Beat Pilot Tone (BPT): Simultaneous MRI and RF motion sensing at arbitrary frequencies.

PURPOSE: To introduce a simple system exploitation with the potential to turn MRI scanners into general-purpose radiofrequency (RF) motion monitoring systems. METHODS: Inspired by Pilot Tone (PT), this work proposes Beat Pilot Tone (BPT), in which two or more RF tones at arbitrary frequencies are transmitted continuously during the scan. These tones create motion-modulated standing wave patterns that are sensed by the receiver coil array, incidentally mixed by intermodulation in the receiver chain, and digitized simultaneously with the MRI data. BPT can operate at almost any frequency as long as the intermodulation products lie within the bandwidth of the receivers. BPT's mechanism is explained in electromagnetic simulations and validated experimentally. RESULTS: Phantom and volunteer experiments over a range of transmit frequencies suggest that BPT may offer frequency-dependent sensitivity to motion. Using a semi-flexible anterior receiver array, BPT appears to sense cardiac-induced body vibrations at microwave frequencies ( ≥ $$ \ge $$ 1.2 GHz). At lower frequencies, it exhibits a similar cardiac signal shape to PT, likely due to blood volume changes. Other volunteer experiments with respiratory, bulk, and head motion show that BPT can achieve greater sensitivity to motion than PT and greater separability between motion types. Basic multiple-input multiple-output ( 4 × 22 $$ 4\times 22 $$ MIMO) operation with simultaneous PT and BPT in head motion is demonstrated using two transmit antennas and a 22-channel head-neck coil. CONCLUSION: BPT may offer a rich source of motion information that is frequency-dependent, simultaneous, and complementary to PT and the MRI exam.

Multiphoton simultaneous multislice imaging.

PURPOSE: To develop multiphoton excitation techniques for simultaneous multislice (SMS) imaging and evaluate their performance and specific absorption rate (SAR) benefit. To improve multiphoton SMS reconstruction quality with a novel CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration) design. THEORY AND METHODS: When a conventional single-slice RF field is applied together with an oscillating gradient field, the two can combine to generate multiphoton excitation at multiple discrete spatial locations. Because the conventional RF is reused at multiple spatial locations, multiphoton excitation offers reduced SAR for SMS applications. CAIPIRINHA shifts are often used to improve parallel-imaging acceleration. Interestingly, CAIPIRINHA-type shifts can be obtained for multiphoton SMS by updating the oscillating gradient phase at every phase encode. In this work, both a gradient-echo and a spin-echo sequence with multiphoton CAIPIRINHA-SMS excitation pulses are implemented for in vivo human imaging at 3 T. RESULTS: For three slices, multiphoton SMS provides a 51% reduction in SAR compared with conventional superposition SMS, whereas for five slices, SAR is reduced by 66%. Multiphoton SMS outperforms PINS (power independent of number of slices) and MultiPINS in terms of SAR reduction especially when the pulse duration is short, slices are thin, and/or the slice spacing is large. A custom CAIPIRINHA phase-encoding design for multiphoton SMS significantly improves reconstruction quality. CONCLUSION: Multiphoton SMS excitation can be obtained by combining conventional single-slice RF pulses with an oscillating gradient and offers significant SAR benefits compared with conventional superposition SMS. A novel CAIPIRINHA design allows higher multiband factors for multiphoton SMS imaging.

K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets.

Although deep learning (DL) methods are powerful for solving inverse problems, their reliance on high-quality training data is a major hurdle. This is significant in high-dimensional (dynamic/volumetric) magnetic resonance imaging (MRI), where acquisition of high-resolution fully sampled k-space data is impractical. We introduce a novel mathematical framework, dubbed k-band, that enables training DL models using only partial, limited-resolution k-space data. Specifically, we introduce training with stochastic gradient descent (SGD) over k-space subsets. In each training iteration, rather than using the fully sampled k-space for computing gradients, we use only a small k-space portion. This concept is compatible with different sampling strategies; here we demonstrate the method for k-space "bands", which have limited resolution in one dimension and can hence be acquired rapidly. We prove analytically that our method stochastically approximates the gradients computed in a fully-supervised setup, when two simple conditions are met: (i) the limited-resolution axis is chosen randomly-uniformly for every new scan, hence k-space is fully covered across the entire training set, and (ii) the loss function is weighed with a mask, derived here analytically, which facilitates accurate reconstruction of high-resolution details. Numerical experiments with raw MRI data indicate that k-band outperforms two other methods trained on limited-resolution data and performs comparably to state-of-the-art (SoTA) methods trained on high-resolution data. k-band hence obtains SoTA performance, with the advantage of training using only limited-resolution data. This work hence introduces a practical, easy-to-implement, self-supervised training framework, which involves fast acquisition and self-supervised reconstruction and offers theoretical guarantees.