| 13:45 | 0182.
 | The first MR Electrical Properties Tomography (MR-EPT) reconstruction challenge: preliminary results of simulated data Stefano Mandija1,2, Alessandro Arduino*3, Cornelis A.T. van den Berg*1,2, Patrick Fuchs*4, Ilias Giannakopoulos*5, Yusuf Ziya Ider*6, Kyu-Jin Jung*7, Ulrich Katscher*8, Dong-Hyun Kim*7, Riccardo Lattanzi*5,9, Thierry G. Meerbothe*1,2, Khin-Khin Tha*10, and Luca Zilberti*3
1Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands, 2Computational Imaging Group for MR Therapy and Diagnostics, University Medical Center Utrecht, Utrecht, Netherlands, 3Istituto Nazionale di Ricerca Metrologica (INRiM), Torino, Italy, 4Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 5The Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 6Department of Biomedical Engineering, Baskent University, Ankara, Turkey, 7Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 8Philips Research Hamburg, Hamburg, Germany, 9Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 10Hokkaido University Faculty of Medicine, Hokkaido, Japan Keywords: Electromagnetic Tissue Properties, Electromagnetic Tissue Properties, Conductivity Motivation: To benchmark MR-Electrical Properties Tomography (MR-EPT) reconstruction methods. Goal(s): To present an overview of the first MR-EPT reconstruction challenge participation and the results of its phase 1. Approach: The challenge consisted of 3 phases:1) reconstructions from a simulated (blind) dataset (ground-truth EPs not provided);2) reconstructions from several simulated dataset (ground-truth EPs provided for few training dataset for tuning algorithm parameters);3) EPs reconstructions from measured data. Results: 52 participants registered to the challenge; 39 submitted their results. For phase 1, all participants submitted a reconstructed conductivity map; 12 submitted a reconstructed permittivity map. The results show large variability in reconstruction accuracy and precision. Impact: The results of phase 1 of the first MR-EPT
reconstruction challenge show large variations in the estimated conductivity
and permittivity maps demonstrating the need of benchmarking reconstruction
methods on common datasets. |
| 13:57 | 0183.
 | Artifact free Projection onto Dipole Fields via a Generalized Frequency-domain Discrete Dipole Kernel Carlos Milovic1, Mathias Lambert2, Patrick Fuchs3, Oliver Kiersnowski3, Chaoyue Wang4, Zheng Wang5, and Cristian Tejos2
1School of Electrical Engineering, Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile, 2Pontificia Universidad Catolica de Chile, Santiago, Chile, 3Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 4SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Shanghai Jiaotong University School of Medicine, Shanghai, China, 5School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China Keywords: Susceptibility/QSM, Quantitative Susceptibility mapping, Background field removal Motivation: Overcoming striping artifacts in the background removal step is a common challenge, especially in non-orthogonal (oblique) B0 field orientations.
Goal(s): Develop a robust solution to eliminate striping artifacts while improving the accuracy of QSM images.
Approach: We introduce a novel approach, employing a generalized discrete kernel to suppress striping artifacts generated by the Projection onto Dipole Fields method.
Results: Our approach successfully addresses striping artifacts and enhances the accuracy of PDF solutions, even at non-orthogonal B0 field angles, promising artifact-free results.
Impact: Our
work promises to benefit the EMTP community by providing a more
robust solution for addressing striping artifacts. This can lead to
improved diagnostic accuracy and higher-quality imaging, ultimately
enhancing patient care and advancing MRI technology. |
| 14:09 | 0184.
 | Data-driven Electrical Conductivity Reconstructions via transceive phase and signal magnitude gradient data from the three imaging directions Chan-Hee Park1, Thierry G. Meerbothe2,3, Kyu-Jin Jung1, Chuanjiang Cui1, Mina Park4, Yoonho Nam5, Cornelis A.T. van den Berg2,3, Stefano Mandija2,3, and Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2Department of Radiotherapy, Division of Imaging and Oncology, UMC Utrecht, Utrecht, Netherlands, 3Computational Imaging Group for MR Diagnostics and Therapy, UMC Utrecht, Utrecht, Netherlands, 4Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of, 5Divison of Biomeidcal Engineering, Hankuk University of Foreign Studies, Yongin-Si, Korea, Republic of Keywords: Electromagnetic Tissue Properties, Electromagnetic Tissue Properties Motivation: Phase-based conductivity reconstructions suffer from poor structural information and lack of conductivity information from through plane (z-direction) phase variations. Goal(s): To present an end-to-end process that utilizes gradient information from the transceive phase and tissue magnitude in all three directions (in-plane: x/y and through plane: z) to address the issue of boundary artifacts in conductivity reconstructions and lack of conductivity information from the z-direction. Approach: This method was trained on simulated data (SNR=50), and tested both on simulated and measured in-vivo data. Results: This approach reduces boundary errors and shows higher accuracy in conductivity reconstructions compared to conventional methods. Impact: In contrast to existing tissue conductivity reconstruction algorithms that operate under the assumption of negligible through-plane (z) transceive phase contributions, our approach demonstrates enhanced efficacy and more accurate conductivity reconstructions by explicitly considering through-plane phase variations. |
| 14:21 | 0185.
 | A human brain atlas of χ-separation (chi-separation) for normative iron and myelin distributions Kyeongseon Min1, Beomseok Sohn2, Woo Jung Kim3,4, Chae Jung Park5, Soohwa Song6, Dong Hoon Shin6, Kyung Won Chang7, Na-Young Shin8, Minjun Kim1, Hyeong-Geol Shin9,10, Phil Hyu Lee11, and Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Department of Radiology, Samsung Medical Center, Seoul, Korea, Republic of, 3Institute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Korea, Republic of, 4Department of Psychiatry, Yongin Severance Hospital, Yongin, Korea, Republic of, 5Department of Radiology, Yongin Severance Hospital, Yongin, Korea, Republic of, 6Heuron Co., Ltd, Seoul, Korea, Republic of, 7Department of Neurosurgery, Severance Hospital, Seoul, Korea, Republic of, 8Department of Radiology, Severance Hospital, Seoul, Korea, Republic of, 9Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 10F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, 11Department of Neurology, Severance Hospital, Seoul, Korea, Republic of Keywords: Susceptibility/QSM, Software Tools, Susceptibility source separation, Atlas, Iron imaging, Myelin imaging Motivation: Abnormal iron and myelin distributions are associated with neurodegenerative diseases. An advanced susceptibility mapping technique, χ-separation, can disentangle paramagnetic iron and diamagnetic myelin contributions in quantitative susceptibility mapping. Goal(s): In this study, a normative χ-separation atlas is created from 106 healthy volunteers. Approach: To this end, individual χ-separation maps were registered to a common space and averaged across subjects. Results: The resulting χ-separation atlas reflects well-known iron and myelin-rich structures in the brain. The analysis based on regions of interest revealed distinct characteristics of normative para- and diamagnetic susceptibility profiles throughout subcortical nuclei, thalamic nuclei, and white matter fibers. Impact: Our χ-separation atlas would be utilized as a reference for
imaging susceptibility in the brain and may assist in accurate localization of
targets for intervention such as deep brain stimulation or high-intensity
focused ultrasound. |
| 14:33 | 0186.
 | Electrical Property Mapping using Vision Transformers and Canny Edge Detection Ilias Giannakopoulos1, Xinling Yu2, Giuseppe Carluccio3, Gregor Koerzdoerfer4, Karthik Lakshmanan1,5, Hector Lise de Moura1, Jose Cruz Serralles1, Jerzy Walczyk,1, Zheng Zhang2, and Riccardo Lattanzi1,5,6
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States, 2UC Santa Barbara, Santa Barbara, CA, United States, 3Universita di Napoli Federico II, Napoli, Italy, 4Siemens Medical Solutions, New York, NY, United States, 5Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States, 6Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, United States Keywords: Electromagnetic Tissue Properties, Electromagnetic Tissue Properties, Machine Learning Motivation: To estimate tissue electrical properties (EP) non-invasively for specific absorption rate management and as biomarkers for pathology characterization. Goal(s): To train neural networks for mapping transmit magnetic fields (B1+) onto EP. Approach: We developed a 3D vision transformer that takes the B1+ and an edge mask based on Canny filtering of the MR image as the inputs. The targets were the EP of the object. We trained on simulated tissue mimicking objects and fine-tuned on realistic head models. Results: Our network successfully reconstructed the EP in a phantom experiment, and detected a synthetic cyst in a realistic head model in simulation. Impact: We propose a supervised learning approach using vision transformers and Canny edge detection to perform electrical property (EP) mapping. The network successfully reconstructs the EP using experimentally measured fields and is a promising first step towards clinically-usable in-vivo EP reconstructions. |
| 14:45 | 0187.
 | Distinguishing microgliosis and tau deposition in the mouse brain using paramagnetic and diamagnetic susceptibility source separation Jayvik Joshi1, Minmin Yao2,3, Wenzhen Duan2,3, and Manisha Aggarwal4
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, 2Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 3Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 4Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States Keywords: Susceptibility/QSM, Microstructure, Brain Motivation: Susceptibility source separation methods to disentangle sub-voxel paramagnetic and diamagnetic susceptibility sources may provide higher specificity to distinguish tissue microstructural alterations. Goal(s): Our goal was to investigate sub-cellular histopathological alterations in an established tauopathy mouse model using quantitative susceptibility source separation. Approach: Brains of PS19 mice and wild-type controls (n = 5 each) were imaged at 11.7 T. We used the DECOMPOSE-QSM model to calculate paramagnetic and diamagnetic component susceptibility maps. Results: Susceptibility maps revealed significant localized alterations in specific regions of the hippocampus and entorhinal cortex, which were found to correspond to regional microgliosis and tau deposition seen with immunohistology. Impact: Our findings demonstrate unique sensitivity of paramagnetic and diamagnetic susceptibility
changes to distinguish regional microgliosis and tau deposition in the brain. Quantitative
magnetic susceptibility source separation may therefore provide a sensitive method
to assess sub-cellular histopathological alterations in tauopathies. |
| 14:57 | 0188.
 | Rapid High Resolution Integrated Structural and Functional Susceptibility and Conductivity Mapping in the Human Brain Oliver C Kiersnowski1, Patrick Fuchs1, Jannette Nassar1, Oriana Arsenov1, Jierong Luo1, Anita Karsa1, Stephen Wastling2,3, and Karin Shmueli1
1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 2Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, 3Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom Keywords: Susceptibility/QSM, Quantitative Susceptibility mapping, Electrical Properties Tomography, EPT, fMRI, fQSM, fQCM Motivation: Quantitative susceptibility mapping (QSM), electrical conductivity mapping (EPT) and fMRI show promise in characterising neurodegenerative diseases but each currently needs a separate time-consuming acquisition. Goal(s): To develop a single, rapid acquisition for simultaneous structural and functional QSM and EPT, providing multi-modal contrasts to facilitate development of biomarkers for neurological diseases. Approach: We developed a multi-echo 2D EPI sequence with 1.3 mm isotropic resolution and 4.02 s TR enabling acquisition of 70 timepoints in 6 min 15 s. We optimised QSM, EPT and fQSM reconstruction pipelines. Results: We obtained high-quality structural QSM and EPT, alongside fMRI and fQSM activations from a visual stimulus. Impact: Demonstrating that this efficient multi-echo EPI
acquisition rapidly produces high-quality simultaneous QSM, fQSM and EPT
reconstructions alongside conventional T2*-weighted, SWI and fMRI contrasts in
6 min 15 s will allow it to be incorporated into clinical studies of
neurodegenerative diseases. |
| 15:09 | 0189.
 | Potential of phase-based electrical conductivity in evaluating lumbar intervertebral disc degeneration Khin Khin Tha1, Maho Kitagawa2, Daiki Sakamoto2, Hiroyuki Hamaguchi3, and Ulrich Katscher4
1Global Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, Sapporo, Japan, 2Laboratory for Biomarker Imaging Science, Hokkaido University Graduate School of Biomedical Science and Engineering, Sapporo, Japan, 3Hokkaido University Graduate School of Biomedical Science and Engineering, Sapporo, Japan, 4Philips Research Laboratories, Hamburg, Germany Keywords: Electromagnetic Tissue Properties, Electromagnetic Tissue Properties, intervertebral disc, lumbar, degeneration Motivation: Visual assessment of T2-weighted image constitutes the mainstay in evaluating the severity of intervertebral disc degeneration (IVD) degeneration. Quantitative MRI indices that strongly correlate with the degree of degeneration are lacking. Goal(s): This study aimed to evaluate if electrical conductivity (σ) derived from phase-based EPT was sensitive to the degenerative changes of lumbar IVD. Approach: EPT was conducted, along with DWI, T1ρ, and T2* imaging, in 54 patients with lumbar IVD degeneration. The diagnostic performance of σ was compared with that of ADC, T1rho, and T2*. Results: σ can compliment the other quantitative MRI indices in evaluating lumbar IVD degeneration. Impact: This is the first study which evaluated the potential clinical usefulness of σ derived from phase-based EPT in evaluating the severity of degeneration of lumbar IVD. |
| 15:21 | 0190.
 | Feasibility of susceptibility separation using single-echo gradient-echo and MPRAGE Nashwan Naji1, Jeff Snyder1, and Alan Wilman1
1Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada Keywords: Susceptibility/QSM, Quantitative Susceptibility mapping, Susceptibility separation, R2*, SWI, MPRAGE, single-echo GRE Motivation: Susceptibility separation enables exploring sub-voxel contributions of iron/myelin but requires multi-echo gradient-echo to calculate the R2* map. Extending its applicability to single-echo measurements such as in SWI-focused studies, would allow wider usage. Goal(s): To develop and validate at 3T an approach that produces brain para- and diamagnetic maps from SWI with information from MPRAGE images typically collected for structural imaging. Approach: R2* was estimated from SWI and MPRAGE using Bloch simulations, followed by production of para- and diamagnetic maps using calculated R2* and R2 maps, and SWI phase. Results: Comparable maps to those produced from multi-echo images were obtained. Impact: The
proposed method enables producing para- and diamagnetic maps from SWI studies, with
the possibility of retrospective application if SWI raw phase and T1w images
exist. |
| 15:33 | 0191.
 | A Self-supervised Physics-informed Reconstruction Error Compensation Neural Network for Magnetic Resonance Electrical Property Tomography Ruian Qin1, Adan Jafet Garcia Inda2, Zhongchao Zhou1, Tianyi Yang1, Nevrez Imamoglu3, Jose Gomez-Tames1,4, Shao Ying Huang5,6, and Wenwei Yu1,4
1Department of Medical Engineering, Chiba University, Chiba, Japan, 2Science & Technology Research Laboratories, Cresco, Tokyo, Japan, 3Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, 4Center for Frontier Medical Engineering, Chiba University, Chiba, Japan, 5Engineering Product Development Department, Singapore University of Technology and Design, Singapore, Singapore, 6Department of Surgery, National University of Singapore, Singapore, Singapore Keywords: Electromagnetic Tissue Properties, Electromagnetic Tissue Properties Motivation: The recent physics-informed neural network (PINN) for Magnetic resonance electrical properties tomography (MREPT) still reply on ground truth as boundary conditions for back propagations. Goal(s): It is aimed to propose a PINN that uses only the residuals of an MREPT analytic model rather than ground truth data. Approach: A PINN framework which uses the aforementioned residuals to guide the network learning process of an neural network, enhancing the accuracy and reliability of the reconstruction, was proposed to compensate for the conductivity reconstruction errors of the Stabilized-EPT. Results: The results show increased accuracy of the reconstruction of conductivity for both normal and tumorous tissues. Impact: Feasibility of more accurate conductivity reconstruction without any ground truth information is demonstrated. This may lead to practical cancer detection. |