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Guo L, Nguyen-Trong N, Ai-Saffar A, Stancombe A, Bialkowski K, Abbosh A. Calibrated Frequency-Division Distorted Born Iterative Tomography for Real-Life Head Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1087-1103. [PMID: 34855589 DOI: 10.1109/tmi.2021.3132000] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The clinical use of microwave tomography (MT) requires addressing the significant mismatch between simulated environment, which is used in the forward solver, and real-life system. To alleviate this mismatch, a calibrated tomography, which uses two homogeneous calibration phantoms and a modified distorted Born iterative method (DBIM), is presented. The two phantoms are used to derive a linear model that matches the forward solver to real-life measurements. Moreover, experimental observations indicate that signal quality at different frequencies varies between different antennas due to inevitably inconsistent manufacturing tolerance and variances in radio-frequency chains. An optimum frequency, at which the simulated and measured signals of the antenna present maximum similarity when irradiating the calibrated phantoms, is thus calculated for each antenna. A frequency-division DBIM (FD-DBIM), in which different antennas in the array transmit their corresponding optimum frequencies, is subsequently developed. A clinical brain scanner is then used to assess performance of the algorithm in lab and healthy volunteers' tests. The linear calibration model is first used to calibrate the measured data. After that FD-DBIM is used to solve the problem and map the dielectric properties of the imaged domain. The simulated and experimental results confirm validity of the presented approach and its superiority to other tomographic method.
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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.
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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
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Han J, Gao Y, Nan X, Yu X, Liu F, Xin SX. Effect of radiofrequency inhomogeneity on water-content based electrical properties tomography and its correction by flip angle maps. Magn Reson Imaging 2021; 78:25-34. [PMID: 33450296 DOI: 10.1016/j.mri.2020.12.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 12/24/2020] [Accepted: 12/31/2020] [Indexed: 10/22/2022]
Abstract
Water-content based electrical properties tomography (wEPT) can retrieve electrical properties (EPs) from water-content maps. B1+ field information is not involved in the traditional magnetic resonance electrical properties tomography approach. wEPT can be performed through conventional MR scanning, such as T1-weighted spin-echo imaging, which provides convenient access to multiple clinical applications. However, the inhomogeneous radiofrequency (RF) field induced by RF coils would cause inaccuracy in wEPT reconstructions during MR scanning. We conducted a detailed investigation to evaluate the effect of inhomogeneous RF field on wEPT reconstructions to guarantee that EP mapping is desired for clinical practice. Two important considerations are involved, namely, multiple typical coil configurations and various flip angles (FAs). We proposed a correction scheme with actual FA mapping to calibrate the RF inhomogeneity and finally validated it by using human imaging at 3 T. This study illustrates a detailed evaluation for wEPT under imperfect RF homogeneity and further provides a feasible correction procedure to mitigate it. The profound knowledge of wEPT provided in our work will benefit its performance in clinical applications.
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Affiliation(s)
- Jijun Han
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Yunyu Gao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Xiang Nan
- Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Xuefei Yu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Sherman Xuegang Xin
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China; School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.
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Liu C, Guo L, Li M, Chen H, Jin J, Chen W, Liu F, Crozier S. Divergence-Based Magnetic Resonance Electrical Properties Tomography. IEEE Trans Biomed Eng 2021; 68:192-203. [DOI: 10.1109/tbme.2020.3003460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Han J, Gao Y, Nan X, Liu F, Xin SX. Statistical analysis of the accuracy of water content-based electrical properties tomography. NMR IN BIOMEDICINE 2020; 33:e4273. [PMID: 32048385 DOI: 10.1002/nbm.4273] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 01/04/2020] [Accepted: 01/24/2020] [Indexed: 06/10/2023]
Abstract
Water content-based electrical properties tomography (wEPT) can retrieve electrical properties (EPs) from water content maps, thereby eliminating the need for B1 field measurement in the traditional magnetic resonance electrical properties tomography method. The wEPT is performed by conventional MR scanning, such as T1 -weighted spin-echo imaging, and thus can be directly applied to clinical settings. However, the random noise propagation involved in wEPT causes inaccuracy in EP mapping. To guarantee the EP estimates desired for clinical practice, this study statically investigates the noise-specific uncertainty of wEPT through probability density function models. We calculated the probability distribution of EP maps with different noise levels and examined the effects of scan parameters on reconstruction accuracy with various flip angles (FAs) and repetition time (TR) settings. The theoretical derivation was validated by Monte Carlo simulations and human imaging experiment at 3 T. Results showed that a serious deviation could occur in tissues with large conductivity value at a low signal-to-noise ratio and quantitatively demonstrate that such deviation could be mitigated by increased FAs or TRs. This study provided useful information for the setup of scan parameters, evaluation of accuracy of the wEPT under specific SNR levels, and promote its clinical applications.
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Affiliation(s)
- Jijun Han
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Yunyu Gao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Xiang Nan
- Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Sherman Xuegang Xin
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
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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.
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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
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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.
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