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SU‐E‐I‐35: Development of Stand‐Alone Filtered Backprojection and Iterative Reconstruction Methods Using the Raw CT Data Exported From Clinical Lung Screening Scans
Author(s) -
Young S,
Hoffman J,
Noo F,
McNittGray M
Publication year - 2015
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 - 10.1118/1.4924032
Subject(s) - iterative reconstruction , kernel (algebra) , scanner , artificial intelligence , computer science , iterative method , pipeline (software) , radon transform , algorithm , computer vision , mathematics , pattern recognition (psychology) , nuclear medicine , medicine , combinatorics , programming language
Purpose: We are developing a research pipeline for generating CT image series that represent a wide variety of acquisition and reconstruction conditions. As part of this effort, we need stand‐alone filtered backprojection (FBP) and iterative reconstruction methods that: (1) can operate on the raw CT data from clinical scans and (2) can be integrated into an acquisition/reconstruction pipeline for evaluating effects of acquisition and reconstruction settings on Quantitative Imaging metrics and CAD algorithms. Methods: Two reconstruction methods were developed: (1) a weighted FBP method, and (2) an iterative method based on sequential minimization of a penalized least‐squares objective function (i.e. iterative coordinate descent). Both methods were adapted from previously‐published algorithms. Using information about the raw CT data format obtained through a research agreement with Siemens Healthcare, we extracted the sinogram from a low‐dose lung screening case acquired on a Sensation 64 scanner as part of the National Lung Screening Trial. We reconstructed the raw data on the scanner with a B50 kernel and again with each of our standalone reconstruction methods. A relatively sharp kernel was used in our FBP method to match the appearance of the B50 kernel. The iterative method used a regularization parameter of 1 and a stopping criterion of 200 iterations. The reconstructed field of view was 29 cm for all methods. Results: Reconstructed images from our FBP method agreed very well with images reconstructed at the scanner. Computation speed was a limiting factor for the iterative method, but initial downsampled results and images of a thin slab of the scanned volume demonstrated substantial potential. Various artifacts should be addressed before direct comparisons of image quality can be made. Conclusion: Our stand‐alone FBP and iterative reconstruction methods show potential for developing a general acquisition/reconstruction research pipeline that can be applied to Quantitative Imaging and CAD applications. NCI grant U01 CA181156 (Quantitative Imaging Network) and Tobacco Related Disease Research Project grant 22RT‐0131.