Search

Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation. To make it possible to use these same-modality measures, image synthesis is used to create proxy images for the opposite modality as well as intensity-normalized images from each of the two available images. The new deformable registration framework was evaluated by performing intra-subject deformation recovery, intra-subject boundary alignment, and inter-subject label transfer experiments using multi-contrast magnetic resonance brain imaging data. Three different multi-channel registration algorithms were evaluated, revealing that the framework is robust to the multi-channel deformable registration algorithm that is used. With a single exception, all results demonstrated improvements when compared against single channel registrations using the same algorithm with mutual information.

Objectives Current robotic training approaches lack the criteria for automatically assessing and tracking (over time) technical skills separately from clinical proficiency. We describe the development and validation of a novel automated and objective framework for the assessment of training. Methods We are able to record all system variables (stereo instrument video, hand and instrument motion, buttons and pedal events) from the da Vinci surgical systems using a portable archival system integrated with the robotic surgical system. Data can be collected unsupervised, and the archival system does not change system operations in any way. Our open-ended multicenter protocol is collecting surgical skill benchmarking data from 24 trainees to surgical proficiency, subject only to their continued availability. Two independent experts performed structured (objective structured assessment of technical skills) assessments on longitudinal data from 8 novice and 4 expert surgeons to generate baseline data for training and to validate our computerized statistical analysis methods in identifying the ranges of operational and clinical skill measures. Results Objective differences in operational and technical skill between known experts and other subjects were quantified. The longitudinal learning curves and statistical analysis for trainee performance measures are reported. Graphic representations of the skills developed for feedback to the trainees are also included. Conclusions We describe an open-ended longitudinal study and automated motion recognition system capable of objectively differentiating between clinical and technical operational skills in robotic surgery. Our results have demonstrated a convergence of trainee skill parameters toward those derived from expert robotic surgeons during the course of our training protocol.