Automatic Intraoperative Correction of Brain Shift for Accurate Neuronavigation

Histomolecular Validation of [18F]-FACBC in Gliomas Using Image-Localized Biopsies

BACKGROUND

Gliomas have a heterogeneous nature, and identifying the most aggressive parts of the tumor and defining tumor borders are important for histomolecular diagnosis, surgical resection, and radiation therapy planning. This study evaluated [18F]-FACBC PET for glioma tissue classification.

METHODS

Pre-surgical [18F]-FACBC PET/MR images were used during surgery and image-localized biopsy sampling in patients with high- and low-grade glioma. TBR was compared to histomolecular results to determine optimal threshold values, sensitivity, specificity, and AUC values for the classification of tumor tissue. Additionally, PET volumes were determined in patients with glioblastoma based on the optimal threshold. [18F]-FACBC PET volumes and diagnostic accuracy were compared to ce-T1 MRI. In total, 48 biopsies from 17 patients were analyzed.

RESULTS

[18F]-FACBC had low uptake in non-glioblastoma tumors, but overall higher sensitivity and specificity for the classification of tumor tissue (0.63 and 0.57) than ce-T1 MRI (0.24 and 0.43). Additionally, [18F]-FACBC TBR was an excellent classifier for IDH1-wildtype tumor tissue (AUC: 0.83, 95% CI: 0.71-0.96). In glioblastoma patients, PET tumor volumes were on average eight times larger than ce-T1 MRI volumes and included 87.5% of tumor-positive biopsies compared to 31.5% for ce-T1 MRI.

CONCLUSION

The addition of [18F]-FACBC PET to conventional MRI could improve tumor classification and volume delineation.

Navigated Intraoperative Ultrasound Offers Effective and Efficient Real-Time Analysis of Intracranial Tumor Resection and Brain Shift

BACKGROUND AND OBJECTIVES

Neuronavigation is a fundamental tool in the resection of intracranial tumors. However, it is limited by its calibration to preoperative neuroimaging, which loses accuracy intraoperatively after brain shift. Therefore, surgeons rely on anatomic landmarks or tools like intraoperative MRI to assess the extent of tumor resection (EOR) and update neuronavigation. Recent studies demonstrate that intraoperative ultrasound (iUS) provides point-of-care imaging without the cost or resource utilization of an intraoperative MRI, and advances in neuronavigation-guided iUS provide an opportunity for real-time imaging overlaid with neuronavigation to account for brain shift. We assessed the feasibility, efficacy, and benefits of navigated iUS to assess the EOR and restore stereotactic accuracy in neuronavigation after brain shift.

METHODS

This prospective single-center study included patients presenting with intracranial tumors (gliomas, metastasis) to an academic medical center. Navigated iUS images were acquired preresection, midresection, and postresection. The EOR was determined by the surgeon intraoperatively and compared with the postoperative MRI report by an independent neuroradiologist. Outcome measures included time to perform the iUS sweep, time to process ultrasound images, and EOR predicted by the surgeon intraoperatively compared with the postoperative MRI.

RESULTS

This study included 40 patients consisting of gliomas (n = 18 high-grade gliomas, n = 4 low-grade gliomas, n = 4 recurrent) and metastasis (n = 18). Navigated ultrasound sweeps were performed in all patients (n = 83) with a median time to perform of 5.5 seconds and a median image processing time of 29.9 seconds. There was 95% concordance between the surgeon's and neuroradiologist's determination of EOR using navigated iUS and postoperative MRI, respectively. The sensitivity was 100%, and the specificity was 94%.

CONCLUSION

Navigated iUS was successfully used for EOR determination in glioma and metastasis resection. Incorporating navigated iUS into the surgical workflow is safe and efficient and provides a real-time assessment of EOR while accounting for brain shift in intracranial tumor surgeries.

The utility of intraoperative ultrasonography for spinal cord surgery

OBJECTIVES

Intraoperative ultrasonography (IOUS) offers the advantage of providing real-time imaging features, yet it is not generally used. This study aims to discuss the benefits of utilizing IOUS in spinal cord surgery and review related literature.

MATERIALS AND METHODS

Patients who underwent spinal cord surgery utilizing IOUS at a single institution were retrospectively collected and analyzed to evaluate the benefits derived from the use of IOUS.

RESULTS

A total of 43 consecutive patients were analyzed. Schwannoma was the most common tumor (35%), followed by cavernous angioma (23%) and ependymoma (16%). IOUS confirmed tumor extent and location before dura opening in 42 patients (97.7%). It was particularly helpful for myelotomy in deep-seated intramedullary lesions to minimize neural injury in 13 patients (31.0% of 42 patients). IOUS also detected residual or hidden lesions in 3 patients (7.0%) and verified the absence of hematoma post-tumor removal in 23 patients (53.5%). In 3 patients (7.0%), confirming no intradural lesions after removing extradural tumors avoided additional dural incisions. IOUS identified surrounding blood vessels and detected dural defects in one patient (2.3%) respectively.

CONCLUSIONS

The IOUS can be a valuable tool for spinal cord surgery in identifying the exact location of the pathologic lesions, confirming the completeness of surgery, and minimizing the risk of neural and vascular injury in a real-time fashion.

D2BGAN: Dual Discriminator Bayesian Generative Adversarial Network for Deformable MR-Ultrasound Registration Applied to Brain Shift Compensation

During neurosurgical procedures, the neuro-navigation system's accuracy is affected by the brain shift phenomenon. One popular strategy is to compensate for brain shift using intraoperative ultrasound (iUS) registration with pre-operative magnetic resonance (MR) scans. This requires a satisfactory multimodal image registration method, which is challenging due to the low image quality of ultrasound and the unpredictable nature of brain deformation during surgery. In this paper, we propose an automatic unsupervised end-to-end MR-iUS registration approach named the Dual Discriminator Bayesian Generative Adversarial Network (D2BGAN). The proposed network consists of two discriminators and a generator optimized by a Bayesian loss function to improve the functionality of the generator, and we add a mutual information loss function to the discriminator for similarity measurements. Extensive validation was performed on the RESECT and BITE datasets, where the mean target registration error (mTRE) of MR-iUS registration using D2BGAN was determined to be 0.75 ± 0.3 mm. The D2BGAN illustrated a clear advantage by achieving an 85% improvement in the mTRE over the initial error. Moreover, the results confirmed that the proposed Bayesian loss function, rather than the typical loss function, improved the accuracy of MR-iUS registration by 23%. The improvement in registration accuracy was further enhanced by the preservation of the intensity and anatomical information of the input images.

Technical evolution of pediatric neurosurgery: the evolution of intraoperative imaging

Imaging has always been fundamental to neurosurgery, and its evolution over the last century has made a dramatic transformation in the ability of neurosurgeons to define pathology and preserve normal tissue during their operations. In the mid-70 s, the development of computerized cross-sectional imaging with CT scan and subsequently MRI have revolutionized the practice of neurosurgery. Later, further advances in computer technology and medical engineering have allowed the combination of many modalities to bring them into the operating theater. This evolution has allowed real-time intraoperative imaging, in the hope of helping neurosurgeons achieve accuracy, maximal safe resection, and the implementation of minimally invasive techniques in brain and spine pathologies. Augmented reality and robotic technologies are also being applied as useful intra-operative techniques that will improve surgical planning and outcomes in the future. In this article, we will review imaging modalities and provide our institutional perspective on how we have integrated them into our practice.

Mechanisms of Resistance and Current Treatment Options for Glioblastoma Multiforme (GBM)

Glioblastoma multiforme (GBM) is a highly aggressive form of brain cancer that is difficult to treat due to its resistance to both radiation and chemotherapy. This resistance is largely due to the unique biology of GBM cells, which can evade the effects of conventional treatments through mechanisms such as increased resistance to cell death and rapid regeneration of cancerous cells. Additionally, the blood–brain barrier makes it difficult for chemotherapy drugs to reach GBM cells, leading to reduced effectiveness. Despite these challenges, there are several treatment options available for GBM. The standard of care for newly diagnosed GBM patients involves surgical resection followed by concurrent chemoradiotherapy and adjuvant chemotherapy. Emerging treatments include immunotherapy, such as checkpoint inhibitors, and targeted therapies, such as bevacizumab, that attempt to attack specific vulnerabilities in GBM cells. Another promising approach is the use of tumor-treating fields, a type of electric field therapy that has been shown to slow the growth of GBM cells. Clinical trials are ongoing to evaluate the safety and efficacy of these and other innovative treatments for GBM, intending to improve with outcomes for patients.

A Brief Explanation on Surgical Approaches for Treatment of Different Brain Tumors

The main goal of brain tumor surgery is to achieve gross total tumor resection without postoperative complications and permanent new deficits. However, when the lesion is located close or within eloquent brain areas, cranial nerves, and/or major brain vessels, it is imperative to balance the extent of resection with the risk of harming the patient, by following a so-called maximal safe resection philosophy. This view implies a shift from an approach-guided attitude, in which few standard surgical approaches are used to treat almost all intracranial tumors, to a pathology-guided one, with surgical approaches actually tailored to the specific tumor that has to be treated with specific dedicated pre- and intraoperative tools and techniques. In this chapter, the basic principles of the most commonly used neurosurgical approaches in brain tumors surgery are presented and discussed along with an overview on all available modern tools able to improve intraoperative visualization, extent of resection, and postoperative clinical outcome.

Modular robotic platform for precision neurosurgery with a bio-inspired needle: System overview and first in-vivo deployment

Over the past 10 years, minimally invasive surgery (MIS) has shown significant benefits compared to conventional surgical techniques, with reduced trauma, shorter hospital stays, and shorter patient recovery times. In neurosurgical MIS procedures, inserting a straight tool (e.g. catheter) is common practice in applications ranging from biopsy and laser ablation, to drug delivery and fluid evacuation. How to handle tissue deformation, target migration and access to deep-seated anatomical structures remain an open challenge, affecting both the preoperative planning phase and eventual surgical intervention. Here, we present the first neurosurgical platform in the literature, able to deliver an implantable steerable needle for a range of diagnostic and therapeutic applications, with a short-term focus on localised drug delivery. This work presents the system's architecture and first in vivo deployment with an optimised surgical workflow designed for pre-clinical trials with the ovine model, which demonstrate appropriate function and safe implantation.

Navigated 3D ultrasound-guided resection of high-grade gliomas: A case series and review

Background:

The crux in high-grade glioma surgery remains maximizing resection without affecting eloquent brain areas. Toward this, a myriad of adjunct tools and techniques has been employed to enhance surgical safety and efficacy. Despite intraoperative MRI and advanced neuronavigational techniques, as well as augmented reality, to date, the only true real-time visualization tool remains the ultrasound (US). Neuroultrasonography is a cost-efficient imaging modality that offers instant, real-time information about the changing anatomical landscape intraoperatively. Recent advances in technology now allow for the integration of intraoperative US with neuronavigation.

Case Description:

In this report, we present the resection technique for three cases of high-grade gliomas (two glioblastomas and one anaplastic astrocytoma). The patient presented with a variable clinical spectrum. All three cases have been performed using the Brainlab^® neuronavigation system (BrainLAB, Munich, Germany) and the bk5000 US Machine^® (BK Medical, Analogic Corporation, Peabody, Massachusetts, USA).

Conclusion:

Gross total resection was achieved in all three cases. The use of 3D navigated US was a reliable adjunct surgical tool in achieving favorable resection outcomes in these patients.

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.

Intraoperative MR Imaging during Glioma Resection

One of the major issues in the surgical treatment of gliomas is the concern about maximizing the extent of resection while minimizing neurological impairment. Thus, surgical planning by carefully observing the relationship between the glioma infiltration area and eloquent area of the connecting fibers is crucial. Neurosurgeons usually detect an eloquent area by functional MRI and identify a connecting fiber by diffusion tensor imaging. However, during surgery, the accuracy of neuronavigation can be decreased due to brain shift, but the positional information may be updated by intraoperative MRI and the next steps can be planned accordingly. In addition, various intraoperative modalities may be used to guide surgery, including neurophysiological monitoring that provides real-time information (e.g., awake surgery, motor-evoked potentials, and sensory evoked potential); photodynamic diagnosis, which can identify high-grade glioma cells; and other imaging techniques that provide anatomical information during the surgery. In this review, we present the historical and current context of the intraoperative MRI and some related approaches for an audience active in the technical, clinical, and research areas of radiology, as well as mention important aspects regarding safety and types of devices.

Anatomical aspects of the insula, opercula and peri-insular white matter for a transcortical approach to insular glioma resection

The insula is a lobe located deep in each hemisphere of the brain and is surrounded by eloquent cortical, white matter, and basal ganglia structures. The aim of this study was to provide an anatomical description of the insula and white matter tracts related to surgical treatment of gliomas through a transcortical approach. The study also discusses surgical implications in terms of intraoperative brain mapping. Five adult brains were prepared according to the Klingler technique. Cortical anatomy was evaluated with the naked eye, whereas white matter dissection was performed with the use of a microscope. The widest exposure of the insular surface was noted through the temporal operculum, mainly in zones III and IV according to the Berger-Sanai classification. By going through the pars triangularis in all cases, the anterior insular point and most of zone I were exposed. The narrowest and deepest operating field was observed by going through the parietal operculum. This method provided a suitable approach to zone II, where the corticospinal tract is not covered by the basal ganglia and is exposed just under the superior limiting sulcus. At the subcortical level, the identification of the inferior frontoocipital fasciculus at the level of the limen insulae is critical in terms of preserving the lenticulostriate arteries. Detailed knowledge of the anatomy of the insula and subcortical white matter that is exposed through each operculum is essential in preoperative planning as well as in the intraoperative decision-making process in terms of intraoperative brain mapping.

Electromagnetic Navigation Systems and Intraoperative Neuromonitoring: Reliability and Feasibility Study

BACKGROUND

A recent influx of intraoperative technology is being used in neurosurgery, but few reports investigate the accuracy and safety of these technologies when used simultaneously.

OBJECTIVE

To assess the ability to use an electromagnetic navigation system alongside multimodal intraoperative neurophysiological monitoring (IONM).

METHODS

Single-institution prospective cohort study of patients requiring craniotomy for brain tumor resection operated using an electromagnetic navigation system (AxiEM, Medtronic®). motor evoked potentials, somatosensory evoked potentials (SSEPs), electroencephalography, and electromyography were recorded and analyzed with AxiEM on (with/without filters) and off. The neurological outcomes of the patients were recorded.

RESULTS

A total of 15 patients were included (8 males/7 females, mean age 52.13 yr). Even though the raw acquisition is affected by the electromagnetic field (particularly SSEPs), no significant difference was detected in the morphology, amplitude, and latency of the different monitoring modalities (AxiEM off vs on) after the appropriate software filter application. Adjustments to the frequency of SSEP stimulation and number of averages, and reductions to the low-pass filters were applied. Notch filters were used appropriately and changes to the physical setup of the IONM and electromagnetic navigation system equipment reduced noise. Postoperatively, none of the patients developed new focal deficits; 7 patients showed improvement in their motor deficit (4 recovered fully).

CONCLUSION

The information provided by the IONM in intracranial neurosurgery patients whilst also using electromagnetic navigation systems is reliable for monitoring, mapping, and detecting intraoperative complications, provided that the appropriate software filters and tools are applied.

Machine Vision for Real-Time Intraoperative Anatomic Guidance: A Proof-of-Concept Study in Endoscopic Pituitary Surgery

BACKGROUND

Current intraoperative orientation methods either rely on preoperative imaging, are resource-intensive to implement, or difficult to interpret. Real-time, reliable anatomic recognition would constitute another strong pillar on which neurosurgeons could rest for intraoperative orientation.

OBJECTIVE

To assess the feasibility of machine vision algorithms to identify anatomic structures using only the endoscopic camera without prior explicit anatomo-topographic knowledge in a proof-of-concept study.

METHODS

We developed and validated a deep learning algorithm to detect the nasal septum, the middle turbinate, and the inferior turbinate during endoscopic endonasal approaches based on endoscopy videos from 23 different patients. The model was trained in a weakly supervised manner on 18 and validated on 5 patients. Performance was compared against a baseline consisting of the average positions of the training ground truth labels using a semiquantitative 3-tiered system.

RESULTS

We used 367 images extracted from the videos of 18 patients for training, as well as 182 test images extracted from the videos of another 5 patients for testing the fully developed model. The prototype machine vision algorithm was able to identify the 3 endonasal structures qualitatively well. Compared to the baseline model based on location priors, the algorithm demonstrated slightly but statistically significantly (P < .001) improved annotation performance.

CONCLUSION

Automated recognition of anatomic structures in endoscopic videos by means of a machine vision model using only the endoscopic camera without prior explicit anatomo-topographic knowledge is feasible. This proof of concept encourages further development of fully automated software for real-time intraoperative anatomic guidance during surgery.

Detection of glioma infiltration at the tumor margin using quantitative stimulated Raman scattering histology

In the management of diffuse gliomas, the identification and removal of tumor at the infiltrative margin remains a central challenge. Prior work has demonstrated that fluorescence labeling tools and radiographic imaging are useful surgical adjuvants with macroscopic resolution. However, they lose sensitivity at the tumor margin and have limited clinical utility for lower grade histologies. Fiber-laser based stimulated Raman histology (SRH) is an optical imaging technique that provides microscopic tissue characterization of unprocessed tissues. It remains unknown whether SRH of tissues taken from the infiltrative glioma margin will identify microscopic residual disease. Here we acquired glioma margin specimens for SRH, histology, and tumor specific tissue characterization. Generalized linear mixed models were used to evaluate agreement. We find that SRH identified residual tumor in 82 of 167 margin specimens (49%), compared to IHC confirming residual tumor in 72 of 128 samples (56%), and H&E confirming residual tumor in 82 of 169 samples (49%). Intraobserver agreements between all 3 modalities were confirmed. These data demonstrate that SRH detects residual microscopic tumor at the infiltrative glioma margin and may be a promising tool to enhance extent of resection.

Current Limitations of Intraoperative Ultrasound in Brain Tumor Surgery

While benefits of intraoperative ultrasound (IOUS) have been frequently described, data on IOUS limitations are relatively sparse. Suboptimal ultrasound imaging of some pathologies, various types of ultrasound artifacts, challenging patient positioning during some IOUS-guided surgeries, and absence of an optimal IOUS probe depicting the entire sellar region during transsphenoidal pituitary surgery are some of the most important pitfalls. This review aims to summarize prominent limitations of current IOUS systems, and to present possibilities to reduce them by using ultrasound technology suitable for a specific procedure and by proper scanning techniques. In addition, future trends of IOUS imaging optimization are described in this article.

Brain Shift in Neuronavigation of Brain Tumors: An Updated Review of Intra-Operative Ultrasound Applications

Neuronavigation using pre-operative imaging data for neurosurgical guidance is a ubiquitous tool for the planning and resection of oncologic brain disease. These systems are rendered unreliable when brain shift invalidates the patient-image registration. Our previous review in 2015, Brain shift in neuronavigation of brain tumours: A review offered a new taxonomy, classification system, and a historical perspective on the causes, measurement, and pre- and intra-operative compensation of this phenomenon. Here we present an updated review using the same taxonomy and framework, focused on the developments of intra-operative ultrasound-based brain shift research from 2015 to the present (2020). The review was performed using PubMed to identify articles since 2015 with the specific words and phrases: “Brain shift” AND “Ultrasound”. Since 2015, the rate of publication of intra-operative ultrasound based articles in the context of brain shift has increased from 2–3 per year to 8–10 per year. This efficient and low-cost technology and increasing comfort among clinicians and researchers have allowed unique avenues of development. Since 2015, there has been a trend towards more mathematical advancements in the field which is often validated on publicly available datasets from early intra-operative ultrasound research, and may not give a just representation to the intra-operative imaging landscape in modern image-guided neurosurgery. Focus on vessel-based registration and virtual and augmented reality paradigms have seen traction, offering new perspectives to overcome some of the different pitfalls of ultrasound based technologies. Unfortunately, clinical adaptation and evaluation has not seen as significant of a publication boost. Brain shift continues to be a highly prevalent pitfall in maintaining accuracy throughout oncologic neurosurgical intervention and continues to be an area of active research. Intra-operative ultrasound continues to show promise as an effective, efficient, and low-cost solution for intra-operative accuracy management. A major drawback of the current research landscape is that mathematical tool validation based on retrospective data outpaces prospective clinical evaluations decreasing the strength of the evidence. The need for newer and more publicly available clinical datasets will be instrumental in more reliable validation of these methods that reflect the modern intra-operative imaging in these procedures.

The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

With the recent developments in machine learning and modern graphics processing units (GPUs), there is a marked shift in the way intra-operative ultrasound (iUS) images can be processed and presented during surgery. Real-time processing of images to highlight important anatomical structures combined with in-situ display, has the potential to greatly facilitate the acquisition and interpretation of iUS images when guiding an operation. In order to take full advantage of the recent advances in machine learning, large amounts of high-quality annotated training data are necessary to develop and validate the algorithms. To ensure efficient collection of a sufficient number of patient images and external validity of the models, training data should be collected at several centers by different neurosurgeons, and stored in a standard format directly compatible with the most commonly used machine learning toolkits and libraries. In this paper, we argue that such effort to collect and organize large-scale multi-center datasets should be based on common open source software and databases. We first describe the development of existing open-source ultrasound based neuronavigation systems and how these systems have contributed to enhanced neurosurgical guidance over the last 15 years. We review the impact of the large number of projects worldwide that have benefited from the publicly available datasets “Brain Images of Tumors for Evaluation” (BITE) and “Retrospective evaluation of Cerebral Tumors” (RESECT) that include MR and US data from brain tumor cases. We also describe the need for continuous data collection and how this effort can be organized through the use of a well-adapted and user-friendly open-source software platform that integrates both continually improved guidance and automated data collection functionalities.

Evaluation of the precision of operative augmented reality compared to standard neuronavigation using a 3D-printed skull

OBJECTIVE

Augmented reality (AR) in cranial surgery allows direct projection of preregistered overlaid images in real time on the microscope surgical field. In this study, the authors aimed to compare the precision of AR-assisted navigation and standard pointer-based neuronavigation (NV) by using a 3D-printed skull in surgical conditions.

METHODS

A commercial standardized 3D-printed skull was scanned, fused, and referenced with an MR image and a CT scan of a patient with a 2 × 2-mm right frontal sinus defect. The defect was identified, registered, and integrated into NV. The target was physically marked on the 3D-printed skull replicating the right frontal sinus defect. Twenty-six subjects participated, 25 of whom had no prior NV or AR experience and 1 with little AR experience. The subjects were briefly trained in how to use NV, AR, and AR recalibration tools. Participants were asked to do the following: 1) "target the center of the defect in the 3D-printed skull with a navigation pointer, assisted only by NV orientation," and 2) "use the surgical microscope and AR to focus on the center of the projected object" under conventional surgical conditions. For the AR task, the number of recalibrations was recorded. Confidence regarding NV and AR precision were assessed prior to and after the experiment by using a 9-level Likert scale.

RESULTS

The median distance to target was statistically lower for AR than for NV (1 mm [Q1: 1 mm, Q3: 2 mm] vs 3 mm [Q1: 2 mm, Q3: 4 mm] [p < 0.001]). In the AR task, the median number of recalibrations was 4 (Q1: 4, Q3: 4.75). The number of recalibrations was significantly correlated with the precision (Spearman rho: -0.71, p < 0.05). The trust assessment after performing the experiment scored a median of 8 for AR and 5.5 for NV (p < 0.01).

CONCLUSIONS

This study shows for the first time the superiority of AR over NV in terms of precision. AR is easy to use. The number of recalibrations performed using reference structures increases the precision of the navigation. The confidence regarding precision increases with experience.

The state-of-the-art in ultrasound-guided spine interventions

During the last two decades, intra-operative ultrasound (iUS) imaging has been employed for various surgical procedures of the spine, including spinal fusion and needle injections. Accurate and efficient registration of pre-operative computed tomography or magnetic resonance images with iUS images are key elements in the success of iUS-based spine navigation. While widely investigated in research, iUS-based spine navigation has not yet been established in the clinic. This is due to several factors including the lack of a standard methodology for the assessment of accuracy, robustness, reliability, and usability of the registration method. To address these issues, we present a systematic review of the state-of-the-art techniques for iUS-guided registration in spinal image-guided surgery (IGS). The review follows a new taxonomy based on the four steps involved in the surgical workflow that include pre-processing, registration initialization, estimation of the required patient to image transformation, and a visualization process. We provide a detailed analysis of the measurements in terms of accuracy, robustness, reliability, and usability that need to be met during the evaluation of a spinal IGS framework. Although this review is focused on spinal navigation, we expect similar evaluation criteria to be relevant for other IGS applications.

Automatic segmentation of brain tumor resections in intraoperative ultrasound images using U-Net

To compensate for the intraoperative brain tissue deformation, computer-assisted intervention methods have been used to register preoperative magnetic resonance images with intraoperative images. In order to model the deformation due to tissue resection, the resection cavity needs to be segmented in intraoperative images. We present an automatic method to segment the resection cavity in intraoperative ultrasound (iUS) images. We trained and evaluated two-dimensional (2-D) and three-dimensional (3-D) U-Net networks on two datasets of 37 and 13 cases that contain images acquired from different ultrasound systems. The best overall performing method was the 3-D network, which resulted in a 0.72 mean and 0.88 median Dice score over the whole dataset. The 2-D network also had good results with less computation time, with a median Dice score over 0.8. We also evaluated the sensitivity of network performance to training and testing with images from different ultrasound systems and image field of view. In this application, we found specialized networks to be more accurate for processing similar images than a general network trained with all the data. Overall, promising results were obtained for both datasets using specialized networks. This motivates further studies with additional clinical data, to enable training and validation of a clinically viable deep-learning model for automated delineation of the tumor resection cavity in iUS images.

Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge

In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.

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.