Integrating Retraction Modeling Into an Atlas-Based Framework for Brain Shift Prediction

A review on organ deformation modeling approaches for reliable surgical navigation using augmented reality

Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient's body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative anatomy. To enable reliable navigation in augmented surgery, modeling of intraoperative deformation to obtain an accurate alignment of the preoperative organ model with the intraoperative anatomy is indispensable. Despite the existence of various methods proposed to model intraoperative organ deformation, there are still few literature reviews that systematically categorize and summarize these approaches. This review aims to fill this gap by providing a comprehensive and technical-oriented overview of modeling methods for intraoperative organ deformation in augmented reality in surgery. Through a systematic search and screening process, 112 closely relevant papers were included in this review. By presenting the current status of organ deformation modeling methods and their clinical applications, this review seeks to enhance the understanding of organ deformation modeling in AR-guided surgery, and discuss the potential topics for future advancements.

Fabrication of a positional brain shift phantom through the utilization of the frozen intermediate hydrogel state

Synthetic models (phantoms) of the brain-skull system are useful tools for the study of surgical events that are otherwise difficult to study directly in humans. To date, very few studies can be found which replicate the full anatomical brain-skull system. Such models are required to study the more global mechanical events that can occur in neurosurgery, such as positional brain shift. Presented in this work is a novel workflow for the fabrication of a biofidelic brain-skull phantom which features a full hydrogel brain with fluid-filled ventricle/fissure spaces, elastomer dural septa and fluid-filled skull. Central to this workflow is the utilization of the frozen intermediate curing state of an established brain tissue surrogate, which allows for a novel moulding and skull installation approach that permits a much fuller recreation of the anatomy. The mechanical realism of the phantom was validated through indentation testing of the phantom's brain and simulation of the supine to prone brain shift event, while the geometric realism was validated through magnetic resonance imaging. The developed phantom captured a novel measurement of the supine to prone brain shift event with a magnitude that accurately reproduces that seen in the literature.

Computational Imaging to Compensate for Soft-Tissue Deformations in Image-Guided Breast Conserving Surgery

OBJECTIVE

During breast conserving surgery (BCS), magnetic resonance (MR) images aligned to accurately display intraoperative lesion locations can offer improved understanding of tumor extent and position relative to breast anatomy. Unfortunately, even under consistent supine conditions, soft tissue deformation compromises image-to-physical alignment and results in positional errors.

METHODS

A finite element inverse modeling technique has been developed to nonrigidly register preoperative supine MR imaging data to the surgical scene for improved localization accuracy during surgery. Registration is driven using sparse data compatible with acquisition during BCS, including corresponding surface fiducials, sparse chest wall contours, and the intra-fiducial skin surface. Deformation predictions were evaluated at surface fiducial locations and subsurface tissue features that were expertly identified and tracked. Among n = 7 different human subjects, an average of 22 ± 3 distributed subsurface targets were analyzed in each breast volume.

RESULTS

The average target registration error (TRE) decreased significantly when comparing rigid registration to this nonrigid approach (10.4 ± 2.3 mm vs 6.3 ± 1.4 mm TRE, respectively). When including a single subsurface feature as additional input data, the TRE significantly improved further (4.2 ± 1.0 mm TRE), and in a region of interest within 15 mm of a mock biopsy clip TRE was 3.9 ± 0.9 mm.

CONCLUSION

These results demonstrate accurate breast deformation estimates based on sparse-data-driven model predictions.

SIGNIFICANCE

The data suggest that a computational imaging approach can account for image-to-surgery shape changes to enhance surgical guidance during BCS.

[microsurgery 'burr hole' for intracranial tumors and mesial temporal lobe epilepsy]

PURPOSE

In recent years, neurosurgery has been characterized by a clear tendency towards the development of minimally invasive and less traumatic surgical approaches. To minimize the degree of injury to the brain tissue, we have proposed burr hole-based microsurgical approaches.

MATERIAL AND METHODS

In the period between February 2016 and February 2019, more than 500 microsurgical interventions were performed through a 14 mm burr hole using a technique that we called burr-hole microneurosurgery; to date, 200 of these have been analyzed. The age of patients varied from 16 to 79 years (median, 38 years). Female patients predominated - 1.6:1. Surgery for intracranial lesions with various locations was performed in 176 cases; in the remaining 24 cases, patients with hippocampal sclerosis underwent selective amygdalohippocampectomy.

RESULTS

Various surgical approaches were used: transcortical approach in 81 (40.5%) cases; retro-sigmoid approach in 38 (19%); sub-temporal approach in 32 (16%); infratentorial supracerebellar approach in 25 (12.5%); interhemispheric approach in 17 (8.5%); telovelar approach in 5 (2.5%); trans-eyebrow approach in 2 cases. The resection degree was evaluated in 167 patients with planned maximum tumor resection. Resection was total and almost total in 145 (87%) patients, subtotal in 15 (9%), and partial in 7 (4%). The surgery duration varied from 35 to 300 min (mean, 80 min). The extubation time after surgery ranged from 5 min to 5 days (mean, 70 min). In 195 (97.5%) cases, patients were verticalized within the first 3 days after surgery.

CONCLUSION

The proposed burr hole technique enables successful surgery in patients with various intracranial pathologies, using a smaller trepanation window compared to that in keyhole surgery. The proposed burr hole technique minimizes injury to the brain substance, significantly reduces patient's exposure to anesthesia, and decreases the entire duration of surgery.

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

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

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

Intraoperative brain shift during neurosurgical procedures is a well-known phenomenon caused by gravity, tissue manipulation, tumor size, loss of cerebrospinal fluid (CSF), and use of medication. For the use of image-guided systems, this phenomenon greatly affects the accuracy of the guidance. During the last several decades, researchers have investigated how to overcome this problem. The purpose of this paper is to present a review of publications concerning different aspects of intraoperative brain shift especially in a tumor resection surgery such as intraoperative imaging systems, quantification, measurement, modeling, and registration techniques. Clinical experience of using intraoperative imaging modalities, details about registration, and modeling methods in connection with brain shift in tumor resection surgery are the focuses of this review. In total, 126 papers regarding this topic are analyzed in a comprehensive summary and are categorized according to fourteen criteria. The result of the categorization is presented in an interactive web tool. The consequences from the categorization and trends in the future are discussed at the end of this work.

Android application for determining surgical variables in brain-tumor resection procedures

The fidelity of image-guided neurosurgical procedures is often compromised due to the mechanical deformations that occur during surgery. In recent work, a framework was developed to predict the extent of this brain shift in brain-tumor resection procedures. The approach uses preoperatively determined surgical variables to predict brain shift and then subsequently corrects the patient's preoperative image volume to more closely match the intraoperative state of the patient's brain. However, a clinical workflow difficulty with the execution of this framework is the preoperative acquisition of surgical variables. To simplify and expedite this process, an Android, Java-based application was developed for tablets to provide neurosurgeons with the ability to manipulate three-dimensional models of the patient's neuroanatomy and determine an expected head orientation, craniotomy size and location, and trajectory to be taken into the tumor. These variables can then be exported for use as inputs to the biomechanical model associated with the correction framework. A multisurgeon, multicase mock trial was conducted to compare the accuracy of the virtual plan to that of a mock physical surgery. It was concluded that the Android application was an accurate, efficient, and timely method for planning surgical variables.

Brain shift in neuronavigation of brain tumors: A review

PURPOSE

Neuronavigation based on preoperative imaging data is a ubiquitous tool for image guidance in neurosurgery. However, it is rendered unreliable when brain shift invalidates the patient-to-image registration. Many investigators have tried to explain, quantify, and compensate for this phenomenon to allow extended use of neuronavigation systems for the duration of surgery. The purpose of this paper is to present an overview of the work that has been done investigating brain shift.

METHODS

A review of the literature dealing with the explanation, quantification and compensation of brain shift is presented. The review is based on a systematic search using relevant keywords and phrases in PubMed. The review is organized based on a developed taxonomy that classifies brain shift as occurring due to physical, surgical or biological factors.

RESULTS

This paper gives an overview of the work investigating, quantifying, and compensating for brain shift in neuronavigation while describing the successes, setbacks, and additional needs in the field. An analysis of the literature demonstrates a high variability in the methods used to quantify brain shift as well as a wide range in the measured magnitude of the brain shift, depending on the specifics of the intervention. The analysis indicates the need for additional research to be done in quantifying independent effects of brain shift in order for some of the state of the art compensation methods to become useful.

CONCLUSION

This review allows for a thorough understanding of the work investigating brain shift and introduces the needs for future avenues of investigation of the phenomenon.

Toward a generic real-time compression correction framework for tracked ultrasound

PURPOSE

Tissue compression during ultrasound imaging leads to error in the location and geometry of subsurface targets during soft tissue interventions. We present a novel compression correction method, which models a generic block of tissue and its subsurface tissue displacements resulting from application of a probe to the tissue surface. The advantages of the new method are that it can be realized independent of preoperative imaging data and is capable of near-video framerate compression compensation for real-time guidance.

METHODS

The block model is calibrated to the tip of any tracked ultrasound probe. Intraoperative digitization of the tissue surface is used to measure the depth of compression and provide boundary conditions to the biomechanical model of the tissue. The tissue displacement field solution of the model is inverted to nonrigidly transform the ultrasound images to an estimation of the tissue geometry prior to compression. This method was compared to a previously developed method using a patient-specific model and within the context of simulation, phantom, and clinical data.

RESULTS

Experimental results with gel phantoms demonstrated that the proposed generic method reduced the mock tumor margin modified Hausdorff distance (MHD) from 5.0 ± 1.6 to 2.1 ± 0.7 mm and reduced mock tumor centroid alignment error from 7.6 ± 2.6 to 2.6 ± 1.1mm. The method was applied to a clinical case and reduced the in vivo tumor margin MHD error from 5.4 ± 0.1 to 2.9 ± 0.1mm, and the centroid alignment error from 7.2 ± 0.2 to 3.8 ± 0.4 mm.

CONCLUSIONS

The correction method was found to effectively improve alignment of ultrasound and tomographic images and was more efficient compared to a previously proposed correction.

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.