Abstract
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.
Highlights
Malignant gliomas are the most common primary and metastatic brain tumors, accounting for ∼70% of the 22,500 new cases of primary brain tumors diagnosed annually in adults in the United States [1, 2]
We evaluate the accuracy and performance of the Adaptive Physics-Based Non-Rigid Registration (A-PBNRR) method for modeling brain deformation in the presence of tumor resection
For a more comprehensive evaluation, the accuracy was assessed using both qualitative, and quantitative criteria (Hausdorff Distance-based error metric, and a landmark-based error measured by Dr Chengjun Yao)
Summary
Malignant gliomas are the most common primary and metastatic brain tumors, accounting for ∼70% of the 22,500 new cases of primary brain tumors diagnosed annually in adults in the United States [1, 2]. Gross total resection is difficult to achieve because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. Oncologic outcomes clearly depend on the extent of tumor resection, yet functional preservation is critical for quality of life and survival, so tumor surgery is a delicate balance between removing as much tumor as possible and preserving important functional areas of the brain [3,4,5]. Preoperative anatomical Magnetic Resonance Imaging (MRI) can be combined with functional MRI (fMRI) to map out areas of the brain near the tumor that are involved with important function such as vision, speech and language, or motor control [7,8,9,10,11,12]. Diffusion Tensor Imaging (DTI) can be used to map out white matter tracts that connect to these important regions and run near or through the tumor [13,14,15,16,17,18,19]
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