Affiliation:
1. Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernardin Cancer Center Loyola University of Chicago Maywood Illinois USA
2. Varian Imaging Laboratory Baden Switzerland
Abstract
AbstractBackgroundDual‐energy (DE)‐CBCT represents a promising imaging modality that can produce virtual monoenergetic (VM) CBCT images. VM images, which provide enhanced contrast and reduced imaging artifacts, can be used to assist in soft‐tissue visualization during image‐guided radiotherapy.PurposeThis work reports the development of TIGRE‐DE, a module in the open‐source TIGRE toolkit for the performance of DE‐CBCT and the production of VM CBCT images. This module is created to make DE‐CBCT tools accessible in a wider range of clinical and research settings.MethodsWe developed an add‐on (TIGRE‐DE) to the TIGRE toolkit that performs DE material decomposition. To verify its performance, sequential CBCT scans at 80 and 140 kV of a Catphan 604 phantom were decomposed into equivalent thicknesses of aluminum (Al) and polymethyl‐methylacrylate (PMMA) basis materials. These basis material projections were used to synthesize VM projections for a range of x‐ray energies, which were then reconstructed using the Feldkamp‐Davis‐Kress (FDK) algorithm. Image quality was assessed by computing Hounsfield units (HU) and contrast‐to‐noise ratios (CNR) for the material inserts of the phantom and comparing with the constituent 80 and 140 kV images.ResultsAll VM images generated using TIGRE‐DE showed good general agreement with the theoretical HU values of the material inserts of the phantom. Apart from the highest‐density inserts imaged at the extremes of the energy range, the measured HU values agree with theoretical HUs within the clinical tolerance of ±50 HU. CNR measurements for the various inserts showed that, of the energies selected, 60 keV provided the highest CNR values. Moreover, 60 keV VM images showed average CNR enhancements of 63% and 66% compared to the 80 and 140 kV full‐fan protocols.ConclusionsTIGRE‐DE successfully implements DE‐CBCT material decomposition and VM image creation in an accessible, open‐source platform.