A robust and semi-automatic quantitative measurement of patellofemoral instability based on four dimensional computed tomography

Differences in Patellofemoral Alignment Between Static and Dynamic Extension Positions in Patients With Patellofemoral Pain

Background

Considering that patellofemoral pain (PFP) is related to dynamic factors, dynamic extension on 4-dimensional computed tomography (4-DCT) may better reflect the influence of muscles and surrounding soft tissue than static extension.

Purpose

To compare the characteristics of patellofemoral alignment between the static and dynamic knee extension position in patients with PFP and controls via 4-DCT.

Study Design

Cross-sectional study; Level of evidence, 3.

Methods

Included were 39 knees (25 patients) with PFP and 37 control knees (24 participants). For each knee, an image of the dynamic extension position (a single frame of the knee in full extension [flexion angle of -5° to 0°] selected from 21 frames of continuous images acquired by 4-DCT during active flexion and extension) and an image of the static extension position (acquired using the same equipment with the knee fully extended and the muscles relaxed) were selected. Patellofemoral alignment was evaluated between the dynamic and static extension positions and between the PFP and control groups with the following parameters: patella-patellar tendon angle (P-PTA), Blackburne-Peel ratio, bisect-offset (BO) index, lateral patellar tilt (LPT), and tibial tuberosity-trochlear groove (TT-TG) distance.

Results

In both PFP patients and controls, the P-PTA, Blackburne-Peel ratio, and BO index in the static extension position were significantly lower (P < .001 for all), while the LPT and TT-TG distance in the static extension position were significantly higher (P ≤ .034 and P < .001, respectively) compared with values in the dynamic extension position. In the comparison between groups, only P-PTA in the static extension position was significantly different (134.97° ± 4.51° [PFP] vs 137.82° ± 5.63° [control]; P = .027). No difference was found in the rate of change from the static to the dynamic extension position of any parameter between the study groups.

Conclusion

The study results revealed significant differences in patellofemoral alignment characteristics between the static and dynamic extension positions of PFP patients and controls. Multiplanar measurements may have a role in subsequent patellofemoral alignment evaluation.

Knee landmarks detection via deep learning for automatic imaging evaluation of trochlear dysplasia and patellar height

OBJECTIVES

To develop and validate a deep learning-based approach to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee magnetic resonance imaging (MRI) scans.

METHODS

A total of 763 knee MRI slices from 95 patients were included in the study, and 3393 anatomical landmarks were annotated for measuring sulcus angle (SA), trochlear facet asymmetry (TFA), trochlear groove depth (TGD) and lateral trochlear inclination (LTI) to assess trochlear dysplasia, and Insall-Salvati index (ISI), modified Insall-Salvati index (MISI), Caton Deschamps index (CDI) and patellotrochlear index (PTI) to assess patellar height. A U-Net based network was implemented to predict the landmarks' locations. The successful detection rate (SDR) and the mean absolute error (MAE) evaluation metrics were used to evaluate the performance of the network. The intraclass correlation coefficient (ICC) was also used to evaluate the reliability of the proposed framework to measure the mentioned PFI indices.

RESULTS

The developed models achieved good accuracy in predicting the landmarks' locations, with a maximum value for the MAE of 1.38 ± 0.76 mm. The results show that LTI, TGD, ISI, CDI and PTI can be measured with excellent reliability (ICC > 0.9), and SA, TFA and MISI can be measured with good reliability (ICC > 0.75), with the proposed framework.

CONCLUSIONS

This study proposes a reliable approach with promising applicability for automatic patellar height and trochlear dysplasia assessment, assisting the radiologists in their clinical practice.

CLINICAL RELEVANCE STATEMENT

The objective knee landmarks detection on MRI images provided by artificial intelligence may improve the reproducibility and reliability of the imaging evaluation of trochlear anatomy and patellar height, assisting radiologists in their clinical practice in the patellofemoral instability assessment.

KEY POINTS

• Imaging evaluation of patellofemoral instability is subjective and vulnerable to substantial intra and interobserver variability. • Patellar height and trochlear dysplasia are reliably assessed in MRI by means of artificial intelligence (AI). • The developed AI framework provides an objective evaluation of patellar height and trochlear dysplasia enhancing the clinical practice of the radiologists.

Length Changes of the Medial Patellofemoral Ligament During In Vivo Knee Motion: An Evaluation Using Dynamic Computed Tomography

BACKGROUND

Medial patellofemoral ligament (MPFL) reconstruction is associated with high complication rates because of graft overloading from incorrect graft positioning. To improve clinical outcomes, it is crucial to gain a better understanding of MPFL elongation patterns.

PURPOSE

To assess MPFL length changes in healthy knees from 0° to 90° of dynamic flexion and their relationship with anatomic parameters of the patellofemoral joint.

STUDY DESIGN

Descriptive laboratory study.

METHODS

Dynamic computed tomography scans of an active flexion-extension-flexion movement in 115 knees from 63 healthy participants were evaluated to construct knee joint models. Using these models, the MPFL length was measured as the shortest wrapping path from the Schöttle point on the femur to 3 insertion points on the superomedial border of the patella (proximal, central, and distal). MPFL length changes (%) relative to the length in full extension were calculated, and their correlations with the tibial tuberosity-trochlear groove distance, Caton-Deschamps index, and lateral trochlear inclination were analyzed.

RESULTS

The proximal fiber was the longest in full extension and progressively decreased to a median length of -6.0% at 90° of flexion. The central fiber exhibited the most isometric pattern during knee flexion, showing a median maximal decrease of 2.8% relative to the full extension length and no evident elongation. The distal fiber first slightly decreased in length but increased at deeper flexion angles. The median overall length changes were 4.6, 4.7, and 5.7 mm for the proximal, central, and distal patellar insertion, respectively. These values were either not or very weakly correlated with the tibial tuberosity-trochlear groove distance, Caton-Deschamps index, and lateral trochlear inclination when the anatomic parameters were within the healthy range.

CONCLUSION

The median MPFL length changed by approximately 5 mm between 0° and 90° of flexion. Proximally, the length continuously decreased, indicating slackening behavior. Distally, the length increased at deeper flexion angles, indicating tightening behavior.

CLINICAL RELEVANCE

In MPFL reconstruction techniques utilizing the Schöttle point to establish the femoral insertion, one should avoid distal patellar insertion, as it causes elongation of the ligament, which may increase the risk for complications due to overloading.

Computed Tomography: State-of-the-Art Advancements in Musculoskeletal Imaging

ABSTRACT

Although musculoskeletal magnetic resonance imaging (MRI) plays a dominant role in characterizing abnormalities, novel computed tomography (CT) techniques have found an emerging niche in several scenarios such as trauma, gout, and the characterization of pathologic biomechanical states during motion and weight-bearing. Recent developments and advancements in the field of musculoskeletal CT include 4-dimensional, cone-beam (CB), and dual-energy (DE) CT. Four-dimensional CT has the potential to quantify biomechanical derangements of peripheral joints in different joint positions to diagnose and characterize patellofemoral instability, scapholunate ligamentous injuries, and syndesmotic injuries. Cone-beam CT provides an opportunity to image peripheral joints during weight-bearing, augmenting the diagnosis and characterization of disease processes. Emerging CBCT technologies improved spatial resolution for osseous microstructures in the quantitative analysis of osteoarthritis-related subchondral bone changes, trauma, and fracture healing. Dual-energy CT-based material decomposition visualizes and quantifies monosodium urate crystals in gout, bone marrow edema in traumatic and nontraumatic fractures, and neoplastic disease. Recently, DE techniques have been applied to CBCT, contributing to increased image quality in contrast-enhanced arthrography, bone densitometry, and bone marrow imaging. This review describes 4-dimensional CT, CBCT, and DECT advances, current logistical limitations, and prospects for each technique.

Automatic measurement of the patellofemoral joint parameters in the Laurin view: a deep learning-based approach

OBJECTIVES

To explore the performance of a deep learning-based algorithm for automatic patellofemoral joint (PFJ) parameter measurements from the Laurin view.

METHODS

A total of 1431 consecutive Laurin views of the PFJ were retrospectively collected and divided into two parts: (1) the model development dataset (dataset 1, n = 1230) and (2) the hold-out test set (dataset 2, n = 201). Dataset 1 was used to develop the U-shaped fully convolutional network (U-Net) model to segment the landmarks of the PFJ. Based on the predicted landmarks, the PFJ parameters were calculated, including the sulcus angle (SA), congruence angle (CA), patellofemoral ratio (PFR), and lateral patellar tilt (LPT). Dataset 2 was used to assess the model performance. The mean of three radiologists who independently measured the PFJ parameters was defined as the reference standard. Model performance was assessed by the intraclass correlation coefficient (ICC), mean absolute difference (MAD), and root mean square (RMS) compared to the reference standard. Ninety-five percent limits of agreement (95% LoA) were calculated pairwise for each radiologist, reference standard, and model.

RESULTS

Compared with the reference standard, U-Net showed good performance for predicting SA, CA, PFR, and LPT, with ICC = 0.85-0.97, MAD = 0.06-5.09, and RMS = 0.09-6.90 in the hold-out test set. Except for the PFR, the remaining parameters measured between the reference standard and the model were within the 95% LoA in the hold-out test dataset.

CONCLUSIONS

The U-Net-based deep learning approach had a relatively high model performance in automatically measuring SA, CA, PFR, and LPT.

KEY POINTS

• The U-Net model could be used to segment the landmarks of the PFJ and calculate the SA, CA, PFR, and LPT, which could be used to evaluate the patellar instability. • In the hold-out test, the automatic measurement model yielded comparable performance with reference standard. • The automatic measurement model could still accurately predict SA, CA, PFR, and LPT in patients with PI and/or PFOA.

Future Directions in Patellofemoral Imaging and 3D Modeling

PURPOSE OF REVIEW

Patellofemoral instability involves complex, three-dimensional pathological anatomy. However, current clinical evaluation and diagnosis relies on attempting to capture the pathology through numerous two-dimensional measurements. This current review focuses on recent advancements in patellofemoral imaging and three-dimensional modeling.

RECENT FINDINGS

Several studies have demonstrated the utility of dynamic imaging modalities. Specifically, radiographic patellar tracking correlates with symptomatic instability, and quadriceps activation and weightbearing alter patellar kinematics. Further advancements include the study of three-dimensional models. Automation of commonly utilized measurements such as tibial tubercle-trochlear groove (TT-TG) distance has the potential to resolve issues with inter-rater reliability and fluctuation with knee flexion or tibial rotation. Future directions include development of robust computational models (e.g., finite element analysis) capable of incorporating patient-specific data for surgical planning purposes. While several studies have utilized novel dynamic imaging and modeling techniques to enhance our understanding of patellofemoral joint mechanics, these methods have yet to find a definitive clinical utility. Further investigation is required to develop practical implementation into clinical workflow.