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PI‐line‐based image reconstruction in helical cone‐beam computed tomography with a variable pitch
Author(s) -
Zou Yu,
Pan Xiaochuan,
Xia Dan,
Wang Ge
Publication year - 2005
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1902530
Subject(s) - cone beam computed tomography , helical scan , iterative reconstruction , beam (structure) , algorithm , detector , mathematics , geometry , imaging phantom , physics , optics , computer science , artificial intelligence , computed tomography , acoustics , medicine , magnetic tape , tape recorder , radiology
Current applications of helical cone‐beam computed tomography (CT) involve primarily a constant pitch where the translating speed of the table and the rotation speed of the source‐detector remain constant. However, situations do exist where it may be more desirable to use a helical scan with a variable translating speed of the table, leading a variable pitch. One of such applications could arise in helical cone‐beam CT fluoroscopy for the determination of vascular structures through real‐time imaging of contrast bolus arrival. Most of the existing reconstruction algorithms have been developed only for helical cone‐beam CT with constant pitch, including the backprojection‐filtration (BPF) and filtered‐backprojection (FBP) algorithms that we proposed previously. It is possible to generalize some of these algorithms to reconstruct images exactly for helical cone‐beam CT with a variable pitch. In this work, we generalize our BPF and FBP algorithms to reconstruct images directly from data acquired in helical cone‐beam CT with a variable pitch. We have also performed a preliminary numerical study to demonstrate and verify the generalization of the two algorithms. The results of the study confirm that our generalized BPF and FBP algorithms can yield exact reconstruction in helical cone‐beam CT with a variable pitch. It should be pointed out that our generalized BPF algorithm is the only algorithm that is capable of reconstructing exactly region‐of‐interest image from data containing transverse truncations.