Int J Comput Assist Radiol Surg - Scaling calibration in region of interest reconstruction with the 1D and 2D ATRACT algorithm.

Tópicos

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Resumo

RPOSE: Recently, a reconstruction algorithm for region of interest (ROI) imaging in C-arm CT was published, named Approximate Truncation Robust Algorithm for Computed Tomography (ATRACT). Even in the presence of substantial data truncation, the algorithm is able to reconstruct images without the use of explicit extrapolation or prior knowledge. However, the method suffers from a scaling and offset artifact in the reconstruction. Hence, the reconstruction results are not quantitative. It is our goal to reduce the scaling and offset artifact so that Hounsfield unit (HU) values can be used for diagnosis.METHODS: In this paper, we investigate two variants of the ATRACT method and present the analytical derivations of these algorithms in the Fourier domain. Then, we propose an empirical correction measure that can be applied to the ATRACT algorithm, to effectively compensate the scaling and offset issue. The proposed method is evaluated on ten clinical datasets in the presence of different degrees of artificial truncation.RESULTS: With the proposed correction approach, we achieved an average relative root-mean-square error (rRMSE) of 2.81% with respect to non-truncated Feldkamp, Davis, and Kress reconstruction, even for severely truncated data. The rRMSE is reduced to as little as 10% of the image reconstructed without the scaling calibration.CONCLUSIONS: The reconstruction results show that ROI reconstruction of high accuracy can be achieved since the scaling and offset artifact are effectively eliminated by the proposed method. With this improvement, the HU values may be used for post-processing operations such as bone or soft tissue segmentation if some tolerance is accepted.

Resumo Limpo

rpose recent reconstruct algorithm region interest roi imag carm ct publish name approxim truncat robust algorithm comput tomographi atract even presenc substanti data truncat algorithm abl reconstruct imag without use explicit extrapol prior knowledg howev method suffer scale offset artifact reconstruct henc reconstruct result quantit goal reduc scale offset artifact hounsfield unit hu valu can use diagnosismethod paper investig two variant atract method present analyt deriv algorithm fourier domain propos empir correct measur can appli atract algorithm effect compens scale offset issu propos method evalu ten clinic dataset presenc differ degre artifici truncationresult propos correct approach achiev averag relat rootmeansquar error rrmse respect nontrunc feldkamp davi kress reconstruct even sever truncat data rrmse reduc littl imag reconstruct without scale calibrationconclus reconstruct result show roi reconstruct high accuraci can achiev sinc scale offset artifact effect elimin propos method improv hu valu may use postprocess oper bone soft tissu segment toler accept

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