A comparative analysis of coregistered ultrasound and magnetic resonance imaging in neurosurgery

Full-course resection control strategy in glioma surgery using both intraoperative ultrasound and intraoperative MRI

Background

Intraoperative ultrasound(iUS) and intraoperative MRI (iMRI) are effective ways to perform resection control during glioma surgery. However, most published studies employed only one modality. Few studies have used both during surgery. How to combine these two techniques reasonably, and what advantages they could have for glioma surgery are still open questions.

Methods

We retrospectively reviewed a series of consecutive patients who underwent initial surgical treatment of supratentorial gliomas in our center. We utilized a full-course resection control strategy to combine iUS and iMRI: IUS for pre-resection assessment and intermediate resection control; iMRI for final resection control. The basic patient characteristics, surgical results, iMRI/iUS findings, and their impacts on surgical procedures were evaluated and reported.

Results

A total of 40 patients were included. The extent of resection was 95.43 ± 10.37%, and the gross total resection rate was 72.5%. The median residual tumor size was 6.39 cm3 (range 1.06–16.23 cm3). 5% (2/40) of patients had permanent neurological deficits after surgery. 17.5% (7/40) of patients received further resection after the first iMRI scan, resulting in four (10%) more patients achieving gross total resection. The number of iMRI scans per patient was 1.18 ± 0.38. The surgical time was 4.5 ± 3.6 hours. The pre-resection iUS scan revealed that an average of 3.8 borders of the tumor were beside sulci in 75% (30/40) patients. Intermediate resection control was utilized in 67.5% (27/40) of patients. In 37.5% (15/40) of patients, the surgical procedures were changed intraoperatively based on the iUS findings. Compared with iMRI, the sensitivity and specificity of iUS for residual tumors were 46% and 96%, respectively.

Conclusion

The full-course resection control strategy by combining iUS and iMRI could be successfully implemented with good surgical results in initial glioma surgeries. This strategy might stabilize resection control quality and provide the surgeon with more intraoperative information to tailor the surgical strategy. Compared with iMRI-assisted glioma surgery, this strategy might improve efficiency by reducing the number of iMRI scans and shortening surgery time.

Tumor Volume Assessment in Low-Grade Gliomas: A Comparison of Preoperative Magnetic Resonance Imaging to Coregistered Intraoperative 3-Dimensional Ultrasound Recordings

BACKGROUND

Image guidance based on magnetic resonance imaging (MRI) and/or ultrasound (US) is widely used to aid decision making in glioma surgery, but tumor delineation based on these 2 modalities does not always correspond.

OBJECTIVE

To analyze volumes of diffuse low-grade gliomas (LGGs) based on preoperative 3-D FLAIR MRIs compared to intraoperative 3-D US image recordings to quantitatively assess potential discrepancies between the 2 imaging modalities.

METHODS

Twenty-three patients with supratentorial WHO grade II gliomas undergoing primary surgery guided by neuronavigation based on preoperative FLAIR MRI and navigated 3-D US were included. Manual volume segmentation was performed twice in 3-D Slicer version 4.0.0 to assess intrarater variabilities and compare modalities with regard to tumor volume. Factors possibly related to correspondence between MRI and US were also explored.

RESULTS

In 20 out of 23 patients (87%), the LGG tumor volume segmented from intraoperative US data was smaller than the tumor volume segmented from the preoperative 3-D FLAIR MRI. The median difference between MRI and US volumes was 7.4 mL (range: -4.9-58.7 mL, P < .001) with US LGG volumes corresponding to a median of 74% (range: 42%-183%) of the MRI LGG volumes. However, there was considerable intraobserver variability for US volumes. The correspondence between MRI and US data was higher for astrocytomas (92%).

CONCLUSION

The tumor volumes of LGGs segmented from intraoperative US images were most often smaller than the tumor volumes segmented from preoperative MRIs. There was a much better match between the 2 modalities in astrocytomas.

Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: Accuracy and generality in multi-site data

Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (iUS) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy iUS. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. High-dimensional texture attributes were used instead of image intensities for image registration and the standard difference-based attribute matching was replaced with correlation-based attribute matching. A strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images was proposed. Key parameters were optimized across independent MR-iUS brain tumor datasets acquired at 3 institutions, with a total of 43 tumor patients and 758 reference landmarks for evaluating the accuracy of the proposed algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, the algorithm is able to reduce landmark errors prior to registration in three data sets (5.37±4.27, 4.18±1.97 and 6.18±3.38 mm, respectively) to a consistently low level (2.28±0.71, 2.08±0.37 and 2.24±0.78 mm, respectively). This algorithm was tested against 15 other algorithms and it is competitive with the state-of-the-art on multiple datasets. We show that the algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). Landmark errors were further characterized according to brain regions and tumor types, a topic so far missing in the literature.

Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data

In this paper, a method of vascular structure identification in intraoperative 3D Contrast-Enhanced Ultrasound (CEUS) data is presented. Ultrasound imaging is commonly used in brain tumor surgery to investigate in real time the current status of cerebral structures. The use of an ultrasound contrast agent enables to highlight tumor tissue, but also surrounding blood vessels. However, these structures can be used as landmarks to estimate and correct the brain shift. This work proposes an alternative method for extracting small vascular segments close to the tumor as landmark. The patient image dataset involved in brain tumor operations includes preoperative contrast T1MR (cT1MR) data and 3D intraoperative contrast enhanced ultrasound data acquired before (3D-iCEUS(start) and after (3D-iCEUS(end) tumor resection. Based on rigid registration techniques, a preselected vascular segment in cT1MR is searched in 3D-iCEUS(start) and 3D-iCEUS(end) data. The method was validated by using three similarity measures (Normalized Gradient Field, Normalized Mutual Information and Normalized Cross Correlation). Tests were performed on data obtained from ten patients overcoming a brain tumor operation and it succeeded in nine cases. Despite the small size of the vascular structures, the artifacts in the ultrasound images and the brain tissue deformations, blood vessels were successfully identified.

Deformable registration of preoperative MR, pre-resection ultrasound, and post-resection ultrasound images of neurosurgery

PURPOSE

Sites that use ultrasound (US) in image-guided neurosurgery (IGNS) of brain tumors generally have three sets of imaging data: preoperative magnetic resonance (MR) image, pre-resection US, and post-resection US. The MR image is usually acquired days before the surgery, the pre-resection US is obtained after the craniotomy but before the resection, and finally, the post-resection US scan is performed after the resection of the tumor. The craniotomy and tumor resection both cause brain deformation, which significantly reduces the accuracy of the MR-US alignment.

METHOD

Three unknown transformations exist between the three sets of imaging data: MR to pre-resection US, pre- to post-resection US, and MR to post-resection US. We use two algorithms that we have recently developed to perform the first two registrations (i.e., MR to pre-resection US and pre- to post-resection US). Regarding the third registration (MR to post-resection US), we evaluate three strategies. The first method performs a registration between the MR and pre-resection US, and another registration between the pre- and post-resection US. It then composes the two transformations to register MR and post-resection US; we call this method compositional registration. The second method ignores the pre-resection US and directly registers the MR and post-resection US; we refer to this method as direct registration. The third method is a combination of the first and second: it uses the solution of the compositional registration as an initial solution for the direct registration method. We call this method group-wise registration.

RESULTS

We use data from 13 patients provided in the MNI BITE database for all of our analysis. Registration of MR and pre-resection US reduces the average of the mean target registration error (mTRE) from 4.1 to 2.4 mm. Registration of pre- and post-resection US reduces the average mTRE from 3.7 to 1.5 mm. Regarding the registration of MR and post-resection US, all three strategies reduce the mTRE. The initial average mTRE is 5.9 mm, which reduces to 3.3 mm with the compositional method, 2.9 mm with the direct technique, and 2.8 mm with the group-wise method.

CONCLUSION

Deformable registration of MR and pre- and post-resection US images significantly improves their alignment. Among the three methods proposed for registering the MR to post-resection US, the group-wise method gives the lowest TRE values. Since the running time of all registration algorithms is less than 2 min on one core of a CPU, they can be integrated into IGNS systems for interactive use during surgery.

Intraoperative patient registration using volumetric true 3D ultrasound without fiducials

PURPOSE

Accurate patient registration is crucial for effective image-guidance in open cranial surgery. Typically, it is accomplished by matching skin-affixed fiducials manually identified in the operating room (OR) with their counterparts in the preoperative images, which not only consumes OR time and personnel resources but also relies on the presence (and subsequent fixation) of the fiducials during the preoperative scans (until the procedure begins). In this study, the authors present a completely automatic, volumetric image-based patient registration technique that does not rely on fiducials by registering tracked (true) 3D ultrasound (3DUS) directly with preoperative magnetic resonance (MR) images.

METHODS

Multistart registrations between binary 3DUS and MR volumes were first executed to generate an initial starting point without incorporating prior information on the US transducer contact point location or orientation for subsequent registration between grayscale 3DUS and MR via maximization of either mutual information (MI) or correlation ratio (CR). Patient registration was then computed through concatenation of spatial transformations.

RESULTS

In ten (N = 10) patient cases, an average fiducial (marker) distance error (FDE) of 5.0 mm and 4.3 mm was achieved using MI or CR registration (FDE was smaller with CR vs MI in eight of ten cases), which are comparable to values reported for typical fiducial- or surface-based patient registrations. The translational and rotational capture ranges were found to be 24.0 mm and 27.0° for binary registrations (up to 32.8 mm and 36.4°), 12.2 mm and 25.6° for MI registrations (up to 18.3 mm and 34.4°), and 22.6 mm and 40.8° for CR registrations (up to 48.5 mm and 65.6°), respectively. The execution time to complete a patient registration was 12-15 min with parallel processing, which can be significantly reduced by confining the 3DUS transducer location to the center of craniotomy in MR before registration (an execution time of 5 min is achievable).

CONCLUSIONS

Because common features deep in the brain and throughout the surgical volume of interest are used, intraoperative fiducial-less patient registration is possible on-demand, which is attractive in cases where preoperative patient registration is compromised (e.g., from loss∕movement of skin-affixed fiducials) or not possible (e.g., in cases of emergency when external fiducials were not placed in time). CR registration was more robust than MI (capture range about twice as big) and appears to be more accurate, although both methods are comparable to or better than fiducial-based registration in the patient cases evaluated. The results presented here suggest that 3DUS image-based patient registration holds promise for clinical application in the future.

Image guidance and neuromonitoring in neurosurgery

INTRODUCTION

The localization of tumors and epileptogenic foci within the somatosensory or language cortex of the brain of a child poses unique neurosurgical challenges. In the past, lesions in these regions were not treated aggressively for fear of inducing neurological deficits. As a result, while function may have been preserved, the underlying disease may not have been optimally treated, and repeat neurosurgical procedures were frequently required. Today, with the advent of preoperative brain mapping, image guidance or neuronavigation, and intraoperative monitoring, peri-Rolandic and language cortex lesions can be approached directly and definitively with a high degree of confidence that neurosurgical function will be maintained.

METHODS AND RESULTS

The preoperative brain maps can now be achieved with magnetic resonance imaging (MRI), functional MRI, magnetoencephalography, and diffusion tensor imaging. Image guidance systems have improved significantly and include the use of the intraoperative MRI. Somatosensory, motor, and brainstem auditory-evoked potentials are used as standard neuromonitoring techniques in many centers around the world. Added to this now is the use of continuous train-of-five monitoring of the integrity of the corticospinal tract while operating in the peri-Rolandic region.

CONCLUSION

We are in an era where continued advancements can be expected in mapping additional pathways such as visual, memory, and hearing pathways. With these new advances, neurosurgeons can expect to significantly improve their surgical outcomes further.

Estimation of brain deformation for volumetric image updating in protoporphyrin IX fluorescence-guided resection

INTRODUCTION

Fluorescence-guided resection (FGR) of brain tumors is an intuitive, practical and emerging technology for visually delineating neoplastic tissue exposed intraoperatively. Image guidance is the standard technique for producing 3-dimensional spatially coregistered information for surgical decision making. Both technologies together are synergistic: the former detects surface fluorescence as a biomarker of the current surgical margin while the latter shows coregistered volumetric neuroanatomy but can be degraded by intraoperative brain shift. We present the implementation of deformation modeling for brain shift compensation in protoporphyrin IX FGR, integrating these two sources of information for maximum surgical benefit.

METHODS

Two patients underwent FGR coregistered with conventional image guidance. Histopathological analysis, intraoperative fluorescence and image space coordinates were recorded for biopsy specimens acquired during surgery. A biomechanical brain deformation model driven by intraoperative ultrasound data was used to generate updated MR images.

RESULTS

Combined use of fluorescence signatures and updated MR image information showed substantially improved accuracy compared to fluorescence or the original (i.e., nonupdated) MR images, detecting only true positives and true negatives, and no instances of false positives or false negatives.

CONCLUSION

Implementation of brain deformation modeling in FGR shows promise for increasing the accuracy of neurosurgical guidance in the delineation and resection of brain tumors.

Data assimilation using a gradient descent method for estimation of intraoperative brain deformation

Biomechanical models that simulate brain deformation are gaining attention as alternatives for brain shift compensation. One approach, known as the "forced-displacement method", constrains the model to exactly match the measured data through boundary condition (BC) assignment. Although it improves model estimates and is computationally attractive, the method generates fictitious forces and may be ill-advised due to measurement uncertainty. Previously, we have shown that by assimilating intraoperatively acquired brain displacements in an inversion scheme, the Representer algorithm (REP) is able to maintain stress-free BCs and improve model estimates by 33% over those without data guidance in a controlled environment. However, REP is computationally efficient only when a few data points are used for model guidance because its costs scale linearly in the number of data points assimilated, thereby limiting its utility (and accuracy) in clinical settings. In this paper, we present a steepest gradient descent algorithm (SGD) whose computational complexity scales nearly invariantly with the number of measurements assimilated by iteratively adjusting the forcing conditions to minimize the difference between measured and model-estimated displacements (model-data misfit). Solutions of full linear systems of equations are achieved with a parallelized direct solver on a shared-memory, eight-processor Linux cluster. We summarize the error contributions from the entire process of model-updated image registration compensation and we show that SGD is able to attain model estimates comparable to or better than those obtained with REP, capturing about 74-82% of tumor displacement, but with a computational effort that is significantly less (a factor of 4-fold or more reduction relative to REP) and nearly invariant to the amount of sparse data involved when the number of points assimilated is large. Based on five patient cases, an average computational cost of approximately 2 min for estimating whole-brain deformation has been achieved with SGD using 100 sparse data points, suggesting the new algorithm is sufficiently fast with adequate accuracy for routine use in the operating room (OR).

Brain-skull contact boundary conditions in an inverse computational deformation model

Biomechanical models simulating brain motion under loading and boundary conditions in the operating room (OR) are gaining attention as alternatives for brain shift compensation during open cranial neurosurgeries. Although the significance of brain-skull boundary conditions (BCs) in these models has been explored in dynamic simulations, it has not been fully investigated in models representing the quasi-static brain motion that prevails during neurosurgery. In this study, we extend the application of a brain-skull contact BC by incorporating it into an inversion estimation scheme for the deformation field using the steepest gradient descent (SGD) framework. The technique allows parenchymal surface motion normal to the skull while maintaining stress-free BCs at the craniotomy and minimizing the effect of measurement noise. Application of the algorithm in five clinical cases using sparse data generated at the tumor boundary confirms the significance of brain-skull BCs in the model response. Specifically, the results demonstrate that the contact BC enhances model flexibility and achieves improved or comparable performance at the tumor boundary (recovering about 85% of the deformation) relative to that obtained when normal motion of the parenchymal surface is not allowed. It also significantly improves model estimation accuracy at the craniotomy (1.6mm on average), especially when the normal motion is large. The importance of the method is that model performance significantly improves when brain-skull contact influences the deformation field but does not degrade when the contact is less critical and simpler BCs would suffice. The computational cost of the technique is currently 3.9 min on average, but may be further reduced by applying an iterative solver to the linear systems of equations involved and/or by local refinement of the mesh in regions of interest.

Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery

An image-based re-registration scheme has been developed and evaluated that uses fiducial registration as a starting point to maximize the normalized mutual information (nMI) between intraoperative ultrasound (iUS) and preoperative magnetic resonance images (pMR). We show that this scheme significantly (p<0.001) reduces tumor boundary misalignment between iUS pre-durotomy and pMR from an average of 2.5 mm to 1.0 mm in six resection surgeries. The corrected tumor alignment before dural opening provides a more accurate reference for assessing subsequent intraoperative tumor displacement, which is important for brain shift compensation as surgery progresses. In addition, we report the translational and rotational capture ranges necessary for successful convergence of the nMI registration technique (5.9 mm and 5.2 deg, respectively). The proposed scheme is automatic, sufficiently robust, and computationally efficient (<2 min), and holds promise for routine clinical use in the operating room during image-guided neurosurgical procedures.