Parametric imaging based on horizontally normalized weight-adjustable Shannon entropy for tissue characterization

Abstract

Ultrasonic B-mode imaging provides real-time and non-invasive imaging for soft tissue diagnosis in clinical use, but its limited contrast leads to the challenge of detection accuracy. Quantitative ultrasound techniques have been proposed as a promising method for soft tissue characterization and reflecting the microstructure of lesions. This study proposed a novel entropy called horizontally normalized weight-adjustable Shannon entropy (hNWASE). An adjustable weight factor was added to this entropy, so that the entropy value can be changed and the imaging performance can be adjusted to lesions according to different positions and acoustic characteristics. The relationship between the adjustable parameter n and the imaging performance was explored. Moreover, the performance of the proposed hNWASE imaging was compared with weighted Shannon entropy (WSE) imaging, and horizontally normalized Shannon entropy (hNSE) imaging by both simulations and clinical data. hNSE imaging obtained a Matthews correlation coefficient (MCC) of 0.68 ± 0.11 in the thyroid nodule diagnostic tests, which underestimated the periphery of the nodule. WSE imaging got the largest area difference of 3.70 ± 1.4 mm2 between the ground truth and predicted area, which indicated that the delineation of the nodule boundary by the WSE was too large. hNWASE imaging got superior lesion area prediction with the MCC of 0.81 ± 0.06, F1 score of 0.81 ± 0.07, and generalized contrast-to-noise ratio of 0.98 ± 0.03. These findings suggested that hNWASE imaging could improve image quality and be a promising technique for tissue characterization.