Self-calibrating wave-encoded 3D turbo spin echo imaging using subspace model based autofocusing

Practical utilization of nonlinear spatial encoding: Fast field mapping and FRONSAC-wave

PURPOSE

To study the additional value of FRONSAC encoding in 2D and 3D wave sequences, implementing a simple strategy to trajectory mapping for FRONSAC encoding gradients.

THEORY AND METHODS

The nonlinear gradient trajectory for each voxel was estimated by exploiting the sparsity of the point spread function in the frequency domain. Simulations and in-vivo experiments were used to analyze the performance of combinations of wave and FRONSAC encoding.

RESULTS

Field mapping using the simplified approach produced similar image quality with much shorter calibration time than the comprehensive mapping schemes utilized in previous work. In-vivo human brain images showed that the addition of FRONSAC encoding could improve wave image quality, particularly at very high undersampling factors and in the context of limited wave amplitudes. These results were further supported by g-factor maps.

CONCLUSION

Results show that FRONSAC can be used to improve image quality of wave at very high undersampling rates or in slew-limited acquisitions. Our study illustrates the potential of the proposed fast field mapping approach.

Accelerated submillimeter wave-encoded magnetic resonance imaging via deep untrained neural network

BACKGROUND

Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto-calibration signals (ACS) signal acquisition and is time-consuming, while the latter requires a large amount of training data.

PURPOSE

To tackle the above issues, an untrained neural network (UNN) model incorporating wave-encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS- and training data-free.

METHODS

Generally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k-space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k-space linear predictability. And then, the iterative paradigm to recover the full k-space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six-fold acceleration, respectively.

RESULTS

In vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice.

CONCLUSIONS

The model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time-consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency.

An artificial intelligence-accelerated 2-minute multi-shot echo planar imaging protocol for comprehensive high-quality clinical brain imaging

PURPOSE

We introduce and validate an artificial intelligence (AI)-accelerated multi-shot echo-planar imaging (msEPI)-based method that provides T1w, T2w, T 2 ∗ , T2-FLAIR, and DWI images with high SNR, high tissue contrast, low specific absorption rates (SAR), and minimal distortion in 2 minutes.

METHODS

The rapid imaging technique combines a novel machine learning (ML) scheme to limit g-factor noise amplification and improve SNR, a magnetization transfer preparation module to provide clinically desirable contrast, and high per-shot EPI undersampling factors to reduce distortion. The ML training and image reconstruction incorporates a tunable parameter for controlling the level of denoising/smoothness. The performance of the reconstruction method is evaluated across various acceleration factors, contrasts, and SNR conditions. The 2-minute protocol is directly compared to a 10-minute clinical reference protocol through deployment in a clinical setting, where five representative cases with pathology are examined.

RESULTS

Optimization of custom msEPI sequences and protocols was performed to balance acquisition efficiency and image quality compared to the five-fold longer clinical reference. Training data from 16 healthy subjects across multiple contrasts and orientations were used to produce ML networks at various acceleration levels. The flexibility of the ML reconstruction was demonstrated across SNR levels, and an optimized regularization was determined through radiological review. Network generalization toward novel pathology, unobserved during training, was illustrated in five clinical case studies with clinical reference images provided for comparison.

CONCLUSION

The rapid 2-minute msEPI-based protocol with tunable ML reconstruction allows for advantageous trade-offs between acquisition speed, SNR, and tissue contrast when compared to the five-fold slower standard clinical reference exam.

Aliasing-free reduced field-of-view parallel imaging

PURPOSE

To reconstruct aliasing-free full field-of-view (FOV) images for reduced FOV (rFOV) parallel imaging (PI) with Cartesian and Wave sampling, which suffers from aliasing artifacts using existing PI methods.

THEORY AND METHODS

The sensitivity encoding method (SENSE) was extended to the Soft-SENSE models supporting multiple-set coil sensitivity maps (CSM) and point spread functions (PSF) for Cartesian and Wave sampled rFOV PI, respectively. The multiple-set CSM and PSF were created from full FOV CSM and PSF according to the image folding process induced by rFOV sampling. The Soft-SENSE reconstructions could be solved by the same algorithms for the conventional full FOV SENSE reconstruction.

RESULTS

Soft-SENSE using multiple-set full FOV CSM and PSF successfully reconstruct aliasing-free full FOV image from rFOV PI data with Cartesian and Wave sampling. The proposed rFOV PI enables flexible control of the aliasing and achieves comparable geometry factors as the standard full FOV PI with the same net acceleration factor. Reduced FOV PI improves the computational efficiency of iterative compressed sensing (CS) and PI reconstruction, especially for high-resolution volumetric imaging, thanks to the reduced fast Fourier transform (FFT) size. Moreover, rFOV PI reconstruction provides a potential alternative to the phase oversampling for the FOV aliasing problem.

CONCLUSION

The proposed Soft-SENSE using full FOV CSM and PSF could reconstruct aliasing-free full FOV image for rFOV PI, and make it a viable solution enabling more flexible PI acceleration and effectively improving the computational efficiency of iterative CSPI reconstruction.