Comparison of Registration Accuracy of Skin- and Bone-Implanted Fiducials for Frameless Stereotaxis of the Brain: A Prospective Study

Ammirati, Mario; Gross, Jeffrey D.; Ammirati, Giuseppe; Dugan, Sharon

doi:10.1055/s-2002-33458-1

Cited by 22 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The typical accuracy of skin-affixed fiducial-based patient registration has been well documented to be 2–5 mm (Helm and Eckel, 1998; Ammirati et al, 2002; and Wolfsberger et al, 2002), and we have found similar results in our experience (Roberts et al, 1998; Roberts et al, 1999; Hartov et al, 2008; Ji et al, 2008a). US scan-head calibration error can be as high as 2–3 mm (Hartov et al, 1999).…”

Section: Methodssupporting

confidence: 88%

Data assimilation using a gradient descent method for estimation of intraoperative brain deformation

Hartov

Roberts

et al. 2009

Medical Image Analysis

View full text Add to dashboard Cite

Biomechanical models that simulate brain deformation are gaining attention as alternatives for brain shift compensation. One approach, known as the “forced-displacement method”, constrains the model to exactly match the measured data through boundary condition (BC) assignment. Although it improves model estimates and is computationally attractive, the method generates fictitious forces and may be ill-advised due to measurement uncertainty. Previously, we have shown that by assimilating intraoperatively acquired brain displacements in an inversion scheme, the Representer algorithm (REP) is able to maintain stress-free BCs and improve model estimates by 33% over those without data guidance in a controlled environment. However, REP is computationally efficient only when a few data points are used for model guidance because its costs scale linearly in the number of data points assimilated, thereby limiting its utility (and accuracy) in clinical settings. In this paper, we present a steepest gradient descent algorithm (SGD) whose computational complexity scales nearly invariantly with the number of measurements assimilated by iteratively adjusting the forcing conditions to minimize the difference between measured and model-estimated displacements (model-data misfit). Solutions of full linear systems of equations are achieved with a parallelized direct solver on a shared-memory, eight-processor Linux cluster. We summarize the error contributions from the entire process of model-updated image registration compensation and we show that SGD is able to attain model estimates comparable to or better than those obtained with REP, capturing about 74% to 82% of tumor displacement, but with a computational effort that is significantly less (a factor of 4-fold or more reduction relative to REP) and nearly invariant to the amount of sparse data involved when the number of points assimilated is large. Based on five patient cases, an average computational cost of approximately 2 minutes for estimating whole-brain deformation has been achieved with SGD using 100 sparse data points, suggesting the new algorithm is sufficiently fast with adequate accuracy for routine use in the operating room (OR).

show abstract

Section: Methodssupporting

confidence: 88%

Data assimilation using a gradient descent method for estimation of intraoperative brain deformation

Hartov

Roberts

et al. 2009

Medical Image Analysis

View full text Add to dashboard Cite

show abstract

“…Further, they are based on CT scans, which do not have the issues of magnetic susceptibility, gradient inhomogeneities, and chemical shift that can distort the images of fatty markers on the scalp by 1–2 mm in MR images. When a direct comparison was made on MR images, Ammirati et al [2002] found only a small improvement in bone implanted markers as compared to skin markers (average FREs of 2.25 and 2.76 mm were obtained, respectively).…”

Section: Discussionmentioning

confidence: 99%

Validation of a method for coregistering scalp recording locations with 3D structural MR images

Whalen

Maclin

Fabiani

et al. 2007

Human Brain Mapping

139

138

View full text Add to dashboard Cite

A common problem in brain imaging is how to most appropriately coregister anatomical and functional data sets into a common space. For surface-based recordings such as the event related optical signal (EROS), near-infrared spectroscopy (NIRS), event-related potentials (ERPs), and magnetoencephalography (MEG), alignment is typically done using either (1) a landmark-based method involving placement of surface markers that can be detected in both modalities; or (2) surface-fitting alignment that samples many points on the surface of the head in the functional space and aligns those points to the surface of the anatomical image. Here we compare these two approaches and advocate a combination of the two in order to optimize coregistration of EROS and NIRS data with structural magnetic resonance images (sMRI). Digitized 3D sensor locations obtained with a Polhemus digitizer can be effectively coregistered with sMRI using fiducial alignment as an initial guess followed by a Marquardt-Levenberg least-squares rigid-body transform (df = 6) to match the surfaces. Additional scaling parameters (df = 3) and point-by-point surface constraints can also be employed to further improve fitting. These alignment procedures place the lower-bound residual error at 1.3 +/- 0.1 mm (micro +/- s) and the upper-bound target registration error at 4.4 +/- 0.6 mm (micro +/- s). The dependence of such errors on scalp segmentation, number of registration points, and initial guess is also investigated. By optimizing alignment techniques, anatomical localization of surface recordings can be improved in individual subjects.

show abstract

“…1 Commercial image-guided systems provide real-time information on where surgical instruments (e.g., a stylus probe, an operating microscope, or an ultrasound scan head) are located relative to the coregistered preoperative scans. The accuracy of this form of navigation is typically between 2 and 5 mm 10 when measured by fiducial registration error (FRE), although its validity relies on maintaining a rigid relationship between the patient’s brain tissue and pMR image data.…”

mentioning

confidence: 99%

Intraoperative image updating for brain shift following dural opening

Fan¹,

Roberts

Schaewe

et al. 2016

JNS

View full text Add to dashboard Cite

OBJECTIVE Preoperative magnetic resonance images (pMR) are typically coregistered to provide intraoperative navigation, the accuracy of which can be significantly compromised by brain deformation. In this study, the authors generated updated MR images (uMR) in the operating room (OR) to compensate for brain shift due to dural opening, and evaluated the accuracy and computational efficiency of the process. METHODS In 20 open cranial neurosurgical cases, a pair of intraoperative stereovision (iSV) images was acquired after dural opening to reconstruct a 3D profile of the exposed cortical surface. The iSV surface was registered with pMR to detect cortical displacements that were assimilated by a biomechanical model to estimate whole-brain nonrigid deformation and produce uMR in the OR. The uMR views were displayed on a commercial navigation system and compared side by side with the corresponding coregistered pMR. A tracked stylus was used to acquire coordinate locations of features on the cortical surface that served as independent positions for calculating target registration errors (TREs) for the coregistered uMR and pMR image volumes. RESULTS The uMR views were visually more accurate and well aligned with the iSV surface in terms of both geometry and texture compared with pMR where misalignment was evident. The average misfit between model estimates and measured displacements was 1.80 ± 0.35 mm, compared with the average initial misfit of 7.10 ± 2.78 mm between iSV and pMR, and the average TRE was 1.60 ± 0.43 mm across the 20 patients in the uMR image volume, compared with 7.31 ± 2.82 mm on average in the pMR cases. The iSV also proved to be accurate with an average error of 1.20 ± 0.37 mm. The overall computational time required to generate the uMR views was 7–8 minutes. CONCLUSIONS This study compensated for brain deformation caused by intraoperative dural opening using computational model–based assimilation of iSV cortical surface displacements. The uMR proved to be more accurate in terms of model-data misfit and TRE in the 20 patient cases evaluated relative to pMR. The computational time was acceptable (7–8 minutes) and the process caused minimal interruption of surgical workflow.

show abstract

Comparison of Registration Accuracy of Skin- and Bone-Implanted Fiducials for Frameless Stereotaxis of the Brain: A Prospective Study

Cited by 22 publications

References 14 publications

Data assimilation using a gradient descent method for estimation of intraoperative brain deformation

Data assimilation using a gradient descent method for estimation of intraoperative brain deformation

Validation of a method for coregistering scalp recording locations with 3D structural MR images

Intraoperative image updating for brain shift following dural opening

Contact Info

Product

Resources

About