Search

Purpose: To demonstrate that interleaved MR thermometry can monitor temperature in water and fat with adequate temporal resolution. This is relevant for high intensity focused uUltrasounds (HIFU) treatment of bone lesions, which are often found near aqueous tissues, as muscle, or embedded in adipose tissues, as subcutaneous fat and bone marrow., Methods: Proton resonance frequency shift (PRFS)-based thermometry scans and T 1 -based 2D variable flip angle (2D-VFA) thermometry scans were acquired alternatingly over time. Temperature in water was monitored using PRFS thermometry, and in fat by 2D-VFA thermometry with slice profile effect correction. The feasibility of interleaved water/fat temperature monitoring was studied ex vivo in porcine bone during MR-HIFU sonication. Precision and stability of measurements in vivo were evaluated in a healthy volunteer under non-heating conditions., Results: The method allowed observing temperature change over time in muscle and fat, including bone marrow, during MR-HIFU sonication, with a temporal resolution of 6.1 s. In vivo, the apparent temperature change was stable on the time scale of the experiment: In 7 min the systematic drift was <0.042°C/min in muscle (PRFS after drift correction) and <0.096°C/min in bone marrow (2D-VFA). The SD of the temperature change averaged over time was 0.98°C (PRFS) and 2.7°C (2D-VFA)., Conclusions: Interleaved MR thermometry allows temperature measurements in water and fat with a temporal resolution high enough for monitoring HIFU ablation. Specifically, combined fat and water thermometry provides uninterrupted information on temperature changes in tissue close to the bone cortex., (© The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Real-time MR-imaging has been clinically adapted for monitoring thermal therapies since it can provide on-the-fly temperature maps simultaneously with anatomical information. However, proton resonance frequency based thermometry of moving targets remains challenging since temperature artifacts are induced by the respiratory as well as physiological motion. If left uncorrected, these artifacts lead to severe errors in temperature estimates and impair therapy guidance. In this study, we evaluated deep learning for on-line correction of motion related errors in abdominal MR-thermometry. For this, a convolutional neural network (CNN) was designed to learn the apparent temperature perturbation from images acquired during a preparative learning stage prior to hyperthermia. The input of the designed CNN is the most recent magnitude image and no surrogate of motion is needed. During the subsequent hyperthermia procedure, the recent magnitude image is used as an input for the CNN-model in order to generate an on-line correction for the current temperature map. The method's artifact suppression performance was evaluated on 12 free breathing volunteers and was found robust and artifact-free in all examined cases. Furthermore, thermometric precision and accuracy was assessed for in vivo ablation using high intensity focused ultrasound. All calculations involved at the different stages of the proposed workflow were designed to be compatible with the clinical time constraints of a therapeutic procedure., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Purpose: To demonstrate that fluid filling of the digestive tract improves the performance of respiratory motion-compensated proton resonance frequency shift (PRFS)-based magnetic resonance (MR) thermometry in the pancreas., Materials and Methods: In seven volunteers (without heating), we evaluated PRFS thermometry in the pancreas with and without filling of the surrounding digestive tract. All data acquisition was performed at 1.5T, then all datasets were analyzed and compared with three different PRFS respiratory motion-compensated thermometry methods: gating, multibaseline, and referenceless. The temperature precision of the different methods was evaluated by assessing temperature standard deviation over time, while a simulation experiment was used to study the accuracy of the methods., Results: Without fluid intake, errors in temperature precision in the pancreas up to 10°C were observed for all evaluated methods. After liquid intake, temperature precision improved to median values between 1.8 and 2.9°C. The simulations showed that gating had the lowest accuracy, with errors up to 7°C. Multibaseline and referenceless thermometry performed better, with a median error in the pancreas between -3 and +3°C after fluid intake, for all volunteers., Conclusion: Preparation of the digestive tract near the pancreas by filling it with fluid improved MR thermometry precision and accuracy for all common respiratory motion-compensated methods evaluated. These improvements are attributed to reducing field inhomogeneity in the pancreas., Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:692-701., (© 2017 International Society for Magnetic Resonance in Medicine.)