Abstract: A system that includes: a force sensor assembly adapted to monitor a load as applied on a subject’s knee joint when the force sensor assembly remains in direct contact with the subject’s lower extremity and the load is monitored from inside a main magnet of an MRI scanner; a mobile unit comprising tracks configured to adjust a position of the force sensor assembly; a stationary base on which the mobile unit and the force sensor assembly are located, the mobile unit translatable solely axially on the stationary base; and a processor coupled to the force sensor assembly and programmed to read information encoding the load being monitored by the force sensor assembly, wherein an MRI scan of the knee j oint is initiated only when a predetermined load has been applied to the subject’s knee joint for a pre-determined period of time.

Abstract: A system that includes: a force sensor assembly adapted to monitor a load as applied on a subject's knee joint when the force sensor assembly remains in direct contact with the subject's lower extremity and the load is monitored from inside a main magnet of an MRI scanner; a mobile unit comprising tracks configured to adjust a position of the force sensor assembly; a stationary base on which the mobile unit and the force sensor assembly are located, the mobile unit translatable solely axially on the stationary base; and a processor coupled to the force sensor assembly and programmed to read information encoding the load being monitored by the force sensor assembly, wherein an MRI scan of the knee joint is initiated only when a pre-determined load has been applied to the subject's knee joint for a pre-determined period of time.

Abstract: Systems and methods for training and implementing a machine learning algorithm to generate feature maps depicting spatial patterns of features associated with osteolysis, synovitis, or both. MRI data, including multispectral imaging data, are input to the trained machine learning algorithm to generate the feature maps, which may indicate features such as a location and probability of a pathology classification, a severity of synovitis, a type of synovitis, a synovial membrane thickness, and other features associated with osteolysis or synovitis. In some implementations, synovial anatomy are segmented in the MRI data before inputting the MRI data to the machine learning algorithm. These segmented MRI data may be generated using another trained machine learning algorithm.

Abstract: A method includes: accessing MRI data acquired from a joint area, the MRI data including a series of spatially mapped spectral data points; generating MRI images of the joint area; receiving information encoding a region of interest that encompasses a suspected metal particle deposition area over at least one of the MRI images; constructing magnetic field maps using the MRI data, each representing off-resonance frequency shifts over the joint area; removing a background of off-resonance field inhomogeneity from the magnetic field map such that the region of interest is free from off-resonance field inhomogeneity; identifying clusters from the magnetic field maps with the background of off-resonance field inhomogeneity removed, the clusters defined over a first dimension of offset frequencies and a second dimension of cluster volumes; and computing a quantitative metric by combining information from the identified clusters according to both the first dimension and the second dimension.

Abstract: A system that includes: a force sensor assembly adapted to monitor a load as applied on a subject's knee joint when the force sensor assembly remains in direct contact with the subject's lower extremity and the load is monitored from inside a main magnet of an MRI scanner; a mobile unit comprising tracks configured to adjust a position of the force sensor assembly; a stationary base on which the mobile unit and the force sensor assembly are located, the mobile unit translatable solely axially on the stationary base; and a processor coupled to the force sensor assembly and programmed to read information encoding the load being monitored by the force sensor assembly, wherein an MRI scan of the knee joint is initiated only when a pre-determined load has been applied to the subject's knee joint for a pre-determined period of time.

Abstract: A method includes: accessing MRI data acquired from a joint area, the MRI data including a series of spatially mapped spectral data points; generating MRI images of the joint area; receiving information encoding a region of interest that encompasses a suspected metal particle deposition area over at least one of the MRI images; constructing magnetic field maps using the MRI data, each representing off-resonance frequency shifts over the joint area; removing a background of off-resonance field inhomogeneity from the magnetic field map such that the region of interest is free from off-resonance field inhomogeneity; identifying clusters from the magnetic field maps with the background of off-resonance field inhomogeneity removed, the clusters defined over a first dimension of offset frequencies and a second dimension of cluster volumes; and computing a quantitative metric by combining information from the identified clusters according to both the first dimension and the second dimension.