Soft Tissue Artifact Research Articles

Dynamic Soft Tissue Artifacts during Impulsive Loads: Measurement Errors Vary With Wearable Inertial Measurement Unit Sensor Design.

Characterize and model Inertial Measurement Unit (IMU) errors due to transient dynamic soft tissue artifacts excited by impulsive loads, such as foot strikes during running and jumping. We instrumented 10 participants (5 female, 5 male) with IMUs on the dominant leg. An ankle IMU measured reference vertical accelerations during impulsive loads and was cross-validated against vertical force measures. Two IMUs on the posterior shank and anterior shank were used to characterize errors caused by dynamic soft tissue artifacts with respect to the reference. Shank sensors' masses were varied to explore their effect on dynamic soft tissue artifacts. Both the posterior IMU and anterior IMU overestimated peak vertical accelerations during the impulsive load (gain of 2.18 ± 0.63 and 1.55 ± 0.35 respectively). The post- impulsive load oscillation duration and natural frequency varied with sensor mass according to an underdamped second-order system, with posterior IMU and anterior IMU durations of 326 ± 75ms and 151 ± 50ms respectively and natural frequencies of 9.79 ± 2.68Hz and 18.22 ± 12.10Hz respectively. Low-pass filtering reduced overestimation of peak vertical accelerations, but also attenuated the reference measure. Our study suggests dynamic soft tissue artifacts result in transient, but substantial measurement errors that may not be appropriately mitigated through low-pass filtering. However, these dynamic soft tissue artifacts can be modeled using an underdamped second-order system and used to estimate material properties of underlying soft tissue. We demonstrate that dynamic soft tissue artifacts can be modeled and potentially mitigated to improve accuracy in applications necessitating measurement of impulsive loads such as foot strikes.

Image quality of virtual monochromatic and material density iodine images for evaluation of head and neck neoplasms using deep learning-based CT image reconstruction – A retrospective observational study

PurposeTo compare the quality of deep learning image reconstructed (DLIR) virtual monochromatic images (VMI) and material density (MD) iodine images from dual-energy computed tomography (DECT) for the evaluation of head and neck neoplasms with CT scans from a conventional single-energy protocol. MethodA total of 294 head and neck CT scans (98 VMIs operated at 60 keV, 102 MD iodine images, and 94 images from a 120 kVp single-energy CT (SECT) protocol) were retrospectively evaluated. VMIs and MD iodine images were generated using the Gemstone Spectral Imaging (GSI) mode using DLIR and metal artifact reduction (MAR) algorithms. SECT images were generated using adaptive statistical iterative reconstruction (ASIR-V). Images were scored by two independent readers on a 6-point Likert-type scale for overall image quality, vessel contrast, soft tissue contrast, noise texture, noise intensity, artifact reduction, and sharpness. ResultsSubjective overall image quality was rated as superior or excellent in 98 % of DLIR-based MD iodine images and VMIs, but only in 55 % of ASIR-V-based SECT images. For each individual quality criterion, image quality of VMIs and MD iodine images was rated as better than that of SECT images (p < 0.001 in each case). Noise texture and intensity were rated better in MD iodine images than in VMIs. ConclusionDECT using both DLIR and MAR for the generation of VMIs and MD iodine images resulted in higher subjective quality of oncologic head and neck images than ASIR-V-based SECT. Noise reduction and noise texture were best achieved with DLIR-based MD iodine images.

Lumbar spine marker placement errors and soft tissue artifact during dynamic flexion/extension and lateral bending in individuals with chronic low back pain

Quantitative in vivo biomechanical assessments are typically performed with optoelectronic motion capture (MoCap) using retroreflective markers attached to skin. This technique inherently contains measurement errors from both marker placement on palpated bony landmarks and skin motion relative to the underlying bone (i.e., soft tissue artifact (STA)). Research on lumbar spine STA is scarce and limited to young, healthy participants in static positions. This study aimed to evaluate static placement errors, lumbar spine STA from MoCap marker clusters (MMC), and linear relationships between STA and patient characteristics. Thirty-nine participants with cLBP performed three trials each of flexion/extension and lateral bending while imaged simultaneously by MoCap (120 Hz) and dynamic biplane radiography DBR (20 Hz). MMCs were placed 29.5 ± 18.0 mm and 27.1 ± 13.4 mm superior to the most prominent aspect of the L1 and L5 spinous process, respectively. L1 relative to L5 STA was larger during flexion/extension (8.6 ± 5.7°) than lateral bending (4.5 ± 2.1°) (p < 0.001). After correcting for marker placement errors, components of the L1 and L5 STA averaged as much as 16.3 mm and 11.4° during flexion/extension, but only 4.0 mm and 4.8° or less during lateral bending. On average, STA for individual L1 and L5 vertebrae increased as participants moved away from the upright neutral position. STA was participant-dependent, however, age and BMI did not model STA well. Given the inaccuracy in marker placement and wide range of patterns of STA, caution is urged when making clinical decisions or when using computational models to estimate spine tissue loading based upon lumbar spine kinematics obtained from skin-mounted markers.

Comparison of IMU-Based Knee Kinematics with and without Harness Fixation against an Optical Marker-Based System.

The use of inertial measurement units (IMUs) as an alternative to optical marker-based systems has the potential to make gait analysis part of the clinical standard of care. Previously, an IMU-based system leveraging Rauch-Tung-Striebel smoothing to estimate knee angles was assessed using a six-degrees-of-freedom joint simulator. In a clinical setting, however, accurately measuring abduction/adduction and external/internal rotation of the knee joint is particularly challenging, especially in the presence of soft tissue artefacts. In this study, the in vivo IMU-based joint angles of 40 asymptomatic knees were assessed during level walking, under two distinct sensor placement configurations: (1) IMUs fixed to a rigid harness, and (2) IMUs mounted on the skin using elastic hook-and-loop bands (from here on referred to as "skin-mounted IMUs"). Estimates were compared against values obtained from a harness-mounted optical marker-based system. The comparison of these three sets of kinematic signals (IMUs on harness, IMUs on skin, and optical markers on harness) was performed before and after implementation of a REference FRame Alignment MEthod (REFRAME) to account for the effects of differences in coordinate system orientations. Prior to the implementation of REFRAME, in comparison to optical estimates, skin-mounted IMU-based angles displayed mean root-mean-square errors (RMSEs) up to 6.5°, while mean RMSEs for angles based on harness-mounted IMUs peaked at 5.1°. After REFRAME implementation, peak mean RMSEs were reduced to 4.1°, and 1.5°, respectively. The negligible differences between harness-mounted IMUs and the optical system after REFRAME revealed that the IMU-based system was capable of capturing the same underlying motion pattern as the optical reference. In contrast, obvious differences between the skin-mounted IMUs and the optical reference indicated that the use of a harness led to fundamentally different joint motion being measured, even after accounting for reference frame misalignments. Fluctuations in the kinematic signals associated with harness use suggested the rigid device oscillated upon heel strike, likely due to inertial effects from its additional mass. Our study proposes that optical systems can be successfully replaced by more cost-effective IMUs with similar accuracy, but further investigation (especially in vivo and upon heel strike) against moving videofluoroscopy is recommended.

A new method called MiKneeSoTA to minimize knee soft-tissue artifacts in kinematic analysis

The use of marker-based optical motion capture to estimate joint kinematics during gait is currently limited by errors associated with soft-tissue-induced motion artefacts (STIMA) and ambiguity in landmark palpation. This study therefore presents a novel protocol aiming to Minimize Knee Soft-Tissue Artefacts (MiKneeSoTA) and their effect on kinematic estimates. Relying on an augmented marker set and a new inverse kinematics approach, our method leverages frame-by-frame optimization to adjust best-fit cylinders that have been automatically generated based on the relative position of lower limb markers during an initial static trial. Tibiofemoral rotations and translations are then calculated along the anatomical joint axes based on the relative 3D motion of these cylinders. When compared against the conventional Helen-Hayes approach, in vivo assessment of fifteen healthy subjects revealed the MiKneeSoTA approach led to kinematic profiles with significantly lower standard deviations in joint rotations across trials, and even visibly reduced the presence of high frequency fluctuations presumably associated with e.g. soft-tissue vibration. In addition to agreeing with previously published bone pin and fluoroscopy datasets, our results illustrate MiKneeSoTA’s ability to abate the effect of STIMA induced by lateral knee ligaments. Our findings indicate that MiKneeSoTA is in fact a promising approach to mitigate knee joint STIMA and thus enable the previously unattainable accurate estimation of translational knee joint motion with an optoelectronic system.

Experimental Comparison between 4D Stereophotogrammetry and Inertial Measurement Unit Systems for Gait Spatiotemporal Parameters and Joint Kinematics.

(1) Background: Traditional gait assessment methods have limitations like time-consuming procedures, the requirement of skilled personnel, soft tissue artifacts, and high costs. Various 3D time scanning techniques are emerging to overcome these issues. This study compares a 3D temporal scanning system (Move4D) with an inertial motion capture system (Xsens) to evaluate their reliability and accuracy in assessing gait spatiotemporal parameters and joint kinematics. (2) Methods: This study included 13 healthy people and one hemiplegic patient, and it examined stance time, swing time, cycle time, and stride length. Statistical analysis included paired samples t-test, Bland-Altman plot, and the intraclass correlation coefficient (ICC). (3) Results: A high degree of agreement and no significant difference (p > 0.05) between the two measurement systems have been found for stance time, swing time, and cycle time. Evaluation of stride length shows a significant difference (p < 0.05) between Xsens and Move4D. The highest root-mean-square error (RMSE) was found in hip flexion/extension (RMSE = 10.99°); (4) Conclusions: The present work demonstrated that the system Move4D can estimate gait spatiotemporal parameters (gait phases duration and cycle time) and joint angles with reliability and accuracy comparable to Xsens. This study allows further innovative research using 4D (3D over time) scanning for quantitative gait assessment in clinical practice.

Marker position uncertainty in joint angle analysis for normal human gait — A new error-modelling approach

Measurement uncertainty is present when using optical motion capture to investigate joint angles during human gait. The aim of this work is to analyse the effect of uncertainty in the position of optical markers on the calculation of joint angles during human gait.The uncertainty is based on known errors of the measurement of marker positions (e.g. measurement inaccuracies, marker placement errors and soft tissue artefacts) and is modelled with epistemic uncertainty. Based on optical motion capture data of normal human gait, the epistemic uncertainty is propagated through the joint angle calculations using the Graph Follower algorithm. While being well suited to model the uncertainty present in motion capture, epistemic uncertainty has not yet been considered to model errors in the marker positions.The main contribution of this work is a new model for marker position errors and a new method to analyse the effects of these errors on the subsequent joint angle calculations during gait. This allows for an efficient worst case analysis, based on a real dataset, providing information on the largest possible error in the joint angle calculation based on given marker position errors. It is also capable of providing the opposite information, i.e. a given limit on joint angle errors can be used to determine acceptable marker errors. Further, it is also possible to investigate the effect of specific markers and their contribution to the joint angle analysis.

Validation of Inertial-Measurement-Unit-Based Ex Vivo Knee Kinematics during a Loaded Squat before and after Reference-Frame-Orientation Optimisation.

Recently, inertial measurement units have been gaining popularity as a potential alternative to optical motion capture systems in the analysis of joint kinematics. In a previous study, the accuracy of knee joint angles calculated from inertial data and an extended Kalman filter and smoother algorithm was tested using ground truth data originating from a joint simulator guided by fluoroscopy-based signals. Although high levels of accuracy were achieved, the experimental setup leveraged multiple iterations of the same movement pattern and an absence of soft tissue artefacts. Here, the algorithm is tested against an optical marker-based system in a more challenging setting, with single iterations of a loaded squat cycle simulated on seven cadaveric specimens on a force-controlled knee rig. Prior to the optimisation of local coordinate systems using the REference FRame Alignment MEthod (REFRAME) to account for the effect of differences in local reference frame orientation, root-mean-square errors between the kinematic signals of the inertial and optical systems were as high as 3.8° ± 3.5° for flexion/extension, 20.4° ± 10.0° for abduction/adduction and 8.6° ± 5.7° for external/internal rotation. After REFRAME implementation, however, average root-mean-square errors decreased to 0.9° ± 0.4° and to 1.5° ± 0.7° for abduction/adduction and for external/internal rotation, respectively, with a slight increase to 4.2° ± 3.6° for flexion/extension. While these results demonstrate promising potential in the approach's ability to estimate knee joint angles during a single loaded squat cycle, they highlight the limiting effects that a reduced number of iterations and the lack of a reliable consistent reference pose inflicts on the sensor fusion algorithm's performance. They similarly stress the importance of adapting underlying assumptions and correctly tuning filter parameters to ensure satisfactory performance. More importantly, our findings emphasise the notable impact that properly aligning reference-frame orientations before comparing joint kinematics can have on results and the conclusions derived from them.

Reconstruction of occluded pelvis markers during marker-based motion capture with industrial exoskeletons

Industrial back support exoskeletons are a promising solution to alleviate lumbar musculoskeletal strain. Due to the complexity of spinal loading, evaluation of EMG data alone has been considered insufficient to assess their support effects, and complementary kinematic and dynamic data are required. However, the acquisition of marker-based kinematics is challenging with exoskeletons, as anatomical reference points, particularly on the pelvis, are occluded by exoskeleton structures. The aim of this study was therefore to develop and validate a method to reliably reconstruct the occluded pelvic markers. The movement data of six subjects, for whom pelvic markers could be placed while wearing an exoskeleton, were used to test the reconstructions and compare them to anatomical landmarks during lifting, holding and walking. Two separate approaches were used for the reconstruction. One used a reference coordinate system based on only exoskeleton markers (EXO), as has been suggested in the literature, while our proposed method adds a technical marker in the lumbar region (LUMB) to compensate for any shifting between exoskeleton and pelvis. Reconstruction with EXO yielded on average an absolute linear deviation of 54 mm ± 16 mm (mean ± 1SD) compared to anatomical markers. The additional marker in LUMB reduced mean deviations to 14 mm ± 7 mm (mean ± 1SD). Both methods were compared to reference values from the literature for expected variances due to marker placement and soft tissue artifacts. For LUMB 99% of reconstructions were within the defined threshold of 24 mm ±9 mm while for EXO 91% were outside.

Does IMU redundancy improve multi-body optimization results to obtain lower-body kinematics? A preliminary study says no

Inertial Measurement Units (IMUs) have been proposed as an ecological alternative to optoelectronic systems for obtaining human body joint kinematics. Tremendous work has been done to reduce differences between kinematics obtained with IMUs and optoelectronic systems, by improving sensor-to-segment calibration, fusion algorithms, and by using Multibody Kinematics Optimization (MKO). However, these improvements seem to reach a barrier, particularly on transverse and frontal planes. Inspired by marker-based MKO approach performed via OpenSim, this study proposes to test whether IMU redundancy with MKO could improve lower-limb kinematics obtained from IMUs. For this study, five subjects were equipped with 11 IMUs and 30 reflective markers tracked by 18 optoelectronic cameras. They then performed gait, cycling, and running actions. Four different lower-limb kinematics were computed: one kinematics based on markers after MKO, one kinematics based on IMUs without MKO, and two based on IMUs after MKO performed with OpenSense (one with, and one without, sensor redundancy). Kinematics were compared via Root Mean Square Difference and correlation coefficients to kinematics based on markers after MKO. Results showed that redundancy does not reduce differences with the kinematics based on markers after MKO on frontal and transverse planes comparatively to classic IMU MKO. Sensor redundancy does not seem to impact lower-limb kinematics on frontal and transverse planes, due to the likelihood of the “rigid component” of soft-tissue artefact impacting all sensors located on one segment.

Estimating 3D kinematics and kinetics from virtual inertial sensor data through musculoskeletal movement simulations.

Portable measurement systems using inertial sensors enable motion capture outside the lab, facilitating longitudinal and large-scale studies in natural environments. However, estimating 3D kinematics and kinetics from inertial data for a comprehensive biomechanical movement analysis is still challenging. Machine learning models or stepwise approaches performing Kalman filtering, inverse kinematics, and inverse dynamics can lead to inconsistencies between kinematics and kinetics. We investigated the reconstruction of 3D kinematics and kinetics of arbitrary running motions from inertial sensor data using optimal control simulations of full-body musculoskeletal models. To evaluate the feasibility of the proposed method, we used marker tracking simulations created from optical motion capture data as a reference and for computing virtual inertial data such that the desired solution was known exactly. We generated the inertial tracking simulations by formulating optimal control problems that tracked virtual acceleration and angular velocity while minimizing effort without requiring a task constraint or an initial state. To evaluate the proposed approach, we reconstructed three trials each of straight running, curved running, and a v-cut of 10 participants. We compared the estimated inertial signals and biomechanical variables of the marker and inertial tracking simulations. The inertial data was tracked closely, resulting in low mean root mean squared deviations for pelvis translation (≤20.2mm), angles (≤1.8 deg), ground reaction forces (≤1.1 BW%), joint moments (≤0.1 BWBH%), and muscle forces (≤5.4 BW%) and high mean coefficients of multiple correlation for all biomechanical variables . Accordingly, our results showed that optimal control simulations tracking 3D inertial data could reconstruct the kinematics and kinetics of individual trials of all running motions. The simulations led to mutually and dynamically consistent kinematics and kinetics, which allows researching causal chains, for example, to analyze anterior cruciate ligament injury prevention. Our work proved the feasibility of the approach using virtual inertial data. When using the approach in the future with measured data, the sensor location and alignment on the segment must be estimated, and soft-tissue artifacts are potential error sources. Nevertheless, we demonstrated that optimal control simulation tracking inertial data is highly promising for estimating 3D kinematics and kinetics for a comprehensive biomechanical analysis.

Quantification of soft tissue artifacts using CT registration and subject-specific multibody modeling

The potential use of gait analysis for quantitative preoperative planning in total hip arthroplasty (THA) has previously been demonstrated. However, the joint kinematic data measured through this process tend to be unreliable for surgical planning due to distortions caused by soft tissue artifacts (STAs). In this study, we developed a novel motion capture framework by combining computed tomography (CT)-based postural calibration and subject-specific multibody dynamics modeling to prevent the effect of STAs in measuring hip kinematics. Three subjects with femoroacetabular impingement syndrome were recruited, and CT data for each patient were collected by attaching marker clusters near the hip. A subject-specific multibody hip joint model was developed based on reconstructed CT data. Spring–dashpot network calculations were performed to minimize the distance between the anatomical landmark and its corresponding infrared reflective marker. The STAs of the thigh was described as six degrees of freedom viscoelastic bushing elements, and their parameter values were identified via smooth orthogonal decomposition. Least squares optimization was used to modify the pelvic rotations to compensate for the rigid components of STAs. The results showed that CT-assisted motion tracking enabled the successful identification of STA influences in gait and squat positions. Furthermore, STA effects were found to alter maximal pelvis tilt and hip rotations during a squat. Compared to other techniques, such as dual fluoroscopic imaging, the adopted framework does not require additional medical imaging for patients undergoing robot-assisted THA surgery and is thus a practical way of evaluating hip joint kinematics for preoperative surgical planning.

Bi-Graph Reasoning for Masticatory Muscle Segmentation From Cone-Beam Computed Tomography.

Automated segmentation of masticatory muscles is a challenging task considering ambiguous soft tissue attachments and image artifacts of low-radiation cone-beam computed tomography (CBCT) images. In this paper, we propose a bi-graph reasoning model (BGR) for the simultaneous detection and segmentation of multi-category masticatory muscles from CBCTs. The BGR exploits the local and long-range interdependencies of regions of interest and category-specific prior knowledge of masticatory muscles by reasoning on the category graph and the region graph. The category graph of the learnable muscle prior knowledge handles high-level dependencies of muscle categories, enhancing the feature representation with noise-agnostic category knowledge. The region graph models both local and global dependencies of the candidate muscle regions of interest. The proposed BGR accommodates the high-level dependencies and enhances the region features in the presence of entangled soft tissue and image artifacts. We evaluated the proposed approach by segmenting masticatory muscles on clinically acquired CBCTs. Extensive experimental results show that the BGR effectively segments masticatory muscles with state-of-the-art accuracy.

A simulation study to investigate an extension to the point cluster technique

Joint kinematics are an important and widely utilized metric in quantitative human movement analysis. Typically, trajectory data for skin-mounted markers are collected using stereophotogrammetry, sometimes referred to as optical motion capture, and processed using various mathematical models to estimate joint kinematics (e.g., angles). Among the various sources of noise in optical motion capture data, soft tissue artifacts (STAs) remain a critical source of error. This study investigates the performance of the point cluster technique (PCT), an extension of the PCT using perturbation theory (PCT-PT), and singular value decomposition least squares (SVD-LS) method (as a reference) for 100 different marker configurations on the thigh and shank during treadmill walking. This study provides additional evidence that the PCT method is significantly limited by the underlying mathematical constraints governing its optimization process. Furthermore, the results suggest the PCT-PT method outperforms the PCT method across all performance metrics for both body segments during the entire gait cycle. For position-based metrics, the PCT-PT method provides better estimates than the SVD-LS method for the thigh during majority of the stance phase and provides comparable estimates for the shank during the entire gait cycle. For knee angle estimates, the PCT-PT method provides equivalent results as the SVD-LS method.

Influence of marker weights optimization on scapular kinematics estimated with a multibody kinematic optimization

Scapular kinematic estimates are altered by soft tissue artefacts, therefore experimental and numerical methods should be developed to improve their accuracy. This study aimed to assess the influence of weights applied to the scapula markers within a closed-loop multibody kinematic optimization on scapular kinematic estimates. Fifteen healthy volunteers performed static postures mimicking analytical, daily living and sport movements. Scapulo-thoracic angles were computed either from a scapula locator as the reference, or from a closed-loop multibody-kinematic optimization (MKO) including a participant-specific point-on-ellipsoid scapulothoracic joint. Weights applied to scapula markers in the MKO were optimized to minimize the difference in scapular orientation from the reference. Optimizing weighting sets significantly (p < 0.0001) improved scapular orientation from 0.9° to 12.1° in comparison to scapular kinematics estimated with non-optimized weighting sets. The mean optimized weighting set contained no neglectable weight for all markers from the acromion to the medial border of the scapular spine but showed no significant difference (p = 0.547) compared to homogeneous weights. Optimized weighting sets were participant- and movement- specific. To conclude, homogenous weights applied on redundant markers located from acromion to scapular medial border spine are recommended when estimating scapular kinematics in upper limb MKO.

On the accuracy of the Conventional gait Model: Distinction between marker misplacement and soft tissue artefact errors

There is a lack of knowledge about the accuracy of the Conventional Gait Model (CGM), compared to the true bone motion. Accuracy is hindered by both marker misplacement and soft-tissue artefact (STA).The effect of the lateral knee marker (KNE) misplacement and STA was determined from a secondary analysis of 13 subjects equipped with a total knee prothesis for which simultaneous dual-plane fluoroscopy and marker-based motion capture was available.In average, STA alone led to 3.3°, 2.9° and 6.7° errors for knee flexion, knee abduction, and the absolute hip rotation respectively. In comparison, marker misplacement led to 0.9°, 4.0° and 12.3° errors for the same kinematics. We showed that STA alone may lead to knee flexion-adduction cross-talk. This finding has clinical repercussions for the use of knee cross talk as a qualitative indicator of knee axis alignment. Our study showed that cumulative effects of marker misplacement and STA affect the transverse plane angles, making challenging to track internal/external rotation with less than 5° of errors.

Combining virtual monoenergetic imaging and iterative metal artifact reduction in first-generation photon-counting computed tomography of patients with dental implants

ObjectivesWhile established for energy-integrating detector computed tomography (CT), the effect of virtual monoenergetic imaging (VMI) and iterative metal artifact reduction (iMAR) in photon-counting detector (PCD) CT lacks thorough investigation. This study evaluates VMI, iMAR, and combinations thereof in PCD-CT of patients with dental implants.Material and methodsIn 50 patients (25 women; mean age 62.0 ± 9.9 years), polychromatic 120 kVp imaging (T3D), VMI, T3DiMAR, and VMIiMAR were compared. VMIs were reconstructed at 40, 70, 110, 150, and 190 keV. Artifact reduction was assessed by attenuation and noise measurements in the most hyper- and hypodense artifacts, as well as in artifact-impaired soft tissue of the mouth floor. Three readers subjectively evaluated artifact extent and soft tissue interpretability. Furthermore, new artifacts through overcorrection were assessed.ResultsiMAR reduced hyper-/hypodense artifacts (T3D 1305.0/−1418.4 versus T3DiMAR 103.2/−46.9 HU), soft tissue impairment (106.7 versus 39.7 HU), and image noise (16.9 versus 5.2 HU) compared to non-iMAR datasets (p ≤ 0.001). VMIiMAR ≥ 110 keV subjectively enhanced artifact reduction over T3DiMAR (p ≤ 0.023). Without iMAR, VMI displayed no measurable artifact reduction (p ≥ 0.186) and facilitated no significant denoising over T3D (p ≥ 0.366). However, VMI ≥ 110 keV reduced soft tissue impairment (p ≤ 0.009). VMIiMAR ≥ 110 keV resulted in less overcorrection than T3DiMAR (p ≤ 0.001). Inter-reader reliability was moderate/good for hyperdense (0.707), hypodense (0.802), and soft tissue artifacts (0.804).ConclusionWhile VMI alone holds minimal metal artifact reduction potential, iMAR post-processing enabled substantial reduction of hyperdense and hypodense artifacts. The combination of VMI ≥ 110 keV and iMAR resulted in the least extensive metal artifacts.Clinical relevanceCombining iMAR with VMI represents a potent tool for maxillofacial PCD-CT with dental implants achieving substantial artifact reduction and high image quality.Key Points• Post-processing of photon-counting CT scans with an iterative metal artifact reduction algorithm substantially reduces hyperdense and hypodense artifacts arising from dental implants.• Virtual monoenergetic images presented only minimal metal artifact reduction potential.• The combination of both provided a considerable benefit in subjective analysis compared to iterative metal artifact reduction alone.

Differences in angular kinematics when using thigh, implant, or medial knee markers in osseointegrated transfemoral prosthetic gait

BackgroundThe Helen Hayes anatomical model is commonly used in clinical gait analysis with standard medial/lateral knee and thigh markers. MethodsTo quantify soft-tissue artifacts associated with the thigh marker following osseointegration surgery, we added an “implant marker” on the implant extending from the femur, with the objective of identifying the differences in the angular kinematics when using the standard versus implant marker. One female adult with an osseointegrated transfemoral prosthesis walked overground for three trials, and common kinematic measures were calculated from motion capture data. FindingsThe results indicated that, when using the thigh marker, a peak of knee varus occurred during the swing phase on the prosthetic side, which is unusual during gait and not feasible for hinge joint prostheses. When using the implant marker, knee varus/valgus was closer to normative. Using the thigh marker, the results showed an internal hip rotation at the start of stance and during the mid and terminal swing phases. In contrast, external hip rotation occurred in both stance and swing phases using the implant marker. Moreover, when selecting the medial knee marker instead of the thigh marker, the angular kinematics and range of motion of knee varus/valgus and hip rotation were comparable to those for the implant marker. InterpretationThis finding suggests that when studying osseointegration gait, using an implant marker will result in more accurate femoral and knee joint motion than using the thigh marker. Changing the selection of markers can reduce the errors of knee varus/valgus and hip kinematics in osseointegrated transfemoral prosthetic gait.

Moving fluoroscopy-based analysis of THA kinematics during unrestricted activities of daily living.

Introduction: Knowledge of the accurate in-vivo kinematics of total hip arthroplasty (THA) during activities of daily living can potentially improve the in-vitro or computational wear and impingement prediction of hip implants. Fluoroscopy- based techniques provide more accurate kinematics compared to skin marker-based motion capture, which is affected by the soft tissue artefact. To date, stationary fluoroscopic machines allowed the measurement of only restricted movements, or only a portion of the whole motion cycle. Methods: In this study, a moving fluoroscopic robot was used to measure the hip joint motion of 15 THA subjects during whole cycles of unrestricted activities of daily living, i.e., overground gait, stair descent, chair rise and putting on socks. Results: The retrieved hip joint motions differed from the standard patterns applied for wear testing, demonstrating that current pre-clinical wear testing procedures do not reflect the experienced in-vivo daily motions of THA. Discussion: The measured patient-specific kinematics may be used as input to in vitro and computational simulations, in order to investigate how individual motion patterns affect the predicted wear or impingement.

Technological Solutions for Human Movement Analysis in Obese Subjects: A Systematic Review.

Obesity has a critical impact on musculoskeletal systems, and excessive weight directly affects the ability of subjects to realize movements. It is important to monitor the activities of obese subjects, their functional limitations, and the overall risks related to specific motor tasks. From this perspective, this systematic review identified and summarized the main technologies specifically used to acquire and quantify movements in scientific studies involving obese subjects. The search for articles was carried out on electronic databases, i.e., PubMed, Scopus, and Web of Science. We included observational studies performed on adult obese subjects whenever reporting quantitative information concerning their movement. The articles must have been written in English, published after 2010, and concerned subjects who were primarily diagnosed with obesity, thus excluding confounding diseases. Marker-based optoelectronic stereophotogrammetric systems resulted to be the most adopted solution for movement analysis focused on obesity; indeed, wearable technologies based on magneto-inertial measurement units (MIMUs) were recently adopted for analyzing obese subjects. Further, these systems are usually integrated with force platforms, so as to have information about the ground reaction forces. However, few studies specifically reported the reliability and limitations of these approaches due to soft tissue artifacts and crosstalk, which turned out to be the most relevant problems to deal with in this context. In this perspective, in spite of their inherent limitations, medical imaging techniques-such as Magnetic Resonance Imaging (MRI) and biplane radiography-should be used to improve the accuracy of biomechanical evaluations in obese people, and to systematically validate less-invasive approaches.