Knee cartilage MRI with in situ mechanical loading using prospective motion correction

Abstract

Purpose To assess the feasibility of high resolution knee cartilage MRI with in situ mechanical loading using optical tracking to compensate for motion. Methods In vivo cartilage MRI with in situ mechanical loading is demonstrated on a clinical 3T system for the patellofemoral as well as for the tibiofemoral knee joint using a T1-weighted spoiled 3D gradient-echo sequence. Prospective motion correction is performed with a moiré phase tracking system consisting of an in-bore camera and a single tracking marker attached to the skin. Results Rigid-body approximation required for prospective correction with optical motion tracking is fulfilled well enough for the patellofemoral as well as for the tibiofemoral joint when the tracking marker is attached to the knee cap and the shin, respectively. Presaturation proves to be efficient in suppressing pulsation artifacts from the popliteal artery and residual motion artifacts primarily arising from non-rigid motion of the posterior knee compartment. Conclusion The proposed technique enables knee cartilage imaging under in situ mechanical loading with sub-millimeter spatial resolution devoid of significant motion artifacts and thus appropriate for cartilage volumetry. It has the potential to provide new insight into the biomechanics of the knee and might complement the panoply of diagnostic MR methods for osteoarthritis.

Comparative T2 and T1ρ mapping of patellofemoral cartilage under in situ mechanical loading with prospective motion correction

“…The ROI was restricted to the patellar cartilage, which has an approximately rigid coupling to the tracking marker on the knee cap while the femur typically undergoes a slight displacement under loading. Since patellofemoral cartilage compression under in situ loading is predominantly observed in the lateral joint compartment, the delineated ROI included the whole lateral patellar cartilage. Furthermore, the ROI was divided into two equally thick layers: a superficial and a deep cartilage zone, which were evaluated individually.…”

“…Recently, PMC based on optical tracking with an in‐bore camera has been demonstrated to provide submillimeter accuracy . Originally devised for brain measurements, the method has also been shown to be advantageous for knee MRI in a proof‐of‐concept study, enabling the imaging of the tibiofemoral as well as patellofemoral joint compartments with in situ loading . It has been demonstrated that motion artifacts arising from small knee motion induced by thigh muscle activation can be efficiently suppressed, thus enabling high‐resolution morphological cartilage MRI for quantification of load‐induced cartilage compression.…”

Optical tracking with two markers for robust prospective motion correction for brain imaging

“…Prospective motion correction (PMC) for MRI using external tracking systems has proven effective in mitigating the effects of subject motion for a variety of MRI modalities without increasing the scanning time [3-9]. Several external motion tracking systems used for MR are optical in nature, consisting of a camera (or cameras) and one or more markers attached to the surface of the object to be imaged (e.g., the head for brain MRI).…”

Quantification of patellofemoral cartilage deformation and contact area changes in response to static loading via high‐resolution MRI with prospective motion correction

“…As a suitable countermeasure, prospective motion correction (PMC) based on an optical camera and a tracking marker has recently been proposed. It has been demonstrated that PMC can effectively suppress motion artifacts arising from small involuntary knee motion during loaded MRI scans of the knee joint …”

“…It has been demonstrated that PMC can effectively suppress motion artifacts arising from small involuntary knee motion during loaded MRI scans of the knee joint. 21,24 In quantitative knee MRI studies, manual, semiautomatic, or automatic segmentation approaches can be used for extraction of bone and cartilage surfaces. Automatic and semiautomatic segmentation algorithms provide more consistent labels of the knee structures in a shorter time than manual segmentation.…”