ArticleJournal of applied clinical medical physics2026
WE-UNet: A Wavelet-enhanced U-Net framework for radiation dose reduction in chest radiography.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundReducing ionizing radiation exposure is a critical goal guided by the as-low-as-reasonably-achievable (ALARA) principle. Aggressively lowering radiation doses in radiography, however, amplifies image noise, compromising diagnostic quality. PURPOSE: To evaluate a hybrid Wavelet Enhanced-UNet (WE-UNet) model for denoising chest radiographs (CXRs), enabling substantial radiation dose reduction while maximizing diagnostic utility.
methodsA training dataset of 3000 images was created by simulating low-dose conditions (70%-90% dose reduction) on CXRs from a public NIH dataset using Poisson-Gaussian noise modeling. WE-UNet was compared against four established architectures (DnCNN, REDNet, U-Net, MWCNN) using quantitative metrics (MSE, MAE, PSNR, SSIM, edge preservation). Qualitative assessment was performed by two board-certified radiologists in a blinded review.
resultsAmong five architectures, WE-UNet achieved the highest SSIM (0.963 ± 0.007) and edge preservation (0.740 ± 0.116; p < 0.001), while DnCNN showed comparable but not significantly better MSE and PSNR. Denoised images maintained PSNR of 35.23 ± 1.11 dB at 90% dose reduction. In blinded review, radiologist quality ratings for denoised images were statistically equivalent to original full-dose acquisitions (p = 0.029, non-significant after correction).
conclusionsWE-UNet outperformed baseline models in structural preservation while maintaining image quality similar to standard-dose acquisitions across dose reductions of 70%-90%, supporting its potential for significant radiation dose reduction.
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