Morphological-consistent Diffusion Network for Ultrasound Coronal Image Enhancement

TL;DR

This paper introduces a diffusion-based multi-stage image enhancement framework that leverages morphological information from multi-depth ultrasound images to improve image quality and measurement accuracy in scoliosis assessment.

Contribution

It proposes a novel fusion operation with a learnable tuner to calibrate high-quality image generation, preserving spinal pose consistency for better scoliosis diagnosis.

Findings

Significantly outperforms existing enhancement methods in ultrasound image quality

Achieves high intra- and inter-rater ICCs for curve angle measurements

Facilitates automated scoliosis diagnosis with improved measurement reliability

Abstract

Ultrasound curve angle (UCA) measurement provides a radiation-free and reliable evaluation for scoliosis based on ultrasound imaging. However, degraded image quality, especially in difficult-to-image patients, can prevent clinical experts from making confident measurements, even leading to misdiagnosis. In this paper, we propose a multi-stage image enhancement framework that models high-quality image distribution via a diffusion-based model. Specifically, we integrate the underlying morphological information from images taken at different depths of the 3D volume to calibrate the reverse process toward high-quality and high-fidelity image generation. This is achieved through a fusion operation with a learnable tuner module that learns the multi-to-one mapping from multi-depth to high-quality images. Moreover, the separate learning of the high-quality image distribution and the spinal features guarantees the preservation of consistent spinal pose descriptions in the generated images, which is crucial in evaluating spinal deformities. Remarkably, our proposed enhancement algorithm significantly outperforms other enhancement-based methods on ultrasound images in terms of image quality. Ultimately, we conduct the intra-rater and inter-rater measurements of UCA and higher ICC (0.91 and 0.89 for thoracic and lumbar angles) on enhanced images, indicating our method facilitates the measurement of ultrasound curve angles and offers promising prospects for automated scoliosis diagnosis.