4D flow imaging with 2D‐selective excitation

Pulsatility analysis of the circle of Willis

Purpose

To evaluate the phenomenological significance of cerebral blood pulsatility imaging in aging research.

Methods

N = 38 subjects from 20 to 72 years of age (24 females) were imaged with ultrafast MRI with a sampling rate of 100 ms and simultaneous acquisition of pulse oximetry data. Of these, 28 subjects had acceptable MRI and pulse data, with 16 subjects between 20 and 28 years of age, and 12 subjects between 61 and 72 years of age. Pulse amplitude in the circle of Willis was assessed with the recently developed method of analytic phase projection to extract blood volume waveforms.

Results

Arteries in the circle of Willis showed pulsatility in the MRI for both the young and old age groups. Pulse amplitude in the circle of Willis significantly increased with age (p = 0.01) but was independent of gender, heart rate, and head motion during MRI.

Discussion and conclusion

Increased pulse wave amplitude in the circle of Willis in the elderly suggests a phenomenological significance of cerebral blood pulsatility imaging in aging research. The physiologic origin of increased pulse amplitude (increased pulse pressure vs. change in arterial morphology vs. re-shaping of pulse waveforms caused by the heart, and possible interaction with cerebrospinal fluid pulsatility) requires further investigation.

Multi-task convolutional neural network-based design of radio frequency pulse and the accompanying gradients for magnetic resonance imaging

Modern MRI systems usually load the predesigned RFs and the accompanying gradients during clinical scans, with minimal adaption to the specific requirements of each scan. Here, we describe a neural network-based method for real-time design of excitation RF pulses and the accompanying gradients' waveforms to achieve spatially two-dimensional selectivity. Nine thousand sets of radio frequency (RF) and gradient waveforms with two-dimensional spatial selectivity were generated as the training dataset using the Shinnar-Le Roux (SLR) method. Neural networks were created and trained with five strategies (TS-1 to TS-5). The neural network-designed RF and gradients were compared with their SLR-designed counterparts and underwent Bloch simulation and phantom imaging to investigate their performances in spin manipulations. We demonstrate a convolutional neural network (TS-5) with multi-task learning to yield both the RF pulses and the accompanying two channels of gradient waveforms that comply with the SLR design, and these design results also provide excitation spatial profiles comparable with SLR pulses in both simulation (normalized root mean square error [NRMSE] of 0.0075 ± 0.0038 over the 400 sets of testing data between TS-5 and SLR) and phantom imaging. The output RF and gradient waveforms between the neural network and SLR methods were also compared, and the joint NRMSE, with both RF and the two channels of gradient waveforms considered, was 0.0098 ± 0.0024 between TS-5 and SLR. The RF and gradients were generated on a commercially available workstation, which took ~130 ms for TS-5. In conclusion, we present a convolutional neural network with multi-task learning, trained with SLR transformation pairs, that is capable of simultaneously generating RF and two channels of gradient waveforms, given the desired spatially two-dimensional excitation profiles.

DeepControl: 2DRF pulses facilitating B 1 + inhomogeneity and B0 off-resonance compensation in vivo at 7 T

PURPOSE

Rapid 2DRF pulse design with subject-specific B 1 + inhomogeneity and B₀ off-resonance compensation at 7 T predicted from convolutional neural networks is presented.

METHODS

The convolution neural network was trained on half a million single-channel transmit 2DRF pulses optimized with an optimal control method using artificial 2D targets, B 1 + and B₀ maps. Predicted pulses were tested in a phantom and in vivo at 7 T with measured B 1 + and B₀ maps from a high-resolution gradient echo sequence.

RESULTS

Pulse prediction by the trained convolutional neural network was done on the fly during the MR session in approximately 9 ms for multiple hand-drawn regions of interest and the measured B 1 + and B₀ maps. Compensation of B 1 + inhomogeneity and B₀ off-resonances has been confirmed in the phantom and in vivo experiments. The reconstructed image data agree well with the simulations using the acquired B 1 + and B₀ maps, and the 2DRF pulse predicted by the convolutional neural networks is as good as the conventional RF pulse obtained by optimal control.

CONCLUSION

The proposed convolutional neural network-based 2DRF pulse design method predicts 2DRF pulses with an excellent excitation pattern and compensated B 1 + and B₀ variations at 7 T. The rapid 2DRF pulse prediction (9 ms) enables subject-specific high-quality 2DRF pulses without the need to run lengthy optimizations.

4D flow imaging with UNFOLD in a reduced FOV

PURPOSE

Two-dimensional selective excitation (2DRF) allows shortening 4D flow scan times by reducing the FOV, but the longer 2DRF pulse duration decreases the temporal resolution, yielding underestimated peak flow values. Multiple k-space lines per cardiac phase, n_l ≥ 2, are commonly applied in 4D flow MRI to shorten the inherent long scan times. We demonstrate that 2DRF 4D flow with n_l ≥ 2 can be easily combined with UNFOLD (UNaliasing by Fourier-encoding the Overlaps using the temporaL Dimension), a technique that allows regaining nominally the temporal resolution of the respective acquisition with n_l = 1, to assure peak flow quantification.

METHODS

Two different 2DRF pulses with spiral k-space trajectories were designed and integrated into a 4D flow sequence. Flow phantom experiments and 7 healthy control 4D flow in vivo measurements, with and without UNFOLD reconstructions, were compared with conventional reconstruction and 1D slab-selective excitation (1DRF) by evaluating time-resolved flow curves, peak flow, peak velocity, blood flow volume per cardiac cycle, and spatial aliasing.

RESULTS

Applying UNFOLD to 4D flow imaging with 2DRF and reduced FOV increased the quantified in vivo peak flow values significantly by 3.7% ± 2.3% to 5.2% ± 2.4% (P < .05). Accordingly, the peak flow underestimation of 2DRF scans compared with conventional 1DRF scans decreased with UNFOLD. Finally, 2DRF combined with UNFOLD accelerated the 4D flow acquisition 3.5 ± 1.4 fold by reducing the FOV and increasing the effective temporal resolution by 6.7% compared with conventional 1D selective excitation, with 2 k-space lines per cardiac phase.

CONCLUSION

Two-dimensional selective excitation combined with UNFOLD allows limiting the FOV to shorten 4D flow scan times and compensates for the loss in temporal resolution with 2DRF (Δt = 64.8 ms) compared with 1DRF (Δt = 43.2 ms), yielding an effective resolution of Δt_eff = 40.5 ms to enhance peak flow quantification.