Title: Towards monitored tomographic reconstruction: algorithm-dependence and convergence
Issue Date: Aug-2023
Publisher: Самарский национальный исследовательский университет
Citation: Bulatov KB, Ingacheva AS, Gilmanov MI, Kutukova K, Soldatova ZV, Buzmakov AV, Chukalina MV, Zschech E, Arlazarov VV. Towards monitored tomographic reconstruction: algorithm-dependence and convergence to an independent ground truth. Computer Optics 2023; 47(4): 658-667. DOI: 10.18287/2412-6179-CO-1238.
Series/Report no.: 47;4
Abstract: The monitored tomographic reconstruction (MTR) with optimized photon flux technique is a pioneering method for X-ray computed tomography (XCT) that reduces the time for data acquisition and the radiation dose. The capturing of the projections in the MTR technique is guided by a scanning protocol built on similar experiments to reach the predetermined quality of the reconstruction. This method allows achieving a similar average reconstruction quality as in ordinary tomography while using lower mean numbers of projections. In this paper, we, for the first time, systematically study the MTR technique under several conditions: reconstruction algorithm (FBP, SIRT, SIRT-TV, and others), type of tomography setup (micro-XCT and nano-XCT), and objects with different morphology. It was shown that a mean dose reduction for reconstruction with a given quality only slightlyvaries with choice of reconstruction algorithm, and reach up to 12.5 % in case of micro-XCT and 8.5 % for nano-XCT. The obtained results allow to conclude that the monitored tomographic reconstruction approach can be universally combined with an algorithm of choice to perform a controlled trade-off between radiation dose and image quality. Validation of the protocol on independent common ground truth demonstrated a good convergence of all reconstruction algorithms within the MTR protocol.
URI: https://dx.doi.org/10.18287/2412-6179-CO-1238
http://repo.ssau.ru/jspui/handle/123456789/23079
Appears in Collections:Журнал "Компьютерная оптика"

Files in This Item:
File Description SizeFormat 
2412-6179_2023_47_4_658-667.pdf8.09 MBAdobe PDFView/Open


Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.