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


CONCLUSIONS
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.
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada. delaramb@ece.ubc.ca.
Delaram
D
Behnami
School of Computing, Queen's University, Kingston, ON, Canada.
Alireza
A
Sedghi
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Emran Mohammad Abu
EMA
Anas
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Abtin
A
Rasoulian
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Alexander
A
Seitel
Department of Ultrasound, Women's Hospital, Vancouver, BC, Canada.
Victoria
V
Lessoway
School of Computing, Queen's University, Kingston, ON, Canada.
Tamas
T
Ungi
Kingston General Hospital and Department of Surgery, School of Medicine, Queen's University, Kinston, PN, Canada.
David
D
Yen
Department of Anesthesia, St. Paul's Hospital, Vancouver, BC, Canada.
Jill
J
Osborn
School of Computing, Queen's University, Kingston, ON, Canada.
Parvin
P
Mousavi
Departments of Mechanical Engineering, Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Robert
R
Rohling
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Purang
P
Abolmaesumi
D006801:Humans; D007278:Injections, Spinal; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging* / Q000601:surgery; D008279:Magnetic Resonance Imaging / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods; D018084:Ultrasonography, Interventional / Q000379:methods*
Anesthesia guidance; Multimodal registration; Statistical models