In this study, we compared the organization of orientation preference in visual areas V1, V2, and V3. Within these visual areas, we also quantified the relationship between orientation preference and cytochrome oxidase (CO) staining patterns. V1 maps of orientation preference contained both pinwheels and linear zones. The location of CO blobs did not relate in a systematic way to maps of orientation; although, as in other primates, there were approximately twice as many pinwheels as CO blobs. V2 contained bands of high and low orientation selectivity. The bands of high orientation selectivity were organized into pinwheels and linear zones, but iso-orientation domains were twice as large as those in V1. Quantitative comparisons between bands containing high or low orientation selectivity and CO dark and light bands suggested that at least four functional compartments exist in V2, CO dense bands with either high or low orientation selectivity, and CO light bands with either high or low selectivity. We also demonstrated that two functional compartments exist in V3, with zones of high orientation selectivity corresponding to CO dense areas and zones of low orientation selectivity corresponding to CO pale areas. Together with previous findings, these results suggest that the modular organization of V1 is similar across primates and indeed across most mammals. V2 organization in owl monkeys also appears similar to that of other simians but different from that of prosimians and other mammals. Finally, V3 of owl monkeys shows a compartmental organization for orientation selectivity that remains to be demonstrated in other primates.

Indications for liver surgery to treat primary and secondary hepatic malignancies are broadening. Utilizing data from B-mode or 2-dimensional intraoperative ultrasound, it is often challenging to replicate the findings from preoperative CT or MRI scans. Additional data from more recently developed image-guidance technology, which registers preoperative axial imaging to a 3-dimensional real-time model, may be used to improve operative planning, locate difficult to find hepatic tumors, and guide ablations. Laparoscopic liver procedures are often more challenging than their open counterparts. Image-guidance technology can assist in overcoming some of the obstacles to minimally invasive liver procedures by enhancing ultrasound findings and ablation guidance. This manuscript describes a protocol that evaluated an open image-guidance system, and a subsequent protocol that directly compared, for validation, a laparoscopic with an open image-guidance system. Both protocols were limited to ablations within the liver. The laparoscopic image-guidance system successfully creates a 3-D model at both 7 and 14 mm Hg that is similar to the open 3-D model. Ultimately, improving intraoperative image guidance can help expand the ability to perform both laparoscopic and open liver surgeries.

Optical imaging of intrinsic responses to visual stimuli in extrastriate cortex of owl monkeys provided evidence for the dorsal half of the third visual area, V3. Visual stimuli were used to selectively activate locations in dorsolateral V2 and the rostrally adjoining presumptive V3. Consistent with the proposed retinotopies of dorsal V2 and dorsal V3, small bars of drifting gratings along the horizontal meridian of the contralateral hemifield activated cortex along the V2͞V3 border, whereas such stimuli along the vertical meridian activated cortex along the rostral border of V3. Stimuli in limited locations in the lower visual quadrant revealed mirror reversals of retinotopy in dorsal V2 and V3, whereas stimuli in the upper visual quadrant failed to activate either region. Brain sections processed for cytochrome oxidase from the same cases provided architectonic borders of V2 that matched those indicated by the optical imaging. The results support the concept that a narrow dorsal V3 exists in monkeys. V3d borders dorsal V2 and contains a smaller, mirror-image representation of the lower visual quadrant.T he visual cortex of monkeys and other primates is widely recognized as containing a number of visual areas, each specialized to contribute uniquely to visual function (see refs. 1-3 for review). However, of the several dozen visual areas proposed to exist in monkeys, only three areas, the first and second areas, V1 and V2, and the middle temporal area (MT, V5), are well established as valid subdivisions with known architectonic characteristics, retinotopy, types of modular organization, response characteristics to visual stimuli, and patterns of connections (see ref.3). Recently we provided architectonic and connectional evidence for the existence of a third visual area, V3 (see Fig. 1a), in prosimian primates (4), New World monkeys (5, 6), and Old World macaque monkeys (7). Although Zeki (8) and Cragg (9) long ago provided connectional evidence for V3 in macaque monkeys, and evidence from microelectrode mapping experiments accumulated for macaques (10, 11) and large New World cebus monkeys (12, 13), that evidence has been open to other interpretations (e.g., see Fig. 1b; see refs. 3 and 7 for review).The goal of the present study was to address the issue of the validity of V3 with another method of revealing the organization of visual cortex, the use of optical imaging of intrinsic cortical signals evoked by stimuli in different locations in the visual field. In a manner similar to that of functional MRI in humans (e.g., refs. 14-17), optical imaging has the potential of revealing global patterns of retinotopic organization of extrastriate cortex of monkeys. Optical imaging has been used recently to study features of retinotopic organization of V1 in several species of mammals (18-21), but this method has not been widely applied to extrastriate cortex yet. In the present study, we explored the retinotopy of the presumptive dorsal V3 region in owl monkeys using optical images of intrinsic signals. This study wa...

The Explorer™ Liver guidance system represents novel technology that continues to evolve. This initial experience indicates that image guidance is valuable in certain procedures, specifically in cases in which difficult anatomy or tumour location or echogenicity limit the usefulness of traditional guidance methods.

Background Postoperative or remnant liver volume (RLV) following hepatic resection is a critical predictor of perioperative outcome. This study investigates whether the accuracy of liver surgical planning software for predicting postoperative RLV and assessing early regeneration. Study Design Patients eligible for hepatic resection were approached for participation in the study from June 2008 to 2010. All patients underwent cross-sectional imaging (CT or MRI) prior to and early after resection. Planned remnant liver volume (pRLV) (based on the planned resection on the preoperative scan), and postoperative actual remnant liver volume (aRLV) (determined from early postoperative scan) were measured using Scout Liver software (Pathfinder Therapeutics Inc., Nashville, TN). Differences between pRLV and aRLV were analyzed controlling for timing of postoperative imaging. Measured TLV was compared against standard equations for calculating volume. Results Sixty-six patients were enrolled in the study from June 2008 to June 2010 at three treatment centers. Correlation was found between pRLV and aRLV (r=0.941, p<0.001), which improved when timing of postoperative imaging was considered (r=0.953, p<0.001). Relative volume deviation from pRLV to aRLV stratified cases according to timing of postoperative imaging showed evidence of measurable regeneration beginning five days after surgery with stabilization at eight days (p < 0.01). For patients at the upper and lower extremes of liver volumes, TLV was poorly estimated using standard equations (up to 50% in some cases). Conclusions Preoperative virtual planning of future liver remnant accurately predicts postoperative volume following hepatic resection. Early postoperative liver regeneration is measureable on imaging beginning at five days following surgery. Measuring TLV directly from CT scans rather than calculating based on equations accounts for extremes in TLV.