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He Z, Lefebvre PM, Soullié P, Doguet M, Ambarki K, Chen B, Odille F. Phantom evaluation of electrical conductivity mapping by MRI: Comparison to vector network analyzer measurements and spatial resolution assessment. Magn Reson Med 2024; 91:2374-2390. [PMID: 38225861 DOI: 10.1002/mrm.30009] [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: 11/10/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/17/2024]
Abstract
PURPOSE To evaluate the performance of various MR electrical properties tomography (MR-EPT) methods at 3 T in terms of absolute quantification and spatial resolution limit for electrical conductivity. METHODS Absolute quantification as well as spatial resolution performance were evaluated on homogeneous phantoms and a phantom with holes of different sizes, respectively. Ground-truth conductivities were measured with an open-ended coaxial probe connected to a vector network analyzer (VNA). Four widely used MR-EPT reconstruction methods were investigated: phase-based Helmholtz (PB), phase-based convection-reaction (PB-cr), image-based (IB), and generalized-image-based (GIB). These methods were compared using the same complex images from a 1 mm-isotropic UTE sequence. Alternative transceive phase acquisition sequences were also compared in PB and PB-cr. RESULTS In large homogeneous phantoms, all methods showed a strong correlation with ground truth conductivities (r > 0.99); however, GIB was the best in terms of accuracy, spatial uniformity, and robustness to boundary artifacts. In the resolution phantom, the normalized root-mean-squared error of all methods grew rapidly (>0.40) when the hole size was below 10 mm, with simplified methods (PB and IB), or below 5 mm, with generalized methods (PB-cr and GIB). CONCLUSION VNA measurements are essential to assess the accuracy of MR-EPT. In this study, all tested MR-EPT methods correlated strongly with the VNA measurements. The UTE sequence is recommended for MR-EPT, with the GIB method providing good accuracy for structures down to 5 mm. Structures below 5 mm may still be detected in the conductivity maps, but with significantly lower accuracy.
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Affiliation(s)
- Zhongzheng He
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
| | | | - Paul Soullié
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
| | - Martin Doguet
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
- BioSerenity, Paris, France
| | | | - Bailiang Chen
- 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
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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.
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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
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Mukhatov A, Le T, Pham TT, Do TD. A comprehensive review on magnetic imaging techniques for biomedical applications. NANO SELECT 2023. [DOI: 10.1002/nano.202200219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Affiliation(s)
- Azamat Mukhatov
- Department of Robotics School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan
| | - Tuan‐Anh Le
- Department of Physiology and Biomedical Engineering Mayo Clinic Scottsdale Arizona USA
| | - Tri T. Pham
- Department of Biology School of Sciences and Humanities Nazarbayev University Astana Kazakhstan
| | - Ton Duc Do
- Department of Robotics School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan
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Kokko MA, Van Citters DW, Seigne JD, Halter RJ. A particle filter approach to dynamic kidney pose estimation in robotic surgical exposure. Int J Comput Assist Radiol Surg 2022; 17:1079-1089. [PMID: 35511394 DOI: 10.1007/s11548-022-02638-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 04/08/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE Traditional soft tissue registration methods require direct intraoperative visualization of a significant portion of the target anatomy in order to produce acceptable surface alignment. Image guidance is therefore generally not available during the robotic exposure of structures like the kidneys which are not immediately visualized upon entry into the abdomen. This paper proposes guiding surgical exposure with an iterative state estimator that assimilates small visual cues into an a priori anatomical model as exposure progresses, thereby evolving pose estimates for the occluded structures of interest. METHODS Intraoperative surface observations of a right kidney are simulated using endoscope tracking and preoperative tomography from a representative robotic partial nephrectomy case. Clinically relevant random perturbations of the true kidney pose are corrected using this sequence of observations in a particle filter framework to estimate an optimal similarity transform for fitting a patient-specific kidney model at each step. The temporal response of registration error is compared against that of serial rigid coherent point drift (CPD) in both static and simulated dynamic surgical fields, and for varying levels of observation persistence. RESULTS In the static case, both particle filtering and persistent CPD achieved sub-5 mm accuracy, with CPD processing observations 75% faster. Particle filtering outperformed CPD in the dynamic case under equivalent computation times due to the former requiring only minimal persistence. CONCLUSION This proof-of-concept simulation study suggests that Bayesian state estimation may provide a viable pathway to image guidance for surgical exposure in the abdomen, especially in the presence of dynamic intraoperative tissue displacement and deformation.
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Affiliation(s)
- Michael A Kokko
- Thayer School of Engineering, Dartmouth College, 15 Thayer Drive, Hanover, NH, 03755, USA.
| | - Douglas W Van Citters
- Thayer School of Engineering, Dartmouth College, 15 Thayer Drive, Hanover, NH, 03755, USA
| | - John D Seigne
- Dartmouth-Hitchcock Medical Center, Section of Urology, 1 Medical Center Drive, Lebanon, NH, 03756, USA.,Geisel School of Medicine, Dartmouth College, 1 Rope Ferry Road, Hanover, NH, 03755, USA
| | - Ryan J Halter
- Thayer School of Engineering, Dartmouth College, 15 Thayer Drive, Hanover, NH, 03755, USA.,Geisel School of Medicine, Dartmouth College, 1 Rope Ferry Road, Hanover, NH, 03755, USA
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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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