Paper Info
Title
Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D-3D image registration.
Abstract
Generation of digitally reconstructed radiographs (DRRs) is computationally expensive and is typically the rate-lim- iting step in the execution time of intensity-based two-dimensional to three-dimensional (2D-3D) registration algorithms. We address this computational issue by extending the technique of light field rendering from the computer graphics community. The extension of light fields, which we call attenuation fields (AFs), allows most of the DRR computation to be performed in a preprocessing step; after this precomputation step, DRRs can be generated substantially faster than with conventional ray casting. We derive expressions for the physical sizes of the two planes of an AF neces- sary to generate DRRs for a given X-ray camera geometry and all possible object motion within a specified range. Because an AF is a ray-based data structure, it is substantially more memory efficient than a huge table of precomputed DRRs because it eliminates the redundancy of replicated rays. Nonetheless, an AF can require substantial memory, which we address by compressing it using vector quantization. We compare DRRs generated using AFs (AF-DRRs) to those generated using ray casting (RC-DRRs) for a typical C-arm geometry and computed tomography images of several anatomic regions. They are quantitatively very similar: the median peak signal-to-noise ratio of AF-DRRs versus RC-DRRs is greater than 43 dB in all cases. We perform intensity-based 2D-3D registration using AF-DRRs and RC-DRRs and evaluate
Year
DOI
Venue
2005
10.1109/TMI.2005.856749
IEEE Trans. Med. Imaging
Keywords
Field
DocType
3d imaging,ray casting,three dimensional,image reconstruction,data structure,peak signal to noise ratio,image registration,computed tomography,computer graphic,light field
Iterative reconstruction,Computer vision,Data structure,Precomputation,Computer science,Ray casting,Robustness (computer science),Vector quantization,Artificial intelligence,Computer graphics,Image registration
Journal
Volume
Issue
ISSN
24
11
0278-0062
Citations 
PageRank 
References 
43
2.29
33
Authors
7
Name
Order
Citations
PageRank
Daniel B. Russakoff123820.50
Torsten Rohlfing248633.44
Kensaku Mori31125160.28
Daniel Rueckert49338637.58
Anthony Ho5593.48
John R. Adler Jr.6888.29
Calvin R. Maurer Jr.788890.04