Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse Using Root-Flipping: DeepRFSLR

A flexible framework for the design and optimization of water-excitation RF pulses using B-spline interpolation

PURPOSE

To implement a flexible framework, named HydrOptiFrame, for the design and optimization of time-efficient water-excitation (WE) RF pulses using B-spline interpolation, and to characterize their lipid suppression performance.

METHODS

An evolutionary optimization algorithm was used to design WE RF pulses. The algorithm minimizes a composite loss function that quantifies the fat-water contrast using Bloch equation simulations. In a first study, B-spline interpolated optimized (BSIO) pulses designed with HydrOptiFrame with durations of 1 and 0.76 ms were generated for 3 T and characterized in healthy volunteers' knees. The femoral bone marrow SNR was compared to that obtained with to 1-1 WE and lipid insensitive binomial off resonant excitation (LIBRE) pulses. In a second study, in the heart at 1.5 T, the water-fat contrast ratio and coronary artery vessel length obtained with a 2.56 ms BSIO pulse was compared to 1-1 WE and LIBRE pulses in free-running cardiovascular MR.

RESULTS

The 1 ms BSIO pulse resulted in higher fat suppression and lower contrast ratio (CR) in the bone marrow than the state-of-the-art pulses (4.1 ± 0.2 vs. 4.7 ± 0.4 and 4.4 ± 0.3 for the BSIO, the 1-1 WE and LIBRE respectively, p < 0.05 vs. both) at 3 T. At 1.5 T, the BSIO pulse resulted in a higher blood-epicardial fat CR (3.8 ± 1.3 vs. 1.6 ± 0.6 and 2.4 ± 1.1 for the BSIO, 1-1 WE and LIBRE, respectively, p < 0.05 vs. both) and longer traceable left coronary artery vessel length (8.7 ± 1.4 cm vs. 7.0 ± 1.0 cm [p = 0.04] and 7.5 ± 1.2 cm [p = 0.09]).

CONCLUSION

The HydrOptiFrame framework offers a new opportunity to design WE RF pulses that are robust to B0 inhomogeneity at multiple magnetic field strengths and for variable RF pulse durations.

Physics-guided self-supervised learning: Demonstration for generalized RF pulse design

PURPOSE

To introduce a new method for generalized RF pulse design using physics-guided self-supervised learning (GPS), which uses the Bloch equations as the guiding physics model.

THEORY AND METHODS

The GPS framework consists of a neural network module and a physics module, where the physics module is a Bloch simulator for MRI applications. For RF pulse design, the neural network module maps an input target profile to an RF pulse, which is subsequently loaded into the physics module. Through the supervision of the physics module, the neural network module designs an RF pulse corresponding to the target profile. GPS was applied to design 1D selective,B 1 $$ {B}_1 $$ -insensitive, saturation, and multidimensional RF pulses, each conventionally requiring dedicated design algorithms. We further demonstrate our method's flexibility and versatility by compensating for experimental and scanner imperfections through online adaptation.

RESULTS

Both simulations and experiments show that GPS can design a variety of RF pulses with corresponding profiles that agree well with the target input. Despite these verifications, GPS-designed pulses have unique differences compared to conventional designs, such as achievingB 1 $$ {B}_1 $$ -insensitivity using different mechanisms and using non-sampled regions of the conventional design to lower its peak power. Experiments, both ex vivo and in vivo, further verify that it can also be used for online adaptation to correct system imperfections, such asB 0 $$ {B}_0 $$ /B 1 + $$ {B}_1^{+} $$ inhomogeneity.

CONCLUSION

This work demonstrates the generalizability, versatility, and flexibility of the GPS method for designing RF pulses and showcases its utility in several applications.

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer's disease imaging

Magnetic Resonance Imaging (MR Imaging) is routinely employed in diagnosing Alzheimer's Disease (AD), which accounts for up to 60-80% of dementia cases. However, it is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for contrast, acquisition time, and signal-to-noise ratio (SNR). The lack of this expertise contributes to the highly inefficient utilization of MRI services diminishing their clinical value. In this work, we extend our previous effort and demonstrate accelerated MRI via intelligent protocolling of the modified brain screen protocol, referred to as the Gold Standard (GS) protocol. We leverage deep learning-based contrast-specific image-denoising to improve the image quality of data acquired using the accelerated protocol. Since the SNR of MR acquisitions depends on the volume of the object being imaged, we demonstrate subject-specific (SS) image-denoising. The accelerated protocol resulted in a 1.94 × gain in imaging throughput. This translated to a 72.51% increase in MR Value-defined in this work as the ratio of the sum of median object-masked local SNR values across all contrasts to the protocol's acquisition duration. We also computed PSNR, local SNR, MS-SSIM, and variance of the Laplacian values for image quality evaluation on 25 retrospective datasets. The minimum/maximum PSNR gains (measured in dB) were 1.18/11.68 and 1.04/13.15, from the baseline and SS image-denoising models, respectively. MS-SSIM gains were: 0.003/0.065 and 0.01/0.066; variance of the Laplacian (lower is better): 0.104/-0.135 and 0.13/-0.143. The GS protocol constitutes 44.44% of the comprehensive AD imaging protocol defined by the European Prevention of Alzheimer's Disease project. Therefore, we also demonstrate the potential for AD-imaging via automated volumetry of relevant brain anatomies. We performed statistical analysis on these volumetric measurements of the hippocampus and amygdala from the GS and accelerated protocols, and found that 27 locations were in excellent agreement. In conclusion, accelerated brain imaging with the potential for AD imaging was demonstrated, and image quality was recovered post-acquisition using DL-based image denoising models.

High-fidelity control of spin ensemble dynamics via artificial intelligence: from quantum computing to NMR spectroscopy and imaging

High-fidelity control of spin ensemble dynamics is essential for many research areas, spanning from quantum computing and radio-frequency (RF) engineering to NMR spectroscopy and imaging. However, attaining robust and high-fidelity spin operations remains an unmet challenge. Using an evolutionary algorithm and artificial intelligence (AI), we designed new RF pulses with customizable spatial or temporal field inhomogeneity compensation. Compared with the standard RF shapes, the new AI-generated pulses show superior performance for bandwidth, robustness, and tolerance to field imperfections. As a benchmark, we constructed a spin entanglement operator for the weakly coupled two-spin-1/2 system of ¹³CHCl₃, achieving high-fidelity transformations under multiple inhomogeneity sources. We then generated band-selective and ultra-broadband RF pulses typical of biomolecular NMR spectroscopy. When implemented in multipulse NMR experiments, the AI-generated pulses significantly increased the sensitivity of medium-size and large protein spectra relative to standard pulse sequences. Finally, we applied the new pulses to typical imaging experiments, showing a remarkable tolerance to changes in the RF field. These AI-generated RF pulses can be directly implemented in quantum information, NMR spectroscopy of biomolecules, magnetic resonance imaging techniques for in vivo and materials sciences.