An Algorithm for Strain Reconstruction from Irregularly Sampled, Incomplete Measurements

This study proposes a novel algorithm for luminal strain reconstruction from sparse irregularly sampled strain measurements. It is based on the Normalized Convolution (NC) algorithm. The novel extension comprises the multiresolution scheme, which takes into account the fact the sample density of the available strain measurements varies during the cardiac cycle. The algorithm was applied for the luminal strain reconstruction in in-vivo Intravascular Ultrasound (IVUS) pullbacks. The high quality of strain restoration was observed after systematically removing up to 80% of the initial elastographic measurements. The restored distributions accurately reproduced the original strain patterns and the error did not exceed 12%. The experimental validation on 8 in-vivo IVUS pullbacks demonstrated that the relative decrease in number of invalid strain estimates amounts to 92.05 +/- 6.03 and 99.17 +/- 0.92 for the traditional and reconstructive strain computational scheme. In conclusion, implementation of reconstructive compounding scheme boosts the diagnostic value of IVUS Palpography.