In untracked freehand 3D ultrasound (US), image content can be used to infer the trajectory of the transducer without a position tracking device. The nominal relationship between image correlation and elevational separation is established from controlled scans of a speckle phantom and used to determine out-of-plane motion. Unfortunately, this nominal relationship only holds under Rayleigh scattering conditions, which rarely occur in real tissue. This paper presents a method for learning the elevational correlation length of US signals in arbitrary tissue from a set of example synthetic US scans using sparse Gaussian process regression. Experiments on synthetic and real imagery of animal tissue show that the data driven approach generalises well across transducers, yielding results of accuracy superior to a base-line speckle detection approach and comparable to the state of the art [1]. Additionally, the new approach uniquely provides a measure of uncertainty in the estimated correlation length.
['Catherine Laporte', 'Tal Arbel']
Learning a tissue invariant ultrasound speckle decorrelation model