  Dual-energy computed tomography (DECT) is an advanced CT scanning technique
enabling material characterization not possible with conventional CT scans. It
allows the reconstruction of energy decay curves at each 3D image voxel,
representing varying image attenuation at different effective scanning energy
levels. In this paper, we develop novel functional data analysis (FDA)
techniques and adapt them to the analysis of DECT decay curves. More
specifically, we construct functional mixture models that integrate spatial
context in mixture weights, with mixture component densities being constructed
upon the energy decay curves as functional observations. We design unsupervised
clustering algorithms by developing dedicated expectation maximization (EM)
algorithms for the maximum likelihood estimation of the model parameters. To
our knowledge, this is the first article to adapt statistical FDA tools and
model-based clustering to take advantage of the full spectral information
provided by DECT. We evaluate our methods on 91 head and neck cancer DECT
scans. We compare our unsupervised clustering results to tumor contours traced
manually by radiologists, as well as to several baseline algorithms. Given the
inter-rater variability even among experts at delineating head and neck tumors,
and given the potential importance of tissue reactions surrounding the tumor
itself, our proposed methodology has the potential to add value in downstream
machine learning applications for clinical outcome prediction based on DECT
data in head and neck cancer.
