A novel computational procedure for accelerated, calibrationless magnetic resonance image reconstruction (Free-breathing Accelerated MRI Portfolio)
Magnetic Resonance Imaging (MRI) is a versatile noninvasive imaging tool that is routinely used to evaluate many diseases and conditions. MRI offers exquisite soft tissue contrast and high spatial resolution to enable comprehensive structural and functional assessment of internal organs. Recent innovations in data acquisition and processing can expand the clinical applications of MRI.
A major limitation of MRI is slow data acquisition, which can compromise patient comfort, drive up costs, and increase susceptibility to motion. The clinical adoption of volumetric imaging for musculoskeletal and neuro applications, the growing
demand for free-breathing real-time imaging for cardiovascular applications, and the emergence of high dimensional imaging (e.g., 4D/5D whole-heart imaging) demand motion robustness and accelerations that often cannot be realized with the existing technology.
This technology portfolio uses new data acquisition and signal processing techniques to improve MRI offerings. These technologies relate to (i) improving image quality for dynamic or volumetric applications, (ii) improving motion-robustness and suppressing motion-related artifacts, and (iii) accelerating the acquisition process.
These technologies apply to the many clinical offerings where long acquisition times make data acquisition susceptible to motion. Such applications include (i) volumetric cine imaging, (ii) 4D/5D flow imaging, (iii) real-time cardiac imaging, (iv) volumetric abdominal imaging, (v) volumetric brain imaging, and (vi) volumetric musculoskeletal imaging. These technologies can be paired with either optimization-based iterative solvers or deep learning-based image reconstruction methods.
These technologies can help maintain a competitive product portfolio by offering shorter scan times, improved image quality, and fewer instances of “failed” scans.