Statistical image reconstruction for inconsistent CT projection data.

Abstract:

OBJECTIVES:The filtered backprojection is not able to cope with metal-induced inconsistencies in the Radon space which leads to artifacts in reconstructed CT images. A new algorithm is presented that reduces the drawbacks of existing artifact reduction strategies. METHODS:Inconsistent projection data are bridged by directed interpolation. These projections are reconstructed using a weighted maximum likelihood algorithm (lambda-MLEM). The correlation coefficient between images of a torso phantom marked with steel markers reconstructed with lambda-MLEM and images of the same torso slice without markers quantifies the quality achieved. For clinical data, entropy maximization is presented to obtain appropriate weightings. RESULTS:Different interpolation strategies have been applied. The quality of reconstruction sensitively depends on the complexity of interpolation. A directional interpolation gives best results. However, the quality of the images can be further improved by an appropriate weighing within lambda-MLEM. This has been demonstrated with data from a torso phantom, a jaw with amalgam fillings and a hip prosthesis. CONCLUSIONS:lambda-MLEM image reconstruction using data from directional Radon space interpolation is a new approach for metal artifact reduction. The weighting in this statistical approach is used to reduce the influence of residual inconsistencies in a way that optimal artifact suppression is obtained by optimizing a compromise between residual inconsistencies and void data. The image quality is superior compared with other artifact reduction strategies.

journal_name

Methods Inf Med

authors

Buzug T,Oehler M

doi

10.1160/ME9041

subject

Has Abstract

pub_date

2007-01-01 00:00:00

pages

261-9

issue

3

eissn

0026-1270

issn

2511-705X

pii

07030009

journal_volume

46

pub_type

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