Model-based registration of preprocedure MR and intraprocedure US of the lumbar spine

Delaram Behnami; Alireza Sedghi; Emran Mohammad Abu Anas; Abtin Rasoulian; Alexander Seitel; Victoria Lessoway; Tamas Ungi; David Yen; Jill Osborn; Parvin Mousavi; Robert Rohling; Purang Abolmaesumi

doi:10.1007/s11548-017-1552-2

Model-based registration of preprocedure MR and intraprocedure US of the lumbar spine

Int J Comput Assist Radiol Surg. 2017 Jun;12(6):973-982. doi: 10.1007/s11548-017-1552-2. Epub 2017 Mar 18.

Authors

Affiliations

¹ Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada. delaramb@ece.ubc.ca.
² School of Computing, Queen's University, Kingston, ON, Canada.
³ Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
⁴ Department of Ultrasound, Women's Hospital, Vancouver, BC, Canada.
⁵ Kingston General Hospital and Department of Surgery, School of Medicine, Queen's University, Kinston, PN, Canada.
⁶ Department of Anesthesia, St. Paul's Hospital, Vancouver, BC, Canada.
⁷ Departments of Mechanical Engineering, Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.

PMID: 28315990
DOI: 10.1007/s11548-017-1552-2

Abstract

Purpose: Epidural and spinal needle insertions, as well as facet joint denervation and injections are widely performed procedures on the lumbar spine for delivering anesthesia and analgesia. Ultrasound (US)-based approaches have gained popularity for accurate needle placement, as they use a non-ionizing, inexpensive and accessible modality for guiding these procedures. However, due to the inherent difficulties in interpreting spinal US, they yet to become the clinical standard-of-care.

Methods: A novel statistical shape [Formula: see text] pose [Formula: see text] scale (s [Formula: see text] p [Formula: see text] s) model of the lumbar spine is jointly registered to preoperative magnetic resonance (MR) and US images. An instance of the model is created for each modality. The shape and scale model parameters are jointly computed, while the pose parameters are estimated separately for each modality.

Results: The proposed method is successfully applied to nine pairs of preoperative clinical MR volumes and their corresponding US images. The results are assessed using the target registration error (TRE) metric in both MR and US domains. The s [Formula: see text] p [Formula: see text] s model in the proposed joint registration framework results in a mean TRE of 2.62 and 4.20 mm for MR and US images, respectively, on different landmarks.

Conclusion: The joint framework benefits from the complementary features in both modalities, leading to significantly smaller TREs compared to a model-to-US registration approach. The s [Formula: see text] p [Formula: see text] s model also outperforms our previous shape [Formula: see text] pose model of the lumbar spine, as separating scale from pose allows to better capture pose and guarantees equally-sized vertebrae in both modalities. Furthermore, the simultaneous visualization of the patient-specific models on the MR and US domains makes it possible for clinicians to better evaluate the local registration accuracy.

Keywords: Anesthesia guidance; Multimodal registration; Statistical models.

MeSH terms

Humans
Injections, Spinal
Lumbar Vertebrae / diagnostic imaging*
Lumbar Vertebrae / surgery
Magnetic Resonance Imaging / methods*
Multimodal Imaging / methods
Ultrasonography, Interventional / methods*