Anatomical Variability of Intercondylar Fossa Geometry in Patients Diagnosed with Primary Anterior Cruciate Ligament Rupture

K-Space Data Reconstruction Algorithm-Based MRI Diagnosis and Influencing Factors of Knee Anterior Cruciate Ligament Injury

This study was aimed at investigating the diagnostic value of MRI based on K-space data reconstruction algorithm for anterior cruciate ligament (ACL) injury of knee joint and the influencing factors of ligament injury. 96 patients with ACL injury of knee joint were selected, and they were randomly divided into two groups: group A (arthroscopy) and group B (MRI examination), and another 96 healthy volunteers in the same period were selected as the control group. The test results of each indicator were compared. The results showed that the signal-to-noise ratio (SNR) of SMASH algorithm was higher than that of sum of squares (SOS) algorithm. In group A, there were 66 positive and 30 negative tests, and in group B, there were 56 positive and 40 negative tests (P < 0.05). The intercondylar fossa width, the intercondylar fossa width index, and the ratio of tibial intercondylar eminence width to intercondylar fossa width in group B were lower than those in the control group (P < 0.05). Compared with the traditional SOS algorithm, SMASH algorithm can improve the image quality, reduce the impact of damage data on the final synthesis image, and improve the image SNR. In clinical work, the ratio of the width of tibial intercondylar eminence to the width of femoral intercondylar fossa can be measured by imaging data to evaluate the matching between tibial intercondylar eminence and femoral intercondylar fossa, so as to evaluate the risk of ACL rupture.

Automatic segmentation model of intercondylar fossa based on deep learning: a novel and effective assessment method for the notch volume

Background

Notch volume is associated with anterior cruciate ligament (ACL) injury. Manual tracking of intercondylar notch on MR images is time-consuming and laborious. Deep learning has become a powerful tool for processing medical images. This study aims to develop an MRI segmentation model of intercondylar fossa based on deep learning to automatically measure notch volume, and explore its correlation with ACL injury.

Methods

The MRI data of 363 subjects (311 males and 52 females) with ACL injuries incurred during non-contact sports and 232 subjects (147 males and 85 females) with intact ACL were retrospectively analyzed. Each layer of intercondylar fossa was manually traced by radiologists on axial MR images. Notch volume was then calculated. We constructed an automatic segmentation system based on the architecture of Res-UNet for intercondylar fossa and used dice similarity coefficient (DSC) to compare the performance of segmentation systems by different networks. Unpaired t-test was performed to determine differences in notch volume between ACL-injured and intact groups, and between males and females.

Results

The DSCs of intercondylar fossa based on different networks were all more than 0.90, and Res-UNet showed the best performance. The notch volume was significantly lower in the ACL-injured group than in the control group (6.12 ± 1.34 cm³ vs. 6.95 ± 1.75 cm³, P < 0.001). Females had lower notch volume than males (5.41 ± 1.30 cm³ vs. 6.76 ± 1.51 cm³, P < 0.001). Males and females who had ACL injuries had smaller notch than those with intact ACL (p < 0.001 and p < 0.005). Men had larger notches than women, regardless of the ACL injuries (p < 0.001).

Conclusion

Using a deep neural network to segment intercondylar fossa automatically provides a technical support for the clinical prediction and prevention of ACL injury and re-injury after surgery.

Does Femoral Intercondylar Notch Volume Differ in Anterior Cruciate Ligament-Injured Adult Patients Compared to the Uninjured?: A Meta-Analysis

Background

Stenotic femoral intercondylar notch is considered as a risk factor for anterior cruciate ligament (ACL) injury and three-dimensional notch volume is used as a marker for the injury. The primary purpose of this study was to assess the difference in notch volume between the ACL-injured and uninjured in men and women combined or stratified by sex. The secondary purpose was to assess the difference in notch volume between the ACL-intact men and women.

Methods

A search of PubMed/Medline, Scopus, Google Scholar, and Cochrane databases from inception to December 9, 2020, was conducted without restrictions using the following terms: ACL, notch, volume, notch volume, femoral notch volume, and intercondylar notch volume. Studies that compared the ACL-injured with uninjured controls were included. Independent extraction of articles by two authors using predefined data fields including study quality indicators was done. All pooled analyses were based on the inverse-variance weighted random effects model and mean difference was chosen as the effect measure.

Results

Nine studies (1,169 knees) qualified for overall analysis (both sexes combined) and significant heterogeneity was observed, which disappeared after pooling studies with age-sex matched controls and those without. Notch volume in the ACL-injured was 0.75 cm³ (95% confidence interval [CI], 0.53–0.96 cm³), which was smaller than that in the age- and sex-matched controls. Six studies qualified for analysis in men. Notch volume in the ACL-injured men was smaller, especially when non-contact ACL injury was considered (1.40 cm³; 95% CI, 1.08–1.73 cm³). Five studies qualified for analysis in women and ACL-injured women had smaller notch volume irrespective of the mechanism of injury (0.38 cm³; 95% CI, 0.18–0.59 cm³). Notch volume of the uninjured men was larger than that of the uninjured women (1.86 cm³; 95% CI, 1.54–2.18 cm³).

Conclusions

ACL-injured adults have smaller notch volume than the age- and sex-matched controls. Non-contact ACL-injured males have smaller notch volume compared to ACL-intact males. ACL-injured females have smaller notch volume irrespective of the nature of injury. Men have higher notch volume than women. The quality of evidence is very low to low.