Cortical Thickness Estimation: A Comparison of FreeSurfer and Three Voxel-Based Methods in a Test-Retest Analysis and a Clinical Application.

The cortical thickness has been used as a biomarker to assess different cerebral conditions and to detect alterations in the cortical mantle. In this work, we compare methods from the FreeSurfer software, the Computational Anatomy Toolbox (CAT12), a Laplacian approach and a new method here proposed, based on the Euclidean Distance Transform (EDT), and its corresponding computational phantom designed to validate the calculation algorithm. At region of interest (ROI) level, within- and inter-method comparisons were carried out with a test-retest analysis, in a subset comprising 21 healthy subjects taken from the Multi-Modal MRI Reproducibility Resource (MMRR) dataset. From the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) data, classification methods were compared in their performance to detect cortical thickness differences between 23 healthy controls (HC) and 45 subjects with Alzheimer's disease (AD). The validation of the proposed EDT-based method showed a more accurate and precise distance measurement as voxel resolution increased. For the within-method comparisons, mean test-retest measures (percentages differences/intraclass correlation/Pearson correlation) were similar for FreeSurfer (1.80%/0.90/0.95), CAT12 (1.91%/0.83/0.91), Laplacian (1.27%/0.89/0.95) and EDT (2.20%/0.88/0.94). Inter-method correlations showed moderate to strong values (R > 0.77) and, in the AD comparison study, all methods were able to detect cortical alterations between groups. Surface- and voxel-based methods have advantages and drawbacks regarding computational demands and measurement precision, while thickness definition was mainly associated to the cortical thickness absolute differences among methods. However, for each method, measurements were reliable, followed similar trends along the cortex and allowed detection of cortical atrophies between HC and patients with AD.

A novel voxel-based method to estimate cortical sulci width and its application to compare patients with Alzheimer's disease to controls.

A voxel-based method for measuring sulcal width was developed, validated and applied to a database. This method (EDT-based LM) employs the 3D Euclidean Distance Transform (EDT) of the pial surface and a Local Maxima labeling algorithm. A computational phantom was designed to test method performance; results revealed the method's inaccuracy δ, to range between 0.1 and 0.5 voxels, for a width that varied between 1 and 7 voxels. Two morphological descriptors were computed to characterize each defined sulcus: mean sulcal width (MSW) and mean absolute deviation (MAD). The former is the average width for all available width measurements within the sulcus, and the latter is the deviation of these measurements. The EDT-based LM method was applied to the Minimal Interval Resonance Imaging in the Alzheimer's Disease (MIRIAD) database, for a set of high-resolution Magnetic Resonance (MR) images of 66 subjects: 43 patients with Alzheimer Disease (AD) and 23 control subjects. AD causes significant gray matter loss; hence, some sulci were expected to broaden. Methodological results concurred with this hypothesis. After a Wilcoxon test, MSW was grater in the case of all sulci pertaining to AD patients, (p < 0.05, FDR corrected), whereas MAD showed significant differences in 8 sulci (p < 0.05, FDR corrected). This work presents a novel voxel-based method for measuring sulcal width and extracting descriptors to characterize and compare the sulci within and across subjects.

Virtual reality simulation of robotic transsphenoidal brain tumor resection: Evaluating dynamic motion scaling in a master-slave system.

BACKGROUND: Integrating simulators with robotic surgical procedures could assist in designing and testing of novel robotic control algorithms and further enhance patient-specific pre-operative planning and training for robotic surgeries. METHODS: A virtual reality simulator, developed to perform the transsphenoidal resection of pituitary gland tumours, tested the usability of robotic interfaces and control algorithms. It used position-based dynamics to allow soft-tissue deformation and resection with haptic feedback; dynamic motion scaling control was also incorporated into the simulator. RESULTS: Neurosurgeons and residents performed the surgery under constant and dynamic motion scaling conditions (CMS vs DMS). DMS increased dexterity and reduced the risk of damage to healthy brain tissue. Post-experimental questionnaires indicated that the system was well-evaluated by experts. CONCLUSION: The simulator was intuitively and realistically operated. It increased the safety and accuracy of the procedure without affecting intervention time. Future research can investigate incorporating this simulation into a real micro-surgical robotic system.