Comparison of Snellen and Early Treatment Diabetic Retinopathy Study charts using a computer simulation

AIM: To compare accuracy, reproducibility and test duration for the Snellen and the Early Treatment Diabetic Retinopathy Study (ETDRS) charts, two main tools used to measure visual acuity (VA). METHODS: A computer simulation was programmed to run multiple virtual patients, each with a unique set of assigned parameters, including VA, false-positive and false-negative error values. For each virtual patient, assigned VA was randomly chosen along a continuous scale spanning the range between 1.0 to 0.0 logMAR units (equivalent to 20/200 to 20/20). Each of 30 000 virtual patients were run ten times on each of the two VA charts. RESULTS: Average test duration (expressed as the total number of characters presented during the test ±SD) was 12.6±11.1 and 31.2±14.7 characters, for the Snellen and ETDRS, respectively. Accuracy, defined as the absolute difference (± SD) between the assigned VA and the measured VA, expressed in logMAR units, was superior in the ETDRS charts: 0.12±0.14 and 0.08±0.08, for the Snellen and ETDRS charts, respectively. Reproducibility, expressed as test-retest variability, was superior in the ETDRS charts: 0.23±0.17 and 0.11±0.09 logMAR units, for the Snellen and ETDRS charts, respectively. CONCLUSION: A comparison of true (assigned) VA to measured VA, demonstrated, on average, better accuracy and reproducibility of the ETDRS chart, but at the penalty of significantly longer test duration. These differences were most pronounced in the low VA range. The reproducibility using a simulation approach is in line with reproducibility values found in several clinical studies

Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI.

OBJECTIVE:Deep brain stimulation (DBS) requires accurate localization of the anatomical target structure, and the precise placement of the DBS electrode within it. Ultra-high field 7 Tesla (T) MR images can be utilized to create patient-specific anatomical 3D models of the subthalamic nuclei (STN) to enhance pre-surgical DBS targeting as well as post-surgical visualization of the DBS lead position and orientation. We validated the accuracy of the 7T imaging-based patient-specific model of the STN and measured the variability of the location and dimensions across movement disorder patients. METHODS:72 patients who underwent DBS surgery were scanned preoperatively on 7T MRI. Segmentations and 3D volume rendering of the STN were generated for all patients. For 21 STN-DBS cases, microelectrode recording (MER) was used to validate the segmentation. For 12 cases, we computed the correlation between the overlap of the STN and volume of tissue activated (VTA) and the monopolar review for a further validation of the model's accuracy and its clinical relevancy. RESULTS:We successfully reconstructed and visualized the STN in all patients. Significant variability was found across individuals regarding the location of the STN center of mass as well as its volume, length, depth and width. Significant correlations were found between MER and the 7T imaging-based model of the STN (r = 0.86) and VTA-STN overlap and the monopolar review outcome (r = 0.61). CONCLUSION:The results suggest that an accurate visualization and localization of a patient-specific 3D model of the STN can be generated based on 7T MRI. The imaging-based 7T MRI STN model was validated using MER and patient's clinical outcomes. The significant variability observed in the STN location and shape based on a large number of patients emphasizes the importance of an accurate direct visualization of the STN for DBS targeting. An accurate STN localization can facilitate postoperative stimulation parameters for optimized patient outcome

Numerical Investigation of an Axis-based Approach to Rigid Registration

The term rigid registration identifies the process that optimally aligns different data sets whose information has to be merged, as in the case of robot calibration, image-guided surgery or patient-specific gait analysis. One of the most common approaches to rigid registration relies on the identifica-tion of a set of fiducial points in each data set to be registered to compute the rototranslational matrix that optimally aligns them. Both measurement and hu-man errors directly affect the final accuracy of the process. Increasing the number of fiducials may improve registration accuracy but it will also increase the time and complexity of the whole procedure, since correspondence must be estab-lished between fiducials in different data sets. The aim of this paper is to present a new approach that resorts to axes instead of points as fiducial features. The fundamental advantage is that any axis can be easily identified in each data set by least-square linear fitting of multiple, un-sorted measured data. This provides a way to filtering the measurement error within each data set, improving the registration accuracy with a reduced effort. In this work, a closed-form solution for the optimal axis-based rigid registration is presented. The accuracy of the method is compared with standard point-based rigid registration through a numerical test. Axis-based registration results one or-der of magnitude more accurate than point-based registration