UNICORN: Ultrasound Nakagami Imaging via Score Matching and Adaptation for Assessing Hepatic Steatosis

Abstract

Ultrasound imaging is an essential first-line tool for assessing hepatic steatosis. While conventional B-mode ultrasound imaging has limitations in providing detailed tissue characterization, ultrasound Nakagami imaging holds promise for visualizing and quantifying tissue scattering in backscattered signals, with potential applications in fat fraction analysis. However, existing methods for Nakagami imaging struggle with optimal window size selection and suffer from estimator instability, leading to degraded image resolution. To address these challenges, we propose a novel method called UNICORN (Ultrasound Nakagami Imaging via Score Matching and Adaptation), which offers an accurate, closed-form estimator for Nakagami parameter estimation based on the score function of the ultrasound envelope signal. Unlike methods that visualize only specific regions of interest (ROI) and estimate parameters within fixed window sizes, our approach provides comprehensive parameter mapping by providing a pixel-by-pixel estimator, resulting in high-resolution imaging. We demonstrated that our proposed estimator effectively assesses hepatic steatosis and provides visual distinction in the backscattered statistics associated with this condition. Through extensive experiments using real envelope data from patient, we validated that UNICORN enables clinical detection of hepatic steatosis and exhibits robustness and generalizability.

Figures (7)

• Figure 1: Nakagami imaging using conventional methods and our UNICORN framework. (a) Momentum-based approach uses a sliding window for Nakagami parameter calculation. (b) Maximum likelihood method obtains Nakagami image through ML estimation. (c) UNICORN consists of two stages: training a score model to learn RF envelope score function, and inference step estimates Nakagami image in terms of score function.
• Figure 2: Comparison of qualitative results across various methods: (Row 1) MNIST dataset, (Rows 2-3) BUSI Ultrasound Image dataset. Comparison against Momentum, MLE, WMC, and UNICORN. The yellow numbers indicate PSNR. To visualize the results, we normalize the images and convert them to grayscale.
• Figure 3: Comparison of qualitative results obtained using different methods. Each row corresponds to Normal, Mild, and Severe liver cases, with MRI-PDFF values of 0.71, 6.34, and 22.12, respectively. The comparison includes the Momentum, MLE, WMC, and UNICORN methods. Red dashed lines delineate the liver region. Compared to other methods, our approach provides clearer visual cues for distinguishing between normal, mild, and severe fatty liver cases.
• Figure 4: Scatter plots illustrating the relationship between MRI-PDFF and estimated m parameters across various methods and datasets: Columns 1-4 represent Momentum, MLE, WMC, and UNICORN, respectively; PCC denotes the Pearson Correlation Coefficient between MRI-PDFF and the estimated $m$ parameter. The trend line follows a locally weighted linear regression model.
• Figure 5: Box plots illustrating the relationship between MRI-PDFF and estimated m parameters across different methods at each stage: Methods include Momentum, MLE, WMC, and UNICORN; stages are defined as Normal (MRI-PDFF $<$ 5$\%$), Mild (5$\%$$\leq$ MRI-PDFF $<$ 15$\%$), and Severe (MRI-PDFF $>$ 15$\%$). The $p$-values between neighboring stages are indicated within the box plots.

Theorems & Definitions (3)

• Proposition 1
• proof
• proof