Improving Separability of Structures with Similar Attributes in 2D Transfer Function Design

Extended finite element analysis for the 2nd and 3rd metatarsals stress fracture during push-off

Metatarsal stress fractures (MSF), particularly the 2nd and 3rd MSF, are common injuries among athletes. Although there are several practices to reduce foot and ankle injuries, there is no injury prevention program specifically designed to minimize MSF. This is mainly due to the lack of information about the loadings/postures that cause MSF. Therefore, this study aimed to investigate dangerous loadings/postures potentially causing MSF during push-off (PO). The analysis was conducted with Finite Element Modelling (FEM), calibrated with the three-point bending test, and validated with peak plantar pressure (PPP) and fracture force measurement. Extended Finite Element Method was used for MSF simulation such that ten different foot and ankle configurations were designed, with five for each of the 2nd and 3rd MSF under pure vertical loadings. A more complex loading, ankle eversion/inversion during PO, was also examined for the MSF. The average error percentage for the calibration of the model with the three-point bending test was 3.05%. The average error percentages for the validation of the model with PPP and fracture force measurements were 18% and 30%, respectively. The outcomes of pure vertical loadings indicated the higher potential for the 2nd and 3rd MSF at 30% PO and 70% PO, respectively. The results of ankle eversion/inversion loadings represented that the most dangerous posture for MSF was 30° ankle eversion for the 3rd metatarsal at 70% PO. These results provide a guide, including what postures to avoid for the 2nd and 3rd MSF among people who are at high risk of MSF.

Development of a simplified, reproducible, parametric 3D model of the talus

Computational foot models have significant application in surgical decision making, injury and disease diagnosis and prevention, sports performance analysis and footwear engineering. However, due to the substantial time in model building and the heavy computational costs from the complexity of the models, daily clinical application of these foot models has yet to be achieved. Much of the previous research adopted a detailed-geometry approach in modeling bones that potentially contributed to the heavy computational costs. In this research, we developed a computational talus model based on CT section image data, image reconstruction and segmentation, contact surface identification, standard shape fitting, and finite element auto meshing algorithms. Modeling the bones as rigid is common, and modeling the contact surfaces only for the rigid body saves additional computational resources. Priority, therefore, in the shape fitting with optimization is given to the contact surfaces of the talus. Thirteen sets (9 males and 4 females) of CT section data were obtained. Image reconstruction, segmentation and bone labeling were conducted on each set of CT data to identify talus and its adjacent bones. Contact surfaces of the talus were then identified based on bone spatial relationships. Apart from the talar dome surface which was fitted by a 3rd-order polynomial, standard shapes such as ellipsoids and planes were used to fit the selected contact surfaces so that the geometrical parameters maintain physical significance. Based on these parameters, we automatically recreated and meshed the least-squares fitted shapes rapidly with limited elements. Last, mean major contact surfaces of the talus were obtained and fitted by standard shapes. Although the number of samples in this study was relatively small, our method provides sufficient and accurate geometric parameters of these contact surfaces to completely describe and reproduce the talus, on both a subject specific and average basis. The method for describing the talus here helps to parametrize computational models using planes and ellipsoids, improves surgical decision making and implants with a more precise and physically significant measures, and the description provides bone geometric parameters which can later be used to relate risk analysis for bone shape specific injury rates.

Occlusion and Slice-Based Volume Rendering Augmentation for PET-CT

Dual-modality positron emission tomography and computed tomography (PET-CT) depicts pathophysiological function with PET in an anatomical context provided by CT. Three-dimensional volume rendering approaches enable visualization of a two-dimensional slice of interest (SOI) from PET combined with direct volume rendering (DVR) from CT. However, because DVR depicts the whole volume, it may occlude a region of interest, such as a tumor in the SOI. Volume clipping can eliminate this occlusion by cutting away parts of the volume, but it requires intensive user involvement in deciding on the appropriate depth to clip. Transfer functions that are currently available can make the regions of interest visible, but this often requires complex parameter tuning and coupled preprocessing of the data to define the regions. Hence, we propose a new visualization algorithm where an SOI from PET is augmented by volumetric contextual information from a DVR of the counterpart CT so that the obtrusiveness from the CT in the SOI is minimized. Our approach automatically calculates an augmentation depth parameter by considering the occlusion information derived from the voxels of the CT in front of the PET SOI. The depth parameter is then used to generate an opacity weight function that controls the amount of contextual information visible from the DVR. We outline the improvements with our visualization approach compared to other slice-based and our previous approaches. We present the preliminary clinical evaluation of our visualization in a series of PET-CT studies from patients with nonsmall cell lung cancer.