Neuronavigation-Assisted Surgery for Distal Anterior Cerebral Artery Aneurysm

Neuronavigation-assisted microsurgical clipping of pericallosal aneurysms: A single-center retrospective study

Surgical clipping of pericallosal artery aneurysm is technically challenging since it is fragile and tends to rupture accidentally during the operation. This study was aimed to evaluate the efficacy and safety of MRI-neuronavigation-assisted microsurgery for pericallosal artery aneurysm clipping. Forty patients diagnosed with pericallosal artery aneurysms who underwent craniotomy clipping were enrolled. Among these patients, 18 cases accepted routine surgical approaches, while another 22 cases accepted MRI-neuronavigation-assisted microsurgery. Design of craniotomy, operation pathway, operation duration, intraoperative cerebral protection and superior drainage vein protection were analyzed retrospectively. All the 40 cases underwent aneurysm clipping by pre-coronal inter-hemispheric approach, and all aneurysms were clipped completely confirmed by postoperative CTA or DSA. During the operations, MRI-neuronavigation provided precise spatial configuration of pericallosal artery aneurysms, and allowed accurate and real-time identification for the adjacent arteries and brain structures, and no aneurysms ruptured accidentally during the operations. Functional cortex and draining veins were protected well. Compared with routine surgical approaches, the MRI-neuronavigation-assisted microsurgery showed less operation duration, few adverse events induced by accurate location for aneurysm and less invasion to draining veins. Therefore, MRI-neuronavigation-assisted microsurgery could precisely locate the pericallosal artery aneurysm, optimize surgical approaches, and help to cerebral protection. It is expected to reduce the surgical risk and improve the precision and security, can be regarded as an effective technology in the clipping of pericallosal artery aneurysms.

Frameless neuronavigation based only on 3D digital subtraction angiography using surface-based facial registration

OBJECT

Cerebrovascular lesions can have complicated abnormal anatomy that is not completely characterized by CT or MR angiography. Although 3D rotational angiography provides superior spatial and temporal resolution, catheter angiograms are not easily registered to the patient, limiting the use of these images as a source for neuronavigation. However, 3D digital subtraction angiography (DSA) contains not only vascular anatomy but also facial surface anatomy data. The authors report a novel technique to register 3D DSA images by using only the surface anatomy contained within the data set without having to fuse the DSA image set to other imaging modalities or use fiducial markers.

METHODS

A cadaver model was first created to assess the accuracy of neuronavigation based on 3D DSA images registered by facial surface anatomy. A 3D DSA scan was obtained of a formalin-fixed cadaver head, with acquisitions of mask and contrast runs. The right common carotid artery was injected prior to the contrast run with a 45% contrast solution diluted with water-soluble red liquid latex. One week later, the head was registered to a neuronavigation system loaded with the 3D DSA images acquired earlier using facial surface anatomy. A right pterional craniotomy was performed and 10 different vascular landmarks were identified and measured for accuracy using the neuronavigation system. Neuronavigation based only on 3D DSA was then used to guide an open clipping procedure for a patient who presented with a ruptured distal lenticulostriate aneurysm.

RESULTS

The accuracy of the measurements for the cadaver model was 0.71 ± 0.25 mm (mean ± SE), which is superior to the 1.8-5 mm reported for neuronavigation. The 3D DSA-based navigation-assisted surgery for the distal lenticulostriate aneurysm aided in localization, resulting in a small craniotomy and minimal brain dissection.

CONCLUSIONS

This is the first example of frameless neuronavigation based on 3D catheter angiography registered by only the surface anatomy data contained within the 3D DSA image set. This is an easily applied technique that is beneficial for accurately locating vascular pathological entities and reducing the dissection burden of vascular lesions.

Genealogy of Training in Vascular Neurosurgery

Remarkable advances and changes in the landscape of neurovascular disease have occurred recently. Concurrently, a paradigm shift in training and resident education is underway. This crossroad of unique opportunities and pressures necessitates creative change in the training of future vascular neurosurgeons to allow incorporation of surgical advances, new technology, and supplementary treatment modalities in a setting of reduced work hours and increased public scrutiny. This article discusses the changing landscape in neurovascular disease treatment, followed by the recent changes in resident training, and concludes with our view of the future of training in vascular neurosurgery.

Advancing neurosurgery with image-guided robotics

Robotic systems are being introduced into surgery to extend human ability. NeuroArm represents a potential change in the way surgery is performed; this is the first image-guided, MR-compatible surgical robot capable of both microsurgery and stereotaxy. This paper presents the first surgical application of neuroArm in an investigation of microsurgical performance, navigation accuracy, and Phase I clinical studies. To evaluate microsurgical performance, 2 surgeons performed microsurgery (splenectomy, bilateral nephrectomy, and thymectomy) in a rodent model using neuroArm and conventional techniques. Two senior residents served as controls, using the conventional technique only (8 rats were used in each of the 3 treatment groups; the 2 surgeons each treated 4 rats from each group). Total surgery time, blood loss, thermal injury, vascular injury, and animal death due to surgical error were recorded and converted to an overall performance score. All values are reported as the mean +/- SEM when normally distributed and as the median and interquartile range when not. Surgeons were slower using neuroArm (1047 +/- 69 seconds) than with conventional microsurgical techniques (814 +/- 54 seconds; p = 0.019), but overall performance was equal (neuroArm: 1110 +/- 82 seconds; microsurgery: 1075 +/- 136 seconds; p = 0.825). Using microsurgery, the surgeons had overall performance scores equal to those of the control resident surgeons (p = 0.141). To evaluate navigation accuracy, the localization error of neuroArm was compared with an established system. Nanoparticles were implanted at predetermined bilateral targets in a cadaveric model (4 specimens) using image guidance. The mean localization error of neuroArm (4.35 +/- 1.68 mm) proved equal to that of the conventional navigation system (10.4 +/- 2.79 mm; p = 0.104). Using the conventional system, the surgeon was forced to retract the biopsy tool to correct the angle of entry in 2 of 4 trials. To evaluate Phase I clinical integration, the role of neuroArm was progressively increased in 5 neurosurgical procedures. The impacts of neuroArm on operating room (OR) staff, hardware, software, and registration system performance were evaluated. NeuroArm was well received by OR staff and progressively integrated into patient cases, starting with draping in Case 1. In Case 2 and all subsequent cases, the robot was registered. It was used for tumor resection in Cases 3-5. Three incidents involving restrictive cable length, constrictive draping, and reregistration failure were resolved. In Case 5, the neuroArm safety system successfully mitigated a hardware failure. NeuroArm performs as well and as accurately as conventional techniques, with demonstrated safety technology. Clinical integration was well received by OR staff, and successful tumor resection validates the surgical applicability of neuroArm.