# Automatic ultrasound image alignment for diagnosis of pediatric distal forearm fractures

## TL;DR

This study develops an automatic method to align ultrasound images for diagnosing pediatric forearm fractures, aiming to reduce reliance on X-rays and radiation exposure.

## Contribution

A fully automatic pipeline for ultrasound image alignment using deep learning and optimization constraints is introduced.

## Key findings

- The method achieved high alignment accuracy compared to X-rays in terms of boundary distances.
- Fracture classification and angulation measurement using merged ultrasound images showed performance comparable to X-rays.
- Surgeons found many results sufficient for diagnosis, indicating clinical potential.

## Abstract

The study aims to develop an automatic method to align ultrasound images of the distal forearm for diagnosing pediatric fractures. This approach seeks to bypass the reliance on X-rays for fracture diagnosis, thereby minimizing radiation exposure and making the process less painful, as well as creating a more child-friendly diagnostic pathway.

We present a fully automatic pipeline to align paired POCUS images. We first leverage a deep learning model to delineate bone boundaries, from which we obtain key anatomical landmarks. These landmarks are finally used to guide the optimization-based alignment process, for which we propose three optimization constraints: aligning specific points, ensuring parallel orientation of the bone segments, and matching the bone widths.

The method demonstrated high alignment accuracy compared to reference X-rays in terms of boundary distances. A morphology experiment including fracture classification and angulation measurement presents comparable performance when based on the merged ultrasound images and conventional X-rays, justifying the effectiveness of our method in these cases.

The study introduced an effective and fully automatic pipeline for aligning ultrasound images, showing potential to replace X-rays for diagnosing pediatric distal forearm fractures. Initial tests show that surgeons find many of our results sufficient for diagnosis. Future work will focus on increasing dataset size to improve diagnostic accuracy and reliability.

The online version contains supplementary material available at 10.1007/s11548-025-03361-w.