Two interpolating filters for scatter estimation.
We have previously reported on a dual-measurement sample-and-estimate technique for scatter correction. In this paper, we present a scatter-correction technique that uses the previous sampling scheme but a different method of estimation. To provide samples of the scatter directly, an array of small, uniformly spaced lead disks is placed immediately before the object during only the first measurement. Interpolating from these samples we form an estimate of the scatter. We subtract this estimate from the second measurement to form a scatter-corrected image. Previously, we used least-squares interpolation to estimate the scatter. Because the samples are uniformly spaced, classical sampling theory motivated the investigation of interpolating filters for scatter estimation. To form the scatter image, we convolved the sample set with two different interpolating filters--a sinc function from classical sampling theory and a jinc function because the scatter function is radially symmetric. Using phantoms as objects, we applied both filters for scatter correction in vessel imaging and energy-subtraction imaging. Initial corrected images contained an artifact attributed to aliasing. We modified the filter widths to reduce the aliasing. Although improvements in image quality were measured and the artifact was less pronounced, the artifact was still present. We present the phantom results obtained with this class of filters and discuss methods for its improved performance.
Wagner
F C
FC
Information Systems Laboratory, Stanford University, California 94305-4055.
Macovski
A
A
Nishimura
D G
DG
D000465:Algorithms; D015901:Angiography, Digital Subtraction; D005374:Filtration / Q000295:instrumentation; D006283:Health Physics; D006801:Humans; D008961:Models, Structural; D015900:Radiography, Dual-Energy Scanned Projection; D012542:Scattering, Radiation