Validation of a hybrid Doppler ultrasound vessel-based registration algorithm for neurosurgery

Automatic rigid image Fusion of preoperative MR and intraoperative US acquired after craniotomy

BACKGROUND

Neuronavigation of preoperative MRI is limited by several errors. Intraoperative ultrasound (iUS) with navigated probes that provide automatic superposition of pre-operative MRI and iUS and three-dimensional iUS reconstruction may overcome some of these limitations. Aim of the present study is to verify the accuracy of an automatic MRI - iUS fusion algorithm to improve MR-based neuronavigation accuracy.

METHODS

An algorithm using Linear Correlation of Linear Combination (LC2)-based similarity metric has been retrospectively evaluated for twelve datasets acquired in patients with brain tumor. A series of landmarks were defined both in MRI and iUS scans. The Target Registration Error (TRE) was determined for each pair of landmarks before and after the automatic Rigid Image Fusion (RIF). The algorithm has been tested on two conditions of the initial image alignment: registration-based fusion (RBF), as given by the navigated ultrasound probe, and different simulated course alignments during convergence test.

RESULTS

Except for one case RIF was successfully applied in all patients considering the RBF as initial alignment. Here, mean TRE after RBF was significantly reduced from 4.03 (± 1.40) mm to (2.08 ± 0.96 mm) (p = 0.002), after RIF. For convergence test, the mean TRE value after initial perturbations was 8.82 (± 0.23) mm which has been reduced to a mean TRE of 2.64 (± 1.20) mm after RIF (p < 0.001).

CONCLUSIONS

The integration of an automatic image fusion method for co-registration of pre-operative MRI and iUS data may improve the accuracy in MR-based neuronavigation.

Photoacoustic-MR Image Registration Based on a Co-Sparse Analysis Model to Compensate for Brain Shift

Brain shift is an important obstacle to the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging to update the image-guided surgery systems. However, due to the innate limitations of the current imaging modalities, accurate brain shift compensation continues to be a challenging task. In this study, the application of intra-operative photoacoustic imaging and registration of the intra-operative photoacoustic with pre-operative MR images are proposed to compensate for brain deformation. Finding a satisfactory registration method is challenging due to the unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for photoacoustic-MR image registration, which can capture the interdependency of the two modalities. The proposed algorithm works based on the minimization of mapping transform via a pair of analysis operators that are learned by the alternating direction method of multipliers. The method was evaluated using an experimental phantom and ex vivo data obtained from a mouse brain. The results of the phantom data show about 63% improvement in target registration error in comparison with the commonly used normalized mutual information method. The results proved that intra-operative photoacoustic images could become a promising tool when the brain shift invalidates pre-operative MRI.

Co-Sparse Analysis Model Based Image Registration to Compensate Brain Shift by Using Intra-Operative Ultrasound Imaging

Notwithstanding the widespread use of image guided neurosurgery systems in recent years, the accuracy of these systems is strongly limited by the intra-operative deformation of the brain tissue, the so-called brain shift. Intra-operative ultrasound (iUS) imaging as an effective solution to compensate complex brain shift phenomena update patients coordinate during surgery by registration of the intra-operative ultrasound and the pre-operative MRI data that is a challenging problem.In this work a non-rigid multimodal image registration technique based on co-sparse analysis model is proposed. This model captures the interdependency of two image modalities; MRI as an intensity image and iUS as a depth image. Based on this model, the transformation between the two modalities is minimized by using a bimodal pair of analysis operators which are learned by optimizing a joint co-sparsity function using a conjugate gradient.Experimental validation of our algorithm confirms that our registration approach outperforms several of other state-of-the-art registration methods quantitatively. The evaluation was performed using seven patient dataset with the mean registration error of only 1.83 mm. Our intensity-based co-sparse analysis model has improved the accuracy of non-rigid multimodal medical image registration by 15.37% compared to the curvelet based residual complexity as a powerful registration method, in a computational time compatible with clinical use.

Retrospective study comparing model-based deformation correction to intraoperative magnetic resonance imaging for image-guided neurosurgery

Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of [Formula: see text] tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift ([Formula: see text], [Formula: see text] measurements per case), the alignment error due to brain shift reduced from [Formula: see text] to [Formula: see text], representing [Formula: see text] correction. These first steps toward validation are promising for model-based strategies.

Geometric modeling of hepatic arteries in 3D ultrasound with unsupervised MRA fusion during liver interventions

PURPOSE

Modulating the chemotherapy injection rate with regard to blood flow velocities in the tumor-feeding arteries during intra-arterial therapies may help improve liver tumor targeting while decreasing systemic exposure. These velocities can be obtained noninvasively using Doppler ultrasound (US). However, small vessels situated in the liver are difficult to identify and follow in US. We propose a multimodal fusion approach that non-rigidly registers a 3D geometric mesh model of the hepatic arteries obtained from preoperative MR angiography (MRA) acquisitions with intra-operative 3D US imaging.

METHODS

The proposed fusion tool integrates 3 imaging modalities: an arterial MRA, a portal phase MRA and an intra-operative 3D US. Preoperatively, the arterial phase MRA is used to generate a 3D model of the hepatic arteries, which is then non-rigidly co-registered with the portal phase MRA. Once the intra-operative 3D US is acquired, we register it with the portal MRA using a vessel-based rigid initialization followed by a non-rigid registration using an image-based metric based on linear correlation of linear combination. Using the combined non-rigid transformation matrices, the 3D mesh model is fused with the 3D US.

RESULTS

3D US and multi-phase MRA images acquired from 10 porcine models were used to test the performance of the proposed fusion tool. Unimodal registration of the MRA phases yielded a target registration error (TRE) of [Formula: see text] mm. Initial rigid alignment of the portal MRA and 3D US yielded a mean TRE of [Formula: see text] mm, which was significantly reduced to [Formula: see text] mm ([Formula: see text]) after affine image-based registration. The following deformable registration step allowed for further decrease of the mean TRE to [Formula: see text] mm.

CONCLUSION

The proposed tool could facilitate visualization and localization of these vessels when using 3D US intra-operatively for either intravascular or percutaneous interventions to avoid vessel perforation.

A combined registration and finite element analysis method for fast estimation of intraoperative brain shift; phantom and animal model study

BACKGROUND

Finite element models for estimation of intraoperative brain shift suffer from huge computational cost. In these models, image registration and finite element analysis are two time-consuming processes.

METHODS

The proposed method is an improved version of our previously developed Finite Element Drift (FED) registration algorithm. In this work the registration process is combined with the finite element analysis. In the Combined FED (CFED), the deformation of whole brain mesh is iteratively calculated by geometrical extension of a local load vector which is computed by FED.

RESULTS

While the processing time of the FED-based method including registration and finite element analysis was about 70 s, the computation time of the CFED was about 3.2 s. The computational cost of CFED is almost 50% less than similar state of the art brain shift estimators based on finite element models.

CONCLUSIONS

The proposed combination of registration and structural analysis can make the calculation of brain deformation much faster.

Applications of Ultrasound in the Resection of Brain Tumors

Neurosurgery makes use of preoperative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of preoperative imaging for neuronavigation, however, is diminished by the well-characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography, has dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies.

Curvelet based residual complexity objective function for non-rigid registration of pre-operative MRI with intra-operative ultrasound images

Intra-operative ultrasound as an imaging based method has been recognized as an effective solution to compensate non rigid brain shift problem in recent years. Measuring brain shift requires registration of the pre-operative MRI images with the intra-operative ultrasound images which is a challenging task. In this study a novel hybrid method based on the matching echogenic structures such as sulci and tumor boundary in MRI with ultrasound images is proposed. The matching echogenic structures are achieved by optimizing the Residual Complexity (RC) in the curvelet domain. At the first step, the probabilistic map of the MR image is achieved and the residual image as the difference between this probabilistic map and intra-operative ultrasound is obtained. Then curvelet transform as a sparse function is used to minimize the complexity of residual image. The proposed method is a compromise between feature-based and intensity-based approaches. Evaluation was performed using 14 patients data set and the mean of registration error reached to 1.87 mm. This hybrid method based on RC improves accuracy of nonrigid multimodal image registration by 12.5% in a computational time compatible with clinical use.

Superficial vessel reconstruction with a multiview camera system

We aim at reconstructing superficial vessels of the brain. Ultimately, they will serve to guide the deformation methods to compensate for the brain shift. A pipeline for three-dimensional (3-D) vessel reconstruction using three mono-complementary metal-oxide semiconductor cameras has been developed. Vessel centerlines are manually selected in the images. Using the properties of the Hessian matrix, the centerline points are assigned direction information. For correspondence matching, a combination of methods was used. The process starts with epipolar and spatial coherence constraints (geometrical constraints), followed by relaxation labeling and an iterative filtering where the 3-D points are compared to surfaces obtained using the thin-plate spline with decreasing relaxation parameter. Finally, the points are shifted to their local centroid position. Evaluation in virtual, phantom, and experimental images, including intraoperative data from patient experiments, shows that, with appropriate camera positions, the error estimates (root-mean square error and mean error) are [Formula: see text].

A hybrid method for non-rigid registration of intra-operative ultrasound images with pre-operative MR images

In recent years intra-operative ultrasound images have been used for many procedures in neurosurgery. The registration of intra-operative ultrasound images with preoperative magnetic resonance images is still a challenging problem. In this study a new hybrid method based on residual complexity is proposed for this problem. A new two stages method based on the matching echogenic structures such as sulci is achieved by optimizing the residual complexity (RC) value with quantized coefficients between the ultrasound image and the probabilistic map of MR image. The proposed method is a compromise between feature-based and intensity-based approaches. The evaluation is performed on both a brain phantom and patient data set. The results of the phantom data set confirmed that the proposed method outperforms the accuracy of conventional RC by 39%. Also the mean of fiducial registration errors reached to 1.45, 1.94 mm when the method was applied on phantom and clinical data set, respectively. This hybrid method based on RC enables non-rigid multimodal image registration in a computational time compatible with clinical use as well as being accurate.

A shape based rotation invariant method for ultrasound-MR image registration: A phantom study

In this work, a new shape based method to improve the accuracy of Brain Ultrasound-MRI image registration is proposed. The method is based on modified Shape Context (SC) descriptor in combination with CPD algorithm. An extensive experiment was carried out to evaluate the robustness of this method against different initialization conditions. As the results prove, the overall performance of the proposed algorithm outperforms both SC and CPD methods. In order to have control over the registration procedure, we simulated the deformations, missing points and outliers according to our Phantom MRI images.