CORRECTION ALGORITHM FOR BONE-INDUCED SPECTRAL ARTIFACTS IN COMPUTED TOMOGRAPH IMAGING

Patent №

US 6,115,487

Granted

2000-09-05

Filed 1998

Owner

GENERAL ELECTRIC COMPANY

Lab

AI components

1

vision

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

09004397

A spectral correction algorithm for correcting dense object-induced spectral artifacts is described. In one embodiment, a calibration object, representative of typical head scanning conditions is scanned and the data reconstructed to provide an image. A water or water-equivalent cylinder of about the same diameter also is scanned and reconstructed, on the same display field of view (DFOV). These two images are designated respectively by BWEQ and WEQ. The ratio of images BWEQ and WEQ is then evaluated, and a region of interest extracted by multiplying the ratio by a function II(r), to obtain a calibration pattern CP. The calibration pattern is then averaged in azimuth to obtain a calibration vector. This calibration vector is fitted with low--order polynomial, and then divided by the fitting polynomial, to take out from the vector the low frequency components that, for instance, would be introduced on an "ideal" scanner. By subtracting 1.0 from the ratio, and multiplying by a CT number scale factor (ctscale) and an apodizing window Aw(r), a calibration error vector CEV is obtained that is representative of the circularly symmetric image error introduced by the non-corrected bone-induced artifact. The corresponding error calibration vector can be expanded into a circularly symmetric image error pattern I[CEV(r)], and subtracted from the calibration image, to provide a substantially artifact free image. The method can be extended to extract and correlate error vectors on an image segment basis such that the resulting error image pattern is not circularly symmetric.

VisionA61B 6/583A61B 6/5258G01N 23/046A61B 6/027G01N 2223/419Y10S 378/901

AI classification

Vision1.00
Natural language0.00
Machine learning0.00
Evolutionary computation0.00
Speech0.00
Knowledge representation0.00
AI hardware0.00
Planning0.00

Ownership

GENERAL ELECTRIC COMPANY

assignment · 89280287

Assignors

TOTH, THOMAS L., BESSON, GUY M., HSIEH, JIANG, PAN, TIN-SU

On an employer assignment, the assignors are typically the inventors.

From the same owner

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