Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery

DREDge: robust motion correction for high-density extracellular recordings across species

High-density microelectrode arrays have opened new possibilities for systems neuroscience, but brain motion relative to the array poses challenges for downstream analyses. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from action potential data, DREDge enables automated, high-temporal-resolution motion tracking in local field potential data. In human intraoperative recordings, DREDge's local field potential-based tracking reliably recovered evoked potentials and single-unit spike sorting. In recordings of deep probe insertions in nonhuman primates, DREDge tracked motion across centimeters of tissue and several brain regions while mapping single-unit electrophysiological features. DREDge reliably improved motion correction in acute mouse recordings, especially in those made with a recent ultrahigh-density probe. Applying DREDge to recordings from chronic implantations in mice yielded stable motion tracking despite changes in neural activity between experimental sessions. These advances enable automated, scalable registration of electrophysiological data across species, probes and drift types, providing a foundation for downstream analyses of these rich datasets.

Image-to-mesh conversion method for multi-tissue medical image computing simulations

Converting a three-dimensional medical image into a 3D mesh that satisfies both the quality and fidelity constraints of predictive simulations and image-guided surgical procedures remains a critical problem. Presented is an image-to-mesh conversion method called CBC3D. It first discretizes a segmented image by generating an adaptive Body-Centered Cubic (BCC) mesh of high-quality elements. Next, the tetrahedral mesh is converted into a mixed-element mesh of tetrahedra, pentahedra, and hexahedra to decrease element count while maintaining quality. Finally, the mesh surfaces are deformed to their corresponding physical image boundaries, improving the mesh's fidelity. The deformation scheme builds upon the ITK open-source library and is based on the concept of energy minimization, relying on a multi-material point-based registration. It uses non-connectivity patterns to implicitly control the number of extracted feature points needed for the registration and, thus, adjusts the trade-off between the achieved mesh fidelity and the deformation speed. We compare CBC3D with four widely used and state-of-the-art homegrown image-to-mesh conversion methods from industry and academia. Results indicate that the CBC3D meshes (i) achieve high fidelity, (ii) keep the element count reasonably low, and (iii) exhibit good element quality.

Spontaneous unilateral spatial neglect recovery after brain tumour resection: A multimodal diffusion and rs-fMRI case report

Patients with unilateral spatial neglect (USN) are unable to explore or to report stimuli presented in the left personal and extra-personal space. USN is usually caused by lesion of the right parietal lobe: nowadays, it is also clear the key role of structural connections (the second and the third branch of the right Superior Longitudinal Fasciculus, respectively, SLF II and III) and functional networks (Dorsal and Ventral Attention Network, respectively, DAN and VAN) in USN. In this multimodal case report, we have merged those structural and functional information derived from a patient with a right parietal lobe tumour and USN before surgery. Functional, structural and neuropsychological data were also collected 6 months after surgery, when the USN was spontaneously recovered. Diffusion metrics and Functional Connectivity (FC) of the right SLF and DAN, before and after surgery, were compared with the same data of a patient with a tumour in a similar location, but without USN, and with a control sample. Results indicate an impairment in the right SLF III and a reduction of FC of the right DAN in patients with USN before surgery compared to controls; after surgery, when USN was recovered, patient's diffusion metrics and FC showed no differences compared to the controls. This single case and its multimodal approach reinforce the crucial role of the right SLF III and DAN in the development and recovery of egocentric and allocentric extra-personal USN, highlighting the need to preserve these structural and functional areas during brain surgery.

Comparison of physics-based deformable registration methods for image-guided neurosurgery

This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete data. It concludes with preliminary results on leveraging Quantum Computing, a promising new technology for computationally intensive problems like Feature Detection and Block Matching in addition to finite element solver; all three account for 75% of computing time in deformable registration.

Modern preoperative imaging and functional mapping in patients with intracranial glioma

Magnetic resonance imaging (MRI) in therapy-naïve intracranial glioma is paramount for neuro-oncological diagnostics, and it provides images that are helpful for surgery planning and intraoperative guidance during tumor resection, including assessment of the involvement of functionally eloquent brain structures. This study reviews emerging MRI techniques to depict structural information, diffusion characteristics, perfusion alterations, and metabolism changes for advanced neuro-oncological imaging. In addition, it reflects current methods to map brain function close to a tumor, including functional MRI and navigated transcranial magnetic stimulation with derived function-based tractography of subcortical white matter pathways. We conclude that modern preoperative MRI in neuro-oncology offers a multitude of possibilities tailored to clinical needs, and advancements in scanner technology (e. g., parallel imaging for acceleration of acquisitions) make multi-sequence protocols increasingly feasible. Specifically, advanced MRI using a multi-sequence protocol enables noninvasive, image-based tumor grading and phenotyping in patients with glioma. Furthermore, the add-on use of preoperatively acquired MRI data in combination with functional mapping and tractography facilitates risk stratification and helps to avoid perioperative functional decline by providing individual information about the spatial location of functionally eloquent tissue in relation to the tumor mass. KEY POINTS::   · Advanced preoperative MRI allows for image-based tumor grading and phenotyping in glioma.. · Multi-sequence MRI protocols nowadays make it possible to assess various tumor characteristics (incl. perfusion, diffusion, and metabolism).. · Presurgical MRI in glioma is increasingly combined with functional mapping to identify and enclose individual functional areas.. · Advancements in scanner technology (e. g., parallel imaging) facilitate increasing application of dedicated multi-sequence imaging protocols.. CITATION FORMAT: · Sollmann N, Zhang H, Kloth C et al. Modern preoperative imaging and functional mapping in patients with intracranial glioma. Fortschr Röntgenstr 2023; 195: 989 - 1000.

Real-Time Dynamic Data Driven Deformable Registration for Image-Guided Neurosurgery: Computational Aspects

Current neurosurgical procedures utilize medical images of various modalities to enable the precise location of tumors and critical brain structures to plan accurate brain tumor resection. The difficulty of using preoperative images during the surgery is caused by the intra-operative deformation of the brain tissue (brain shift), which introduces discrepancies concerning the preoperative configuration. Intra-operative imaging allows tracking such deformations but cannot fully substitute for the quality of the pre-operative data. Dynamic Data Driven Deformable Non-Rigid Registration (D4NRR) is a complex and time-consuming image processing operation that allows the dynamic adjustment of the pre-operative image data to account for intra-operative brain shift during the surgery. This paper summarizes the computational aspects of a specific adaptive numerical approximation method and its variations for registering brain MRIs. It outlines its evolution over the last 15 years and identifies new directions for the computational aspects of the technique.

Toward Estimating MRI-Ultrasound Registration Error in Image-Guided Neurosurgery

Image-guided neurosurgery allows surgeons to view their tools in relation to preoperatively acquired patient images and models. To continue using neuronavigation systems throughout operations, image registration between preoperative images [typically magnetic resonance imaging (MRI)] and intraoperative images (e.g., ultrasound) is common to account for brain shift (deformations of the brain during surgery). We implemented a method to estimate MRI-ultrasound registration errors, with the goal of enabling surgeons to quantitatively assess the performance of linear or nonlinear registrations. To the best of our knowledge, this is the first dense error estimating algorithm applied to multimodal image registrations. The algorithm is based on a previously proposed sliding-window convolutional neural network that operates on a voxelwise basis. To create training data where the true registration error is known, simulated ultrasound images were created from preoperative MRI images and artificially deformed. The model was evaluated on artificially deformed simulated ultrasound data and real ultrasound data with manually annotated landmark points. The model achieved a mean absolute error (MAE) of 0.977 ± 0.988 mm and a correlation of 0.8 ± 0.062 on the simulated ultrasound data, and an MAE of 2.24 ± 1.89 mm and a correlation of 0.246 on the real ultrasound data. We discuss concrete areas to improve the results on real ultrasound data. Our progress lays the foundation for future developments and ultimately implementation of clinical neuronavigation systems.

Intra-operative applications of augmented reality in glioma surgery: a systematic review

Background

Augmented reality (AR) is increasingly being explored in neurosurgical practice. By visualizing patient-specific, three-dimensional (3D) models in real time, surgeons can improve their spatial understanding of complex anatomy and pathology, thereby optimizing intra-operative navigation, localization, and resection. Here, we aimed to capture applications of AR in glioma surgery, their current status and future potential.

Methods

A systematic review of the literature was conducted. This adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. PubMed, Embase, and Scopus electronic databases were queried from inception to October 10, 2022. Leveraging the Population, Intervention, Comparison, Outcomes, and Study design (PICOS) framework, study eligibility was evaluated in the qualitative synthesis. Data regarding AR workflow, surgical application, and associated outcomes were then extracted. The quality of evidence was additionally examined, using hierarchical classes of evidence in neurosurgery.

Results

The search returned 77 articles. Forty were subject to title and abstract screening, while 25 proceeded to full text screening. Of these, 22 articles met eligibility criteria and were included in the final review. During abstraction, studies were classified as "development" or "intervention" based on primary aims. Overall, AR was qualitatively advantageous, due to enhanced visualization of gliomas and critical structures, frequently aiding in maximal safe resection. Non-rigid applications were also useful in disclosing and compensating for intra-operative brain shift. Irrespective, there was high variance in registration methods and measurements, which considerably impacted projection accuracy. Most studies were of low-level evidence, yielding heterogeneous results.

Conclusions

AR has increasing potential for glioma surgery, with capacity to positively influence the onco-functional balance. However, technical and design limitations are readily apparent. The field must consider the importance of consistency and replicability, as well as the level of evidence, to effectively converge on standard approaches that maximize patient benefit.

Vertical Hemispherotomy: Contribution of Advanced Three-Dimensional Modeling for Presurgical Planning and Training

Vertical hemispherotomy is an effective treatment for many drug-resistant encephalopathies with unilateral involvement. One of the main factors influencing positive surgical results and long-term seizure freedom is the quality of disconnection. For this reason, perfect anatomical awareness is mandatory during each step of the procedure. Although previous groups attempted to reproduce the surgical anatomy through schematic representations, cadaveric dissections, and intraoperative photographs and videos, a comprehensive understanding of the approach may still be difficult, especially for less experienced neurosurgeons. In this work, we reported the application of advanced technology for three-dimensional (3D) modeling and visualization of the main neurova-scular structures during vertical hemispherotomy procedures. In the first part of the study, we built a detailed 3D model of the main structures and landmarks involved during each disconnection phase. In the second part, we discussed the adjunctive value of augmented reality systems for the management of the most challenging etiologies, such as hemimegalencephaly and post-ischemic encephalopathy. We demonstrated the contribution of advanced 3D modeling and visualization to enhance the quality of anatomical representation and interaction between the operator and model according to a surgical perspective, optimizing the quality of presurgical planning, intraoperative orientation, and educational training.

Interfractional dose accumulation for MR-guided liver SBRT: Variation among algorithms is highly patient- and fraction-dependent

BACKGROUND AND PURPOSE

Daily plan adaptations could take the dose delivered in previous fractions into account. Due to high dose delivered per fraction, low number of fractions, steep dose gradients, and large interfractional organ deformations, this might be particularly important for liver SBRT. This study investigates inter-algorithm variation of interfractional dose accumulation for MR-guided liver SBRT.

MATERIALS AND METHODS

We assessed 27 consecutive MR-guided liver SBRT treatments of 67.5 Gy in three (n = 15) or 50 Gy in five fractions (n = 12), both prescribed to the GTV. We calculated fraction doses on daily patient anatomy, warped these doses to the simulation MRI using seven different algorithms, and accumulated the warped doses. Thus, we obtained differences in planned doses and warped or accumulated doses for each algorithm. This enabled us to calculate the inter-algorithm variations in warped doses per fraction and in accumulated doses per treatment course.

RESULTS

The four intensity-based algorithms were more consistent with planned PTV dose than affine or contour-based algorithms. The mean (range) variation of the dose difference for PTV D_95% due to dose warping by these intensity-based algorithms was 10.4 percentage points (0.3 to 43.7) between fractions and 8.6 (0.3 to 24.9) between accumulated treatment doses. As seen by these ranges, the variation was very dependent on the patient and the fraction being analyzed. Nevertheless, no correlations between patient or plan characteristics on the one hand and inter-algorithm dose warping variation on the other hand was found.

CONCLUSION

Inter-algorithm dose accumulation variation is highly patient- and fraction-dependent for MR-guided liver SBRT. We advise against trusting a single algorithm for dose accumulation in liver SBRT.

In vivo measurement of human brain material properties under quasi-static loading

Computational modelling of the brain requires accurate representation of the tissues concerned. Mechanical testing has numerous challenges, in particular for low strain rates, like neurosurgery, where redistribution of fluid is biomechanically important. A finite-element (FE) model was generated in FEBio, incorporating a spring element/fluid-structure interaction representation of the pia-arachnoid complex (PAC). The model was loaded to represent gravity in prone and supine positions. Material parameter identification and sensitivity analysis were performed using statistical software, comparing the FE results to human in vivo measurements. Results for the brain Ogden parameters µ, α and k yielded values of 670 Pa, -19 and 148 kPa, supporting values reported in the literature. Values of the order of 1.2 MPa and 7.7 kPa were obtained for stiffness of the pia mater and out-of-plane tensile stiffness of the PAC, respectively. Positional brain shift was found to be non-rigid and largely driven by redistribution of fluid within the tissue. To the best of our knowledge, this is the first study using in vivo human data and gravitational loading in order to estimate the material properties of intracranial tissues. This model could now be applied to reduce the impact of positional brain shift in stereotactic neurosurgery.

Reducing variations in multi-center Alzheimer's disease classification with convolutional adversarial autoencoder

Based on brain magnetic resonance imaging (MRI), multiple variations ranging from MRI scanners to center-specific parameter settings, imaging protocols, and brain region-of-interest (ROI) definitions pose a big challenge for multi-center Alzheimer's disease characterization and classification. Existing approaches to reduce such variations require intricate multi-step, often manual preprocessing pipelines, including skull stripping, segmentation, registration, cortical reconstruction, and ROI outlining. Such procedures are time-consuming, and more importantly, tend to be user biased. Contrasting costly and biased preprocessing pipelines, the question arises whether we can design a deep learning model to automatically reduce these variations from multiple centers for Alzheimer's disease classification? In this study, we used T1 and T2-weighted structural MRI from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset based on three groups with 375 subjects, respectively: patients with Alzheimer's disease (AD) dementia, with mild cognitive impairment (MCI), and healthy controls (HC); to test our approach, we defined AD classification as classifying an individual's structural image to one of the three group labels. We first introduced a convolutional adversarial autoencoder (CAAE) to reduce the variations existing in multi-center raw MRI scans by automatically registering them into a common aligned space. Afterward, a convolutional residual soft attention network (CRAT) was further proposed for AD classification. Canonical classification procedures demonstrated that our model achieved classification accuracies of 91.8%, 90.05%, and 88.10% for the 2-way classification tasks using the RAW aligned MRI scans, including AD vs. HC, AD vs. MCI, and MCI vs. HC, respectively. Thus, our automated approach achieves comparable or even better classification performance by comparing it with many baselines with dedicated conventional preprocessing pipelines. Furthermore, the uncovered brain hotpots, i.e., hippocampus, amygdala, and temporal pole, are consistent with previous studies.

Superior Longitudinal Fasciculus: A Review of the Anatomical Descriptions With Functional Correlates

The superior longitudinal fasciculus (SLF) is part of the longitudinal association fiber system, which lays connections between the frontal lobe and other areas of the ipsilateral hemisphere. As a dominant association fiber bundle, it should correspond to a well-defined structure with a clear anatomical definition. However, this is not the case, and a lot of confusion and overlap surrounds this entity. In this review/opinion study, we survey relevant current literature on the topic and try to clarify the definition of SLF in each hemisphere. After a comparison of postmortem dissections and data obtained from diffusion MRI studies, we discuss the specifics of this bundle regarding its anatomical landmarks, differences in lateralization, as well as individual variability. We also discuss the confusion regarding the arcuate fasciculus in relation to the SLF. Finally, we recommend a nomenclature based on the findings exposed in this review and finalize with a discussion on relevant functional correlates of the structure.

Computer simulation of tumour resection-induced brain deformation by a meshless approach

Tumour resection requires precise planning and navigation to maximise tumour removal while simultaneously protecting nearby healthy tissues. Neurosurgeons need to know the location of the remaining tumour after partial tumour removal before continuing with the resection. Our approach to the problem uses biomechanical modelling and computer simulation to compute the brain deformations after the tumour is resected. In this study, we use meshless Total Lagrangian explicit dynamics as the solver. The problem geometry is extracted from the patient-specific magnetic resonance imaging (MRI) data and includes the parenchyma, tumour, cerebrospinal fluid and skull. The appropriate non-linear material formulation is used. Loading is performed by imposing intra-operative conditions of gravity and reaction forces between the tumour and surrounding healthy parenchyma tissues. A finite frictionless sliding contact is enforced between the skull (rigid) and parenchyma. The meshless simulation results are compared to intra-operative MRI sections. We also calculate Hausdorff distances between the computed deformed surfaces (ventricles and tumour cavities) and surfaces observed intra-operatively. Over 80% of points on the ventricle surface and 95% of points on the tumour cavity surface were successfully registered (results within the limits of two times the original in-plane resolution of the intra-operative image). Computed results demonstrate the potential for our method in estimating the tissue deformation and tumour boundary during the resection.

Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT.

Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon.

Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in <2 min, achieving desirable speed for use in a clinical setting.

Conclusions: The A-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room. A-PBNRR, coupled with the mixed reality system, presents a powerful and affordable solution compared to current neuronavigation systems.

Challenges and Opportunities of Intraoperative 3D Ultrasound With Neuronavigation in Relation to Intraoperative MRI

Introduction

Neuronavigation greatly improves the surgeons ability to approach, assess and operate on brain tumors, but tends to lose its accuracy as the surgery progresses and substantial brain shift and deformation occurs. Intraoperative MRI (iMRI) can partially address this problem but is resource intensive and workflow disruptive. Intraoperative ultrasound (iUS) provides real-time information that can be used to update neuronavigation and provide real-time information regarding the resection progress. We describe the intraoperative use of 3D iUS in relation to iMRI, and discuss the challenges and opportunities in its use in neurosurgical practice.

Methods

We performed a retrospective evaluation of patients who underwent image-guided brain tumor resection in which both 3D iUS and iMRI were used. The study was conducted between June 2020 and December 2020 when an extension of a commercially available navigation software was introduced in our practice enabling 3D iUS volumes to be reconstructed from tracked 2D iUS images. For each patient, three or more 3D iUS images were acquired during the procedure, and one iMRI was acquired towards the end. The iUS images included an extradural ultrasound sweep acquired before dural incision (iUS-1), a post-dural opening iUS (iUS-2), and a third iUS acquired immediately before the iMRI acquisition (iUS-3). iUS-1 and preoperative MRI were compared to evaluate the ability of iUS to visualize tumor boundaries and critical anatomic landmarks; iUS-3 and iMRI were compared to evaluate the ability of iUS for predicting residual tumor.

Results

Twenty-three patients were included in this study. Fifteen patients had tumors located in eloquent or near eloquent brain regions, the majority of patients had low grade gliomas (11), gross total resection was achieved in 12 patients, postoperative temporary deficits were observed in five patients. In twenty-two iUS was able to define tumor location, tumor margins, and was able to indicate relevant landmarks for orientation and guidance. In sixteen cases, white matter fiber tracts computed from preoperative dMRI were overlaid on the iUS images. In nineteen patients, the EOR (GTR or STR) was predicted by iUS and confirmed by iMRI. The remaining four patients where iUS was not able to evaluate the presence or absence of residual tumor were recurrent cases with a previous surgical cavity that hindered good contact between the US probe and the brainsurface.

Conclusion

This recent experience at our institution illustrates the practical benefits, challenges, and opportunities of 3D iUS in relation to iMRI.

A review on multiplatform evaluations of semi-automatic open-source based image segmentation for cranio-maxillofacial surgery

BACKGROUND AND OBJECTIVES

Computer-assisted technologies, such as image-based segmentation, play an important role in the diagnosis and treatment support in cranio-maxillofacial surgery. However, although many segmentation software packages exist, their clinical in-house use is often challenging due to constrained technical, human or financial resources. Especially technological solutions or systematic evaluations of open-source based segmentation approaches are lacking. The aim of this contribution is to assess and review the segmentation quality and the potential clinical use of multiple commonly available and license-free segmentation methods on different medical platforms.

METHODS

In this contribution, the quality and accuracy of open-source segmentation methods was assessed on different platforms using patient-specific clinical CT-data and reviewed with the literature. The image-based segmentation algorithms GrowCut, Robust Statistics Segmenter, Region Growing 3D, Otsu & Picking, Canny Segmentation and Geodesic Segmenter were investigated in the mandible on the platforms 3D Slicer, MITK and MeVisLab. Comparisons were made between the segmentation algorithms and the ground truth segmentations of the same anatomy performed by two clinical experts (n = 20). Assessment parameters were the Dice Score Coefficient (DSC), the Hausdorff Distance (HD), and Pearsons correlation coefficient (r).

RESULTS

The segmentation accuracy was highest with the GrowCut (DSC 85.6%, HD 33.5 voxel) and the Canny (DSC 82.1%, HD 8.5 voxel) algorithm. Statistical differences between the assessment parameters were not significant (p < 0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the segmentation methods and the ground truth schemes. Functionally stable and time-saving segmentations were observed.

CONCLUSION

High quality image-based semi-automatic segmentation was provided by the GrowCut and the Canny segmentation method. In the cranio-maxillofacial complex, these segmentation methods provide algorithmic alternatives for image-based segmentation in the clinical practice for e.g. surgical planning or visualization of treatment results and offer advantages through their open-source availability. This is the first systematic multi-platform comparison that evaluates multiple license-free, open-source segmentation methods based on clinical data for the improvement of algorithms and a potential clinical use in patient-individualized medicine. The results presented are reproducible by others and can be used for clinical and research purposes.

A comparison of thin-plate spline deformation and finite element modeling to compensate for brain shift during tumor resection

PURPOSE

Brain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons' ability to achieve optimal resections. This paper compares two methods that have been presented in the literature to compensate for brain shift: a thin-plate spline deformation model and a finite element method (FEM). For this comparison, both methods are driven by identical sparse data. Specifically, both methods are driven by displacements between automatically detected and matched feature points from intraoperative 3D ultrasound (iUS). Both methods have been shown to be fast enough for intraoperative brain shift correction (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018; Luo et al. in J Med Imaging (Bellingham) 4(3):035003, 2017). However, the spline method requires no preprocessing and ignores physical properties of the brain while the FEM method requires significant preprocessing and incorporates patient-specific physical and geometric constraints. The goal of this work was to explore the relative merits of these methods on recent clinical data.

METHODS

Data acquired during 19 sequential tumor resections in Brigham and Women's Hospital's Advanced Multi-modal Image-Guided Operating Suite between December 2017 and October 2018 were considered for this retrospective study. Of these, 15 cases and a total of 24 iUS to iUS image pairs met inclusion requirements. Automatic feature detection (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018) was used to detect and match features in each pair of iUS images. Displacements between matched features were then used to drive both the spline model and the FEM method to compensate for brain shift between image acquisitions. The accuracies of the resultant deformation models were measured by comparing the displacements of manually identified landmarks before and after deformation.

RESULTS

The mean initial subcortical registration error between preoperative MRI and the first iUS image averaged 5.3 ± 0.75 mm. The mean subcortical brain shift, measured using displacements between manually identified landmarks in pairs of iUS images, was 2.5 ± 1.3 mm. Our results showed that FEM was able to reduce subcortical registration error by a small but statistically significant amount (from 2.46 to 2.02 mm). A large variability in the results of the spline method prevented us from demonstrating either a statistically significant reduction in subcortical registration error after applying the spline method or a statistically significant difference between the results of the two methods.

CONCLUSIONS

In this study, we observed less subcortical brain shift than has previously been reported in the literature (Frisken et al., in: Miller (ed) Biomechanics of the brain, Springer, Cham, 2019). This may be due to the fact that we separated out the initial misregistration between preoperative MRI and the first iUS image from our brain shift measurements or it may be due to modern neurosurgical practices designed to reduce brain shift, including reduced craniotomy sizes and better control of intracranial pressure with the use of mannitol and other medications. It appears that the FEM method and its use of geometric and biomechanical constraints provided more consistent brain shift correction and better correction farther from the driving feature displacements than the simple spline model. The spline-based method was simpler and tended to give better results for small deformations. However, large variability in the spline results and relatively small brain shift prevented this study from demonstrating a statistically significant difference between the results of the two methods.

Visualization of Brain Shift Corrected Functional Magnetic Resonance Imaging Data for Intraoperative Brain Mapping

Background

Brain tumor surgery requires careful balance between maximizing tumor excision and preserving eloquent cortex. In some cases, the surgeon may opt to perform an awake craniotomy including intraoperative mapping of brain function by direct cortical stimulation (DCS) to assist in surgical decision-making. Preoperatively, functional magnetic resonance imaging (fMRI) facilitates planning by identification of eloquent brain areas, helping to guide DCS and other aspects of the surgical plan. However, brain deformation (shift) limits the usefulness of preoperative fMRI during surgery. To address this, an integrated visualization method for fMRI and DCS results is developed that is intuitive for the surgeon.

Methods

An image registration pipeline was constructed to display preoperative fMRI data corrected for brain shift overlaid on images of the exposed cortical surface at the beginning and completion of DCS mapping. Preoperative fMRI and DCS data were registered for a range of misalignments, and the residual registration errors were calculated. The pipeline was validated on imaging data from five brain tumor patients who underwent awake craniotomy.

Results

Registration errors were well under 5 mm (the approximate spatial resolution of DCS) for misalignments of up to 25 mm and approximately 10–15°. For rotational misalignments up to 20°, the success rate was 95% for an error tolerance of 5 mm. Failures were negligible for rotational misalignments up to 10°. Good quality registrations were observed for all five patients.

Conclusions

A proof-of-concept image registration pipeline is presented with acceptable accuracy for intraoperative use, providing multimodality visualization with potential benefits for intraoperative brain mapping.

Preliminary Results Comparing Thin Plate Splines with Finite Element Methods for Modeling Brain Deformation during Neurosurgery using Intraoperative Ultrasound

Brain shift compensation attempts to model the deformation of the brain which occurs during the surgical removal of brain tumors to enable mapping of presurgical image data into patient coordinates during surgery and thus improve the accuracy and utility of neuro-navigation. We present preliminary results from clinical tumor resections that compare two methods for modeling brain deformation, a simple thin plate spline method that interpolates displacements and a more complex finite element method (FEM) that models physical and geometric constraints of the brain and its material properties. Both methods are driven by the same set of displacements at locations surrounding the tumor. These displacements were derived from sets of corresponding matched features that were automatically detected using the SIFT-Rank algorithm. The deformation accuracy was tested using a set of manually identified landmarks. The FEM method requires significantly more preprocessing than the spline method but both methods can be used to model deformations in the operating room in reasonable time frames. Our preliminary results indicate that the FEM deformation model significantly out-performs the spline-based approach for predicting the deformation of manual landmarks. While both methods compensate for brain shift, this work suggests that models that incorporate biophysics and geometric constraints may be more accurate.

Computed tomography data collection of the complete human mandible and valid clinical ground truth models

Image-based algorithmic software segmentation is an increasingly important topic in many medical fields. Algorithmic segmentation is used for medical three-dimensional visualization, diagnosis or treatment support, especially in complex medical cases. However, accessible medical databases are limited, and valid medical ground truth databases for the evaluation of algorithms are rare and usually comprise only a few images. Inaccuracy or invalidity of medical ground truth data and image-based artefacts also limit the creation of such databases, which is especially relevant for CT data sets of the maxillomandibular complex. This contribution provides a unique and accessible data set of the complete mandible, including 20 valid ground truth segmentation models originating from 10 CT scans from clinical practice without artefacts or faulty slices. From each CT scan, two 3D ground truth models were created by clinical experts through independent manual slice-by-slice segmentation, and the models were statistically compared to prove their validity. These data could be used to conduct serial image studies of the human mandible, evaluating segmentation algorithms and developing adequate image tools.

Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action

INTRODUCTION

Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However-due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice.

MATERIAL AND METHODS

In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance.

RESULTS

Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p<0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the two groups.

DISCUSSION

Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works.

A Systematic Review of 10 Years of Augmented Reality Usability Studies: 2005 to 2014

Augmented Reality (AR) interfaces have been studied extensively over the last few decades, with a growing number of user-based experiments. In this paper, we systematically review 10 years of the most influential AR user studies, from 2005 to 2014. A total of 291 papers with 369 individual user studies have been reviewed and classified based on their application areas. The primary contribution of the review is to present the broad landscape of user-based AR research, and to provide a high-level view of how that landscape has changed. We summarize the high-level contributions from each category of papers, and present examples of the most influential user studies. We also identify areas where there have been few user studies, and opportunities for future research. Among other things, we find that there is a growing trend toward handheld AR user studies, and that most studies are conducted in laboratory settings and do not involve pilot testing. This research will be useful for AR researchers who want to follow best practices in designing their own AR user studies.

Tractography Verified by Intraoperative Magnetic Resonance Imaging and Subcortical Stimulation During Tumor Resection Near the Corticospinal Tract

BACKGROUND

Tractography is a popular tool for visualizing the corticospinal tract (CST). However, results may be influenced by numerous variables, eg, the selection of seeding regions of interests (ROIs) or the chosen tracking algorithm.

OBJECTIVE

To compare different variable sets by correlating tractography results with intraoperative subcortical stimulation of the CST, correcting intraoperative brain shift by the use of intraoperative MRI.

METHODS

Seeding ROIs were created by means of motor cortex segmentation, functional MRI (fMRI), and navigated transcranial magnetic stimulation (nTMS). Based on these ROIs, tractography was run for each patient using a deterministic and a probabilistic algorithm. Tractographies were processed on pre- and postoperatively acquired data.

RESULTS

Using a linear mixed effects statistical model, best correlation between subcortical stimulation intensity and the distance between tractography and stimulation sites was achieved by using the segmented motor cortex as seeding ROI and applying the probabilistic algorithm on preoperatively acquired imaging sequences. Tractographies based on fMRI or nTMS results differed very little, but with enlargement of positive nTMS sites the stimulation-distance correlation of nTMS-based tractography improved.

CONCLUSION

Our results underline that the use of tractography demands for careful interpretation of its virtual results by considering all influencing variables.

Challenges and techniques for presurgical brain mapping with functional MRI

Functional magnetic resonance imaging (fMRI) is increasingly used for preoperative counseling and planning, and intraoperative guidance for tumor resection in the eloquent cortex. Although there have been improvements in image resolution and artifact correction, there are still limitations of this modality. In this review, we discuss clinical fMRI's applications, limitations and potential solutions. These limitations depend on the following parameters: foundations of fMRI, physiologic effects of the disease, distinctions between clinical and research fMRI, and the design of the fMRI study. We also compare fMRI to other brain mapping modalities which should be considered as alternatives or adjuncts when appropriate, and discuss intraoperative use and validation of fMRI. These concepts direct the clinical application of fMRI in neurosurgical patients.

Models and tissue mimics for brain shift simulations

Capturing the deformation of human brain during neurosurgical operations is an extremely important task to improve the accuracy or surgical procedure and minimize permanent damage in patients. This study focuses on the development of an accurate numerical model for the prediction of brain shift during surgical procedures and employs a tissue mimic recently developed to capture the complexity of the human tissue. The phantom, made of a composite hydrogel, was designed to reproduce the dynamic mechanical behaviour of the brain tissue in a range of strain rates suitable for surgical procedures. The use of a well-controlled, accessible and MRI compatible alternative to real brain tissue allows us to rule out spurious effects due to patient geometry and tissue properties variability, CSF amount uncertainties, and head orientation. The performance of different constitutive descriptions is evaluated using a brain-skull mimic, which enables 3D deformation measurements by means of MRI scans. Our combined experimental and numerical investigation demonstrates the importance of using accurate constitutive laws when approaching the modelling of this complex organic tissue and supports the proposal of a hybrid poro-hyper-viscoelastic material formulation for the simulation of brain shift.

Quality assurance of radiotherapy in the ongoing EORTC 1219-DAHANCA-29 trial for HPV/p16 negative squamous cell carcinoma of the head and neck: Results of the benchmark case procedure

BACKGROUND AND PURPOSE

The phase III EORTC 1219-DAHANCA 29 intergroup trial evaluates the influence of nimorazole in patients with locally advanced head and neck cancer when treated with accelerated radiotherapy (RT) in combination with chemotherapy. This article describes the results of the RT Benchmark Case (BC) performed before patient inclusion.

MATERIALS AND METHODS

The participating centers were asked to perform a 2-step BC, consisting of (1) a delineation and (2) a planning exercise according to the protocol guidelines. Submissions were prospectively centrally reviewed and feedback was given to the submitting centers. Sørensen-Dice similarity index (DSI) and the 95th percentile Hausdorff distance (HD) were retrospectively used to evaluate the agreement between the centers and the expert contours.

RESULTS

Fifty-four submissions (34 delineation and 20 planning exercises) from 19 centers were reviewed. Nine (47%) centers needed to perform the delineation step twice and three (16%) centers 3 times before receiving an approval. An increase in DSI-value and a decrease in HD, in particular for the prophylactic Clinical Target Volume (pCTV), could be found for the resubmitted cases. No unacceptable variations could be found for the planning exercise.

CONCLUSIONS

These BC-results highlight the need for effective and prospective RTQA in clinical trials. Even with clearly defined protocol guidelines, delineation and not planning remain the main reason for unacceptable protocol variations. The introduction of more objective quantitative analysis methods, such as the HD and DSI, in future trials might strengthen the evaluation by experts.

Fast single image super-resolution using estimated low-frequency k-space data in MRI

PURPOSE

Single image super-resolution (SR) is highly desired in many fields but obtaining it is often technically limited in practice. The purpose of this study was to propose a simple, rapid and robust single image SR method in magnetic resonance (MR) imaging (MRI).

METHODS

The idea is based on the mathematical formulation of the intrinsic link in k-space between a given (modulus) low-resolution (LR) image and the desired SR image. The method consists of two steps: 1) estimating the low-frequency k-space data of the desired SR image from a single LR image; 2) reconstructing the SR image using the estimated low-frequency and zero-filled high-frequency k-space data. The method was evaluated on digital phantom images, physical phantom MR images and real brain MR images, and compared with existing SR methods.

RESULTS

The proposed SR method exhibited a good robustness by reaching a clearly higher PSNR (25.77dB) and SSIM (0.991) averaged over different noise levels in comparison with existing edge-guided nonlinear interpolation (EGNI) (PSNR=23.78dB, SSIM=0.983), zero-filling (ZF) (PSNR=24.09dB, SSIM=0.985) and total variation (TV) (PSNR=24.54dB, SSIM=0.987) methods while presenting the same order of computation time as the ZF method but being much faster than the EGNI or TV method. The average PSNR or SSIM over different slice images of the proposed method (PSNR=26.33 dB or SSIM=0.955) was also higher than the EGNI (PSNR=25.07dB or SSIM=0.952), ZF (PSNR=24.97dB or SSIM=0.950) and TV (PSNR=25.70dB or SSIM=0.953) methods, demonstrating its good robustness to variation in anatomical structure of the images. Meanwhile, the proposed method always produced less ringing artifacts than the ZF method, gave a clearer image than the EGNI method, and did not exhibit any blocking effect presented in the TV method. In addition, the proposed method yielded the highest spatial consistency in the inter-slice dimension among the four methods.

CONCLUSIONS

This study proposed a fast, robust and efficient single image SR method with high spatial consistency in the inter-slice dimension for clinical MR images by estimating the low-frequency k-space data of the desired SR image from a single spatial modulus LR image.

Performance Assessment for Brain MR Imaging Registration Methods

BACKGROUND AND PURPOSE

Clinical brain MR imaging registration algorithms are often made available by commercial vendors without figures of merit. The purpose of this study was to suggest a rational performance comparison methodology for these products.

MATERIALS AND METHODS

Twenty patients were imaged on clinical 3T scanners by using 4 sequences: T2-weighted, FLAIR, susceptibility-weighted angiography, and T1 postcontrast. Fiducial landmark sites (n = 1175) were specified throughout these image volumes to define identical anatomic locations across sequences. Multiple registration algorithms were applied by using the T2 sequence as a fixed reference. Euclidean error was calculated before and after each registration and compared with a criterion standard landmark registration. The Euclidean effectiveness ratio is the fraction of Euclidean error remaining after registration, and the statistical effectiveness ratio is similar, but accounts for dispersion and noise.

RESULTS

Before registration, error values for FLAIR, susceptibility-weighted angiography, and T1 postcontrast were 2.07 ± 0.55 mm, 2.63 ± 0.62 mm, and 3.65 ± 2.00 mm, respectively. Postregistration, the best error values for FLAIR, susceptibility-weighted angiography, and T1 postcontrast were 1.55 ± 0.46 mm, 1.34 ± 0.23 mm, and 1.06 ± 0.16 mm, with Euclidean effectiveness ratio values of 0.493, 0.181, and 0.096 and statistical effectiveness ratio values of 0.573, 0.352, and 0.929 for rigid mutual information, affine mutual information, and a commercial GE registration, respectively.

CONCLUSIONS

We demonstrate a method for comparing the performance of registration algorithms and suggest the Euclidean error, Euclidean effectiveness ratio, and statistical effectiveness ratio as performance metrics for clinical registration algorithms. These figures of merit allow registration algorithms to be rationally compared.

Automated white matter fiber tract identification in patients with brain tumors

We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography.

Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions.

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Spectral clustering machine learning approach for white matter tract identification

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Data-driven white matter parcellation learned from healthy subjects tractography

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White matter parcellation applied to 18 consecutive patients with brain tumors

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Arcuate fasciculus and corticospinal tracts identified in all patients

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All tracts within 3 mm of corresponding patient-specific functional activations

Multi-channel registration of fractional anisotropy and T1-weighted images in the presence of atrophy: application to multiple sclerosis

Co-registration of structural T1-weighted (T1w) scans and diffusion tensor imaging (DTI)-derived fractional anisotropy (FA) maps to a common space is of particular interest in neuroimaging, as T1w scans can be used for brain segmentation while DTI can provide microstructural tissue information. While the effect of lesions on registration has been tackled and solutions are available, the issue of atrophy is still open to discussion. Multi-channel (MC) registration algorithms have the advantage of maintaining anatomical correspondence between different contrast images after registration to any target space. In this work, we test the performance of an MC registration approach applied to T1w and FA data using simulated brain atrophy images. Experimental results are compared with a standard single-channel registration approach. Multi-channel registration of fractional anisotropy and T1-weighted images in the presence of atrophy: application to multiple sclerosis Both qualitative and quantitative evaluations are presented, showing that the MC approach provides better alignment with the target while maintaining better T1w and FA co-alignment.

Topology preserving non-rigid image registration using time-varying elasticity model for MRI brain volumes

In this paper, we present a new non-rigid image registration method that imposes a topology preservation constraint on the deformation. We propose to incorporate the time varying elasticity model into the deformable image matching procedure and constrain the Jacobian determinant of the transformation over the entire image domain. The motion of elastic bodies is governed by a hyperbolic partial differential equation, generally termed as elastodynamics wave equation, which we propose to use as a deformation model. We carried out clinical image registration experiments on 3D magnetic resonance brain scans from IBSR database. The results of the proposed registration approach in terms of Kappa index and relative overlap computed over the subcortical structures were compared against the existing topology preserving non-rigid image registration methods and non topology preserving variant of our proposed registration scheme. The Jacobian determinant maps obtained with our proposed registration method were qualitatively and quantitatively analyzed. The results demonstrated that the proposed scheme provides good registration accuracy with smooth transformations, thereby guaranteeing the preservation of topology.

Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery

With the recent advances in computing, the opportunities to translate computational models to more integrated roles in patient treatment are expanding at an exciting rate. One area of considerable development has been directed towards correcting soft tissue deformation within image guided neurosurgery applications. This review captures the efforts that have been undertaken towards enhancing neuronavigation by the integration of soft tissue biomechanical models, imaging and sensing technologies, and algorithmic developments. In addition, the review speaks to the evolving role of modeling frameworks within surgery and concludes with some future directions beyond neurosurgical applications.

Volumetric Measurements of Brain Shift Using Intraoperative Cone-Beam Computed Tomography: Preliminary Study

BACKGROUND

Cerebrospinal fluid leakage and ventricular compression during open surgery may lead to brain deformation called brain shift. Brain shift may affect intraoperative navigation that is based on image-based preoperative planning. Tools to correct or predict these anatomic modifications can be important to maintain precision during open guided neurosurgery.

OBJECTIVE

To obtain a reliable intraoperative volumetric deformation vector field describing brain shift during intracranial neurosurgical procedures.

METHODS

We acquired preoperative and intraoperative cone-beam computed tomography enhanced with intravenous injection of iodine contrast. These data sets were preprocessed and elastically registered to obtain the volumetric brain shift deformation vector fields.

RESULTS

We obtained the brain shift deformation vector field in 9 cases. The deformation fields proved to be highly nonlinear, particularly around the ventricles. Interpatient variability was considerable, with a maximum deformation ranging from 8.1 to 26.6 mm and a standard deviation ranging from 0.9 to 4.9 mm.

CONCLUSION

Contrast-enhanced cone-beam computed tomography provides a feasible technique for intraoperatively determining brain shift deformation vector fields. This technique can be used perioperatively to adjust preoperative planning and coregistration during neurosurgical procedures.

A nonrigid registration method for correcting brain deformation induced by tumor resection

PURPOSE

This paper presents a nonrigid registration method to align preoperative MRI with intraoperative MRI to compensate for brain deformation during tumor resection. This method extends traditional point-based nonrigid registration in two aspects: (1) allow the input data to be incomplete and (2) simulate the underlying deformation with a heterogeneous biomechanical model.

METHODS

The method formulates the registration as a three-variable (point correspondence, deformation field, and resection region) functional minimization problem, in which point correspondence is represented by a fuzzy assign matrix; Deformation field is represented by a piecewise linear function regularized by the strain energy of a heterogeneous biomechanical model; and resection region is represented by a maximal simply connected tetrahedral mesh. A nested expectation and maximization framework is developed to simultaneously resolve these three variables.

RESULTS

To evaluate this method, the authors conducted experiments on both synthetic data and clinical MRI data. The synthetic experiment confirmed their hypothesis that the removal of additional elements from the biomechanical model can improve the accuracy of the registration. The clinical MRI experiments on 25 patients showed that the proposed method outperforms the ITK implementation of a physics-based nonrigid registration method. The proposed method improves the accuracy by 2.88 mm on average when the error is measured by a robust Hausdorff distance metric on Canny edge points, and improves the accuracy by 1.56 mm on average when the error is measured by six anatomical points.

CONCLUSIONS

The proposed method can effectively correct brain deformation induced by tumor resection.

Contouring variability of human- and deformable-generated contours in radiotherapy for prostate cancer

This study was designed to evaluate contouring variability of human-and deformable-generated contours on planning CT (PCT) and CBCT for ten patients with low-or intermediate-risk prostate cancer. For each patient in this study, five radiation oncologists contoured the prostate, bladder, and rectum, on one PCT dataset and five CBCT datasets. Consensus contours were generated using the STAPLE method in the CERR software package. Observer contours were compared to consensus contour, and contour metrics (Dice coefficient, Hausdorff distance, Contour Distance, Center-of-Mass [COM] Deviation) were calculated. In addition, the first day CBCT was registered to subsequent CBCT fractions (CBCTn: CBCT2-CBCT5) via B-spline Deformable Image Registration (DIR). Contours were transferred from CBCT1 to CBCTn via the deformation field, and contour metrics were calculated through comparison with consensus contours generated from human contour set. The average contour metrics for prostate contours on PCT and CBCT were as follows: Dice coefficient-0.892 (PCT), 0.872 (CBCT-Human), 0.824 (CBCT-Deformed); Hausdorff distance-4.75 mm (PCT), 5.22 mm (CBCT-Human), 5.94 mm (CBCT-Deformed); Contour Distance (overall contour)-1.41 mm (PCT), 1.66 mm (CBCT-Human), 2.30 mm (CBCT-Deformed); COM Deviation-2.01 mm (PCT), 2.78 mm (CBCT-Human), 3.45 mm (CBCT-Deformed). For human contours on PCT and CBCT, the difference in average Dice coefficient between PCT and CBCT (approx. 2%) and Hausdorff distance (approx. 0.5 mm) was small compared to the variation between observers for each patient (standard deviation in Dice coefficient of 5% and Hausdorff distance of 2.0 mm). However, additional contouring variation was found for the deformable-generated contours (approximately 5.0% decrease in Dice coefficient and 0.7 mm increase in Hausdorff distance relative to human-generated contours on CBCT). Though deformable contours provide a reasonable starting point for contouring on CBCT, we conclude that contours generated with B-Spline DIR require physician review and editing if they are to be used in the clinic.

Intraoperative Magnetic Resonance Imaging in Intracranial Glioma Resection: A Single-Center, Retrospective Blinded Volumetric Study

BACKGROUND

Intraoperative magnetic resonance imaging (IoMRI) was devised to overcome brain shifts during craniotomies. Yet, the acceptance of IoMRI is limited.

OBJECTIVE

To evaluate impact of IoMRI on intracranial glioma resection outcome including overall patient survival.

METHODS

A retrospective review of records was performed on a cohort of 164 consecutive patients who underwent resection surgery for newly diagnosed intracranial gliomas either with or without IoMRI technology performed by 2 neurosurgeons in our center. Patient follow-up was at least 5 years. Extent of resection (EOR) was calculated using pre- and postoperative contrast-enhanced and T2-weighted MR-images. Adjusted analysis was performed to compare gross total resection (GTR), EOR, permanent surgery-associated neurologic deficit, and overall survival between the 2 groups.

RESULTS

Overall median EOR was 92.1%, and 97.45% with IoMRI use and 89.9% without IoMRI, with crude (unadjusted) P < 0.005. GTR was achieved in 49.3% of IoMRI cases, versus in only 21.4% of no-IoMRI cases, P < 0.001. GTR achieved was more with the use of IoMRI among gliomas located in both eloquent and noneloquent brain areas, P = 0.017 and <0.001, respectively. Permanent surgery-associated neurologic deficit was not (statistically) more significant with no-IoMRI, P = 0.284 (13.8% vs. 6.7%). In addition, the IoMRI group had better 5-year overall survival, P < 0.001.

CONCLUSION

This study shows that the use of IoMRI was associated with greater rates of EOR and GTR, and better overall 5-year survival in both eloquent brain areas located and non-eloquent brain areas located gliomas, with no increased risk of neurologic complication.

A Kalman Filtering Perspective for Multiatlas Segmentation

In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity-neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.