Personalized local SAR prediction for parallel transmit neuroimaging at 7T from a single T1‐weighted dataset

Abstract

Parallel RF transmission (PTx) is one of the key technologies enabling high quality imaging at ultra-high fields (≥7T). Compliance with regulatory limits on the local specific absorption rate (SAR) typically involves over-conservative safety margins to account for intersubject variability, which negatively affect the utilization of ultra-high field MR. In this work, we present a method to generate a subject-specific body model from a single T1-weighted dataset for personalized local SAR prediction in PTx neuroimaging at 7T. Methods: Multi-contrast data were acquired at 7T (N = 10) to establish ground truth segmentations in eight tissue types. A 2.5D convolutional neural network was trained using the T1-weighted data as input in a leave-one-out cross-validation study. The segmentation accuracy was evaluated through local SAR simulations in a quadrature birdcage as well as a PTx coil model. Results:The network-generated segmentations reached Dice coefficients of 86.7% ± 6.7% (mean ± SD) and showed to successfully address the severe intensity bias and contrast variations typical to 7T. Errors in peak local SAR obtained were below 3.0% in the quadrature birdcage. Results obtained in the PTx configuration indicated that a safety margin of 6.3% ensures conservative local SAR estimates in 95% of the random RF shims, compared to an average overestimation of 34% in the generic "one-size-fits-all" approach. Conclusion: A subject-specific body model can be automatically generated from a single T1-weighted dataset by means of deep learning, providing the necessary inputs for accurate and personalized local SAR predictions in PTx neuroimaging at 7T.

Rapid estimation of 2D relative B1+‐maps from localizers in the human heart at 7T using deep learning

“…Some related DL approaches in UHF imaging have been recently reported for subject‐specific calibration of the transmit $$$ {\mathrm{B}}_1^{+} $$$‐fields 25–29 . Abbasi‐Rad et al 28 predicted channel‐combined $$$ {\mathrm{B}}_1^{+} $$$‐magnitudes from a standard localizer to determine a FA scaling factor for adiabatic RF pulses.…”

“…Some related DL approaches in UHF imaging have been recently reported for subject-specific calibration of the transmit B + 1 -fields. [25][26][27][28][29] Abbasi-Rad et al 28 predicted…”

“…Furthermore, different neural networks have been suggested to approximate channel-combined or channel-wise B + 1 -distributions in various application scenarios. [25][26][27][28][29] Abbasi-Rad et al 28 reduced the specific absorption rate (SAR) in T 2 -FLAIR imaging at UHF in the human head by determining a scaling factor for adiabatic RF pulses from a predicted channel-combined B + 1 -magnitude. Plumley et al 29 presented a system of conditional generative adversarial networks to predict how rigid motion changes complex, channel-wise B + 1 -distributions in the head.…”

“…Vinding et al 23 proposed a DL‐based RF pulse design for high‐FA 2D RF pulses at 7T with a pulse prediction time of under 10 milliseconds. Furthermore, different neural networks have been suggested to approximate channel‐combined or channel‐wise $$$ {\mathrm{B}}_1^{+} $$$‐distributions in various application scenarios 25–29 . Abbasi‐Rad et al 28 reduced the specific absorption rate (SAR) in T 2 ‐FLAIR imaging at UHF in the human head by determining a scaling factor for adiabatic RF pulses from a predicted channel‐combined $$$ {\mathrm{B}}_1^{+} $$$‐magnitude.…”

Actual patient position versus safety models: Specific Absorption Rate implications of initial head position for Ultrahigh Field Magnetic Resonance Imaging

“…Further to translational/rotational position of the body models, partial positional variations (i.e., position of the limbs relative to each other and to the body) can also have a profound effect on local SAR 67,68 . A recent study has demonstrated that using personalised SAR models can reduce the effect of the differences between the simulated models and the actual subject on SAR underestimation 69 . Such models would inherently be patient‐position aware and, therefore, can potentially be used to overcome positional variation‐related SAR underestimation, especially if the associated ~30‐min workflow for pTx observed in the initial implementation 69 can be reduced to clinically feasible times.…”