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

• Published in: International journal for computer assisted radiology and surgery Vol. 12; no. 6; pp. 973 - 982
• Main Authors: Behnami, Delaram, Sedghi, Alireza, Anas, Emran Mohammad Abu, Rasoulian, Abtin, Seitel, Alexander, Lessoway, Victoria, Ungi, Tamas, Yen, David, Osborn, Jill, Mousavi, Parvin, Rohling, Robert, Abolmaesumi, Purang
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2017; Springer Nature B.V
• Subjects: Analgesia; Anesthesia; Computer Imaging; Computer Science; Health Informatics; Humans; Imaging; Injections, Spinal; Lumbar Vertebrae - diagnostic imaging; Lumbar Vertebrae - surgery; Magnetic resonance; Magnetic Resonance Imaging - methods; Mathematical models; Medicine; Medicine & Public Health; Multimodal Imaging - methods; Original Article; Parameter estimation; Pattern Recognition and Graphics; Radiology; Registration; Scale models; Spine; Statistical methods; Surgery; Ultrasonography, Interventional - methods; Vertebrae; Vision; Anesthesia guidance; Multimodal registration; Statistical models
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-017-1552-2

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  +  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. 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  +  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. 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  +  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.