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.

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.

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.

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.

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.

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.

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.

A multi-vertebrae CT to US registration of the lumbar spine in clinical data.

PURPOSE: Spinal needle injections are widely applied to alleviate back pain and for anesthesia. Current treatment is performed either blindly with palpation or using fluoroscopy or computed tomography (CT). Both fluoroscopy and CT guidance expose patients to ionizing radiation. Ultrasound (US) guidance for spinal needle procedures is becoming more prevalent as an alternative. It is challenging to use US as the sole imaging modality for intraoperative guidance of spine needle injections due to the acoustic shadows created by the bony structures of the vertebra that limit visibility of the target areas for injection. We propose registration of CT and the US images to augment anatomical visualization for the clinician during spinal interventions guided by US. METHODS: The proposed method involves automatic global and multi-vertebrae registration to find the closest alignment between CT and US data. This is performed by maximizing the similarity between the two modalities using voxel intensity information as well as features extracted from the input volumes. In our method, the lumbar spine is first globally aligned between the CT and US data using intensity-based registration followed by point-based registration. To account for possible curvature change of the spine between the CT and US volumes, a multi-vertebrae registration step is also performed. Springs are used to constrain the movement of the individually transformed vertebrae to ensure the optimal alignment is a pose of the lumbar spine that is physically possible. RESULTS: Evaluation of the algorithm is performed on 10 clinical patient datasets. The registration approach was able to align CT and US datasets from initial misalignments of up to 25 mm, with a mean TRE of 1.37 mm. These results suggest that the proposed approach has the potential to offer a sufficiently accurate registration between clinical CT and US data.

Statistical Biomechanical Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions.

A common challenge when performing surface-based registration of images is ensuring that the surfaces accurately represent consistent anatomical boundaries. Image segmentation may be difficult in some regions due to either poor contrast, low slice resolution, or tissue ambiguities. To address this, we present a novel non-rigid surface registration method designed to register two partial surfaces, capable of ignoring regions where the anatomical boundary is unclear. Our probabilistic approach incorporates prior geometric information in the form of a statistical shape model (SSM), and physical knowledge in the form of a finite element model (FEM). We validate results in the context of prostate interventions by registering pre-operative magnetic resonance imaging (MRI) to 3D transrectal ultrasound (TRUS). We show that both the geometric and physical priors significantly decrease net target registration error (TRE), leading to TREs of 2.35 ± 0.81 mm and 2.81 ± 0.66 mm when applied to full and partial surfaces, respectively. We investigate robustness in response to errors in segmentation, varying levels of missing data, and adjusting the tunable parameters. Results demonstrate that the proposed surface registration method is an efficient, robust, and effective solution for fusing data from multiple modalities.

Biomechanically Constrained Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions.

In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions. To address these, we present a novel non-rigid registration method. For establishing correspondences, we use a probabilistic framework based on a Gaussian mixture model (GMM) that treats one surface as a potentially partial observation. To extrapolate and constrain the deformation field, we incorporate biomechanical prior knowledge in the form of a finite element model (FEM). We validate the algorithm, referred to as GMM-FEM, in the context of prostate interventions. Our method leads to a significant reduction in target registration error (TRE) compared to similar state-of-the-art registration algorithms in the case of missing data up to 30%, with a mean TRE of 2.6 mm. The method also performs well when full segmentations are available, leading to TREs that are comparable to or better than other surface-based techniques. We also analyze robustness of our approach, showing that GMM-FEM is a practical and reliable solution for surface-based registration.

Ultrasound-guided spinal injections: a feasibility study of a guidance system.

PURPOSE: Facet joint injections of analgesic agents are widely used to treat patients with lower back pain. The current standard-of-care for guiding the injection is fluoroscopy, which exposes the patient and physician to significant radiation. As an alternative, several ultrasound guidance systems have been proposed, but have not become the standard-of-care, mainly because of the difficulty in image interpretation by the anesthesiologist unfamiliar with the complex spinal sonography. METHODS: We introduce an ultrasound-based navigation system that allows for live 2D ultrasound images augmented with a patient-specific statistical model of the spine and relating this information to the position of the tracked injection needle. The model registration accuracy is assessed on ultrasound data obtained from nine subjects who had prior CT images as the gold standard for the statistical model. The clinical validity of our method is evaluated on four subjects (of an ongoing in vivo study) which underwent facet joint injections. RESULTS: The statistical model could be registered to the bone structures in the ultrasound volume with an average RMS accuracy of 2.3±0.4 mm. The shape of the individual vertebrae could be estimated from the US volume with an average RMS surface distance error of 1.5±0.4 mm. The facet joints could be identified by the statistical model with an average accuracy of 5.1 ± 1.5 mm. CONCLUSIONS: The results of this initial feasibility assessment suggest that this ultrasound-based system is capable of providing information sufficient to guide facet joint injections. Further clinical studies are warranted.

Towards real-time, tracker-less 3D ultrasound guidance for spine anaesthesia.

PURPOSE: Epidural needle insertions and facet joint injections play an important role in spine anaesthesia. The main challenge of safe needle insertion is the deep location of the target, resulting in a narrow and small insertion channel close to sensitive anatomy. Recent approaches utilizing ultrasound (US) as a low-cost and widely available guiding modality are promising but have yet to become routinely used in clinical practice due to the difficulty in interpreting US images, their limited view of the internal anatomy of the spine, and/or inclusion of cost-intensive tracking hardware which impacts the clinical workflow. METHODS: We propose a novel guidance system for spine anaesthesia. An efficient implementation allows us to continuously align and overlay a statistical model of the lumbar spine on the live 3D US stream without making use of additional tracking hardware. The system is evaluated in vivo on 12 volunteers. RESULTS: The in vivo study showed that the anatomical features of the epidural space and the facet joints could be continuously located, at a volume rate of 0.5 Hz, within an accuracy of 3 and 7 mm, respectively. CONCLUSIONS: A novel guidance system for spine anaesthesia has been presented which augments a live 3D US stream with detailed anatomical information of the spine. Results from an in vivo study indicate that the proposed system has potential for assisting the physician in quickly finding the target structure and planning a safe insertion trajectory in the spine.

Bone enhancement in ultrasound using local spectrum variations for guiding percutaneous scaphoid fracture fixation procedures.

PURPOSE: The scaphoid bone is the most frequently fractured bone in the wrist. When fracture fixation is indicated, a screw is inserted into the bone either in an open surgical procedure or percutaneously under fluoroscopic guidance. Due to the complex geometry of the wrist, fracture fixation is a challenging task. Fluoroscopic guidance exposes both the patient and the physician to ionizing radiation. Ultrasound-based guidance has been suggested as a real-time, radiation-free alternative. The main challenge of using ultrasound is the difficulty in interpreting the images due to the low contrast and noisy nature of the data. METHODS: We propose a bone enhancement method that exploits local spectrum features of the ultrasound image. These features are utilized to design a set of quadrature band-pass filters and subsequently estimate the local phase symmetry, where high symmetry is expected at the bone locations. We incorporate the shadow information below the bone surfaces to further enhance the bone responses. The extracted bone surfaces are then used to register a statistical wrist model to ultrasound volumes, allowing the localization and interpretation of the scaphoid bone in the volumes. RESULTS: Feasibility experiments were performed using phantom and in vivo data. For phantoms, we obtain a surface distance error 1.08 mm and an angular deviation from the main axis of the scaphoid bone smaller than 5°, which are better compared to previously presented approaches. CONCLUSION: The results are promising for further development of a surgical guidance system to enable accurate anatomy localization for guiding percutaneous scaphoid fracture fixations.

Temporal-lobe morphology differs between healthy adolescents and those with early-onset of depression.

Major depressive disorder (MDD) has previously been linked to structural changes in several brain regions, particularly in the medial temporal lobes (Bellani, Baiano, Brambilla, 2010; Bellani, Baiano, Brambilla, 2011). This has been determined using voxel-based morphometry, segmentation algorithms, and analysis of shape deformations (Bell-McGinty et al., 2002; Bergouignan et al., 2009; Posener et al., 2003; Vasic et al., 2008; Zhao et al., 2008): these are methods in which information related to the shape and the pose (the size, and anatomical position and orientation) of structures is lost. Here, we incorporate information about shape and pose to measure structural deformation in adolescents and young adults with and without depression (as measured using the Beck Depression Inventory and Diagnostic and Statistical Manual of Mental Disorders criteria). As a hypothesis-generating study, a significance level of p < 0.05, uncorrected for multiple comparisons, was used, so that subtle morphological differences in brain structures between adolescent depressed individuals and control participants could be identified. We focus on changes in cortical and subcortical temporal structures, and use a multi-object statistical pose and shape model to analyze imaging data from 16 females (aged 16-21) and 3 males (aged 18) with early-onset MDD, and 25 female and 1 male normal control participants, drawn from the same age range. The hippocampus, parahippocampal gyrus, putamen, and superior, inferior and middle temporal gyri in both hemispheres of the brain were automatically segmented using the LONI Probabilistic Brain Atlas (Shattuck et al., 2008) in MNI space. Points on the surface of each structure in the atlas were extracted and warped to each participant's structural MRI. These surface points were analyzed to extract the pose and shape features. Pose differences were detected between the two groups, particularly in the left and right putamina, right hippocampus, and left and right inferior temporal gyri. Shape differences were detected between the two groups, particularly in the left hippocampus and in the left and right parahippocampal gyri. Furthermore, pose measures were significantly correlated with BDI score across the whole (clinical and control) sample. Since the clinical participants were experiencing their very first episodes of MDD, morphological alteration in the medial temporal lobe appears to be an early sign of MDD, and is unlikely to result from treatment with antidepressants. Pose and shape measures of morphology, which are not usually analyzed in neuromorphometric studies, appear to be sensitive to depressive symptomatology.

Local phase tensor features for 3-D ultrasound to statistical shape+pose spine model registration.

Most conventional spine interventions are performed under X-ray fluoroscopy guidance. In recent years, there has been a growing interest to develop nonionizing imaging alternatives to guide these procedures. Ultrasound guidance has emerged as a leading alternative. However, a challenging problem is automatic identification of the spinal anatomy in ultrasound data. In this paper, we propose a local phase-based bone feature enhancement technique that can robustly identify the spine surface in ultrasound images. The local phase information is obtained using a gradient energy tensor filter. This information is used to construct local phase tensors in ultrasound images, which highlight the spine surface. We show that our proposed approach results in a more distinct enhancement of the bone surfaces compared to recently proposed techniques based on monogenic scale-space filters and logarithmic Gabor filters. We also demonstrate that registration accuracy of a statistical shape+pose model of the spine to 3-D ultrasound images can be significantly improved, using the proposed method, compared to those obtained using monogenic scale-space filters and logarithmic Gabor filters.

Lumbar spine segmentation using a statistical multi-vertebrae anatomical shape+pose model.

Segmentation of the spinal column from computed tomography (CT) images is a preprocessing step for a range of image-guided interventions. One intervention that would benefit from accurate segmentation is spinal needle injection. Previous spinal segmentation techniques have primarily focused on identification and separate segmentation of each vertebra. Recently, statistical multi-object shape models have been introduced to extract common statistical characteristics between several anatomies. These models can be used for segmentation purposes because they are robust, accurate, and computationally tractable. In this paper, we develop a statistical multi-vertebrae shape+pose model and propose a novel registration-based technique to segment the CT images of spine. The multi-vertebrae statistical model captures the variations in shape and pose simultaneously, which reduces the number of registration parameters. We validate our technique in terms of accuracy and robustness of multi-vertebrae segmentation of CT images acquired from lumbar vertebrae of 32 subjects. The mean error of the proposed technique is below 2 mm, which is sufficient for many spinal needle injection procedures, such as facet joint injections.

Statistical shape model to 3D ultrasound registration for spine interventions using enhanced local phase features.

Accurate registration of ultrasound images to statistical shape models is a challenging problem in percutaneous spine injection procedures due to the typical imaging artifacts inherent to ultrasound. In this paper we propose a robust and accurate registration method that matches local phase bone features extracted from ultrasound images to a statistical shape model. The local phase information for enhancing the bone surfaces is obtained using a gradient energy tensor filter, which combines advantages of the monogenic scale-space and Gaussian scale-space filters, resulting in an improved simultaneous estimation of phase and orientation information. A novel statistical shape model was built by separating the pose statistics from the shape statistics. This model is then registered to the local phase bone surfaces using an iterative expectation maximization registration technique. Validation on 96 in vivo clinical scans obtained from eight patients resulted in a root mean square registration error of 2 mm (SD: 0.4 mm), which is below the clinically acceptable threshold of 3.5 mm. The improvement achieved in registration accuracy using the new features was also significant (p < 0.05) compared to state of the art local phase image processing methods.

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.

Group-wise registration of point sets for statistical shape models.

This paper presents a novel, fast, group-wise registration technique based on establishing soft correspondences between groups of point sets. The registration approach is used to create a statistical shape model, capable of learning the shape variations within a training set. The shape model consists of a mean shape and its transformations to all training shapes. We decouple the procedure into two steps: updating the mean shape and registering it to the training shapes. The algorithm alternates between these two steps until convergence. Following the generation of the statistical shape model, we use the soft correspondence approach to register the model to a new observation. We perform extensive experiments on two data sets: lumbar spine and hippocampi. We compare our algorithm to available state-of- the-art group-wise registration algorithms including feature-based and volume-based approaches. We demonstrate improved generalization, specificity and compactness compared to these algorithms.