Accuracy Assessment of CBCT-Based Volumetric Brain Shift Field

Smit-Ockeloen, Ingrid M Iris; Ruijters, Daniël; Breeuwer, Marcel; Babić, Draženko; Brina, Olivier; Pereira, Vítor Mendes

doi:10.1007/978-3-319-31808-0_1

Cited by 3 publications

(6 citation statements)

References 19 publications

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“…Apparently, the outlier detection of the registration framework successfully ignores the streak artifacts, making the streak correction in the vessel segmentation not strictly necessary, while the few true vessels that are accidentally removed, are informative for finding the correct transformation. We also validated the method of Smit-Ockeloen et al [8], which has a mean registration error of 2.89 mm -slightly worse than ours. A more detailed look at the results (Fig.…”

Section: Registrationmentioning

confidence: 62%

“…Since the preprocessing part was adopted from [8], we only validate the more challenging intra-operative cortex segmentation here. We found that, over all images, the mean distance between manual and automatic cortex segmentation was 1.05 mm.…”

Section: Results and Validationmentioning

confidence: 99%

“…Then, the pre-and intra-operative images are rigidly registered, using the sum of squared differences as a similarity measure. Finally, the brain is segmented using the brain extraction tool proposed by Smith [10] adapted for CBCT [8].…”

Section: Preprocessingmentioning

confidence: 99%

“…However, in practice these images suffer from streak artifacts from the metal stereotactic frame. Smit-Ockeloen et al [8] propose a method where the CBCT angiographies are first preprocessed to reduce streak artifacts, then landmark enhanced and finally elastically registered to find the displacement field. The landmark enhancement comprises applying a vesselness filter and zeroing all voxels inside the ventricle and outside the brain segmentations.…”

Section: Introductionmentioning

confidence: 99%

“…Since metal streak artifacts are present in the intra-operative images, we focus on reducing their influence on the segmentation and registration. We evaluate our method on a clinical dataset of eight patients and make a quantitative comparison with the method of Smit-Ockeloen et al [8].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Inferring brain deformation during open neurosurgery using CBCT angiography

Verheyen

Robben

Ruijters

et al. 2016

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

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During neurosurgery the brain can deform, which is known as brain shift. The deformation can be as large as 20 mm and limits the accuracy of image-guided surgery. In this work we use pre-and intra-operative CBCT angiography to infer this deformation. We first segment cortex and vessels in both images and then perform surface registration between the pre-and intra-operative segmentations. The resulting deformation field is assumed to correspond to the brain deformation and can be used to update other preoperative images. Since the intra-operative image quality is deteriorated by streak artifacts originating from the metallic stereotactic frame, our approach explores robust segmentation and registration methods that reduce the influence of these artifacts. Our method is tested on a clinical data set of 8 patients who underwent neurosurgery. We also compare with a recently published method and rigid registration as a baseline method, showing the improved accuracy of our approach.

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Section: Registrationmentioning

confidence: 62%

Section: Results and Validationmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Inferring brain deformation during open neurosurgery using CBCT angiography

Verheyen

Robben

Ruijters

et al. 2016

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

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Intraoperative Brain Shift Compensation Using a Hybrid Mixture Model

Bayer

Ravikumar

Strumia

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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Augmented reality navigation for cranial biopsy and external ventricular drain insertion

Skyrman

Lai

Edström

et al. 2021

Neurosurgical Focus

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OBJECTIVE The aim of this study was to evaluate the accuracy (deviation from the target or intended path) and efficacy (insertion time) of an augmented reality surgical navigation (ARSN) system for insertion of biopsy needles and external ventricular drains (EVDs), two common neurosurgical procedures that require high precision. METHODS The hybrid operating room–based ARSN system, comprising a robotic C-arm with intraoperative cone-beam CT (CBCT) and integrated video tracking of the patient and instruments using nonobtrusive adhesive optical markers, was used. A 3D-printed skull phantom with a realistic gelatinous brain model containing air-filled ventricles and 2-mm spherical biopsy targets was obtained. After initial CBCT acquisition for target registration and planning, ARSN was used for 30 cranial biopsies and 10 EVD insertions. Needle positions were verified by CBCT. RESULTS The mean accuracy of the biopsy needle insertions (n = 30) was 0.8 mm ± 0.43 mm. The median path length was 39 mm (range 16–104 mm) and did not correlate to accuracy (p = 0.15). The median device insertion time was 149 seconds (range 87–233 seconds). The mean accuracy for the EVD insertions (n = 10) was 2.9 mm ± 0.8 mm at the tip with a 0.7° ± 0.5° angular deviation compared with the planned path, and the median insertion time was 188 seconds (range 135–400 seconds). CONCLUSIONS This study demonstrated that ARSN can be used for navigation of percutaneous cranial biopsies and EVDs with high accuracy and efficacy.

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Accuracy Assessment of CBCT-Based Volumetric Brain Shift Field

Cited by 3 publications

References 19 publications

Inferring brain deformation during open neurosurgery using CBCT angiography

Inferring brain deformation during open neurosurgery using CBCT angiography

Intraoperative Brain Shift Compensation Using a Hybrid Mixture Model

Augmented reality navigation for cranial biopsy and external ventricular drain insertion

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