HIFU Tissue Degeneration Classification Based on Multifractal Detrending Fluctuation Analysis and Vision Transformer

For high-intensity focused ultrasound (HIFU) thermal ablation to be safe and effective, real-time, high-precision monitoring of tissue coagulative necrosis is essential. However, decoding the ultra-long, non-stationary radio frequency (RF) echoes produced during tissue phase transitions usually results in severe feature aliasing and high computational costs for conventional deep learning models. This paper suggests a highly interpretable, asymmetric classification framework that combines a lightweight Vision Transformer (ViT) with multifractal detrended fluctuation analysis (MFDFA) in order to overcome this obstacle. In terms of methodology, ViT may independently capture cross-scale thermodynamic dependencies without local inductive biases by using MFDFA as a physical prior to compress 1D RF sequences into dense 2D fractal tensors. This method greatly improved the algorithmic recognition of the extremely elusive “partially degenerated” transient state, achieving a strong 96.5% classification accuracy when validated on an ex vivo pig liver dataset. Furthermore, by firmly attaching its classifications to the macroscopic statistical correlates of the acoustic scattering process, the model achieves great decision transparency instead of functioning as an opaque black box. Importantly, this MFDFA-ViT architecture provides an ideal accuracy–latency trade-off with only 3.45M parameters and an end-to-end inference latency of 20.6 ms. This offers a real-time, intelligent monitoring paradigm that is highly deployable and specifically designed for upcoming clinical HIFU applications.