Development of a digital energy modulation framework in projectional radiography

Digital energy modulation is a novel framework with the potential to enhance projectional x-ray imaging by enabling translation between different x-ray energy domains. We evaluate the feasibility of integrating machine learning methods into this approach by leveraging digitally reconstructed radiographs (DRRs) generated from dual-energy CT datasets.

DRRs were created in 15° increments from 0° to 90°, producing 3500 images per energy domain (2 polyenergetic, 4 monoenergetic). A supervised deep-learning approach was used to train models for energy translation, focusing on conversions between polyenergetic domains and from polyenergetic to monoenergetic images. Model performance was assessed using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), mean squared error (MSE), and mean absolute percentage error (MAPE). Cross-validation and projection-specific dataset splits were used for evaluation.

The models trained using cross-validation on the various energy translations achieved the following results: PSNR: 29.1 ± 2.0, SSIM: 0.947 ± 0.017, MSE: 169.1 ± 68.3, MAPE: 8.2% ± 1.8%. When translating between polyenergetic high-energy and low-energy domains in projection-specific datasets (anterior-posterior [0°] and lateral [90°] views), models achieved the following results: PSNR: 27.4 ± 0.5, SSIM: 0.909 ± 0.003, MSE: 195.9 ± 39.7, MAPE: 10.4% ± 2.1%.

These findings demonstrate the feasibility of a digital energy modulation framework for projectional x-ray imaging using machine learning for energy translation. The results support the potential of this approach to enhance projectional x-ray imaging, though future work is needed to refine the models and further explore clinical applications.