Feature-based multibody rigid registration of CT and ultrasound images of lumbar spine.

PURPOSE: Fusion of intraprocedure ultrasound and preprocedure CT data is proposed for guidance in percutaneous spinal injections, a common procedure for pain management. CT scan of the lumbar spine is usually collected in a supine position, whereas spinal injections are performed in prone or sitting positions. This leads to a difference in the spine curvature between CT and ultrasound images; as such, a single-body rigid registration approach cannot be used for the whole lumbar vertebrae. METHODS: To compensate for the difference in the spinal curvature between the two imaging modalities, a multibody rigid registration algorithm is presented. The approach utilizes a point-based registration technique based on the unscented Kalman filter (UKF), taking as input segmented vertebrae surfaces in both CT and ultrasound data. Ultrasound images are automatically segmented using a dynamic programming method, while the CT images are semiautomatically segmented using thresholding. The registration approach is designed to simultaneously align individual groups of points segmented from each vertebra in the two imaging modalities. A biomechanical model is developed to constrain the vertebrae transformation parameters during the registration and to ensure convergence. RESULTS: The proposed methodology is evaluated on human spine phantoms and a sheep cadaver. Registrations on phantom data have a mean target registration error (TRE) of 1.99 mm in the region of interest and converged in 87% of cases. Registrations on sheep cadaver have a mean target registration error of 2.2 mm and converged in 82% of cases. CONCLUSIONS: The proposed technique can robustly and simultaneously register several vertebrae extracted from CT images to the ultrasound volumes. The registration error below 2.2 mm is sufficient for most spinal injections.

Utility of prepuncture ultrasound for localization of the thoracic epidural space.

BACKGROUND: Ultrasound has been shown to facilitate accurate identification of the intervertebral level and to predict skin-to-epidural depth in the lumbar epidural space with reliable precision. We hypothesized that we could accurately predict the skin-to-epidural depth and the intervertebral level in the thoracic spine with the use of ultrasound. METHODS: Twenty patients presenting for thoracic surgery were included in a feasibility study. The skin-to-epidural depth was measured using prepuncture ultrasound in the paramedian window, and the predicted depth was compared with the actual needle depth and the depth as measured by computed tomography. In addition, the intervertebral levels were identified by ultrasound using the "counting up" method, and the results were compared with the levels identified by anesthesiologists. RESULTS: The ultrasound-based depth measurements displayed a bias of 3.21 mm with 95% limits of agreement from -7.47 to 13.9 mm compared with the clinically determined needle depth. The intervertebral levels identified by the anesthesiologists and the sonographer matched in only 40% of cases. CONCLUSION: Ultrasound-based measurements of skin-to-epidural depth show acceptable agreement with the actual depth observed during epidural catheterization; however, the limits of agreement are wide, which restricts the predictive value of ultrasound-based measurements. Further study is required to delineate the role of ultrasound in thoracic epidural catheterizations.

Three-Dimensional Ultrasound-Guided Real-Time Midline Epidural Needle Placement with Epiguide: A Prospective Feasibility Study.

Current 2-D ultrasound technology is unable to perform a midline neuraxial needle insertion under real-time ultrasound guidance using a standard needle and without an assistant. The aim of the work described here was to determine the feasibility of a new technology providing such capability, starting with a study evaluating the selected puncture site. A novel 3-D ultrasound imaging technique was designed using thick-slice rendering in conjunction with a custom needle guide (3DUS + Epiguide). A clinical feasibility study evaluated the ability of 3DUS + Epiguide to identify the epidural needle puncture site for a midline insertion in the lumbar spine. We hypothesized that (i) the puncture site identified by 3DUS + Epiguide was within a 5-mm radius from the site chosen by standard palpation, and (ii) the difference between the two puncture sites was not correlated to the patient characteristics age, weight, height, body mass index and gestational age. The mean (±standard deviation) distances between puncture sites determined by 3DUS + Epiguide and palpation were 3.1 (±1.7) mm and 2.8 (±1.3) mm, for the L2-3 and L3-4 interspaces of 20 patients, respectively. Distances were comparable to intra-observer variability, indicating the potential for a thick-slice rendering of 3-D ultrasound along the Epiguide trajectory to select the puncture site of a midline neuraxial needle insertion. The long-term potential benefits of this system include increased efficiency and use of anesthesia, and a reduction in the frequency and severity of the complications from incorrect needle insertions. Epidural success in the most difficult cases (e.g., the obese) will be the focus of future work.

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

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.

Ultrasound-Guided Spine Anesthesia: Feasibility Study of a Guidance System.

Spinal needle injections are guided by fluoroscopy or palpation, resulting in radiation exposure and/or multiple needle re-insertions. Consequently, guiding these procedures with live ultrasound has become more popular, but images are still challenging to interpret. We introduce a guidance system based on augmentation of ultrasound images with a patient-specific 3-D surface model of the lumbar spine. We assessed the feasibility of the system in a study on 12 patients. The system could accurately provide augmentations of the epidural space and the facet joint for all subjects. Following conventional, fluoroscopy-guided needle placement, augmentation accuracy was determined according to the electromagnetically tracked final position of the needle. In 9 of 12 cases, the accuracy was considered sufficient for successfully delivering anesthesia. The unsuccessful cases can be attributed to errors in the electromagnetic tracking reference, which can be avoided by a setup reducing the influence of the metal C-arm.

Automatic Segmentation of Wrist Bones in CT Using a Statistical Wrist Shape + Pose Model.

Segmentation of the wrist bones in CT images has been frequently used in different clinical applications including arthritis evaluation, bone age assessment and image-guided interventions. The major challenges include non-uniformity and spongy textures of the bone tissue as well as narrow inter-bone spaces. In this work, we propose an automatic wrist bone segmentation technique for CT images based on a statistical model that captures the shape and pose variations of the wrist joint across 60 example wrists at nine different wrist positions. To establish the correspondences across the training shapes at neutral positions, the wrist bone surfaces are jointly aligned using a group-wise registration framework based on a Gaussian Mixture Model. Principal component analysis is then used to determine the major modes of shape variations. The variations in poses not only across the population but also across different wrist positions are incorporated in two pose models. An intra-subject pose model is developed by utilizing the similarity transforms at all wrist positions across the population. Further, an inter-subject pose model is used to model the pose variations across different wrist positions. For segmentation of the wrist bones in CT images, the developed model is registered to the edge point cloud extracted from the CT volume through an expectation maximization based probabilistic approach. Residual registration errors are corrected by application of a non-rigid registration technique. We validate the proposed segmentation method by registering the wrist model to a total of 66 unseen CT volumes of average voxel size of 0.38 mm. We report a mean surface distance error of 0.33 mm and a mean Jaccard index of 0.86.

Joint registration of ultrasound, CT and a shape+pose statistical model of the lumbar spine for guiding anesthesia.

PURPOSE: Facet joint injections and epidural needle insertions are widely used for spine anesthesia. Accurate needle placement is important for effective therapy delivery and avoiding complications arising from damage of soft tissue and nerves. Needle guidance is usually performed by fluoroscopy or palpation, resulting in radiation exposure and multiple needle re-insertions. Several ultrasound (US)-based approaches have been proposed but have not found wide acceptance in clinical routine. This is mainly due to difficulties in interpretation of the complex spinal anatomy in US, which leads to clinicians' lack of confidence in relying only on information derived from US for needle guidance. METHODS: We introduce a multimodal joint registration technique that takes advantage of easy-to-interpret preprocedure computed topography (CT) scans of the lumbar spine to concurrently register a shape+pose model to the intraprocedure 3D US. Common shape coefficients are assumed between two modalities, while pose coefficients are specific to each modality. RESULTS: The joint method was evaluated on patient data consisting of ten pairs of US and CT scans of the lumbar spine. It was successfully applied in all cases and yielded an RMS shape error of 2.1 mm compared to the CT ground truth. The joint registration technique was compared to a previously proposed method of statistical model to US registration Rasoulian et al. (Information processing in computer-assisted interventions. Springer, Berlin, pp 51-60, 2013). The joint framework improved registration accuracy to US in 7 out of 17 visible vertebrae, belonging to four patients. In the remaining cases, the two methods were equally accurate. CONCLUSION: The joint registration allows visualization and augmentation of important anatomy in both the US and CT domain and improves the registration accuracy in both modalities. Observing the patient-specific model in the CT domain allows the clinicians to assess the local registration accuracy qualitatively, which is likely to increase their confidence in using the US model for deriving needle guidance decisions.

Registration of a statistical model to intraoperative ultrasound for scaphoid screw fixation.

PURPOSE: Volar percutaneous scaphoid fracture fixation is conventionally performed under fluoroscopy-based guidance, where surgeons need to mentally determine a trajectory for the insertion of the screw and its depth based on a series of 2D projection images. In addition to challenges associated with mapping 2D information to a 3D space, the process involves exposure to ionizing radiation. Three-dimensional ultrasound has been suggested as an alternative imaging tool for this procedure; however, it has not yet been integrated into clinical routine since ultrasound only provides a limited view of the scaphoid and its surrounding anatomy. METHODS: We propose a registration of a statistical wrist shape + scale + pose model to a preoperative CT and intraoperative ultrasound to derive a patient-specific 3D model for guiding scaphoid fracture fixation. The registered model is then used to determine clinically important intervention parameters, including the screw length and the trajectory of screw insertion in the scaphoid bone. RESULTS: Feasibility experiments are performed using 13 cadaver wrists. In 10 out of 13 cases, the trajectory of screw suggested by the registered model meets all clinically important intervention parameters. Overall, an average 94 % of maximum allowable screw length is obtained based on the measurements from gold standard CT. Also, we obtained an average 92 % successful volar accessibility, which indicates that the trajectory is not obstructed by the surrounding trapezium bone. CONCLUSIONS: These promising results indicate that determining clinically important screw insertion parameters for scaphoid fracture fixation is feasible using 3D ultrasound imaging. This suggests the potential of this technology in replacing fluoroscopic guidance for this procedure in future applications.

A multi-center milestone study of clinical vertebral CT segmentation.

A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention.

Nonlocal means filter-based speckle tracking.

The objective of sensorless freehand 3-D ultrasound imaging is to eliminate the need for additional tracking hardware and reduce cost and complexity. However, the accuracy of current out-of-plane pose estimation is main obstacle for full 6-degree-of-freedom (DoF) tracking. We propose a new filter-based speckle tracking framework to increase the accuracy of out-of-plane displacement estimation. In this framework, we use the displacement estimation not only for the specific speckle pattern, but for the entire image. We develop a nonlocal means (NLM) filter based on a probabilistic normal variance mixture model of ultrasound, known as Rician-inverse Gaussian (RiIG). To aggregate the local displacement estimations, Stein's unbiased risk estimate (SURE) is used as a quality measure of the estimations. We derive an explicit analytical form of SURE for the RiIG model and use it as a weight factor. The proposed filter-based speckle tracking framework is formulated and evaluated for three commonly used noise models, including the RiIG model. The out-of-plane estimations are compared with our previously proposed model-based algorithm in a set of ex vivo experiments for different tissue types. We show that the proposed RiIG filter-based method is more accurate and less tissue-dependent than the other methods. The proposed method is also evaluated in vivo on the spines of five different subjects to assess the feasibility of a clinical application. The 6-DoF transform parameters are estimated and compared with the electromagnetic tracker measurements. The results show higher tracking accuracy for typical small lateral displacements and tilt rotations between image pairs.