  Thermal ablation procedures, such as high intensity focused ultrasound (HIFU)
and Radiofrequency Ablation (RFA), are often used to eliminate tumors by
minimally invasively heating a focal region. For this task, real-time 3D
temperature visualization is key to target the diseased tissues while
minimizing damage to the surroundings. Current CT thermometry is based on
energy-integrated CT, tissue-specific experimental data, and linear
relationships between attenuation and temperature. In this letter, we develop a
novel approach using photon-counting CT for material decomposition and a neural
network to predict temperature based on thermal characteristics of base
materials and spectral tomographic measurements of a volume of interest. In our
feasibility study, distilled water, 50 mM CaCl2, and 600 mM CaCl2 are chosen as
the base materials. Their attenuations are measured in four discrete energy
bins at various temperatures. The neural network trained on the experimental
data achieves a mean absolute error of 1.80 {\deg}C and 3.97 {\deg}C on 300 mM
CaCl2 and a milk-based protein shake respectively. These experimental results
indicate that our approach is promising for handling nonlinear thermal
properties for materials that are similar or dissimilar to our base materials.
