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Object This study proposes a scale space based algorithm for automatedsegmentation of single-shot tagged images of modest SNR. Furthermore thealgorithm was designed for analysis of discontinuous or shearing types ofmotion, i.e. segmentation of broken tag patterns.  Materials and methods The proposed algorithm utilizes non-linear scale spacefor automatic segmentation of single-shot tagged images. The algorithm'sability to automatically segment tagged shearing motion was evaluated in anumerical simulation and in vivo. A typical shearing deformation was simulatedin a Shepp-Logan phantom allowing for quantitative evaluation of thealgorithm's success rate as a function of both SNR and the amount ofdeformation. For a qualitative in vivo evaluation tagged images showingdeformations in the calf muscles and eye movement in a healthy volunteer wereacquired.  Results Both the numerical simulation and the in vivo tagged datademonstrated the algorithm's ability for automated segmentation of single-shottagged MR provided that SNR of the images is above 10 and the amount ofdeformation does not exceed the tag spacing. The latter constraint can be metby adjusting the tag delay or the tag spacing.  Conclusion The scale space based algorithm for automatic segmentation ofsingle-shot tagged MR enables the application of tagged MR to complex(shearing) deformation and the processing of datasets with relatively low SNR.

A scale space based algorithm for automated segmentation of single shot tagged MRI of shearing deformation