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.

Open-source image registration for MRI-TRUS fusion-guided prostate interventions.

PURPOSE: We propose two software tools for non-rigid registration of MRI and transrectal ultrasound (TRUS) images of the prostate. Our ultimate goal is to develop an open-source solution to support MRI-TRUS fusion image guidance of prostate interventions, such as targeted biopsy for prostate cancer detection and focal therapy. It is widely hypothesized that image registration is an essential component in such systems. METHODS: The two non-rigid registration methods are: (1) a deformable registration of the prostate segmentation distance maps with B-spline regularization and (2) a finite element-based deformable registration of the segmentation surfaces in the presence of partial data. We evaluate the methods retrospectively using clinical patient image data collected during standard clinical procedures. Computation time and Target Registration Error (TRE) calculated at the expert-identified anatomical landmarks were used as quantitative measures for the evaluation. RESULTS: The presented image registration tools were capable of completing deformable registration computation within 5 min. Average TRE was approximately 3 mm for both methods, which is comparable with the slice thickness in our MRI data. Both tools are available under nonrestrictive open-source license. CONCLUSIONS: We release open-source tools that may be used for registration during MRI-TRUS-guided prostate interventions. Our tools implement novel registration approaches and produce acceptable registration results. We believe these tools will lower the barriers in development and deployment of interventional research solutions and facilitate comparison with similar tools.

An augmented reality system for epidural anesthesia (AREA): prepuncture identification of vertebrae.

We propose an augmented reality system to identify lumbar vertebral levels to assist in spinal needle insertion for epidural anesthesia. These procedures require careful placement of a needle to ensure effective delivery of anesthetics and to avoid damaging sensitive tissue such as nerves. In this system, a trinocular camera tracks an ultrasound transducer during the acquisition of a sequence of B-mode images. The system generates an ultrasound panorama image of the lumbar spine, automatically identifies the lumbar levels in the panorama image, and overlays the identified levels on a live camera view of the patient's back. Validation is performed to test the accuracy of panorama generation, lumbar level identification, overall system accuracy, and the effect of changes in the curvature of the spine during the examination. The results from 17 subjects demonstrate the feasibility and capability of achieving an error within clinically acceptable range for epidural anaesthesia.

Experimental validation of an intrasubject elastic registration algorithm for dynamic-3D ultrasound images.

PURPOSE: In image-guided therapy, real-time visualization of the anatomy and adjustments in the therapy plan due to anatomical motions during the procedure is of outmost importance. 3D ultrasound has the potential to enable this real-time monitoring; however, nonrigid registration of a sequence of 3D ultrasound volumes remains to be a challenging problem. The authors present our recent results on the development of a computationally inexpensive feature-based registration algorithm for elastic alignment of dynamic-3D ultrasound images. METHODS: Our algorithm uses attribute vectors, based on the image intensity and gradient information, to perform feature-based matching in a sequence of 3D ultrasound images. Prior information from both the fixed and previous moving images is utilized to track features throughout the 3D image series. The algorithm has been compared to various publicly available registration techniques, i.e., the B-splines deformable registration, the symmetric forces Demons, and the fast free-form deformable registration method. RESULTS: Using a series of validation experiments on datasets collected from carotid artery, liver, and kidney of 20 subjects, the authors demonstrate that the feature-based, B-splines, Demons, and fast free-form deformable registration techniques can all recover volume deformations in a 3D ultrasound image series with reasonable accuracy; however, the proposed feature-based registration technique has substantial computational advantage over the other approaches. CONCLUSIONS: The proposed feature-based registration technique has the potential for real-time implementation on a computationally inexpensive platform and has the capability of recovering nonrigid deformations in tissue with reasonable accuracy.

AREA: an augmented reality system for epidural anaesthesia.

PURPOSE: Spinal needle injection procedures are used for anesthesia and analgesia, such as lumbar epidurals. These procedures require careful placement of a needle, both to ensure effective therapy delivery and to avoid damaging sensitive tissue such as the spinal cord. An important step in such procedures is the accurate identification of the vertebral levels, which is currently performed using manual palpation with a reported 30% success rate for correct identification. METHODS: An augmented reality system was developed to help identify the lumbar vertebral levels. The system consists of an ultrasound transducer tracked in real time by a trinocular camera system, an automatic ultrasound panorama generation module that provides an extended view of the lumbar vertebrae, an image processing technique that automatically identifies the vertebral levels in the panorama image, and a graphical interface that overlays the identified levels on a live camera view of the patient's back. RESULTS: Validation was performed on ultrasound data obtained from 10 subjects with different spine arching. The average success rate for segmentation of the vertebrae was 85%. The automatic level identification had an average accuracy of 6.6 mm. CONCLUSION: The prototype system demonstrates better accuracy for identifying the vertebrae than traditional manual methods.

Registration of a statistical shape model of the lumbar spine to 3D ultrasound images.

MOTIVATION: Spinal needle injections are technically demanding procedures. The use of ultrasound image guidance without prior CT and MR imagery promises to improve the efficacy and safety of these procedures in an affordable manner. METHODOLOGY: We propose to create a statistical shape model of the lumbar spine and warp this atlas to patient-specific ultrasound images during the needle placement procedure. From CT image volumes of 35 patients, statistical shape model of the L3 vertebra is built, including mean shape and main modes of variation. This shape model is registered to the ultrasound data by simultaneously optimizing the parameters of the model and its relative pose. Ground-truth data was established by printing 3D anatomical models of 3 patients using a rapid prototyping. CT and ultrasound data of these models were registered using fiducial markers. RESULTS: Pairwise registration of the statistical shape model and 3D ultrasound images led to a mean target registration error of 3.4 mm, while 81% of all cases yielded clinically acceptable accuracy below the 3.5 mm threshold.