Quantitative dual-energy imaging of bone marrow edema using multi-source cone-beam CT with model-based decomposition

Abstract

Purpose:We investigated the feasibility of dual-energy (DE) detection of bone marrow edema (BME) using a dedicated extremity cone-beam CT (CBCT) with a unique three-source x-ray unit. The sources can be operated at different energies to enable single-scan DE acquisitions. However, they are arranged parallel to the axis of rotation, resulting in incomplete sampling and precluding the application of DE projection-domain decompositions (PDD) for beam-hardening reduction. Therefore, we propose a novel combination of a model-based "one-step" DE two-material decomposition followed by a constrained image-domain change-of-basis to obtain virtual non-calcium (VNCa) images for BME detection. Methods: DE projections were obtained using an "alternating-kV" protocol by operating the peripheral two sources of the CBCT system at low-energy (60 kV, 0.105 mAs/frame) and the central source at high-energy (100 kV, 0.028 mAs/frame), for a total of 600 frames over 216° of gantry rotation. Projections were processed with detector lag, glare and fast Monte Carlo (MC)-based iterative scatter corrections. Model-based material decomposition (MBMD) was then implemented to obtain aluminum (Al) and polyethylene (PE) volume fraction images with minimal beam-hardening. Statistical ray weights in MBMD were modified to account for regions with highly oblique sampling by the peripheral sources. To generate the VNCa maps, image-domain decomposition (IDD) constrained by the volume conservation principle (VCP) was performed to convert the Al and PE MBMD images into volume fractions of water, fat and cortical bone. Accuracy of BME detection was evaluated using physical phantom data acquired on the multi-source extremity CBCT scanner. Results:The proposed framework estimated the volume of BME with ~10% error. The MC-based scatter corrections and the modified MBMD ray weights were essential to achieve such performance -the error without MC scatter corrections was >30%, whereas the uniformity of estimated VNCa images was 3x improved using the modified weights compared to the conventional weights. Conclusions: The proposed DE decomposition framework was able to overcome challenges of high scatter and incomplete sampling to achieve BME detection on a CBCT system with axially-distributed x-ray sources.

Feasibility of bone marrow edema detection using dual‐energy cone‐beam computed tomography

“…Even smaller water content changes might be discernible in smaller body parts and using narrower collimation or additional means of scatter reduction such as antiscatter grids or extended scan geometries. Additional performance improvements might be possible by adopting advanced model-based decomposition algorithms (VCP-constrained one-step decompositions 33,34 with proper initializations such as the result of the complete PDD-to-IDD approach, or unconstrained decompositions replacing the PE-Al twomaterial PDD step 31 ) and data-driven scatter estimation methods 70,71 in place of the analytical and MC-based approaches investigated in the current work. The results of this feasibility study pave the way for performance evaluation in humans and live animal models in the settings of acute BME.…”

“…Therefore, the imaging chain non-idealities that are often associated with CBCT-ranging from relatively high electronic noise and limited dynamic range of flat-panel detectors (FPD), to FPD lag and glare, 28 to x-ray scatter-remain a substantial challenge in the development of DE CBCT techniques. Nevertheless, our initial simulation studies [29][30][31] demonstrated that BME can be detected in DE CBCT as long as the scatter is controlled using either a grid or a modest collimation (≤ 15 cm); any residual scatter needs to be corrected to within 10% magnitude. In this work, we build on these preliminary investigations to present the first experimental evaluation of BME imaging using DE CBCT with a comprehensive artifact correction framework, including fast Monte Carlo (MC) scatter estimation.…”