HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting

Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on dictio-nary matching to map the temporal MRF signals to quantitative tissue parameters. Such approaches suffer from inherent discretization errors, as well as high computational complexity as the dictionary size grows. To alleviate these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting approach, referred to as HYDRA. Methods: HYDRA involves two stages: a model-based signature restoration phase and a learning-based parameter restoration phase. Signal restoration is implemented using low-rank based de-aliasing techniques while parameter restoration is performed using a deep nonlocal residual convolutional neural network. The designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady state precession (FISP) sequences. In test mode, it takes a temporal MRF signal as input and produces the corresponding tissue parameters. Results: We validated our approach on both synthetic data and anatomical data generated from a healthy subject. The results demonstrate that, in contrast to conventional dictionary-matching based MRF techniques, our approach significantly improves inference speed by eliminating the time-consuming dictionary matching operation, and alleviates discretization errors by outputting continuous-valued parameters. We further avoid the need to store a large dictionary, thus reducing memory requirements. Conclusions: Our approach demonstrates advantages in terms of inference speed, accuracy and storage requirements over competing MRF method

POCS-based reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE): A general algorithm for reducing motion-related artifacts.

PURPOSE: A projection onto convex sets reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE) is developed to reduce motion-related artifacts, including respiration artifacts in abdominal imaging and aliasing artifacts in interleaved diffusion-weighted imaging. THEORY: Images with reduced artifacts are reconstructed with an iterative projection onto convex sets (POCS) procedure that uses the coil sensitivity profile as a constraint. This method can be applied to data obtained with different pulse sequences and k-space trajectories. In addition, various constraints can be incorporated to stabilize the reconstruction of ill-conditioned matrices. METHODS: The POCSMUSE technique was applied to abdominal fast spin-echo imaging data, and its effectiveness in respiratory-triggered scans was evaluated. The POCSMUSE method was also applied to reduce aliasing artifacts due to shot-to-shot phase variations in interleaved diffusion-weighted imaging data corresponding to different k-space trajectories and matrix condition numbers. RESULTS: Experimental results show that the POCSMUSE technique can effectively reduce motion-related artifacts in data obtained with different pulse sequences, k-space trajectories and contrasts. CONCLUSION: POCSMUSE is a general post-processing algorithm for reduction of motion-related artifacts. It is compatible with different pulse sequences, and can also be used to further reduce residual artifacts in data produced by existing motion artifact reduction methods

Compressed Sensing And Joint Acquisition Techniques In Mri

The relatively long scan times in Magnetic Resonance Imaging (MRI) limits some clinical applications and the ability to collect more information in a reasonable period of time. Practically, 3D imaging requires longer acquisitions which can lead to a reduction in image quality due to motion artifacts, patient discomfort, increased costs to the healthcare system and loss of profit to the imaging center. The emphasis in reducing scan time has been to a large degree through using limited k-space data acquisition and special reconstruction techniques. Among these approaches are data extrapolation methods such as constrained reconstruction techniques, data interpolation methods such as parallel imaging, and more recently another technique known as Compressed Sensing (CS). In order to recover the image components from far fewer measurements, CS exploits the compressible nature of MR images by imposing randomness in k-space undersampling schemes. In this work, we explore some intuitive examples of CS reconstruction leading to a primitive algorithm for CS MR imaging. Then, we demonstrate the application of this algorithm to MR angiography (MRA) with the goal of reducing the scan time. Our results showed reconstructions with comparable results to the fully sampled MRA images, providing up to three times faster image acquisition via CS. The CS performance in recovery of the vessels in MRA, showed slightly shrinkage of both the width of and amplitude of the vessels in 20% undersampling scheme. The spatial location of the vessels however remained intact during CS reconstruction. Another direction we pursue is the introduction of joint acquisition for accelerated multi data point MR imaging such as multi echo or dynamic imaging. Keyhole imaging and view sharing are two techniques for accelerating dynamic acquisitions, where some k-space data is shared between neighboring acquisitions. In this work, we combine the concept of CS random sampling with keyhole imaging and view sharing techniques, in order to improve the performance of each method by itself and reduce the scan time. Finally, we demonstrate the application of this new method in multi-echo spin echo (MSE) T2 mapping and compare the results with conventional methods. Our proposed technique can potentially provide up to 2.7 times faster image acquisition. The percentage difference error maps created from T2 maps generated from images with joint acquisition and fully sampled images, have a histogram with a 5-95 percentile of less than 5% error. This technique can potentially be applied to other dynamic imaging acquisitions such as multi flip angle T1 mapping or time resolved contrast enhanced MRA