An axially-variant kernel imaging model applied to ultrasound image reconstruction

Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

1 Sitaatiot (Scopus)

Abstrakti

Existing ultrasound deconvolution approaches unrealistically assume, primarily for computational reasons, that the convolution model relies on a spatially invariant kernel and circulant boundary conditions. We discard both restrictions and introduce an image formation model applicable to ultrasound imaging and deconvolution based on an axially varying kernel, that accounts for arbitrary boundary conditions. Our model has the same computational complexity as the one employing spatially invariant convolution and has negligible memory requirements. To accommodate state-of-the-art deconvolution approaches when applied to a variety of inverse problem formulations, we also provide an equally efficient adjoint expression of our model. Simulation results confirm the tractability of our model for the deconvolution of large images. Moreover, in terms of accuracy metrics, the quality of reconstruction using our model is superior to that obtained using spatially invariant convolution.

AlkuperäiskieliEnglanti
Sivut961-965
Sivumäärä5
JulkaisuIEEE Signal Processing Letters
Vuosikerta25
Numero7
DOI - pysyväislinkit
TilaJulkaistu - heinäkuuta 2018
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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