Int J Comput Assist Radiol Surg - Automated segmentation of blood-flow regions in large thoracic arteries using 3D-cine PC-MRI measurements.

Tópicos

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Resumo

RPOSE: Quantitative analysis of vascular blood flow, acquired by phase-contrast MRI, requires accurate segmentation of the vessel lumen. In clinical practice, 2D-cine velocity-encoded slices are inspected, and the lumen is segmented manually. However, segmentation of time-resolved volumetric blood-flow measurements is a tedious and time-consuming task requiring automation.METHODS: Automated segmentation of large thoracic arteries, based solely on the 3D-cine phase-contrast MRI (PC-MRI) blood-flow data, was done. An active surface model, which is fast and topologically stable, was used. The active surface model requires an initial surface, approximating the desired segmentation. A method to generate this surface was developed based on a voxel-wise temporal maximum of blood-flow velocities. The active surface model balances forces, based on the surface structure and image features derived from the blood-flow data. The segmentation results were validated using volunteer studies, including time-resolved 3D and 2D blood-flow data. The segmented surface was intersected with a velocity-encoded PC-MRI slice, resulting in a cross-sectional contour of the lumen. These cross-sections were compared to reference contours that were manually delineated on high-resolution 2D-cine slices.RESULTS: The automated approach closely approximates the manual blood-flow segmentations, with error distances on the order of the voxel size. The initial surface provides a close approximation of the desired luminal geometry. This improves the convergence time of the active surface and facilitates parametrization.CONCLUSIONS: An active surface approach for vessel lumen segmentation was developed, suitable for quantitative analysis of 3D-cine PC-MRI blood-flow data. As opposed to prior thresholding and level-set approaches, the active surface model is topologically stable. A method to generate an initial approximate surface was developed, and various features that influence the segmentation model were evaluated. The active surface segmentation results were shown to closely approximate manual segmentations.

Resumo Limpo

rpose quantit analysi vascular blood flow acquir phasecontrast mri requir accur segment vessel lumen clinic practic dcine velocityencod slice inspect lumen segment manual howev segment timeresolv volumetr bloodflow measur tedious timeconsum task requir automationmethod autom segment larg thorac arteri base sole dcine phasecontrast mri pcmri bloodflow data done activ surfac model fast topolog stabl use activ surfac model requir initi surfac approxim desir segment method generat surfac develop base voxelwis tempor maximum bloodflow veloc activ surfac model balanc forc base surfac structur imag featur deriv bloodflow data segment result valid use volunt studi includ timeresolv d d bloodflow data segment surfac intersect velocityencod pcmri slice result crosssect contour lumen crosssect compar refer contour manual delin highresolut dcine slicesresult autom approach close approxim manual bloodflow segment error distanc order voxel size initi surfac provid close approxim desir lumin geometri improv converg time activ surfac facilit parametrizationconclus activ surfac approach vessel lumen segment develop suitabl quantit analysi dcine pcmri bloodflow data oppos prior threshold levelset approach activ surfac model topolog stabl method generat initi approxim surfac develop various featur influenc segment model evalu activ surfac segment result shown close approxim manual segment

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