Reproducibility and postacquisition correction methods for quantitative magnetic resonance imaging of the anterior cruciate ligament (ACL)

Abstract

Quantitative magnetic resonance imaging has been used to evaluate the structural integrity of knee joint structures. However, variations in acquisition parameters between scanners pose significant challenges. Understanding the effect of small differences in acquisition parameters for quantitative sequences is vital to the validity of cross‐institutional studies, and for the harmonization of large, heterogeneous datasets to train machine learning models. The study objective was to assess the reproducibility of T2* relaxometry and the constructive interference in steady‐state sequence (CISS) across scanners, with minimal hardware‐necessitated changes to acquisition parameters. It was hypothesized that there would be no significant differences between scanners in anterior cruciate ligament T2* relaxation times and CISS signal intensities (SI). Secondarily, it was hypothesized that differences could be corrected by rescaling the SI distribution to harmonize between scanners. Seven volunteers were scanned on 3T Prisma and Tim Trio scanners (Siemens). Three correction methods were evaluated for T2*: inverse echo time scaling, z‐scoring, and Nyúl histogram matching. For CISS, scans were normalized to cortical bone, scaled by the background noise ratio, and log‐transformed. Before correction, significant mean differences of 6.0 ± 3.2 ms (71.8%; p = 0.02) and 0.49 ± 0.15 units (40.7%; p = 0.02) for T2* and CISS across scanners were observed, respectively. After rescaling, T2* differences decreased to 2.6 ± 2.7 ms (23.9%; p = 0.03), 1.3 ± 2.5 ms (10.9%; p = 0.13), and 1.27 ± 3.0 ms (19.6%; p = 0.40) for inverse echo time, z‐scoring, and Nyúl, respectively, while CISS decreased to 0.01 ± 0.11 units (4.0%; p = 0.87). These findings suggest that small acquisition parameter differences may lead to large changes in T2* and SI values that must be reconciled to compare data across magnets.

Quantitative MRI Biomarkers to Predict Risk of Reinjury Within 2 Years After Bridge-Enhanced ACL Restoration

“…However, a recently validated harmonization procedure was used to standardize the qMRI images between scanners. 16 Future large-scale prospective studies are needed to confirm the current findings and assess the utility of qMRI in revision prediction after ACLR.…”

“…To aid in translation, we have developed harmonization procedures to standardize qMRI results between scanners. 16 We have also engineered automatic ACL segmentation techniques to reduce the segmentation time from hours to seconds to obtain the qMRI parameters required for the prediction. 14,15 An automatic pipeline that includes image harmonization, ACL segmentation, and the predicted equations to determine the failure properties of the healing ligament is under development.…”

“…Harmonization to standardize the images between the 2 scanners was performed as previously described. 16 The Prisma scans were scaled to the Tim Trio scans due to hardware-necessitated differences in the sequence acquisition parameters (Table 1). The ACL was then segmented from the MRI stack by an observer with >8 years of experience in ACL segmentation (A.M.K.)…”

“…4,6,8,54 Despite promising preclinical and clinical evidence on the utility of qMRI for the noninvasive assessment of the healing ACL structural properties and postoperative remodeling, these techniques have not yet become mainstream. Possible reasons for this include technical challenges in standardizing image acquisition parameters or post hoc harmonization for consistent qMRI results, 16,49 as well as the lack of high-quality evidence on the relative performance of qMRI metrics in predicting ACL surgery outcomes and reinjury risk.…”

Predicting anterior cruciate ligament failure load with T2* relaxometry and machine learning as a prospective imaging biomarker for revision surgery

“…On the Prisma (Brown University; n = 10), T 2 * relaxometry scans were acquired with FA = 12°; TR = 29 ms; TE 1 = 2.5 ms; TE 2 = 6.9 ms; TE 3 = 11. Due to the acquisition parameter differences between scanners imposed by the differing gradient hardware performance limits, all acquisitions were harmonized prior to analysis by z-scoring 47 . Details regarding the MRI harmonization procedure have been previously described 47 .…”