Delaram Behnami1, Alireza Sedghi2, Emran Mohammad Abu Anas3, Abtin Rasoulian3, Alexander Seitel3, Victoria Lessoway4, Tamas Ungi2, David Yen5, Jill Osborn6, Parvin Mousavi2, Robert Rohling7, Purang Abolmaesumi3. 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.
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.
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.
Authors: Mohamed N Ahmed; Sameh M Yamany; Nevin Mohamed; Aly A Farag; Thomas Moriarty Journal: IEEE Trans Med Imaging Date: 2002-03 Impact factor: 10.048
Authors: Denis Tran; Allaudin A Kamani; Elias Al-Attas; Victoria A Lessoway; Simon Massey; Robert N Rohling Journal: Can J Anaesth Date: 2010-04 Impact factor: 5.063
Authors: Abtin Rasoulian; Alexander Seitel; Jill Osborn; Samira Sojoudi; Saman Nouranian; Victoria A Lessoway; Robert N Rohling; Purang Abolmaesumi Journal: Int J Comput Assist Radiol Surg Date: 2015-06-03 Impact factor: 2.924
Authors: Alexander Seitel; Samira Sojoudi; Jill Osborn; Abtin Rasoulian; Saman Nouranian; Victoria A Lessoway; Robert N Rohling; Purang Abolmaesumi Journal: Ultrasound Med Biol Date: 2016-09-02 Impact factor: 2.998
Authors: T De Silva; A Uneri; X Zhang; M Ketcha; R Han; N Sheth; A Martin; S Vogt; G Kleinszig; A Belzberg; D M Sciubba; J H Siewerdsen Journal: Phys Med Biol Date: 2018-10-29 Impact factor: 3.609