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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.)

Practical non-invasive equipment modifications and effective acquisition methods to achieve robust and reliable real-time MR thermometry for monitoring of clinical hepatic microwave ablations were implemented. These included selection of the microwave generator location (inside versus outside the MR scan room), the number of radiofrequency chokes added to the microwave generator's coaxial lines, and the use of copper wool to maximize their electrical grounding. Signal-to-noise ratio (SNR) of MR thermometry images of a small fluid-filled phantom acquired during activation of microwave antenna were used to evaluate image quality as a function of each modification. SNR measurements corresponding to both locations of the microwave generator were comparable and so it was located outside the MR scan room. For this location, addition of one RF choke on the power and four chokes on the sensor coaxial lines was found to be optimal, corresponding to a 68% increase in SNR. Furthermore, image quality strongly depended on the proper electrical grounding of the power and sensor lines. SNR ratio (relative to SNR of baseline images) during activation of microwave generator was found to be 0.49 ± 0.28 without adequate grounding, and 0.88 ± 0.08 with adequate grounding (p = 0.002, Student's t-test). These SNR measurements were sufficiently sensitive to detect issues related to equipment performance and hence formed part of the quality assurance testing performed prior to each clinical treatment. Incorporating these non-invasive approaches resulted in significant improvements to image quality and, importantly while maintaining the clinical integrity of the microwave system which is of paramount importance in a highly regulated healthcare environment., (Copyright © 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Proton resonance frequency shift-based magnetic resonance thermometry is a currently used technique for monitoring temperature during targeted thermal therapies. However, in order to provide temperature updates with very short latency times, fast MR acquisition schemes are usually employed, which in turn might lead to noisy temperature measurements. This will, in general, have a direct impact on therapy control and endpoint detection. In this paper, we address this problem through an improved non-local filtering technique applied on the temperature images. Compared with previous non-local filtering methods, the proposed approach considers not only spatial information but also exploits temporal redundancies. The method is fully automatic and designed to improve the precision of the temperature measurements while at the same time maintaining output accuracy. In addition, the implementation was optimized in order to ensure real-time availability of the temperature measurements while having a minimal impact on latency. The method was validated in three complementary experiments: a simulation, an ex-vivo and an in-vivo study. Compared to the original non-local means filter and two other previously employed temperature filtering methods, the proposed approach shows considerable improvement in both accuracy and precision of the filtered data. Together with the low computational demands of the numerical scheme, the proposed filtering technique shows great potential for improving temperature measurements during real-time MR thermometry dedicated to targeted thermal therapies. [ABSTRACT FROM PUBLISHER]

Laser ablation (LA) is a minimally invasive technique for the treatment of tumors as an alternative to surgical resection. The light absorbed by tissue is converted into heat, and causes irreversible cell damage when temperatures higher than 60 °C are reached. The knowledge in real time of temperature may be particularly beneficial for adjusting laser settings applied during treatment and to be notified in real time about its end-point. As a consequence, several techniques for temperature monitoring within the tissue have been investigated along the last decades. In the field of LA, particularly attractive are non-invasive methods. Among these techniques, thermometry based on the analysis of Magnetic Resonance Imaging (MR-thermometry) has gaining large acceptance in this field. MR-thermometry allows estimating the temperature variation thanks to the thermal dependence of several MRI parameters, among others the most promising are T 1 relaxation time, and proton resonance frequency shift. The aim of this study is to assess the sensitivity of MRI thermometry using three T 1 -weighted sequences (i.e., Inversion Recovery Turbo-FLASH, IRTF, Saturation Recovery Turbo-FLASH, SRTF, and FLASH) using an 1.5-T MR scanner on healthy swine pancreases undergoing LA. The reference temperature was measured by MRI-compatible fiber optic sensors (fiber Bragg grating sensors). The sensitivity of the proposed techniques was estimated and compared. The thermal sensitivity of the three sequences was −1.47 ± 0.08 °C −1 , −0.95 ± 0.05 °C −1 , and −0.56 ± 0.04 °C −1 for IRTF, SRTF and FLASH, respectively. Results show that the proposed technique may be adequate for temperature monitoring during LA. [ABSTRACT FROM AUTHOR]

Thin-film micro-coils are integrated with commercially available ablation catheters, for MR-thermometry during laser interstitial thermal therapies (LITTs). The coils are formed on a flexible polyimide substrate and consist of a two-turn electroplated copper inductor and integrated parallel plate capacitors for tuning and matching. Their performance was assessed during Nd:YAG laser ablations in a static phantom study carried out in a 3 T clinical scanner. Further moving phantom studies were performed to calculate errors due to motion. The temperature accuracy is improved by 1.5–10 times in a radius matching the dimensions of the lesions typically treated. Resolution of 1 mm can be maintained during motion by using short acquisition time sequences while the SNR remains sufficient for accurate MR-thermometry. The temperature error on a moving un-heated phantom under respiratory gating does not exceed 1 °C. This demonstration suggests a possible improvement of the overall control of MR-guided LITTs by local temperature monitoring. [ABSTRACT FROM AUTHOR]