3D Ultrasound Mosaic of the Whole Shoulder: A Feasibility Study

Abstract

A protocol is proposed to acquire a tomographic ultrasound (US) scan of the musculoskeletal (MSK) anatomy in the rotator cuff region. Current clinical US imaging techniques are hindered by occlusions and a narrow field of view and require expert acquisition and interpretation. There is limited literature on 3D US image registration of the shoulder or volumetric reconstruction of the full shoulder complex. We believe that a clinically accurate US volume reconstruction of the entire shoulder can aid in pre-operative surgical planning and reduce the complexity of US interpretation. The protocol was used in generating data for deep learning model training to automatically register US mosaics in real-time. An in vivo 3D US tomographic reconstruction of the entire rotator cuff region was produced by registering 53 sequential 3D US volumes acquired by an MSK sonographer. Anatomical surface thicknesses and distances in the US mosaic were compared to their corresponding MRI measurements as the ground truth. The humeral head surface was marginally thicker in the reconstructed US mosaic than its original thickness observed in a single US volume by 0.65 mm. The humeral head diameter and acromiohumeral distance (ACHD) matched with their measured MRI distances with a reconstruction error of 0 mm and 1.2 mm, respectively. Furthermore, the demonstration of 20 relevant MSK structures was independently graded between 1 and 5 by two sonographers, with higher grades indicating poorer demonstration. The average demonstration grade for each anatomy was as follows: bones = 2, muscles = 3, tendons = 3, ligaments = 4–5 and labrum = 4–5. There was a substantial agreement between sonographers (Cohen’s Weighted kappa of 0.71) on the demonstration of the structures, and they both independently deemed the mosaic clinically acceptable for the visualisation of the bony anatomy. Ligaments and the labrum were poorly observed due to anatomy size, location and inaccessibility in a static scan, and artefact build-up from the registration and compounding approaches.

Keywords

• ultrasound shoulder mosaic
• ultrasound shoulder atlas
• shoulder tomography
• 3D ultrasound panorama
• machine learning
• 3D image registration