Name
Abstract | ||
|---|---|---|
Quantitative MR studies often utilize sequences of coregistered images, where the contrast in each image frame is experimentally manipulated to enable the regression of important physical parameters. However, the potential of these experiments has been limited for high-resolution biological studies because of long acquisition times and limited signal-to-noise ratio. This work presents a new approach for the reconstruction of an image sequence from noisy data, using a statistical model that incorporates an implicit line-site prior to take advantage of the high level of inter-frame correlation between spatial image features. Reconstructions are efficiently computed using a globally-convergent half-quadratic iterative algorithm, and the proposed optimization procedure enables precise characterization of resolution and noise properties. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISBI.2008.4541105 | 2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4 |
Keywords | Field | DocType |
magnetic resonance imaging, image reconstruction, denoising, half-quadratic regularization, image sequences | Noise reduction,Iterative reconstruction,Computer vision,Noisy data,Regression,Pattern recognition,Iterative method,Feature (computer vision),Computer science,Joint reconstruction,Artificial intelligence,Statistical model | Conference |
ISSN | Citations | PageRank |
1945-7928 | 3 | 0.50 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin P. Haldar | 1 | 350 | 35.40 |
| Zhi-Pei Liang | 2 | 522 | 64.94 |