Osteochondral Repair: Evaluation with Sweep Imaging with Fourier Transform in an Equine Model

Abstract

SWIFT enabled assessment of spontaneous osteochondral repair in an equine model.

Multiparametric MRI assessment of human articular cartilage degeneration: Correlation with quantitative histology and mechanical properties

“…The zonal evaluation of the advanced OA samples may have been hindered by tissue erosion which may affect the comparison of the ROIs of the cartilage layers between early and advanced OA. Furthermore, the very high relaxation times observed in the superficial cartilage in advanced OA samples was most likely caused by a small amount of residual PBS solution left on the surfaces of the samples before imaging . However, the thicknesses of the full‐thickness ROIs were compared against the thicknesses derived from microscopy images.…”

In vivo visualization using MRI T2 mapping of induced osteochondrosis and osteochondritis dissecans lesions in goats undergoing controlled exercise

“…Recently, development and validation of novel MRI sequences led to an increased interest in advanced cartilage imaging . Quantitative MRI techniques, including cartilage maps created using T 2 −, RAFF (relaxation along a fictitious field)−, or T 1ρ − relaxation times, were able to identify surgically induced, subclinical lesions of OC ex vivo at various stages in their temporal development .…”

“…Recently, development and validation of novel MRI sequences led to an increased interest in advanced cartilage imaging. [13][14][15] Quantitative MRI techniques, including cartilage maps created using T 2 À, RAFF (relaxation along a fictitious field)À, or T 1r À relaxation times, were able to identify surgically induced, subclinical lesions of OC ex vivo at various stages in their temporal development. 16 Susceptibility weighted imaging and quantitative susceptibility mapping 17,18 have also been used to describe the vascular architecture of the epiphyseal growth cartilage at the distal femoral predilection site of OC in human beings, pigs, horses, and goats ex vivo.…”

Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging

“…Thus, performance of WND-CHRM in the classification of MR images may be substantially improved through the inclusion of additional joint features, such as subchondral bone and meniscus. To be fully effective, this may require use of specialized imaging techniques such as ultrashort TE, sweep imaging with Fourier Transform or near-zero TE 43–45 so that these more rigid structures are more readily visualized. Nevertheless, we achieved substantial success through analysis of cartilage matrix using MR measures that are routinely available in clinical settings.…”