Level localisation for lumbar surgery using ultrasound

Abstract

Wrong-level spine surgery (WLS) is a medical error with severe consequences for the patient and medical staff. The current golden standard to prevent WLS during spinal surgery is fluoroscopy images acquired by a C-arm. However, this approach uses ionising radiation and interrupts the standard surgical workflow. To overcome these disadvantages, ultrasound-based navigation may provide an alternative to prevent WLS during spinal surgery. Baka et al. developed the Lumbar Localiser for ultrasound-based level localisation; however, this has not yet been applied in clinical practice. This project aims to improve and evaluate this level localisation approach in clinical practice.
Chapter 1 highlights the social relevance of ultrasound-based level localisation. Chapter 2 shows in a literature review that navigated ultrasound-based guidance is not yet implemented in clinical practice. Identified recommendations for further research were optimising ease of use, workflow integration, registration accuracy and computation time. Chapter 3 describes a prospective multi-centre study (n=34) which evaluated the accuracy of the level localisation approach and the added value of intraoperative ultrasound acquisition. The accuracy of the improved level localisation approach was too low (53%) to recommend implementation in clinical practice. We showed that pre- and intraoperative ultrasound acquisitions could be integrated efficiently into the operation room (OR) workflow, with clinically negligible time differences compared to the current approach for level localisation. Intraoperative ultrasound acquisition added valuable information, enabling a re-check of the level localisation without the attenuating subcutaneous fat layer between the SP and ultrasound transducer. Chapter 4 describes the development of an automated contour segmentation approach based on magnetic resonance imaging (MRI) using deep learning. A nnU-Net was trained to segment the lumbar spinous process automatically. Subsequently, the posterior contours were extracted based on these segmentations and imported into the Lumbar Localiser. The automatic contours showed a successful matching in all 16 test cases. Chapter 5 provides an overall conclusion and discussion.
In this master’s thesis, the technical aspects and clinical workflow of the Lumbar Localiser were improved to get closer to the clinical application of ultrasound-based level localisation for lumbar surgery. Implemented improvements include adding intraoperative ultrasound acquisition, extension to MRI as a preoperative modality and automatisation of the contour annotation. In conclusion, this approach cannot be applied yet in clinical practice, as an accuracy of around 100% should be achieved to minimise the risk of WLS and thereby prevent irreversible damage to the patient.