Name
Abstract | ||
|---|---|---|
A post-processing ”autofocusing” algorithm for the reduction of motion artifacts in MR images has been developed and tested
on a large clinical data set of high resolution shoulder images. The algorithm uses only the raw (complex) data from the MR
scanner, and requires no knowledge of the patient motion during the scan, deducing that from the raw data itself. It operates
by searching over the space of possible patient motions and optimizing the image quality. Evaluation of this technique on
the clinical data set (for which navigator echo based measured motions and corrected images were available) show that the
algorithm can correct for the effects of global translation during the scan almost as well as the navigator echo approach
and is more robust.
|
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/BFb0056245 | MICCAI |
Keywords | Field | DocType |
clinical shoulder mr images,motion artifacts,complex data,high resolution,image quality | Computer vision,Synthetic aperture radar image,Pattern recognition,Computer science,Raw data,Image quality,Scanner,Artificial intelligence,Motion correction,Patient Motion | Conference |
Volume | ISSN | ISBN |
1496 | 0302-9743 | 3-540-65136-5 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Armando Manduca | 1 | 303 | 60.99 |
| Kiaran P. McGee | 2 | 0 | 1.35 |
| E. Brian Welch | 3 | 45 | 16.66 |
| Joel P Felmlee | 4 | 41 | 6.89 |
| Richard L. Ehman | 5 | 57 | 16.47 |