The human body comes in many sizes and shapes. So, realistic virtual mannequins, which represent body shapes that occur in a target population, are valuable tools for product developers who design near-body products. Statistical shape modeling is a promising approach to map out the variability of a population using 3D scans. A statistical shape model (SSM) can therefore be used as a virtual mannequin. By adapting the parameters of the SSM, a new, realistic surface can be obtained. In this chapter, we present a registration-based framework to build such a statistical shape model. The framework consists of three parts. First, the input surfaces are brought into correspondence with each other by elastic surface registration. Next, a statistical shape model is built. Then, the relationship between the body shape and body measurements is described. Furthermore, a posture model is constructed to filter out posture variances that occur in the database and generate a posture-invariant shape model. The statistical shape model is convertible to a digital human model (DHM) that contains much more 3D information than traditional DHMs (e.g., Jack). The surface prediction from features is an intuitive extension of the traditional anthropometry percentiles and can also be the basis of a new sizing system for near-body products. In this chapter, we propose a fast, skeleton-less, marker-less, data-driven method to generate statistical shape models in a posture-invariant way.

For product developers that design near-body products, virtual mannequins that represent realistic body shapes, are valuable tools. With statistical shape modelling, the variability of such body shapes can be described. Shape variation captured by statistical shape models (SSMs) is often polluted by posture variations, leading to less compact models. In this paper, we propose a framework that has low computational complexity to build a posture invariant SSM, by capturing and correcting the posture of an instance. The posture-normalised SSM is shown to be substantially more compact than the non-posture-normalised SSM. Practitioner summary: Statistical shape modelling is a technique to map out the variability of (body) shapes. This variability is often polluted by variations in posture. In this paper, we propose a framework to build a posture invariant statistical shape model. Abbreviations: SSM: statistical shape model; 1D: one-dimensional; 3D: three-dimensional; DHM: digital human model; LBS: linear blend skinning; PCA: princial component analysis; PC: principal component; TTR: thumb tip reach.

This chapter presents digital human models (DHMs) as supporting tools in user-centered design for smart clothing. Smart clothing represents the new class of design era 2.0 with interactive technologies intended to be attractive, comfortable, and fit for the purpose of the identified user (Scataglini et al., 2019). While DHMs offer the possibility to analyze or predict human posture, motion, and other functions in a high-fidelity, physics-based, three-dimensional, real-time environment, smart clothing allows for monitoring vital (heart rate, breathing rate, temperature) and emotional (heart rate variability, electrodermal activity) parameters, movements, and postures. Effectiveness of functional wear is verified on the integration of DHMs into the design of a smart clothing system. Smart clothing represents a “second skin” that has a close “intimate” relation with the human body. This relation is physiological, psychological, biomechanical, and ergonomical. Physiological, biomechanical, and ergonomic aspects represent a retro-feedback of the user’s function in the iterative co-design workflow for designing smart garments, permitting the redesign and the technological refinement of it.

In this paper we present an innovative approach to design smart clothing using statistical body shape modeling (SBSM) from the CAESAR™ dataset. A combination of different digital technologies and applications are used to create a common co-design workflow for garment design. User and apparel product design and developers can get personalized prediction of cloth sizing, fitting and aesthetics.

Background: Foot morphology has received increasing attention from both biomechanics researches and footwear manufacturers. Usually, the morphology of the foot is quantified by 2D footprints. However, footprint quantification ignores the foot's vertical dimension and hence, does not allow accurate quantification of complex 3D foot shape. Methods: The shape variation of healthy 3D feet in a population of 31 adult women and 31 adult men who live in Belgium was studied using geometric morphometric methods. The effect of different factors such as sex, age, shoe size, frequency of sport activity, Body Mass Index (BMI), foot asymmetry, and foot loading on foot shape was investigated. Correlation between these factors and foot shape was examined using multivariate linear regression. Results: The complex nature of a foot's 3D shape leads to high variability in healthy populations. After normalizing for scale, the major axes of variation in foot morphology are (in order of decreasing variance): arch height, combined ball width and inter-toe distance, global foot width, hallux bone orientation (valgus-varus), foot type (e.g. Egyptian, Greek), and midfoot width. These first six modes of variation capture 92.59% of the total shape variation. Higher BMI results in increased ankle width, Achilles tendon width, heel width and a thicker forefoot along the dorsoplantar axis. Age was found to be associated with heel width, Achilles tendon width, toe height and hallux orientation. A bigger shoe size was found to be associated with a narrow Achilles tendon, a hallux varus, a narrow heel, heel expansion along the posterior direction, and a lower arch compared to smaller shoe size. Sex was found to be associated with differences in ankle width, Achilles tendon width, and heel width. Frequency of sport activity was associated with Achilles tendon width and toe height. Conclusion: A detailed analysis of the 3D foot shape, allowed by geometric morphometrics, provides insights in foot variations in three dimensions that can not be obtained from 2D footprints. These insights could be applied in various scientific disciplines, including orthotics and shoe design.

We present a new approach to estimate geometry parameters of glass fibers in glass fiber-reinforced polymers from simulated X-ray micro-computed tomography scans. Traditionally, these parameters are estimated using a multi-step procedure including image reconstruction, pre-processing, segmentation and analysis of features of interest. Each step in this chain introduces errors that propagate through the pipeline and impair the accuracy of the estimated parameters. In the approach presented in this paper, we reconstruct volumes from a low number of projection angles using an iterative reconstruction technique and then estimate position, direction and length of the contained fibers incorporating a priori knowledge about their shape, modeled as a geometric representation, which is then optimized. Using simulation experiments, we show that our method can estimate those representations even in presence of noisy data and only very few projection angles available.

Purpose: Quantitative T₁ mapping is a magnetic resonance imaging technique that estimates the spin-lattice relaxation time of tissues. Even though T₁ mapping has a broad range of potential applications, it is not routinely used in clinical practice as accurate and precise high resolution T₁ mapping requires infeasibly long acquisition times. Method: To improve the trade-off between the acquisition time, signal-to-noise ratio and spatial resolution, we acquire a set of low resolution T₁-weighted images and directly estimate a high resolution T₁ map by means of super-resolution reconstruction. Results: Simulation and in vivo experiments show an increased spatial resolution of the T₁ map, while preserving a high signal-to-noise ratio and short scan time. Moreover, the proposed method outperforms conventional estimation in terms of root-mean-square error. Conclusion: Super resolution T₁ estimation enables resolution enhancement in T₁ mapping with the use of standard (inversion recovery) T₁ acquisition sequences. Magn Reson Med, 2016.

In quantitative MR T1 mapping, the spin-lattice relaxation time T1 of tissues is estimated from a series of T1-weighted images. As the T1 estimation is a voxel-wise estimation procedure, correct spatial alignment of the T1-weighted images is crucial. Conventionally, the T1-weighted images are first registered based on a general-purpose registration metric, after which the T1 map is estimated. However, as demonstrated in this paper, such a two-step approach leads to a bias in the final T1 map. In our work, instead of considering motion correction as a preprocessing step, we recover the motion-free T1 map using a unified estimation approach. In particular, we propose a unified framework where the motion parameters and the T1 map are simultaneously estimated with a Maximum Likelihood (ML) estimator. With our framework, the relaxation model, the motion model as well as the data statistics are jointly incorporated to provide substantially more accurate motion and T1 parameter estimates. Experiments with realistic Monte Carlo simulations show that the proposed unified ML framework outperforms the conventional two-step approach as well as state-of-the-art modelbased approaches, in terms of both motion and T1 map accuracy and mean-square error. Furthermore, the proposed method was additionally validated in a controlled experiment with real T1-weighted data and with two in vivo human brain T1-weighted data sets, showing its applicability in real-life scenarios.

An important factor influencing the quality of magnetic resonance (MR) images is the reconstruction method that is employed, and specifically, the type of prior knowledge that is exploited during reconstruction. In this work, we introduce a new type of prior knowledge, partial discreteness (PD), where a small number of regions in the image are assumed to be homogeneous and can be well represented by a constant magnitude. In particular, we mathematically formalize the partial discreteness property based on a Gaussian Mixture Model (GMM) and derive a partial discreteness image representation that characterizes the salient features of partially discrete images: a constant intensity in homogeneous areas and texture in heterogeneous areas. The partial discreteness representation is then used to construct a novel prior dedicated to the reconstruction of partially discrete MR images. The strength of the proposed prior is demonstrated on various simulated and real k-space data-based experiments with partially discrete images. Results demonstrate that the PD algorithm performs competitively with state-of-the-art reconstruction methods, being flexible and easy to implement.