Development of bony range of motion (B-ROM) boundary for total hip replacement planning

Simulation of hip bony range of motion (BROM) corresponds to the observed functional range of motion (FROM) for pure flexion, internal rotation in deep flexion, and external rotation in minimal flexion-extension - A cadaver study

BACKGROUND

The study investigated the relationship between computed bony range of motion (BROM) and actual functional range of motion (FROM) as directly measured in cadaveric hips. The hypothesis was that some hip movements are not substantially restricted by soft tissues, and therefore, computed BROM for these movements may effectively represent FROM, providing a reliable parameter for computational pre-operative planning.

METHODS

Maximum passive FROM was measured in nine cadaveric hips using optical tracking. Each hip was measured in at least ninety FROM positions, covering flexion, extension, abduction, flexion-internal rotation (IR), flexion-external rotation (ER), extension-IR, and extension-ER movements. The measured FROM was virtually recreated using 3D models of the femur and pelvis derived from CT scans, and the corresponding BROM was computed. The relationship between FROM and BROM was classified into three groups: close (mean difference<5°), moderate (mean difference 5-15°), and weak (mean difference>15°).

RESULTS

The relationship between FROM and BROM was close for pure flexion (difference = 3.1° ± 3.9°) and IR in deep (>70°) flexion (difference = 4.3° ± 4.6°). The relationship was moderate for ER in minimal flexion (difference = 10.3° ± 5.8°) and ER in minimal extension (difference = 11.7° ± 7.2°). Bony impingement was observed in some cases during these movements. Other movements showed a weak relationship: large differences were observed in extension (51.9° ± 14.4°), abduction (18.6° ± 11.3°), flexion-IR at flexion<70° (37.1° ± 9.4°), extension-IR (79.6° ± 4.8°), flexion-ER at flexion>30° (45.9° ± 11.3°), and extension-ER at extension>20° (15.8° ± 4.8°).

CONCLUSION

BROM simulations of hip flexion, IR in deep flexion, and ER in low flexion/extension may be useful in dynamic pre-operative planning of total hip arthroplasty.

Evaluating computed bony range of motion (BROM) by registering in-vitro cadaver-based functional range of motion (FROM) to a hip motion simulation

BACKGROUND

While modern hip replacement planning relies on hip motion simulation (HMS), it lacks the capability to include soft-tissues and ligaments restraints on computed bony range of motion (BROM), often leading to an overestimation of the in-vivo functional range of motion (FROM). Furthermore, there is a lack of literature on BROM assessment in relation to FROM. Therefore, the study aimed to assess computed BROM using in-vitro cadaver-derived FROM measurements, registered to a CT-based in-house HMS, and to further investigate the effect of functional and anatomical hip joint centres (FHJC and AHJC) on BROM.

METHOD

Seven limiting and three non-limiting circumducted passive FROM of four cadaver hips were measured using optical coordinate measuring machine with reference spheres (RSs) affixed to the pelvis and the femur, following CT-scan of the specimen. The RSs' centres were used to register the measured FROM in HMS, enabling its virtual recreation to compute corresponding BROM by detecting nearest bony impingement. FHJC, estimated from non-limiting FROM, was compared with AHJC to examine their positional differences and effect on BROM.

RESULTS

Differences in BROM and FROM were minimal in deep flexion (3.0° ± 4.1°) and maximum internal rotation (IR) at deep flexion (3.0° ± 2.9°), but substantially greater in extension (53.2° ± 9.5°). Bony impingement was observed during flexion, and IR at deep flexion for two hips. The average positional difference between FHJC and AHJC was 3.1 ± 1.2 mm, resulting in BROM differences of 1°-13° across four motions.

CONCLUSIONS

The study provided greater insight into the applicability and reliability of computed BROM in pre-surgical planning.

Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans

The range of applications of digital surface models of the bones in science and industry is wide. Three-dimensional reconstructions of bones are used in biomechanics, biomedical engineering, medical image processing, orthopedics, traumatology, radiology, patient education, anatomy, anthropometry, forensic anthropology, ergonomics, usability and human factors engineering, or accident and injury analysis and prevention. No open access database or repository of skeletal surface models of the full lower extremities exists. Therefore, the objective of this publication was to provide access to consistent complete bone models of the pelvis and lower limbs of multiple subjects, including biometric data. Segmentations and surface models of the bones of the lower extremities of more than twenty subjects were created from open access postmortem whole-body computed tomography scans. The database provides a broad range of applications by giving access to the data of the complete process chain, from the raw medical imaging data through the segmentations to the surface models.

Estimation of two wear factors for total hip arthroplasty: A simulation study based on musculoskeletal modelling

BACKGROUND

Primary causes of surgical revision after total hip arthroplasty are polyethylene wear and implant loosening. These factors are particularly related to joint friction and thus patients' physical activity. Assessing implant wear over time according to patients' morphology and physical activity level is key to improve follow-up and patients' quality of life.

METHODS

An approach initially proposed for tibiofemoral prosthetic wear estimation was adapted to compute two wear factors (force-velocity, directional wear intensity) using a musculoskeletal model. It was applied on 17 participants with total hip arthroplasty to compute joint angular velocity, contact force, sliding velocity, and wear factors during common daily living activities.

FINDINGS

Differences were observed between gait, sitting down, and standing up tasks. An incremental increase of both global wear factors (time-integral) was observed during gait from slow to fast speeds (p ≤ 0.01). Interestingly, these two wear factors did not result in same trend for sitting down and standing up tasks. Compared to gait, one cycle of sitting down or standing up tends to induce higher friction-related wear but lower cross-shear-related wear. Depending on the wear factor, significant differences can be found between sitting down and gait at slow speed (p ≤ 0.05), and between sitting down (p ≤ 0.05) or standing up (p ≤ 0.05) and gait at fast speed. Furthermore, depending on the activity, wear can be fostered by joint contact force and/or sliding velocity.

INTERPRETATION

This study demonstrated the potential of wear estimation to highlight activities inducing a higher risk of implant wear after total hip arthroplasty from motion capture data.