We introduce PLIKS (Pseudo-Linear Inverse Kinematic Solver) for reconstruction of a 3D mesh of the human body from a single 2D image. Current techniques directly regress the shape, pose, and translation of a parametric model from an input image through a non-linear mapping with minimal flexibility to any external influences. We approach the task as a model-in-the-loop optimization problem. PLIKS is built on a linearized formulation of the parametric SMPL model. Using PLIKS, we can analytically reconstruct the human model via 2D pixel-aligned vertices. This enables us with the flexibility to use accurate camera calibration information when available. PLIKS offers an easy way to introduce additional constraints such as shape and translation. We present quantitative evaluations which confirm that PLIKS achieves more accurate reconstruction with greater than 10% improvement compared to other state-of-the-art methods with respect to the standard 3D human pose and shape benchmarks while also obtaining a reconstruction error improvement of 12.9 mm on the newer AGORA dataset.

Blood flow velocity estimation techniques from 2D fluoroscopy and more recently rotational angiography images represent a topic of wide interest in various clinical research areas. In particular, they can be an important step towards patient-specific flow simulations. Additionally, it can be of diagnostic interest to evaluate volumetric blood flow in stenotic vessel segments; e.g. in a patient's brain. In this work, we present a robust optimization-based approach to estimate mean blood flow velocities from rotational digital subtraction angiography (DSA) images. Our method was extensively evaluated on 70 simulated datasets and 6 clinical datasets with MR phase contrast ground truth data. Our evaluation explores the limitations of image-based velocity estimation; i.e., measurements over short or small vessel segments. Overall, we were able to estimate the mean velocity with average errors as little as 4% for simulation studies, if the vessel segment is sufficiently long, and achieved results within the confines of the MR phase contrast ground truth data for our clinical data, with an average relative error to the centerline measurement of 9.5%±10.5%. The achieved accuracy enables patient-specific hemodynamic simulations and may also be of immediate diagnostic interest.