Semi-quantitative assessment of pulmonary perfusion in children using dynamic contrast-enhanced MRI

Catalin Fetita; William E. Thong; Phalla Ou

doi:10.1117/12.2007920

18 March 2013 Semi-quantitative assessment of pulmonary perfusion in children using dynamic contrast-enhanced MRI

Catalin Fetita, William E. Thong, Phalla Ou

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867033 (2013) https://doi.org/10.1117/12.2007920
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

This paper addresses the study of semi-quantitative assessment of pulmonary perfusion acquired from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in a study population mainly composed of children with pulmonary malformations. The automatic analysis approach proposed is based on the indicator-dilution theory introduced in 1954. First, a robust method is developed to segment the pulmonary artery and the lungs from anatomical MRI data, exploiting 2D and 3D mathematical morphology operators. Second, the time-dependent contrast signal of the lung regions is deconvolved by the arterial input function for the assessment of the local hemodynamic system parameters, ie. mean transit time, pulmonary blood volume and pulmonary blood flow. The discrete deconvolution method implements here a truncated singular value decomposition (tSVD) method. Parametric images for the entire lungs are generated as additional elements for diagnosis and quantitative follow-up. The preliminary results attest the feasibility of perfusion quantification in pulmonary DCE-MRI and open an interesting alternative to scintigraphy for this type of evaluation, to be considered at least as a preliminary decision in the diagnostic due to the large availability of the technique and to the non-invasive aspects.

Citation Download Citation

Catalin Fetita, William E. Thong, and Phalla Ou "Semi-quantitative assessment of pulmonary perfusion in children using dynamic contrast-enhanced MRI", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867033 (18 March 2013); https://doi.org/10.1117/12.2007920

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