Mandible-Specific Noise Reduction in Pediatric Dental CBCT with Simulated Noise: Search Window Selection for the Fast Non-Local Means Algorithm

Accurate assessment of the pediatric mandible using dental cone-beam computed tomography (CBCT) is crucial despite radiation exposure concerns. The fast non-local means (FNLM) algorithm reduces noise in CBCT images, but its performance depends on parameter selection. This study compared FNLM search window sizes for pediatric mandibular CBCT images under simulated noise degradation. Mandibular CBCT images were acquired from 17 pediatric datasets in the MMDental database. Noise degradation was simulated by adding Poisson and Gaussian noise (σ = 0.05). The FNLM algorithm was applied using search windows from 3 × 3 to 21 × 21 pixels and image quality was assessed using noise, similarity, and no-reference metrics and visual evaluation. Most metrics changed progressively with increasing search window size, with smaller changes beyond 7 × 7, and BRISQUE reached its lowest value at 7 × 7. Visual assessment showed noise reduction but decreased sharpness of anatomical boundaries with larger search windows. Among the evaluated conditions, the 7 × 7 search window provided the best compromise between noise reduction and structural preservation. These findings indicate that appropriate search window selection is important for FNLM-based noise reduction in pediatric mandibular CBCT images and should be interpreted within the simulated conditions of this study.