Image registration of ex-vivo MRI to sparsely sectioned histology of hippocampal and neocortical temporal lobe specimens.

Intractable or drug-resistant epilepsy occurs in up to 30% of epilepsy patients, with many of these patients undergoing surgical excision of the affected brain region to achieve seizure control. Recent magnetic resonance imaging (MRI) sequences and analysis techniques have the potential to detect abnormalities not identified with diagnostic MRI protocols. Prospective studies involving pre-operative imaging and collection of surgically-resected tissue provide a unique opportunity for verification and tuning of these image analysis techniques, since direct comparison can be made against histopathology, and can lead to better prediction of surgical outcomes and potentially less invasive procedures. To carry out MRI and histology comparison, spatial correspondence between the MR images and the histology images must be found. Towards this goal, a novel pipeline is presented here for bringing ex-vivo MRI of surgically-resected temporal lobe specimens and digital histology into spatial correspondence. The sparsely-sectioned histology images represent a challenge for 3D reconstruction which we address with a combined 3D and 2D registration algorithm that alternates between slice-based and volume-based registration with the ex-vivo MRI. We evaluated our registration method on specimens resected from patients undergoing anterior temporal lobectomy (N=7) and found our method to have a mean target registration error of 0.76±0.66 and 0.98±0.60 mm for hippocampal and neocortical specimens respectively. This work allows for the spatially-local comparison of histology with post-operative MRI and paves the way for eventual correlation with pre-operative MRI image analysis techniques.

Off-pump positioning of a conventional aortic valve prosthesis through the left ventricular apex with the universal cardiac introducer under sole ultrasound guidance, in the pig.

OBJECTIVE: : To test an alternative to catheter and open-heart techniques, by documenting the feasibility of implanting an unmodified mechanical aortic valve (AoV) in the off pump, beating heart using the universal cardiac introducer (UCI) attached to the left ventricular (LV) apex. METHODS: : In six pigs, the LV apex was exposed by a median sternotomy. The UCI was attached to the apex. A 12-mm punching tool (punch), introduced through the UCI, was used to create a cylindrical opening through the apex. Then, the AoV, secured to a holder, was introduced into the LV, using transesophageal echocardiographic, guided through the apical LV opening, navigated into the LV outflow tract, and positioned within the aortic annulus. Transesophageal echocardiographic guidance was useful for navigation and positioning by superimposing the aortic annulus and prosthetic ring while Doppler imaging verified preserved prosthetic function and absence of perivalvular leaks. The valve function and hemodynamics were observed before termination for macroscopic evaluation. RESULTS: : The punch produced a clean opening without fragmentation or myocardial embolization. During advancement of the mechanical AoV, there were no arrhythmias, mitral valve dysfunctions, evidence of myocardial ischemia, or hemodynamic instability. The AoVs were well seated over the annulus, without obstructing the coronaries or contact with the conduction system. The ring of AoVs was well circumscribed by the aortic annulus. CONCLUSIONS: : This study documented the feasibility of positioning a mechanical AoV on the closed, beating heart. These results should encourage the development of adjunct technologies to deliver current tissue or mechanical AoV with minimal side effects.

Novel multistage three-dimensional medical image segmentation: methodology and validation.

In this paper, we propose a novel multistage method for three-dimensional (3-D) segmentation of medical images and a new radial distance-based segmentation validation approach. For the 3-D segmentation method, we first employ a morphological recursive erosion operation to reduce the connectivity between the region of interest and its surrounding neighborhood; then we design a hybrid segmentation method to achieve an initial result. The hybrid approach integrates an improved fast marching method and a morphological reconstruction algorithm. Finally, a morphological recursive dilation is employed to recover any lost structure from the first stage of the multistage method. This approach is tested on 12 CT and 3 MRI images of the brain, heart, and kidney, to demonstrate the effectiveness and accuracy of this technique across a variety of imaging modalities and organ systems. In order to validate the multistage segmentation method, a novel radial distance-based validation method is proposed that uses a global accuracy (GA) measure. The GA is calculated based on local radial distance errors (LRDE), where LRDE are calculated on the radii emitted from points along the skeleton of the object rather than the centroid, in order to accommodate more complicated organ structures. The experimental results demonstrate that the proposed multistage segmentation method is fast and accurate, with comparable performance to existing segmentation methods, but with a significantly higher execution speed.

Automatic fusion of freehand endoscopic brain images to three-dimensional surfaces: creating stereoscopic panoramas.

A major limitation of the use of endoscopes in minimally invasive surgery is the lack of relative context between the endoscope and its surroundings. The purpose of this work was to fuse images obtained from a tracked endoscope to surfaces derived from three-dimensional (3-D) preoperative magnetic resonance or computed tomography (CT) data, for assistance in surgical planning, training and guidance. We extracted polygonal surfaces from preoperative CT images of a standard brain phantom and digitized endoscopic video images from a tracked neuro-endoscope. The optical properties of the endoscope were characterized using a simple calibration procedure. Registration of the phantom (physical space) and CT images (preoperative image space) was accomplished using fiducial markers that could be identified both on the phantom and within the images. The endoscopic images were corrected for radial lens distortion and then mapped onto the extracted surfaces via a two-dimensional 2-D to 3-D mapping algorithm. The optical tracker has an accuracy of about 0.3 mm at its centroid, which allows the endoscope tip to be localized to within 1.0 mm. The mapping operation allows multiple endoscopic images to be "painted" onto the 3-D brain surfaces, as they are acquired, in the correct anatomical position. This allows panoramic and stereoscopic visualization, as well as navigation of the 3-D surface, painted with multiple endoscopic views, from arbitrary perspectives.