Quantitative Assessment of Thyroid Nodules Through Ultrasound Imaging Analysis

Recent studies have proposed quantitative ultrasound (QUS) to extract the acoustic properties of tissues from pulse-echo data obtained through multiple transmissions. In this paper, we introduce a learning-based approach to identify thyroid nodule malignancy by extracting acoustic attenuation and speed of sound from ultrasound imaging. The proposed method employs a neural model that integrates a convolutional neural network (CNN) for detailed local pulse-echo pattern analysis with a Transformer architecture, enhancing the model’s ability to capture complex correlations among multiple beam receptions. B-mode images are employed as both an input and label to guarantee robust performance regardless of the complex structures present in the human neck, such as the thyroid, blood vessels, and trachea. In order to train the proposed deep neural model, a simulation phantom mimicking the structure of human muscle, fat layers, and the shape of the thyroid gland has been designed. The effectiveness of the proposed method is evaluated through numerical simulations and clinical tests.