Model-Based Image Updating for Brain Shift in Deep Brain Stimulation Electrode Placement Surgery

Porcine-human glioma xenograft model. Immunosuppression and model reproducibility

BACKGROUND

Glioblastoma is the most common primary malignant and treatment-resistant human brain tumor. Rodent models have played an important role in understanding brain cancer biology and treatment. However, due to their small cranium and tumor volume mismatch, relative to human disease, they have been less useful for translational studies. Therefore, development of a consistent and simple large animal glioma xenograft model would have significant translational benefits.

METHODS

Immunosuppression was induced in twelve standard Yucatan minipigs. 3 pigs received cyclosporine only, while 9 pigs received a combined regimen including cyclosporine (55 mg/kg q12 h), prednisone (25 mg, q24 h) and mycophenolate (500 mg q24 h). U87 cells (2 × 106) were stereotactically implanted into the left frontal cortex. The implanted brains were imaged by MRI for monitoring. In a separate study, tumors were grown in 5 additional pigs using the combined regimen, and pigs underwent tumor resection with intra-operative image updating to determine if the xenograft model could accurately capture the spatial tumor resection challenges seen in humans.

RESULTS

Tumors were successfully implanted and grown in 11 pigs. One animal in cyclosporine only group failed to show clinical tumor growth. Clinical tumor growth, assessed by MRI, progressed slowly over the first 10 days, then rapidly over the next 10 days. The average tumor growth latency period was 20 days. Animals were monitored twice daily and detailed records were kept throughout the experimental period. Pigs were sacrificed humanely when the tumor reached 1 - 2 cm. Some pigs experienced decreased appetite and activity, however none required premature euthanasia. In the image updating study, all five pigs demonstrated brain shift after craniotomy, consistent with what is observed in humans. Intraoperative image updating was able to accurately capture and correct for this shift in all five pigs.

CONCLUSION

This report demonstrates the development and use of a human intracranial glioma model in an immunosuppressed, but nongenetically modified pig. While the immunosuppression of the model may limit its utility in certain studies, the model does overcome several limitations of small animal or genetically modified models. For instance, we demonstrate use of this model for guiding surgical resection with intraoperative image-updating technologies. We further report use of a surrogate extracranial tumor that indicates growth of the intracranial tumor, allowing for relative growth assessment without radiological imaging.

Model-based image updating in deep brain stimulation with assimilation of deep brain sparse data

BACKGROUND

Accuracy of electrode placement for deep brain stimulation (DBS) is critical to achieving desired surgical outcomes and impacts the efficacy of treating neurodegenerative diseases. Intraoperative brain shift degrades the accuracy of surgical navigation based on preoperative images.

PURPOSE

We extended a model-based image updating scheme to address intraoperative brain shift in DBS surgery and improved its accuracy in deep brain.

METHODS

We evaluated 10 patients, retrospectively, who underwent bilateral DBS surgery and classified them into groups of large and small deformation based on a 2 mm subsurface movement threshold and brain shift index of 5%. In each case, sparse brain deformation data were used to estimate whole brain displacements and deform preoperative CT (preCT) to generate updated CT (uCT). Accuracy of uCT was assessed using target registration errors (TREs) at the Anterior Commissure (AC), Posterior Commissure (PC), and four calcification points in the sub-ventricular area by comparing their locations in uCT with their ground truth counterparts in postoperative CT (postCT).

RESULTS

In the large deformation group, TREs were reduced from 2.5 mm in preCT to 1.2 mm in uCT (53% compensation); in the small deformation group, errors were reduced from 1.25 to 0.74 mm (41%). Average reduction of TREs at AC, PC and pineal gland were significant, statistically (p ⩽ 0.01).

CONCLUSIONS

With more rigorous validation of model results, this study confirms the feasibility of improving the accuracy of model-based image updating in compensating for intraoperative brain shift during DBS procedures by assimilating deep brain sparse data.

A particle filter approach to dynamic kidney pose estimation in robotic surgical exposure

PURPOSE

Traditional soft tissue registration methods require direct intraoperative visualization of a significant portion of the target anatomy in order to produce acceptable surface alignment. Image guidance is therefore generally not available during the robotic exposure of structures like the kidneys which are not immediately visualized upon entry into the abdomen. This paper proposes guiding surgical exposure with an iterative state estimator that assimilates small visual cues into an a priori anatomical model as exposure progresses, thereby evolving pose estimates for the occluded structures of interest.

METHODS

Intraoperative surface observations of a right kidney are simulated using endoscope tracking and preoperative tomography from a representative robotic partial nephrectomy case. Clinically relevant random perturbations of the true kidney pose are corrected using this sequence of observations in a particle filter framework to estimate an optimal similarity transform for fitting a patient-specific kidney model at each step. The temporal response of registration error is compared against that of serial rigid coherent point drift (CPD) in both static and simulated dynamic surgical fields, and for varying levels of observation persistence.

RESULTS

In the static case, both particle filtering and persistent CPD achieved sub-5 mm accuracy, with CPD processing observations 75% faster. Particle filtering outperformed CPD in the dynamic case under equivalent computation times due to the former requiring only minimal persistence.

CONCLUSION

This proof-of-concept simulation study suggests that Bayesian state estimation may provide a viable pathway to image guidance for surgical exposure in the abdomen, especially in the presence of dynamic intraoperative tissue displacement and deformation.