Material Decomposition from Photon-Counting CT using a Convolutional Neural Network and Energy-Integrating CT Training Labels

Although photon counting detectors (PCD) can offer numerous benefits for CT imaging, it is difficult to generate accurate material decompositions from photon counting (PC) CT images due to spectral distortions. In this work, we present a deep learning (DL) approach for material decomposition from PCCT. To produce training and testing data for this study, we scanned 2 ex-vivo mice using a PCD scan protocol with a dose of 36 mGy and a multi-EID scan protocol with a dose of 296 mGy. PCD images were reconstructed using filtered backprojection. EID images were reconstructed using an iterative algorithm to reduce noise, and decomposed into iodine (I), Compton scattering (CS), and photoelectric effect (PE) material maps by a matrix inversion approach. We then trained a convolutional neural network with a 3D U-net structure using PCD images as inputs and multi-EID material maps as labels, and evaluated its performance. The 3D U-net predictions provided substantially lower RMSE compared to decomposition from PCD images using a matrix inversion approach. Measurements from iodine vials in the test set showed that 3D U-net predictions gave mean values within 0.6 mg/mL of the mean values from the multi-EID material maps and much lower standard deviation than PCD material map measurements. Our results show that the trained 3D U-net enables low-noise, quantitatively accurate material decomposition from a low dose PCD scan.

Duke Scholars

Author Darin Clark Radiology

Author Cristian Tudorel Badea Radiology