title: Motion predicted online dynamic MRI reconstruction from partially sampled k-space data.
abstract: In this work we address the problem of reconstructing dynamic MRI sequences in an online fashion, i.e. reconstructing the current frame given that the previous frames have been already reconstructed. The reconstruction consists of a prediction and a correction step. The prediction step is based on an Auto-Regressive AR(1) model. Assuming that the prediction is good, the difference between the predicted frame and the actual frame (to be reconstructed) will be sparse. In the correction step, the difference between the predicted frame and the actual frame is estimated from partially sampled K-space data via a sparsity promoting least squares minimization problem. We have compared the proposed method with state-of-the-art methods in online dynamic MRI reconstruction. The experiments have been carried out on 2D and 3D Dynamic Contrast Enhanced (DCE) MRI datasets. Results show that our method yields the least reconstruction error.
authors.affiliation: Indraprastha Institute of Information Technology. Electronic address: ansghul@iiitd.ac.in.
authors.forename: Angshul
authors.initials: A
authors.lastname: Majumdar
mesh_terms: D000465:Algorithms; D000818:Animals; D005260:Female; D005583:Fourier Analysis; D007091:Image Processing, Computer-Assisted; D020407:Internet; D008279:Magnetic Resonance Imaging; D051379:Mice; D016688:Mice, Inbred NOD; D016513:Mice, SCID; D009038:Motion; D016011:Normal Distribution; D009862:Online Systems; D012044:Regression Analysis; D015203:Reproducibility of Results; D013997:Time Factors
keywords: Compressed sensing; Dynamic MRI; Motion prediction