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Purpose: Local specific absorption rate (SAR) cannot be measured and is usually evaluated by offline numerical simulations using generic body models that of course will differ from the patient's anatomy. An additional safety margin is needed to include this intersubject variability. In this work, we present a deep learning–based method for image-based subject-specific local SAR assessment. We propose to train a convolutional neural network to learn a “surrogate SAR model” to map the relation between subject-specific (Formula presented.) maps and the corresponding local SAR. Method: Our database of 23 subject-specific models with an 8–transmit channel body array for prostate imaging at 7 T was used to build 5750 training samples. These synthetic complex (Formula presented.) maps and local SAR distributions were used to train a conditional generative adversarial network. Extra penalization for local SAR underestimation errors was included in the loss function. In silico and in vivo validation were performed. Results: In silico cross-validation shows a good qualitative and quantitative match between predicted and ground-truth local SAR distributions. The peak local SAR estimation error distribution shows a mean overestimation error of 15% with 13% probability of underestimation. The higher accuracy of the proposed method allows the use of less conservative safety factors compared with standard procedures. In vivo validation shows that the method is applicable with realistic measurement data with impressively good qualitative and quantitative agreement to simulations. Conclusion: The proposed deep learning method allows online image-based subject-specific local SAR assessment. It greatly reduces the uncertainty in current state-of-the-art SAR assessment methods, reducing the time in the examination protocol by almost 25%.

Objective: To demonstrate imaging performance for cardiac MR imaging at 7 T using a coil array of 8 transmit/receive dipole antennas and 16 receive loops. Materials and methods: An 8-channel dipole array was extended by adding 16 receive-only loops. Average power constraints were determined by electromagnetic simulations. Cine imaging was performed on eight healthy subjects. Geometrical factor (g-factor) maps were calculated to assess acceleration performance. Signal-to-noise ratio (SNR)-scaled images were reconstructed for different combinations of receive channels, to demonstrate the SNR benefits of combining loops and dipoles. Results: The overall image quality of the cardiac functional images was rated a 2.6 on a 4-point scale by two experienced radiologists. Imaging results at different acceleration factors demonstrate that acceleration factors up to 6 could be obtained while keeping the average g-factor below 1.27. SNR maps demonstrate that combining loops and dipoles provides a more than 50% enhancement of the SNR in the heart, compared to a situation where only loops or dipoles are used. Conclusion: This work demonstrates the performance of a combined loop/dipole array for cardiac imaging at 7 T. With this array, acceleration factors of 6 are possible without increasing the average g-factor in the heart beyond 1.27. Combining loops and dipoles in receive mode enhances the SNR compared to receiving with loops or dipoles only.

OBJECTIVES: We studied the feasibility of high-resolution T2-weighted cervical cancer imaging on an ultra-high-field 7.0-T magnetic resonance imaging (MRI) system using an endorectal antenna of 4.7-mm thickness.METHODS: A feasibility study on 20 stage IB1-IIB cervical cancer patients was conducted. All underwent pre-treatment 1.5-T MRI. At 7.0-T MRI, an external transmit/receive array with seven dipole antennae and a single endorectal monopole receive antenna were used. Discomfort levels were assessed. Following individualised phase-based B1+ shimming, T2-weighted turbo spin echo sequences were completed.RESULTS: Patients had stage IB1 (n = 9), IB2 (n = 4), IIA1 (n = 1) or IIB (n = 6) cervical cancer. Discomfort (ten-point scale) was minimal at placement and removal of the endorectal antenna with a median score of 1 (range, 0-5) and 0 (range, 0-2) respectively. Its use did not result in adverse events or pre-term session discontinuation. To demonstrate feasibility, T2-weighted acquisitions from 7.0-T MRI are presented in comparison to 1.5-T MRI. Artefacts on 7.0-T MRI were due to motion, locally destructive B1 interference, excessive B1 under the external antennae and SENSE reconstruction.CONCLUSIONS: High-resolution T2-weighted 7.0-T MRI of stage IB1-IIB cervical cancer is feasible. The addition of an endorectal antenna is well tolerated by patients.KEY POINTS: • High resolution T 2 -weighted 7.0-T MRI of the inner female pelvis is challenging • We demonstrate a feasible approach for T 2 -weighted 7.0-T MRI of cervical cancer • An endorectal monopole receive antenna is well tolerated by participants • The endorectal antenna did not lead to adverse events or session discontinuation.

The progression to 7 Tesla (7 T) magnetic resonance imaging (MRI) yields promises of substantial increase in signal-to-noise (SNR) ratio. This increase can be traded off to increase image spatial resolution or to decrease acquisition time. However, renal 7 T MRI remains challenging due to inhomogeneity of the radiofrequency field and due to specific absorption rate (SAR) constraints. A number of studies has been published in the field of renal 7 T imaging. While the focus initially was on anatomic imaging and renal MR angiography, later studies have explored renal functional imaging. Although anatomic imaging remains somewhat limited by inhomogeneous excitation and SAR constraints, functional imaging results are promising. The increased SNR at 7 T has been particularly advantageous for blood oxygen level-dependent and arterial spin labelling MRI, as well as sodium MR imaging, thanks to changes in field-strength-dependent magnetic properties. Here, we provide an overview of the currently available literature on renal 7 T MRI. In addition, we provide a brief overview of challenges and opportunities in renal 7 T MR imaging.