Elsevier

Journal of Biomechanics

Volume 62, 6 September 2017, Pages 156-164
Journal of Biomechanics

CAT & MAUS: A novel system for true dynamic motion measurement of underlying bony structures with compensation for soft tissue movement

https://doi.org/10.1016/j.jbiomech.2017.04.015Get rights and content

Abstract

Optoelectronic motion capture systems are widely employed to measure the movement of human joints. However, there can be a significant discrepancy between the data obtained by a motion capture system (MCS) and the actual movement of underlying bony structures, which is attributed to soft tissue artefact. In this paper, a computer-aided tracking and motion analysis with ultrasound (CAT & MAUS) system with an augmented globally optimal registration algorithm is presented to dynamically track the underlying bony structure during movement. The augmented registration part of CAT & MAUS was validated with a high system accuracy of 80%. The Euclidean distance between the marker-based bony landmark and the bony landmark tracked by CAT & MAUS was calculated to quantify the measurement error of an MCS caused by soft tissue artefact during movement. The average Euclidean distance between the target bony landmark measured by each of the CAT & MAUS system and the MCS alone varied from 8.32 mm to 16.87 mm in gait. This indicates the discrepancy between the MCS measured bony landmark and the actual underlying bony landmark. Moreover, Procrustes analysis was applied to demonstrate that CAT & MAUS reduces the deformation of the body segment shape modeled by markers during motion. The augmented CAT & MAUS system shows its potential to dynamically detect and locate actual underlying bony landmarks, which reduces the MCS measurement error caused by soft tissue artefact during movement.

Introduction

Optoelectronic motion capture systems (MCS), for example VICON systems (Oxford, UK), are commonly employed to monitor and measure motions of the human body. An optoelectronic MCS is composed of a group of infra-red cameras that capture the trajectories of skin-attached retro-reflective markers to determine the movement of corresponding underlying bony landmarks. However, because of soft tissues between the skin and the underlying bone, for example muscles and fat, sliding, stretching and compressing during movement, the skin-attached markers shift away from the original bony landmarks (Stagni et al., 2005). This leads to a significant discrepancy between the data obtained by an MCS and the actual movement of underlying bone structures and this is attributed to soft tissue artefact (STA). It has been reported that STA can cause an MCS measurement error of up to 30 mm for the thigh (Akbarshahi et al., 2010, Rouhandeh et al., 2014, Stagni et al., 2005).

In order to correct the error caused by soft tissue movement, it is essential to first track and assess the movement of actual underlying bony structures. This can be done by either tracking the bone by attaching invasive pins to the underlying bone landmarks (Robertson et al., 2013) or by imaging the actual bone during movement. Several medical imaging technologies, such as open magnetic resonance imaging (MRI) (Higuchi et al., 2010, Souza et al., 2010) and X-ray radiography (Shellikeri et al., 2016, Stagni et al., 2005), have been employed to image the underlying bony structures and track their motion for comparison to the MCS data in order to quantify the effects of STA. However, these imaging studies have suffered from, for example, the limited field of view of devices or the excessive exposure to radiation which may increase the risk of cancer to the subject (Monk, 2011). Compared to MRI and X-ray imaging, ultrasound (US) is a cheaper, more dynamic, more portable and non-ionizing imaging modality (Nazarian, 2008). It has shown some potential for tracking underlying bony structures instead of skin-attached markers (Herrington et al., 2006, Monk et al., 2013). However, imaging large joints such as the hip and the knee presents its own challenge due to the limited footprint of most US transducers. Moreover, in previous US-related methods, the target structures had to be identified by eye, making US-based measurement laborious and time consuming (Monk et al., 2013). The potential of using ultrasound imaging to very accurately explore human joint motion has also been explored by Masum et al., 2014a, Masum et al., 2014b with a novel multi-probe approach.

A computer-aided tracking and motion analysis with ultrasound (CAT & MAUS) system has been developed by our group to track the underlying bony anatomy in 3D space during routine activities, for example gait. It combines state-of-the-art computer vision techniques, including an automatic US segmentation, a 3D reconstruction and a 3D surface-to-surface registration, with an MCS and a 2D US device. In a previously published paper (Jia et al., 2016d), our CAT & MAUS system was applied to estimate hip joint kinematics during gait. The difference between the actual position of the underlying bony structure and the position captured by marker-based MCS alone during gait was illustrated using CAT & MAUS. However, the 3D bone surfaces for the surface-to-surface registration were reconstructed from 2D US sweeps at static gait poses. Since gait is a continuous sequence, the nature of the examination did not capture the continuity and reality of dynamic gait. This approach is not capable of dynamically measuring hip joint kinematics. The 3D position of the femur surface can be reconstructed for motion data using the skin markers but STA affects the accuracy. Data recorded by the US probe about the bone position during motion can be used to adjust the 3D position of the reconstructed femur surface and in the process compensate for STA.

In this paper, we have augmented our previous CAT & MAUS system with a multiple segments globally optimal registration algorithm to dynamically locate a target bony landmark. The ability of this system to dynamically locate the greater trochanter (GT) in 3D space during gait was explored. In addition the potential for the system to compensate for STA during gait was investigated.

Section snippets

Computer-aided tracking and motion analysis with ultrasound

A computer-aided tracking and motion analysis with ultrasound (CAT & MAUS) system combines a computer-aided post-processing pipeline with a motion analysis with ultrasound (MAUS) system (Monk et al., 2013). A schematic of the CAT & MAUS system is shown in Fig. 1.

The constitution of the MAUS part of the CAT & MAUS system shares similar principles with three-dimensional (3D) freehand US imaging (Jia et al., 2015). Thus, it is comprised of an optoelectronic MCS (VICON, Oxford, UK) and a 2D US

In-vitro validation of multiple segments globally optimal registration

To validate the accuracy of the augmented registration algorithm, a proximal femur phantom with a socket ball joint was used. Four 10 mm diameter retro-reflective markers were attached to four non-coplanar positions of the femur shaft to provide femur location information as the ground truth as illustrated in Fig. 3a. Another four markers were attached at each corner of the water tank to provide position information of the water tank (Fig. 3b). If the calibration box was moved during the

In-vitro validation

A visual comparison of the registration results from the segmented bone contour in a given US frame captured by US during movement to the full reference surface and to each of three split segments are shown in Fig. 5. The blue line is the segmented bone contour of one example US frame, while the red point cloud is the reference surface. Although the results of registration to the full reference surface (Fig. 5b), Segment 2 or Segment 3 (Fig. 5c) are all within the threshold of the L2 norm, it

Conclusions

In this paper, an augmented computer-aided tracking and motion analysis with ultrasound (CAT & MAUS) system has been introduced to dynamically measure the movement of the GT during gait. A multiple segments globally optimal registration has been developed to dynamically locate the underlying bony structure during movement. It has been validated with a proximal femur phantom shown that the multiple segments registration algorithm is more accurate than the full surface registration with a high

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

All the in vivo experiments were in accordance with the ethical standards of the institutional and/or national research committee.

Acknowledgements

The authors would like to thank Orthopaedics Research UK for supporting this project (Grant code: HFR00390) and the China Scholarship Council for funding Rui Jia (CSC No. 201408060234). We sincerely thank all the participants for volunteering in the experiments.

References (20)

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