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Automated identification and reduction of artifacts in cine four-dimensional computed tomography (4DCT) images using respiratory motion model.

Abstract：

PURPOSE:Four-dimensional computed tomography (4DCT) images are often marred by artifacts that substantially degrade image quality and confound image interpretation. Human observation remains the standard method of 4DCT artifact evaluation, which is time-consuming and subjective. We develop a method to automatically identify and reduce artifacts in cine 4DCT images. METHODS:We proposed an algorithm that consisted of two main stages: deformable image registration and respiratory motion simulation. Specifically, each 4DCT phase image was registered to the breath-holding CT image using the block-matching method, with erroneous spatial matches removed by the least median of squares filter and the full displacement vector field generated by the moving least squares interpolation. The lung's respiratory motion trajectory was then recovered from the displacement vector field using the parameterized polynomial function, with fitting parameters estimated by combinatorial optimization. In this way, artifacts were located according to deviations between image points and their motion trajectories and further corrected based on position prediction. RESULTS:The mean spatial error (standard deviation) was 1.00 (0.85) mm after registration as opposed to 6.96 (4.61) mm before registration. In addition, we took human observation conducted by medical experts as the gold standard. The average sensitivity, specificity, and accuracy of the proposed method in artifact identification were 0.97, 0.84, and 0.89, respectively. CONCLUSIONS:The proposed method identified and reduced artifacts accurately and automatically, providing an alternative way to analyze 4DCT image quality and to correct problematic images for radiation therapy.

journal_name

Int J Comput Assist Radiol Surg

journal_title

International journal of computer assisted radiology and surgery

authors

Li M,Castillo SJ,Castillo R,Castillo E,Guerrero T,Xiao L,Zheng X

doi

10.1007/s11548-017-1538-0

subject

Has Abstract

pub_date

2017-09-01 00:00:00

pages

1521-1532

issue

eissn

1861-6410

issn

1861-6429

pii

10.1007/s11548-017-1538-0

journal_volume

pub_type

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Automated identification and reduction of artifacts in cine four-dimensional computed tomography (4DCT) images using respiratory motion model.