Development of a fully automatic, 20-second long deep-learning based calibration procedure for parallel transmission (pTx) in ultrahigh field MR body imaging

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In clinical magnetic resonance imaging (MRI), MRI scanners with a field strength of 1.5 and 3 Tesla are typically used. In addition, so-called ultra-high field (UHF) MRI scanners operating at 7 Tesla and beyond are increasingly being investigated for clinical use as well as for scientific purposes, which allow, amongst others, higher spatial image resolution and faster image acquisition. While the advantage of 7 Tesla is already used diagnostically for clinical applications in the head or extremities, the benefits of imaging the body at 7 Tesla has been investigated rather rarely and it has not yet been approved for routine clinical examinations. The main reason for this and a major problem in UHF body MRI is the spatially highly inhomogeneous signal of the image. This effect arises from the inhomogeneous distribution of the magnetic component (B1+) of the radiofrequency (RF) fields irradiated by the RF antenna to image the nuclear spins. Although this inhomogeneous distribution can be compensated very successfully by so-called "parallel transmission (pTx)" i.e. by using multiple antennas and driving them independently by separate RF pulses, this requires two calibration steps: after taking a fast overview image (localizer), for each patient i) the B1+ map for each antenna has to be measured and ii) the pTx RF pulses have to be calculated. A drawback of this approach is the long calibration time of typically 10-15 minutes in the body, most of which falls on step i). It is this calibration time that severely hinders or prevents not only scientific studies but also clinical UHF applications in the body. Massively reducing this time is the goal of this application. The grant application is based on a recently by the group of the applicant presented preliminary technique, in which the B1+ maps do not need to be measured by a separate scan. Instead, by using neural networks (NN) the maps are estimated from the localizer maps, that is anyway acquired at the beginning of the study. In this application, this technique will be developed further and will be systematically analyzed with respect to precision and robustness. Furthermore, in a separate work performed jointly with collages from Aarhus University, the pTx RF pulse calculation has been massively accelerated using NN, too. In the present grant application, the latter technique will be combined with the advanced NN-based B1+ mapping method to form a single AI-based calibration step that does not require more than 20 seconds of calibration time. This novel method will be tested at different UHF centers in Berlin, in Heidelberg and in Minneaplis, USA. The final calibration method allows for the first time to perform UHF patient studies targeting the human body without the need of lengthy calibration, which will promote future patient studies to investigate the benefit of 7 Tesla for the human body.