Approaches for Three Material Decomposition using a Triple-Layer Flat-Panel Detector.

X-ray spectral imaging has been increasingly investigated in radiography and interventional imaging. Flat-panel detectors with more than one detection layer have demonstrated advantages in providing separate soft tissue and bone images. Current dual-layer flat-panel detectors (DL-FPD) have limited capability to further differentiate between iodinated contrast agent and bony/calcified structures. In this work, we investigate a triple-layer flat-panel detector (TL-FPD) and the feasibility of three-material (water/calcium/iodine) decomposition. A physical model of TL-FPD, including system geometry, spectrum sensitivities, blur and noise models was developed. Using simulated triple-layer projections, three-material decompositions were performed using three different processing methods: polynomial-based, model-based, and a machine learning-based method (ResUnet). We find that the polynomial-based method leads to very noisy images with poor differentiation between calcium and iodine maps. The model-based method achieved much lower noise levels than the polynomial-based method but exhibited residual errors between the iodine and calcium channels. The ResUnet method offered the best decompositions among the investigated methods in terms of root mean square error from the ground truth and noise in the material maps. These preliminary results demonstrate the feasibility of three-material decomposition using TL-FPD and suggest a path for clinical translation of single-shot contrast/iodine differentiation in radiography and fluoroscopy.