Papers Published

1. Zarei, M; Sotoudeh-Paima, S; McCabe, C; Abadi, E; Samei, E, A Physics-Informed Deep Neural Network for Harmonization of CT Images., IEEE Trans Biomed Eng, vol. 71 no. 12 (December, 2024), pp. 3494-3504 [doi] .
   (last updated on 2025/05/31)

   Abstract:
   OBJECTIVE: Computed Tomography (CT) quantification is affected by the variability in image acquisition and rendition. This paper aimed to reduce this variability by harmonizing the images utilizing physics-based deep neural networks (DNNs). METHODS: An adversarial generative network was trained on virtual CT images acquired under various imaging conditions using a virtual imaging platform with 40 computational patient models. These models featured anthropomorphic lungs with different levels of pulmonary diseases, including nodules and emphysema. Imaging was conducted using a validated CT simulator at two dose levels and varying reconstruction kernels. The trained model was tested on an independent virtual test dataset and two clinical datasets. RESULTS: On the virtual test set, the harmonizer improved the structural similarity index from 79.3 16.4% to 95.8 1.7%, normalized mean squared error from 16.7 9.7% to 9.2 1.7%, and peak signal-to-noise ratio from 27.7 3.7 dB to 32.2 1.6 dB. Moreover, the harmonized images yielded more precise quantification of emphysema-based imaging biomarkers for lung attenuation, LAA -950 from 5.6 8.7% to 0.23 0.16%, Perc 15 from 43.4 45.4 HU to 20.0 7.5 HU, and Lung Mass from 0.3 0.3 g to 0.1 0.2 g. In clinical data, the harmonizer reduced biomarker variability by an average of 70%. For lung nodules, harmonized images improved the detectability index by 6.5-fold and DNN-based precision by 6%. CONCLUSION: The proposed harmonizer significantly enhances image quality and quantification accuracy in CT imaging. SIGNIFICANCE: The study demonstrated the potential utility of image harmonization for consistent CT image quality and reliable quantification, which is crucial for clinical applications and patient management.