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
Original Article



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.


A novel statistical shape \(+\) pose \(+\) scale (s \(+\) p \(+\) 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.


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 \(+\) p \(+\) 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.


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 \(+\) p \(+\) s model also outperforms our previous shape \(+\) 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.


Multimodal registration Anesthesia guidance Statistical models 



This work was supported in part by the Natural Sciences and Engineering Research and Council of Canada (NSERC) and in part by the Canadian Institutes of Health Research (CIHR).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© CARS 2017

Authors and Affiliations

  • Delaram Behnami
    • 1
  • Alireza Sedghi
    • 2
  • Emran Mohammad Abu Anas
    • 1
  • Abtin Rasoulian
    • 1
  • Alexander Seitel
    • 1
  • Victoria Lessoway
    • 3
  • Tamas Ungi
    • 2
  • David Yen
    • 4
  • Jill Osborn
    • 5
  • Parvin Mousavi
    • 2
  • Robert Rohling
    • 6
  • Purang Abolmaesumi
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverCanada
  2. 2.School of ComputingQueen’s UniversityKingstonCanada
  3. 3.Department of UltrasoundWomen’s HospitalVancouverCanada
  4. 4.Kingston General Hospital and Department of SurgerySchool of Medicine, Queen’s UniversityKinstonCanada
  5. 5.Department of AnesthesiaSt. Paul’s HospitalVancouverCanada
  6. 6.Departments of Mechanical EngineeringDepartment of Electrical and Computer Engineering, University of British ColumbiaVancouverCanada

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