The variable origin of the lateral circumflex femoral artery: a meta-analysis and proposal for a new classification system.

The lateral circumflex femoral artery (LCFA) is responsible for vascularisation of the head and neck of the femur, greater trochanter, vastus lateralis and the knee. The origin of the LCFA has been reported to vary significantly throughout the literature, with numerous branching patterns described and variable distances to the mid-inguinal point reported. The aim of this study was to determine the estimated population prevalence and pooled means of these anatomical characteristics, and review their associated clinical relevance. A search of the major electronic databases was performed to identify all articles reporting data on the origin of the lateral circumflex femoral artery and its distance to the mid-inguinal point. Additionally, an extensive search of the references of all relevant articles was performed. All data on origin, branching, and distance to mid-inguinal point was extracted and pooled into a meta-analysis. A total of 26 articles (n = 3731 lower limbs) were included in the meta-analysis. Lateral circumflex femoral artery most commonly originates from the deep femoral artery with a pooled prevalence of 76.1% (95% confidence interval 69.4-79.3). The deep femoral artery-derived lateral circumflex femoral artery was found to originate with a mean pooled distance of 51.06 mm (95% confidence interval 44.61-57.51 mm) from the mid-inguinal point. Subgroup analysis of both gender and limb side data were consistent with these findings. Due to variability in the lateral circumflex femoral artery's origin and distance to mid-inguinal point, anatomical knowledge is crucial for clinicians to avoid iatrogenic injuries when performing procedures in the femoral region, and thus radiographic assessment prior to surgery is recommended. Lastly, we propose a new classification system for origin of the lateral circumflex femoral artery.

Detection of fixed points in spatiotemporal signals by a clustering method.

We present a method to determine fixed points in spatiotemporal signals. The method combines a clustering algorithm and a nonlinear analysis method fitting temporal dynamics. A 144-dimensional simulated signal, similar to a Kueppers-Lortz instability, is analyzed and its fixed points are reconstructed.

Probabilistic modeling of single-trial fMRI data.

This paper describes a probabilistic framework for modeling single-trial functional magnetic resonance (fMR) images based on a parametric model for the hemodynamic response and Markov random field (MRF) image models. The model is fitted to image data by maximizing a lower bound on the log likelihood. The result is an approximate maximum a posteriori estimate of the joint distribution over the model parameters and pixel labels. Examples show how this technique can used to segment two-dimensional (2-D) fMR images, or parts thereof, into regions with different characteristics of their hemodynamic response.