Caroline Le Ster¹, Franck Mauconduit¹, Christian Mirkes², Alexandre Vignaud¹, and Nicoal Boulant^1
1Université Paris-Saclay, CNRS, BAOBAB, Neurospin, CEA, Gif-sur-Yvette, France, ²Skope MRT, Zurich, Switzerland

Sub-degree brain temperature rise occurring through RF power deposition during the MR exam was measured with MR thermometry at 7T using a multi-slice EPI sequence and concurrent field monitoring. This sequence was first tested in vitro with field perturbations, an optical probe being used for ground truth measurements, and the accuracy of the method reached 0.02°C. In vivo, this methodology was complemented by a motion compensation step and precision reached 0.05°C. After 20-min of scanning at maximal SAR, temperature rise induced by RF power deposition throughout the inner brain reached 0-0.2°C, well below the values predicted by thermal models. 

Mathijs W. I. Kikken¹, Bart R. Steensma², Cornelis A. T. van den Berg², and Alexander J. E. Raaijmakers^1
1Biomedical Engineering - Medical Imaging Analysis, Eindhoven University of Technology, Eindhoven, Netherlands, ²Center for Image Sciences - Computational Imaging Group, University Medical Center Utrecht, Utrecht, Netherlands

A multi-echo MRT approach is presented for application in RF safety assessment and validation of thermal simulations. By water-fat separation, more accurate determination of the drift field is possible. The method was tested in the thigh at 7T, using multi-transmit coils. Precision and accuracy were improved considerably compared to a previous single-echo fat-referenced method (precision: 0.09 vs 0.19 °C). Comparison of measured temperature distributions to simulated counterparts show good relative agreement in three subjects for multiple RF shim settings. Strikingly, simulated heating magnitudes mostly underestimated the observed heating with varying extent, suggesting a role for subject-specific parameters, such as perfusion.

Huiwen Luo^1,2, Zhangyan Yang^1,2, Pai-feng Yang^2,3, Feng Wang^2,3, Jamie L Reed^2,3, Li Min Chen^2,3, John C Gore^1,2,3, and William A Grissom ^1,2,3
1Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ²Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States, ³Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States

Optogenetic stimulation with BOLD-fMRI has been applied in studies of brain circuits, by selectively exciting optical fluorophores with wavelength-specific light. However, the light used for stimulation may induce non-negligible heating effects. Because BOLD fMRI is mainly based on relatively long TE gradient-recalled echo acquisitions that are also suitable for measuring temperature changes with the proton resonance frequency (PRF) shift, we can monitor simultaneously the neural effects and laser-associated heating. We proposed a processing pipeline to calculate temperatures from BOLD images based on PRF shift thermometry and detect small temperature rises induced by laser-associated heating with general linear modeling.

Dongsuk Sung¹, Benjamin B. Risk², Peter A. Kottke³, Jason W. Allen^1,4,5, Fadi Nahab⁵, Andrei G. Fedorov^3,6, and Candace C. Fleischer^1,4,6
1Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States, ²Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States, ³Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States, ⁴Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States, ⁵Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States, ⁶Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, United States

To advance our understanding of thermal dynamics in the human brain, a thermal modeling framework was previously developed to facilitate temperature predictions in the absence of clinical thermometry. Here, predicted brain temperatures using our fully conserved model were compared with MR thermometry in 21 healthy human subjects. Bland-Altman plots demonstrated agreement between predictions and MR-measurements for average temperature values, but some differences were observed at the lowest and highest temperatures. Regional variations were similar between predicted and measured temperatures. We anticipate our modeling framework will form the necessary baseline for predicting injury-induced brain temperature changes in patients.

Stefano Mandija^1,2 and Cornelis A.T. van den Berg^1,2
1Department of Radiotherapy, UMC Utrecht, Utrecht, Netherlands, ²Computational Imaging Group for MR Diagnostics and Therapy, UMC Utrecht, Utrecht, Netherlands

This work announces the initial design of the first Electrical Properties (EPs) reconstruction challenge and opens up the call for ideas to finalize its implementation and to define the organizing committee members. Several EPs reconstruction methods have been presented in the last decade. However, as emerged from the last EPs workshop (IMEP-Utrecht’19), testing on common data has not been performed yet, nor data are publicly available. The scope of this challenge is therefore to benchmark current reconstructions on the same data. These data will also be made available to favor benchmarking of new methods in the future. 

Kyu-Jin Jung¹, Stefano Mandija^2,3, Jun-Hyung Kim¹, Chuanjiang Cui¹, Sanghyeok Choi¹, Jaeuk Yi¹, Mina Park⁴, Cornelis A.T. van den Berg^2,3, and Dong-Hyun Kim^1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, ²Department of Radiotherapy, 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

Phase-based Electrical properties tomography is a non-invasive imaging technique that uses MRI systems to measure the tissue conductivity. However, the conductivity reconstruction process causes problems such as noise amplification and boundary artifact. To address such limitations, several DL-based reconstruction methods were proposed. Building upon these works,  we propose an ANN-based conductivity reconstruction method trained only on simulation dataset. The proposed method was studied with the aim of: (a) approaching ground-truth conductivity values, (b) noise-robustness, (c) higher image resolution, (d) generalization to clinical data. The feasibility was investigated on simulations and TSE in-vivo data (one healthy volunteer, two meningioma cases).