Qiu Wang

AIR-MRF: Accelerated iterative reconstruction for magnetic resonance fingerprinting

Existing approaches for reconstruction of multiparametric maps with magnetic resonance fingerprinting (MRF) are currently limited by their estimation accuracy and reconstruction time. We aimed to address these issues with a novel combination of iterative reconstruction, fingerprint compression, additional regularization, and accelerated dictionary search methods. The pipeline described here, accelerated iterative reconstruction for magnetic resonance fingerprinting (AIR-MRF), was evaluated with simulations as well as phantom and in vivo scans. We found that the AIR-MRF pipeline provided reduced parameter estimation errors compared to non-iterative and other iterative methods, particularly at shorter sequence lengths. Accelerated dictionary search methods incorporated into the iterative pipeline reduced the reconstruction time at little cost of quality.

Learning a multiscale patch-based representation for image denoising in X-RAY fluoroscopy

Denoising is an indispensable step in processing low-dose X-ray fluoroscopic images that requires development of specialized high-quality algorithms able to operate in near real-time. We address this problem with an efficient deep learning approach based on the process-centric view of traditional iterative thresholding methods. We develop a novel trainable patch-based multiscale framework for sparse image representation. In a computationally efficient way, it allows us to accurately reconstruct important image features on multiple levels of decomposition with patch dictionaries of reduced size and complexity. The flexibility of the chosen machine learning approach allows us to tailor the learned basis for preserving important structural information in the image and noticeably minimize the amount of artifacts. Our denoising results obtained with real clinical data demonstrate significant quality improvement and are computed much faster in comparison with the BM3D algorithm.