Surface-based Registration Techniques Research Articles

Surface‐based registration between CT and US for image‐guided percutaneous renal access – A feasibility study

As a crucial step in accessing the kidney in several minimally invasive interventions, percutaneous renal access (PRA) practicality and safety may be improved through the fusion of computed tomography (CT) and ultrasound (US) data. This work aims to assess the potential of a surface-based registration technique and establish an optimal US acquisition protocol to fuse two-dimensional (2D) US and CT data for image-guided PRA. Ten porcine kidney phantoms with fiducial markers were imaged using CT and three-dimensional (3D) US. Both images were manually segmented and aligned. In a virtual environment, 2D contours were extracted by slicing the 3D US kidney surfaces and using usual PRA US-guided views, while the 3D CT kidney surfaces were transformed to simulate positional variability. Surface-based registration was performed using two methods of the iterative closest point algorithm (point-to-point, ICP1; and point-to-plane, ICP2), while four acquisition variants were studied: (a) use of single-plane (transverse, SPT ; or longitudinal, SPL ) vs bi-plane views (BP); (b) use of different kidney's coverage ranges acquired by a probe's sweep; (c) influence of sweep movements; and (d) influence of the spacing between consecutive slices acquired for a specific coverage range. BP view showed the best performance (TRE=2.26mm) when ICP2 method, a wide kidney coverage range (20°, with slices spaced by 5°), and a large sweep along the central longitudinal view were used, showing a statistically similar performance (P=0.097) to a full 3D US surface registration (TRE=2.28mm). An optimal 2D US acquisition protocol was evaluated. Surface-based registration, using multiple slices and specific sweep movements and views, is here suggested as a valid strategy for intraoperative image fusion using CT and US data, having the potential to be applied to other image modalities and/or interventions.

A computer and image-assisted guidance system for radial head arthroplasty

Accurately positioning components during radial head arthroplasty is difficult due to the complex shape of the radial head. The objective of this study was to develop a navigation system for patient-specific radial head implants using intra-operative surface-based registration techniques. Using an optical tracking system, an implant stem was navigated using real-time visualisation of the implant position and orientation (n = 8). Absolute positioning errors in the ML and AP directions were 0.8 ± 0.5 mm (and 1.1 ± 0.9°) and 0.8 ± 0.6 mm (and 1.2 ± 1.7°), respectively. Registration error was less than 1.0 mm. The current system is an important first step in the development of a computer-assisted system for implant navigation.

A surface-based technique for mapping homotopic interhemispheric connectivity: Development, characterization, and clinical application.

The functional organization of the human brain consists of a high degree of connectivity between interhemispheric homologous regions. The degree of homotopic organization is known to vary across the cortex and homotopic connectivity is high in regions that share cross-hemisphere structural connections or are activated by common input streams (e.g., the visual system). Damage to one or both regions, as well as damage to the connections between homotopic regions, could disrupt this functional organization. Here were introduce and test a computationally efficient technique, surface-based homotopic interhermispheric connectivity (sHIC), that leverages surface-based registration and processing techniques in an attempt to improve the spatial specificity and accuracy of cortical interhemispheric connectivity estimated with resting state functional connectivity. This technique is shown to be reliable both within and across subjects. sHIC is also characterized in a dataset of nearly 1000 subjects. We confirm previous results showing increased interhemispheric connectivity in primary sensory regions, and reveal a novel rostro-caudal functionally defined network level pattern of sHIC across the brain. In addition, we demonstrate a structural-functional relationship between sHIC and atrophy of the corpus callosum in multiple sclerosis (r = 0.2979, p = 0.0461). sHIC presents as a sensitive and reliable measure of cortical homotopy that may prove useful as a biomarker in neurologic disease. Hum Brain Mapp 37:2849-2868, 2016. © 2016 Wiley Periodicals, Inc.

Skuller: A volumetric shape registration algorithm for modeling skull deformities.

We present an algorithm for volumetric registration of 3D solid shapes. In comparison to previous work on image based registration, our technique achieves higher efficiency by leveraging a template tetrahedral mesh. In contrast to point- and surface-based registration techniques, our method better captures volumetric nature of the data, such as bone thickness. We apply our algorithm to study pathological skull deformities caused by a particular condition, i.e., craniosynostosis. The input to our system is a pair of volumetric 3D shapes: a tetrahedral mesh and a voxelized object represented by a set of voxel cells segmented from computed tomography (CT) scans. Our general framework first performs a global registration and then launches a novel elastic registration process that uses as much volumetric information as possible while deforming the generic template tetrahedral mesh of a healthy human skull towards the underlying geometry of the voxel cells. Both data are high-resolution and differ by large non-rigid deformations. Our fully-automatic solution is fast and accurate, as compared with the state of the arts from the reconstruction and medical image registration fields. We use the resulting registration to match the ground-truth surfaces extracted from the medical data as well as to quantify the severity of the anatomical deformity.

Automated landmark identification for human cortical surface-based registration

Volume-based registration (VBR) is the predominant method used in human neuroimaging to compensate for individual variability. However, surface-based registration (SBR) techniques have an inherent advantage over VBR because they respect the topology of the convoluted cortical sheet. There is evidence that existing SBR methods indeed confer a registration advantage over affine VBR. Landmark-SBR constrains registration using explicit landmarks to represent corresponding geographical locations on individual and atlas surfaces. The need for manual landmark identification has been an impediment to the widespread adoption of Landmark-SBR. To circumvent this obstacle, we have implemented and evaluated an automated landmark identification (ALI) algorithm for registration to the human PALS-B12 atlas. We compared ALI performance with that from two trained human raters and one expert anatomical rater (ENR). We employed both quantitative and qualitative quality assurance metrics, including a biologically meaningful analysis of hemispheric asymmetry. ALI performed well across all quality assurance tests, indicating that it yields robust and largely accurate results that require only modest manual correction (<10 min per subject). ALI largely circumvents human error and bias and enables high throughput analysis of large neuroimaging datasets for inter-subject registration to an atlas.

Intraprocedural Registration for Image-Guided Kidney Surgery

This paper reviews the process of using surface-based registration techniques for image-guided kidney surgery and presents data for both open and minimally invasive kidney surgery either by robot or by hand.

Scaled, patient-specific 3D vertebral model reconstruction based on 2D lateral fluoroscopy

Accurate three-dimensional (3D) models of lumbar vertebrae are required for image-based 3D kinematics analysis. MRI or CT datasets are frequently used to derive 3D models but have the disadvantages that they are expensive, time-consuming or involving ionizing radiation (e.g., CT acquisition). An alternative method using 2D lateral fluoroscopy was developed. A technique was developed to reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image and a statistical shape model of the lumbar vertebrae. Four cadaveric lumbar spine segments and two statistical shape models were used for testing. Reconstruction accuracy was determined by comparison of the surface models reconstructed from the single lateral fluoroscopic images to the ground truth data from 3D CT segmentation. For each case, two different surface-based registration techniques were used to recover the unknown scale factor, and the rigid transformation between the reconstructed surface model and the ground truth model before the differences between the two discrete surface models were computed. Successful reconstruction of scaled surface models was achieved for all test lumbar vertebrae based on single lateral fluoroscopic images. The mean reconstruction error was between 0.7 and 1.6 mm. A scaled, patient-specific surface model of the lumbar vertebra from a single lateral fluoroscopic image can be synthesized using the present approach. This new method for patient-specific 3D modeling has potential applications in spine kinematics analysis, surgical planning, and navigation.

Predicting the location of entorhinal cortex from MRI

Entorhinal cortex (EC) is a medial temporal lobe area critical to memory formation and spatial navigation that is among the earliest parts of the brain affected by Alzheimer's disease (AD). Accurate localization of EC would thus greatly facilitate early detection and diagnosis of AD. In this study, we used ultra-high resolution ex vivo MRI to directly visualize the architectonic features that define EC rostrocaudally and mediolaterally, then applied surface-based registration techniques to quantify the variability of EC with respect to cortical geometry, and made predictions of its location on in vivo scans. The results indicate that EC can be localized quite accurately based on cortical folding patterns, within 3 mm in vivo, a significant step forward in our ability to detect the earliest effects of AD when clinical intervention is most likely to be effective.

A comparison of registration techniques for computer- and image-assisted elbow surgery

Purpose: Accurate determination of the flexion-extension axis of the elbow is critical to the successful placement of elbow arthroplasties, articulated external fixators and ligament reconstructions. We expect axis alignment using computer-assisted techniques to improve the outcome of these procedures. For image-based procedures, registration (i.e. the transformation needed to align two sets of points) during surgery is critical for accurate alignment. A surface-based registration technique, employing a hand-held laser scanner, was evaluated against a stand-alone paired-point registration method to determine whether it led to improved alignment of the elbow’s flexion-extension axis. Methods: Twelve cadaveric distal-humeri were selected for registration. To perform paired-point (TP-PP) registration, key anatomical landmarks (capitellum, trochlear sulcus and distal humeral shaft) were digitized using a tracked-probe (TP) and an electromagnetic tracking device (Flock of Birds, Ascension Tech). Using the geometric centers of these landmarks, TP-PP registration to CT data was performed. Surface registration was achieved using the iterative closest point (ICP) least-squares algorithm and the results were evaluated for two devices; registration employing the tracked-probe (TP-ICP) and registration employing a hand-held laser scanner, HHS-ICP (FastSCAN, Polhemus). For surface registration, to be consistent with the amount of the joint exposed during a typical surgical procedure, only the articular surface was used for alignment. Results: Registration error (Figure 1) was lowest for the HHS-ICP method with a mean of 0.8±0.3-mm (maximum error, 1.4-mm) in translation, compared with a mean error of 1.5±0.5-mm (maximum error, 2.4-mm) for the TP-ICP method and 1.9±1.0-mm (maximum error, 4.4-mm) for the TP-PP method (p Conclusions: Overall, the reliability of surface-based registration combined with the implementation of the hand-held laser scanner demonstrated greater registration accuracy. A reliable surface-based registration technique may lead to a more accurate determination of the elbow’s flexion-extension axis during surgical procedures, leading to improved joint motion and implant longevity. The implications of these results can also be extended to other joints that employ comparable computer-assisted surgical techniques.

A comparison of registration techniques for computer- and image-assisted elbow surgery

Optimal function following elbow replacement surgery is dependent on the accurate replication of the elbow's flexion-extension axis. Currently, position and orientation of the axis are estimated from visual landmarks. In order to develop computer-assisted techniques to more accurately define this axis, a surface-based registration technique employing a hand-held laser scanner was evaluated against a conventional paired-point registration method to determine whether it produced improved alignment of the flexion-extension axis of the elbow. Registration error was 0.8 ± 0.3 mm for surface-based registration, compared with 1.9 ± 1.0 mm for the conventional registration method. These results suggest that the implementation of a surface-based registration technique may lead to a more accurate axis determination and improved clinical outcomes following elbow replacement surgery.

Correlation of PET and MRI for comparing assessments on myocardial viability

Spatially correlating images from different imaging modalities could provide complementary information of physiopathology of the heart. In this study, a surface-based registration technique, relied on integrating morphological information extracted from image datasets, was employed to co-register the functional and anatomical information from positron emission tomography (PET) and magnetic resonance imaging (MRI), respectively. We also evaluated the system performance based on external fiducial marker and concluded that the overall registration accuracy is close to intrinsic system limitation. Therefore, comprehensive image information could be performed for comparing assessments on myocardial viability.