Laser range scanning for image‐guided neurosurgery: Investigation of image‐to‐physical space registrations

Cao, Aize; Thompson, Reid C.; Dumpuri, Prashanth; Dawant, Benoît M.; Galloway, Robert L.; Ding, Sha; Miga, Michael I.

doi:10.1118/1.2870216

Cited by 61 publications

(45 citation statements)

References 31 publications

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“…The points are used to provide an initial alignment which then moves on to a standard iterative closest point algorithm to complete the rigid registration process. It has been found that face-based alignment is equivalent to traditional synthetic fiducial alignment in [23]. …”

Section: Methodsmentioning

confidence: 99%

Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases

et al. 2015

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Purpose Brain shift during neurosurgical procedures must be corrected for in order to reestablish accurate alignment for successful image-guided tumor resection. Sparse-data-driven biomechanical models that predict physiological brain shift by accounting for typical deformation-inducing events such as cerebrospinal fluid drainage, hyperosmotic drugs, swelling, retraction, resection, and tumor cavity collapse are an inexpensive solution. This study evaluated the robustness and accuracy of a biomechanical model-based brain shift correction system to assist with tumor resection surgery in 16 clinical cases. Methods Preoperative computation involved the generation of a patient-specific finite element model of the brain and creation of an atlas of brain deformation solutions calculated using a distribution of boundary and deformation-inducing forcing conditions (e.g., sag, tissue contraction, and tissue swelling). The optimum brain shift solution was determined using an inverse problem approach which linearly combines solutions from the atlas to match the cortical surface deformation data collected intraoperatively. The computed deformations were then used to update the preoperative images for all 16 patients. Results The mean brain shift measured ranged on average from 2.5 to 21.3 mm, and the biomechanical model-based correction system managed to account for the bulk of the brain shift, producing a mean corrected error ranging on average from 0.7 to 4.0 mm. Conclusions Biomechanical models are an inexpensive means to assist intervention via correction for brain deformations that can compromise surgical navigation systems. To our knowledge, this study represents the most comprehensive clinical evaluation of a deformation correction pipeline for image-guided neurosurgery.

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Section: Methodsmentioning

confidence: 99%

Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases

et al. 2015

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“…More importantly, because point-pair matching algorithms developed for FBR are designed to minimize the FRE, the target registration error (TRE) in the region of surgical interest, either on the cortical surface or deeper inside the brain, can be ≥5 mm. 2,24 …”

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confidence: 99%

“…28 The accuracy of extracranial SBR (based on external facial or forehead curvature) is reported to be comparable with FBR in terms of TRE. 2 Although the accuracy can be further improved by intraoperative reregistration using the cortical surface (e.g., acquired by a laser range scanner 2,24 or stereovision 6 ) or the brain volume (e.g., acquired by ultrasound 19 ), these intraoperative reregistration methods still require an initial alignment achieved by some form of patient registration, and thus they have not replaced patient registration.…”

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confidence: 99%

Intraoperative fiducial-less patient registration using volumetric 3D ultrasound: a prospective series of 32 neurosurgical cases

Fan¹,

Roberts

et al. 2015

JNS

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Object Fiducial-based registration (FBR) is used widely for patient registration in image-guided neurosurgery. The authors of this study have developed an automatic fiducial-less registration (FLR) technique to find the patient-to-image transformation by directly registering 3D ultrasound (3DUS) with MR images without incorporating prior information. The purpose of the study was to evaluate the performance of the FLR technique when used prospectively in the operating room and to compare it with conventional FBR. Methods In 32 surgical patients who underwent conventional FBR, preoperative T1-weighted MR images (pMR) with attached fiducial markers were acquired prior to surgery. After craniotomy but before dural opening, a set of 3DUS images of the brain volume was acquired. A 2-step registration process was executed immediately after image acquisition: 1) the cortical surfaces from pMR and 3DUS were segmented, and a multistart sum-of-squared-intensity-difference registration was executed to find an initial alignment between down-sampled binary pMR and 3DUS volumes; and 2) the alignment was further refined by a mutual information-based registration between full-resolution grayscale pMR and 3DUS images, and a patient-to-image transformation was subsequently extracted. Results To assess the accuracy of the FLR technique, the following were quantified: 1) the fiducial distance error (FDE); and 2) the target registration error (TRE) at anterior commissure and posterior commissure locations; these were compared with conventional FBR. The results showed that although the average FDE (6.42 ± 2.05 mm) was higher than the fiducial registration error (FRE) from FBR (3.42 ± 1.37 mm), the overall TRE of FLR (2.51 ± 0.93 mm) was lower than that of FBR (5.48 ± 1.81 mm). The results agreed with the intent of the 2 registration techniques: FBR is designed to minimize the FRE, whereas FLR is designed to optimize feature alignment and hence minimize TRE. The overall computational cost of FLR was approximately 4–5 minutes and minimal user interaction was required. Conclusions Because the FLR method directly registers 3DUS with MR by matching internal image features, it proved to be more accurate than FBR in terms of TRE in the 32 patients evaluated in this study. The overall efficiency of FLR in terms of the time and personnel involved is also improved relative to FBR in the operating room, and the method does not require additional image scans immediately prior to surgery. The performance of FLR and these results suggest potential for broad clinical application.

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“…Usually, the image space and patient space were registered by these two approaches then the TRE was measured at a small number of anatomical or artificial landmarks for each method. In some studies the TRE calculated for these two registration methods was similar [5][6][7], while in others the TRE for point matching was smaller than that for surface matching [8][9][10]. According to the research on TRE distribution in point matching [11,12], the TRE varies at different points in the same registration.…”

Section: Introductionmentioning

confidence: 99%

Properties of the target registration error for surface matching in neuronavigation

Wang

Song

2011

Computer Aided Surgery

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Objective: Surface matching is a relatively new method of spatial registration in neuronavigation. Compared to the traditional point matching method, surface matching does not use fiducial markers that must be fixed to the surface of the head before image scanning, and therefore does not require an image acquisition specifically dedicated for navigation purposes. However, surface matching is not widely used clinically, mainly because there is still insufficient knowledge about its application accuracy. This study aimed to explore the properties of the Target Registration Error (TRE) of surface matching in neuronavigation. Materials and Methods: The surface matching process was simulated in the image space of a neuronavigation system so that the TRE could be calculated at any point in that space. For each registration, two point clouds were generated to represent the surface extracted from preoperative images (PC image ) and the surface obtained intraoperatively by laser scanning (PC laser ). The properties of the TRE were studied by performing multiple registrations with PC laser point clouds at different positions and generated by adding different types of error. Results: For each registration, the TRE had a minimal value at a point in the image space, and the iso-valued surface of the TRE was approximately ellipsoid with smaller TRE on the inner surfaces. The position of the point with minimal TRE and the shape of the iso-valued surface were highly random across different registrations, and the surface registration error between the two point clouds was irrelevant to the TRE at a specific point. The overall TRE tended to increase with the increase in errors in PC laser , and a larger PC laser made it less sensitive to these errors. With the introduction of errors in PC laser , the points with minimal TRE tended to be concentrated in the anterior and inferior part of the head. Conclusion:The results indicate that the alignment between the two surfaces could not provide reliable information about the registration accuracy at an arbitrary target point. However, according to the spatial distribution of the target registration error of a single registration, enough application accuracy could be guaranteed by proper visual verification after registration. In addition, surface matching tends to achieve high accuracy in the inferior and anterior part of the head, and a relatively large scanning area is preferable.

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Laser range scanning for image‐guided neurosurgery: Investigation of image‐to‐physical space registrations

Cited by 61 publications

References 31 publications

Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases

Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases

Intraoperative fiducial-less patient registration using volumetric 3D ultrasound: a prospective series of 32 neurosurgical cases

Properties of the target registration error for surface matching in neuronavigation

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