Pre-Operative Femoral Cartilage Ultrasound Characteristics Are Altered in People Who Report Symptoms at 1 year After Anterior Cruciate Ligament Reconstruction

Femoral cartilage ultrasound echo-intensity is a valid measure of cartilage composition

This study aimed to create a conversion equation that accurately predicts cartilage magnetic resonance imaging (MRI) T2 relaxation times using ultrasound echo-intensity and common participant demographics. We recruited 15 participants with a primary anterior cruciate ligament reconstruction between the ages of 18 and 35 years at 1-5 years after surgery. A single investigator completed a transverse suprapatellar scan with the ACLR limb in max knee flexion to image the femoral trochlea cartilage. A single reader manually segmented the femoral cartilage cross-sectional area to assess the echo-intensity (i.e., mean gray-scale pixel value). At a separate visit, a T2 mapping sequence with the MRI beam set to an oblique angle was used to image the femoral trochlea cartilage. A single reader manually segmented the cartilage cross-sectional area on a single MRI slice to assess the T2 relaxation time. A stepwise, multiple linear regression was used to predict T2 relaxation time from cartilage echo-intensity and common demographic variables. We created a conversion equation using the regression betas and then used an ICC and Bland-Altman plot to assess agreement between the estimated and true T2 relaxation time. Cartilage ultrasound echo-intensity and age significantly predicted T2 relaxation time (F = 7.33, p = 0.008, R2  = 0.55). When using the new conversion equation to estimate T2 relaxation time from cartilage echo-intensity and age, there was strong agreement between the estimated and true T2 relaxation time (ICC2,k  = 0.84). This study provides promising preliminary data that cartilage echo-intensity combined with age can be used as a clinically accessible tool for evaluating cartilage composition.

Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images

OBJECTIVE

To validate a semi-automated technique to segment ultrasound-assessed femoral cartilage without compromising segmentation accuracy to a traditional manual segmentation technique in participants with an anterior cruciate ligament injury (ACL).

DESIGN

We recruited 27 participants with a primary unilateral ACL injury at a pre-operative clinic visit. One investigator performed a transverse suprapatellar ultrasound scan with the participant's ACL injured knee in maximum flexion. Three femoral cartilage ultrasound images were recorded. A single expert reader manually segmented the femoral cartilage cross-sectional area in each image. In addition, we created a semi-automatic program to segment the cartilage using a random walker-based method. We quantified the average cartilage thickness and echo-intensity for the manual and semi-automated segmentations. Intraclass correlation coefficients (ICC_2,k) and Bland-Altman plots were used to validate the semi-automated technique to the manual segmentation for assessing average cartilage thickness and echo-intensity. A dice correlation coefficient was used to quantify the overlap between the segmentations created with the semi-automated and manual techniques.

RESULTS

For average cartilage thickness, there was excellent reliability (ICC_2,k = 0.99) and a small mean difference (+0.8%) between the manual and semi-automated segmentations. For average echo-intensity, there was excellent reliability (ICC_2,k = 0.97) and a small mean difference (-2.5%) between the manual and semi-automated segmentations. The average dice correlation coefficient between the manual segmentation and semi-automated segmentation was 0.90, indicating high overlap between techniques.

CONCLUSIONS

Our novel semi-automated segmentation technique is a valid method that requires less technical expertise and time than manual segmentation in patients after ACL injury.