scholarly journals Image Updating for Brain Shift Compensation During Resection

2017 ◽  
Vol 14 (4) ◽  
pp. 402-411 ◽  
Author(s):  
Xiaoyao Fan ◽  
David W Roberts ◽  
Jonathan D Olson ◽  
Songbai Ji ◽  
Timothy J Schaewe ◽  
...  
Keyword(s):  
Biomechanical Model ◽  
Partial Resection ◽  
Computational Time ◽  
Brain Shift ◽  
Registration Errors ◽  
Brain Deformation ◽  
Preoperative Magnetic Resonance ◽  
Surgical Field ◽  
Image Pairs ◽  
Dural Opening

Abstract BACKGROUND In open-cranial neurosurgery, preoperative magnetic resonance (pMR) images are typically coregistered for intraoperative guidance. Their accuracy can be significantly degraded by intraoperative brain deformation, especially when resection is involved. OBJECTIVE To produce model updated MR (uMR) images to compensate for brain shift that occurred during resection, and evaluate the performance of the image-updating process in terms of accuracy and computational efficiency. METHODS In 14 resection cases, intraoperative stereovision image pairs were acquired after dural opening and during resection to generate displacement maps of the surgical field. These data were assimilated by a biomechanical model to create uMR volumes of the evolving surgical field. A tracked stylus provided independent measurements of feature locations to quantify target registration errors (TREs) in the original coregistered pMR and uMR as surgery progressed. RESULTS Updated MR TREs were 1.66 ± 0.27 and 1.92 ± 0.49 mm in the 14 cases after dural opening and after partial resection, respectively, compared to 8.48 ± 3.74 and 8.77 ± 4.61 mm for pMR, respectively. The overall computational time for generating uMRs after partial resection was less than 10 min. CONCLUSION We have developed an image-updating system to compensate for brain deformation during resection using a computational model with data assimilation of displacements measured with intraoperative stereovision imaging that maintains TREs less than 2 mm on average.

scholarly journals Intraoperative image updating for brain shift following dural opening

2016 ◽  
Vol 126 (6) ◽  
pp. 1924-1933 ◽  
Author(s):  
Xiaoyao Fan ◽  
David W. Roberts ◽  
Timothy J. Schaewe ◽  
Songbai Ji ◽  
Leslie H. Holton ◽  
...  
Keyword(s):  
Biomechanical Model ◽  
Magnetic Resonance Images ◽  
Computational Time ◽  
Average Error ◽  
Cortical Surface ◽  
Brain Shift ◽  
Surgical Workflow ◽  
Brain Deformation ◽  
Time Required ◽  
Dural Opening

OBJECTIVEPreoperative 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.METHODSIn 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.RESULTSThe 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.CONCLUSIONSThis 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.

scholarly journals Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Ramy A. Zeineldin ◽  
Mohamed E. Karar ◽  
Jan Coburger ◽  
Christian R. Wirtz ◽  
Franziska Mathis-Ullrich ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Mean Squared Error ◽  
Intraoperative Ultrasound ◽  
Brain Shift ◽  
Resonance Imaging ◽  
Registration Method ◽  
Brain Deformation ◽  
Preoperative Magnetic Resonance ◽  
Magnetic Resonance Imaging Mri

AbstractIntraoperative brain deformation, so-called brain shift, affects the applicability of preoperative magnetic resonance imaging (MRI) data to assist the procedures of intraoperative ultrasound (iUS) guidance during neurosurgery. This paper proposes a deep learning-based approach for fast and accurate deformable registration of preoperative MRI to iUS images to correct brain shift. Based on the architecture of 3D convolutional neural networks, the proposed deep MRI-iUS registration method has been successfully tested and evaluated on the retrospective evaluation of cerebral tumors (RESECT) dataset. This study showed that our proposed method outperforms other registration methods in previous studies with an average mean squared error (MSE) of 85. Moreover, this method can register three 3D MRI-US pair in less than a second, improving the expected outcomes of brain surgery.

scholarly journals Use of Stereovision for Intraoperative Coregistration of a Spinal Surgical Field: A Human Feasibility Study

2017 ◽  
Vol 14 (1) ◽  
pp. 29-35 ◽  
Author(s):  
S Scott Lollis ◽  
Xiaoyao Fan ◽  
Linton Evans ◽  
Jonathan D Olson ◽  
Keith D Paulsen ◽  
...  
Keyword(s):  
Reconstruction Error ◽  
Computational Time ◽  
Registration Accuracy ◽  
Camera System ◽  
Cross Sectional ◽  
Registration Method ◽  
Current Form ◽  
Preoperative Ct ◽  
Cross Sectional Imaging ◽  
Surgical Field

AbstractBACKGROUNDThe use of image guidance during spinal surgery has been limited by several anatomic factors such as intervertebral segment motion and ineffective spine immobilization. In its current form, the surgical field is coregistered with a preoperative computed tomography (CT), often obtained in a different spinal confirmation, or with intraoperative cross-sectional imaging. Stereovision offers an alternative method of registration.OBJECTIVETo demonstrate the feasibility of stereovision-mediated coregistration of a human spinal surgical field using a proof-of-principle study, and to provide preliminary assessments of the technique's accuracy.METHODSA total of 9 subjects undergoing image-guided pedicle screw placement also underwent stereovision-mediated coregistration with preoperative CT imaging. Stereoscopic images were acquired using a tracked, calibrated stereoscopic camera system mounted on an operating microscope. Images were processed, reconstructed, and segmented in a semi-automated manner. A multistart registration of the reconstructed spinal surface with preoperative CT was performed. Registration accuracy, measured as surface-to-surface distance error, was compared between stereovision registration and a standard registration.RESULTSThe mean surface reconstruction error of the stereovision-acquired surface was 2.20 ± 0.89 mm. Intraoperative coregistration with stereovision was performed with a mean error of 1.48 ± 0.35 mm compared to 2.03 ± 0.28 mm using a standard point-based registration method. The average computational time for registration with stereovision was 95 ± 46 s (range 33-184 s) vs 10to 20 min for standard point-based registration.CONCLUSIONSemi-automated registration of a spinal surgical field using stereovision is possible with accuracy that is at least comparable to current landmark-based techniques.

Pre- and Intraoperative Tractographic Evaluation of Corticospinal Tract Shift

Neurosurgery ◽  
2011 ◽  
Vol 69 (3) ◽  
pp. 696-705 ◽  
Author(s):  
Andrea Romano ◽  
Giancarlo D'Andrea ◽  
Luigi Fausto Calabria ◽  
Valeria Coppola ◽  
Camilla Rossi Espagnet ◽  
...  
Keyword(s):  
Corticospinal Tract ◽  
Diffusion Tensor ◽  
Tumor Resection ◽  
Brain Lesions ◽  
Peritumoral Edema ◽  
Brain Shift ◽  
Neuronavigation System ◽  
Dural Opening ◽  
Glioma Resection ◽  
The Brain

Abstract BACKGROUND: Magnetic resonance with diffusion tensor image (DTI) may be able to estimate trajectories compatible with subcortical tracts close to brain lesions. A limit of DTI is brain shifting (movement of the brain after dural opening and tumor resection). OBJECTIVE: To calculate the brain shift of trajectories compatible with the corticospinal tract (CST) in patients undergoing glioma resection and predict the shift directions of CST. METHODS: DTI was acquired in 20 patients and carried out through 12 noncollinear directions. Dedicated software “merged” all sequences acquired with tractographic processing and the whole dataset was sent to the neuronavigation system. Preoperative, after dural opening (in 11) and tumor resection (in all) DTI acquisitions were performed to evaluate CST shifting. The extent of shifting was considered as the maximum distance between the preoperative and intraoperative contours of the trajectories. RESULTS: An outward shift of CST was observed in 8 patients and an inward shift in 10 patients during surgery. In the remaining 2 patients, no intraoperative displacement was detected. Only peritumoral edema showed a statistically significant correlation with the amount of shift. In those patients in which DTI was acquired after dural opening as well (11 patients), an outward shifting of CST was evident in that phase. CONCLUSION: The use of intraoperative DTI demonstrated brain shifting of the CST. DTI evaluation of white matter tracts can be used during surgical procedures only if updated with intraoperative acquisitions.

scholarly journals Photoacoustic-MR Images Registration Based on Co-Sparse Analysis Model to Compensate for Brain Shift

2021 ◽  
Author(s):  
Parastoo Farnia ◽  
Bahador Makkiabadi ◽  
Meysam Alimohammadi ◽  
Ebrahim Najafzadeh ◽  
Maryam Basij ◽  
...  
Keyword(s):  
Image Registration ◽  
Real Time ◽  
Mouse Brain ◽  
Brain Shift ◽  
Challenging Problem ◽  
Analysis Model ◽  
Mr Images ◽  
Registration Method ◽  
Phantom Data ◽  
Brain Deformation

Brain shift is an important obstacle for the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging systems to update the image-guided surgery systems with real-time data. However, due to the innate limitations of the current imaging modalities, accurate and real-time brain shift compensation remains as a challenging problem. In this study, application of the intra-operative photoacoustic (PA) imaging and registration of the intra-operative PA images with pre-operative brain MR images is proposed to compensate brain deformation during surgery. Finding a satisfactory multimodal image registration method is a challenging problem due to complicated and unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for PA-MR image registration which can capture the interdependency of two modalities. The proposed algorithm works based on the minimization of mapping transform by using a pair of analysis operators. These operators are learned by the alternating direction method of multipliers. The method was evaluated using experimental phantom and ex-vivo data obtained from mouse brain. The results of phantom data show about 60% and 63% improvement in root mean square error (RMSE) and target registration error (TRE) in comparison with commonly used normalized mutual information registration method. In addition, the results of mouse brain and phantom data shown more accurate performance for PA versus ultrasound imaging for brain shift calculation. Finally, by using the proposed registration method, the intra-operative PA images could become a promising tool when the brain shift invalidated pre-operative MRI.

scholarly journals Efficient Image Registration for Underwater Optical Mapping Using Geometric Invariants

2019 ◽  
Vol 7 (6) ◽  
pp. 178
Author(s):  
Armagan Elibol ◽  
Nak Young Chong
Keyword(s):  
Image Registration ◽  
Optical Mapping ◽  
Probabilistic Methods ◽  
Computational Time ◽  
Intermediate Step ◽  
Geometrical Properties ◽  
Geometric Invariants ◽  
Salient Points ◽  
Feature Based ◽  
Image Pairs

Image registration is one of the most fundamental and widely used tools in optical mapping applications. It is mostly achieved by extracting and matching salient points (features) described by vectors (feature descriptors) from images. While matching the descriptors, mismatches (outliers) do appear. Probabilistic methods are then applied to remove outliers and to find the transformation (motion) between images. These methods work in an iterative manner. In this paper, an efficient way of integrating geometric invariants into feature-based image registration is presented aiming at improving the performance of image registration in terms of both computational time and accuracy. To do so, geometrical properties that are invariant to coordinate transforms are studied. This would be beneficial to all methods that use image registration as an intermediate step. Experimental results are presented using both semi-synthetically generated data and real image pairs from underwater environments.

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