. Both network configurations were tested on simulated MR data and their conductivity reconstruction accuracy and precision were assessed. Furthermore, both network configurations were used to reconstruct conductivity maps from a healthy volunteer and two cervical cancer patients. DL-based conductivity was compared in vivo and in silico to Helmholtz-based (H-EPT) conductivity. RESULTS Conductivity maps obtained from both network configurations were comparable. Accuracy was assessed by mean error (ME) with respect to ground truth conductivity. On average, ME < 0.1 Sm-1 for all tissues. Maximum MEs were 0.2 Sm-1 for muscle and tumour, and 0.4 Sm-1 for bladder. Precision was indicated with the difference between 90th and 10th conductivity percentiles, and was below 0.1 Sm-1 for fat, bone and muscle, 0.2 Sm-1 for tumour and 0.3 Sm-1 for bladder. In vivo, DL-based conductivity had median values in agreement with H-EPT values, but a higher precision. CONCLUSION Anatomically detailed, noise-robust 3D conductivity maps with good sensitivity to tissue conductivity variations were reconstructed in the pelvis with DL.
Collapse
Affiliation(s)
- Soraya Gavazzi
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR diagnostics and therapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mark H F Savenije
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR diagnostics and therapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - H Petra Kok
- Department of Radiation Oncology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Peter de Boer
- Radiotherapy Institute Friesland, Leeuwarden, The Netherlands
| | - Lukas J A Stalpers
- Department of Radiation Oncology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jan J W Lagendijk
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hans Crezee
- Department of Radiation Oncology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | | |
Collapse
|
42
|
Sun X, Lu L, Qi L, Mei Y, Liu X, Chen W. A robust electrical conductivity imaging method with total variation and wavelet regularization. Magn Reson Imaging 2020; 69:28-39. [PMID: 32145270 DOI: 10.1016/j.mri.2020.02.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Revised: 01/23/2020] [Accepted: 02/27/2020] [Indexed: 11/29/2022]
Abstract
PURPOSE This study aims to develop and evaluate a robust conductivity imaging method that combines total variation and wavelet regularization to enhance the accuracy of conductivity maps. THEORY AND METHODS The proposed approach is based on a gradient-based method. The central equation is derived from Maxwell's equation and describes the relationship between conductivity and the transceive phase. A linear system equation is obtained via a finite-difference method and solved using a least-squares method. Total variation and wavelet transform regularization terms are added to the minimization problem and solved using the Split Bregman method to improve reconstruction stability. The proposed approach is compared with conventional and gradient-based methods. Numerical simulations are performed to validate the accuracy of the developed method, and the effects of noise are determined. Phantom and in vivo experiments are conducted at 3 T to verify the clinical applicability of the proposed method. RESULTS Numerical simulations show that the proposed method is more robust than other methods and can suppress the effects of noise. The quantitative conductivity value of the phantom experiment agrees with the measured value. The in vivo experiment results present a clear structure, and the conductivity value of the tumor region is significantly higher than that around healthy tissues. CONCLUSION The proposed electrical conductivity imaging method can improve the quality of conductivity reconstruction, and thus, has future clinical applications.
Collapse
Affiliation(s)
- Xiangdong Sun
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Li Qi
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Yingjie Mei
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Xiaoyun Liu
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wufan Chen
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| |
Collapse
|
43
|
Amouzandeh G, Mentink-Vigier F, Helsper S, Bagdasarian FA, Rosenberg JT, Grant SC. Magnetic resonance electrical property mapping at 21.1 T: a study of conductivity and permittivity in phantoms, ex vivo tissue and in vivo ischemia. Phys Med Biol 2020; 65:055007. [PMID: 31307020 PMCID: PMC7223161 DOI: 10.1088/1361-6560/ab3259] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Electrical properties (EP), namely conductivity and permittivity, can provide endogenous contrast for tissue characterization. Using electrical property tomography (EPT), maps of EP can be generated from conventional MRI data. This report investigates the feasibility and accuracy of EPT at 21.1 T for multiple RF coils and modes of operation using phantoms. Additionally, it demonstrates the EP of the in vivo rat brain with and without ischemia. Helmholtz-based EPT was implemented in its Full-form, which demands the complex [Formula: see text] field, and a simplified form requiring either just the [Formula: see text] field phase for conductivity or the [Formula: see text] field magnitude for permittivity. Experiments were conducted at 21.1 T using birdcage and saddle coils operated in linear or quadrature transceive mode, respectively. EPT approaches were evaluated using a phantom, ex and in vivo Sprague-Dawley rats under naïve conditions and ischemic stroke via transient middle cerebral artery occlusion. Different conductivity reconstruction approaches applied to the phantom displayed average errors of 12%-73% to the target acquired from dielectric probe measurements. Permittivity reconstructions showed higher agreement and an average 3%-8% error to the target depending on reconstruction approach. Conductivity and permittivity of ex and in vivo rodent brain were measured. Elevated EP in the ischemia region correlated with the increased sodium content and the influx of water intracellularly following ischemia in the lesion were detected. The Full-form technique generated from the linear birdcage provided the best accuracy for EP of the phantom. Phase-based conductivity and magnitude-based permittivity mapping provided reasonable estimates but also demonstrated the limitations of Helmholtz-based EPT at 21.1 T. Permittivity reconstruction was improved significantly over lower fields, suggesting a novel metric for in vivo brain studies. EPT applied to ischemic rat brain proved sensitivity to physiological changes, motivating the future application of more advanced reconstruction approaches.
Collapse
Affiliation(s)
- Ghoncheh Amouzandeh
- Department of Physics, Florida State University, Tallahassee, FL, USA
- The National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA
| | | | - Shannon Helsper
- The National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA
- Department of Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL, USA
| | - F. Andrew Bagdasarian
- The National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA
- Department of Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL, USA
| | - Jens T. Rosenberg
- The National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA
| | - Samuel C. Grant
- The National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA
- Department of Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL, USA
| |
Collapse
|
44
|
Park JA, Kang KJ, Ko IO, Lee KC, Choi BK, Katoch N, Kim JW, Kim HJ, Kwon OI, Woo EJ. In Vivo Measurement of Brain Tissue Response After Irradiation: Comparison of T2 Relaxation, Apparent Diffusion Coefficient, and Electrical Conductivity. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2779-2784. [PMID: 31034410 DOI: 10.1109/tmi.2019.2913766] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Radiation therapy (RT) has been widely used as a powerful treatment tool to address cancerous tissues because of its ability to control cell growth. Its ionizing radiation damages the DNA of cancerous tissues, leading to cell death. Medical imaging, however, still has limitations regarding the reliability of its assessment of tissue response and in predicting the treatment effect because of its inability to provide contrast information on the gradual, minute tissue changes after RT. A recently developed magnetic resonance (MR)-based conductivity imaging method may provide direct, highly sensitive information on this tissue response because its contrast mechanism is based on the concentration and mobility of ions in intracellular and extracellular spaces. In this feasibility study, we applied T2-weighted, diffusion-weighted, and electrical conductivity imaging to mouse brain, thus, using the MR imaging to map the tissue response after radiation exposure. To evaluate the degree of response, we measured the T2 relaxation, apparent diffusion coefficient (ADC), and electrical conductivity of brain tissues before and after irradiation. The conductivity images, which showed significantly higher sensitivity than other MR imaging methods, indicated that the contrast is distinguishable in different ways at different areas of the brain. Future studies will focus on verifying these results and the long-term evaluation of conductivity changes using various irradiation methods for clinical applications.
Collapse
|
45
|
Gavazzi S, Shcherbakova Y, Bartels LW, Stalpers LJA, Lagendijk JJW, Crezee H, van den Berg CAT, van Lier ALHMW. Transceive phase mapping using the PLANET method and its application for conductivity mapping in the brain. Magn Reson Med 2019; 83:590-607. [PMID: 31483520 PMCID: PMC6900152 DOI: 10.1002/mrm.27958] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/25/2019] [Accepted: 07/30/2019] [Indexed: 12/23/2022]
Abstract
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.
Collapse
Affiliation(s)
- Soraya Gavazzi
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yulia Shcherbakova
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lambertus W Bartels
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Image Sciences Institute, Imaging Division, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lukas J A Stalpers
- Department of Radiotherapy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jan J W Lagendijk
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hans Crezee
- Department of Radiotherapy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Cornelis A T van den Berg
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.,Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | | |
Collapse
|
46
|
Automated gradient-based electrical properties tomography in the human brain using 7 Tesla MRI. Magn Reson Imaging 2019; 63:258-266. [PMID: 31425805 DOI: 10.1016/j.mri.2019.08.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 08/01/2019] [Accepted: 08/15/2019] [Indexed: 12/18/2022]
Abstract
Electrical properties of the brain tissues may yield useful biomarkers for neurological disorders and diseases, as well as contribute to safety assurance of ultra-high-field MRI. It has been reported that using B1 maps from a multi-channel RF coil, the spatial variation of the electrical properties can be robustly retrieved. The absolute electrical property values can then be obtained by spatial integration, given that an integration seed point is assigned. In this study, we propose to exploit automatically detected seed points based on tissue piece-wise homogeneity (Helmholtz equation) for spatial integration. Numerical simulations of a numerical brain model and experiments involving 12 healthy volunteers were performed to demonstrate its feasibility and robustness in various noisy conditions and head positions. For in vivo imaging, we consistently observed higher conductivity and permittivity values in the white and gray matter compared to tabulated ex vivo probe measurement results found in the literature, a discrepancy that may be attributed to ex vivo experimental constraints. Our results suggest that the proposed technique produces consistent brain electrical properties in vivo that may contribute to improving diagnostic and therapeutic decisions.
Collapse
|
47
|
Mandija S, Meliadò EF, Huttinga NRF, Luijten PR, van den Berg CAT. Opening a new window on MR-based Electrical Properties Tomography with deep learning. Sci Rep 2019; 9:8895. [PMID: 31222055 PMCID: PMC6586684 DOI: 10.1038/s41598-019-45382-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 06/04/2019] [Indexed: 11/09/2022] Open
Abstract
In the radiofrequency (RF) range, the electrical properties of tissues (EPs: conductivity and permittivity) are modulated by the ionic and water content, which change for pathological conditions. Information on tissues EPs can be used e.g. in oncology as a biomarker. The inability of MR-Electrical Properties Tomography techniques (MR-EPT) to accurately reconstruct tissue EPs by relating MR measurements of the transmit RF field to the EPs limits their clinical applicability. Instead of employing electromagnetic models posing strict requirements on the measured MRI quantities, we propose a data driven approach where the electrical properties reconstruction problem can be casted as a supervised deep learning task (DL-EPT). DL-EPT reconstructions for simulations and MR measurements at 3 Tesla on phantoms and human brains using a conditional generative adversarial network demonstrate high quality EPs reconstructions and greatly improved precision compared to conventional MR-EPT. The supervised learning approach leverages the strength of electromagnetic simulations, allowing circumvention of inaccessible MR electromagnetic quantities. Since DL-EPT is more noise-robust than MR-EPT, the requirements for MR acquisitions can be relaxed. This could be a major step forward to turn electrical properties tomography into a reliable biomarker where pathological conditions can be revealed and characterized by abnormalities in tissue electrical properties.
Collapse
Affiliation(s)
- Stefano Mandija
- Computational Imaging Group for MR diagnostic & therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
| | - Ettore F Meliadò
- Computational Imaging Group for MR diagnostic & therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
- Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Niek R F Huttinga
- Computational Imaging Group for MR diagnostic & therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Peter R Luijten
- Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Cornelis A T van den Berg
- Computational Imaging Group for MR diagnostic & therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| |
Collapse
|
48
|
Liao Y, Lechea N, Magill AW, Worthoff WA, Gras V, Shah NJ. Correlation of quantitative conductivity mapping and total tissue sodium concentration at 3T/4T. Magn Reson Med 2019; 82:1518-1526. [DOI: 10.1002/mrm.27787] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 04/02/2019] [Accepted: 04/07/2019] [Indexed: 01/15/2023]
Affiliation(s)
- Yupeng Liao
- Institute of Neuroscience and Medicine (INM‐4), Forschungszentrum Jülich Jülich Germany
| | - Nazim Lechea
- Institute of Neuroscience and Medicine (INM‐4), Forschungszentrum Jülich Jülich Germany
| | - Arthur W. Magill
- Institute of Neuroscience and Medicine (INM‐4), Forschungszentrum Jülich Jülich Germany
| | - Wieland A. Worthoff
- Institute of Neuroscience and Medicine (INM‐4), Forschungszentrum Jülich Jülich Germany
| | - Vincent Gras
- Institute of Neuroscience and Medicine (INM‐4), Forschungszentrum Jülich Jülich Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine (INM‐4), Forschungszentrum Jülich Jülich Germany
- Institute of Neuroscience and Medicine (INM‐11) JARA, Forschungszentrum Jülich Jülich Germany
- JARA‐BRAIN‐Translational Medicine Aachen Germany
- Department of Neurology RWTH Aachen University Aachen Germany
- Monash Biomedical Imaging, School of Psychology Monash University Melbourne Australia
| |
Collapse
|
49
|
Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topogr 2019; 32:825-858. [PMID: 31054104 PMCID: PMC6708046 DOI: 10.1007/s10548-019-00710-2] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 04/13/2019] [Indexed: 01/01/2023]
Abstract
Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly- and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a three-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR.
Collapse
|
50
|
Gavazzi S, van den Berg CAT, Sbrizzi A, Kok HP, Stalpers LJA, Lagendijk JJW, Crezee H, van Lier ALHMW. Accuracy and precision of electrical permittivity mapping at 3T: the impact of three B 1 + mapping techniques. Magn Reson Med 2019; 81:3628-3642. [PMID: 30737816 PMCID: PMC6593818 DOI: 10.1002/mrm.27675] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 01/07/2019] [Accepted: 01/08/2019] [Indexed: 12/29/2022]
Abstract
Purpose To investigate the sequence‐specific impact of B1+ amplitude mapping on the accuracy and precision of permittivity reconstruction at 3T in the pelvic region. Methods B1+ maps obtained with actual flip angle imaging (AFI), Bloch–Siegert (BS), and dual refocusing echo acquisition mode (DREAM) sequences, set to a clinically feasible scan time of 5 minutes, were compared in terms of accuracy and precision with electromagnetic and Bloch simulations and MR measurements. Permittivity maps were reconstructed based on these B1+ maps with Helmholtz‐based electrical properties tomography. Accuracy and precision in permittivity were assessed. A 2‐compartment phantom with properties and size similar to the human pelvis was used for both simulations and measurements. Measurements were also performed on a female volunteer’s pelvis. Results Accuracy was evaluated with noiseless simulations on the phantom. The maximum B1+ bias relative to the true B1+ distribution was 1% for AFI and BS and 6% to 15% for DREAM. This caused an average permittivity bias relative to the true permittivity of 7% to 20% for AFI and BS and 12% to 35% for DREAM. Precision was assessed in MR experiments. The lowest standard deviation in permittivity, found in the phantom for BS, measured 22.4 relative units and corresponded to a standard deviation in B1+ of 0.2% of the B1+ average value. As regards B1+ precision, in vivo and phantom measurements were comparable. Conclusions Our simulation framework quantitatively predicts the different impact of B1+ mapping techniques on permittivity reconstruction and shows high sensitivity of permittivity reconstructions to sequence‐specific bias and noise perturbation in the B1+ map. These findings are supported by the experimental results.
Collapse
Affiliation(s)
- Soraya Gavazzi
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.,Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alessandro Sbrizzi
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - H Petra Kok
- Department of Radiation Oncology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Lukas J A Stalpers
- Department of Radiation Oncology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jan J W Lagendijk
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hans Crezee
- Department of Radiation Oncology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | | |
Collapse
|