Extended imaging volume in cone-beam x-ray tomography using the weighted simultaneous iterative reconstruction technique. (13th August 2021)

• Record Type: Journal Article
• Authors: Sanctorum, Joaquim G; Van Wassenbergh, Sam; Nguyen, Van; De Beenhouwer, Jan; Sijbers, Jan; Dirckx, Joris J J
• Is Part Of: Physics in medicine & biology. Volume 66:Number 16(2021)
• Publication Date: 2021-08-13
• Subjects: x-ray tomography -- detector offset -- field-of-view -- image intensifier -- cone-beam -- SIRT; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac16bc
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: An issue in computerized x-ray tomography is the limited size of available detectors relative to objects of interest. A solution was provided in the past two decades by positioning the detector in a lateral offset position, increasing the effective field of view (FOV) and thus the diameter of the reconstructed volume. However, this introduced artifacts in the obtained reconstructions, caused by projection truncation and data redundancy. These issues can be addressed by incorporating an additional data weighting step in the reconstruction algorithms, known as redundancy weighting. In this work, we present an implementation of redundancy weighting in the widely-used simultaneous iterative reconstruction technique (SIRT), yielding the weighted SIRT (W-SIRT) method. The new technique is validated using geometric phantoms and a rabbit specimen, by performing both simulation studies as well as physical experiments. The experiments are carried out in a highly flexible stereoscopic x-ray system equipped with x-ray image intensifiers (XRIIs). The simulations showed that higher values of contrast-to-noise ratio could be obtained using the W-SIRT approach as compared to a weighted implementation of the simultaneous algebraic reconstruction technique (SART). The convergence rate of the W-SIRT was accelerated by including a relaxation parameter in the W-SIRT algorithm, creating the aW-SIRT algorithm. This allowed to obtain the same results as the W-SIRT algorithm, but at half the number of iterations, yielding a much shorter computation time. The aW-SIRT algorithm has proven to perform well for both large as well as small regions of overlap, outperforming the pre-convolutional Feldkamp–David–Kress algorithm for small overlap regions (or large detector offsets). The experiments confirmed the results of the simulations. Using the aW-SIRT algorithm, the effective FOV was increased by >75%, only limited by experimental constraints. Although an XRII is used in this work, the method readily applies to flat-panel detectors as well.