Distorted born iterative method reconstruction in high-noise environments using KNN-based machine learning denoising - Nguyen Quang Huy; Nguyen Truong Thang (Universitas Ahmad Dahlan, 2026)
Ultrasound tomography reconstruction using the distorted born iterative method (DBIM) is sensitive to measurement noise, which degrades image fidelity and slows convergence. We propose integrating a k-nearest neighbors (KNN) denoising step within each DBIM iteration to suppress noise adaptively while preserving structural edges. Simulations with a circular cylindrical target and transmit/receive geometry (12×12) were conducted at signal-to-noise ratio (SNR) levels of 6 dB, 3 dB, and 1 dB. Compared with conventional DBIM employing Tikhonov regularization, the KNN-filtered DBIM reduces normalized reconstruction error by up to 57.2% at 1 dB and shows faster error decay over successive iterations. The method is training-free, computationally lightweight, and preserves fine structural details. These properties make KNN-filtered DBIM attractive for noisy or resource-constrained imaging environments. Future work will validate the approach on experimental data and explore adaptive K selection.