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An investigation of the filtering of TEW scatter estimates used to compensate for scatter with ordered subset reconstructions

Conference ·
OSTI ID:513153
; ;  [1]
  1. Univ. of Massachusetts Medical Center, Worcester, MA (United States); and others

With the Triple Energy Window (TEW) scatter correction method, scatter is estimated by interpolation between narrow energy windows placed on either side (or from a single window just below) the photopeak window. The use of narrow windows results in a noisy estimate which requires filtering to prevent significant amplification of noise in the corrected images. Using Monte Carlo simulated projections of a digital model of the anatomy of the torso, we investigated two-dimensional pre-reconstruction filtering of the scatter estimate by the Wiener low-pass filter, and Butterworth low-pass filters with various cutoff frequencies. The scatter estimate was either subtracted from the photopeak window projection or used directly in ordered-subset maximum-likelihood (OS-ML) reconstruction. Using the normalized mean square error (NMSE) between the estimated and true slices as the criterion, it was observed that: (1) low-pass filtering of the TEW scatter estimate dramatically decreases the NMSE compared to that of no filtering of this estimate; (2) the cutoff frequency of the Butterworth filter used to filter the scatter estimate is lower than that typically used for photopeak window images; (3) the cutoff frequency of the Butterworth filter has a broad range of values over which the NMSE is near its minimum value; (4) the cutoff frequency at which the Butterworth reaches its minimum value depends on the number of counts in the TEW window(s), and source distribution; (5) the Wiener low-pass filter adapts to produce a low, but not necessarily the minimum, NMSE; and (6) the inclusion of the scatter estimate directly into OS-ML reconstruction results in a lower NMSE than subtraction of the scatter estimate from the photopeak window prior to reconstruction.

OSTI ID:
513153
Report Number(s):
CONF-961123--; CNN: Grant HL50349
Country of Publication:
United States
Language:
English

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