Koninklijke Philips Corporation N.v.

Krishna Subramanyan

Shalabh Chandra

Kai Eck

Lothar Spies

Scott Kenneth Pohlman

Matthieu Frederic Bal

Hasan Celik

Matthieu Bal

A diagnostic imaging system () corrects metal artifact streaks () emanating from a metal object () in a tomographic image (T). A first processor () reduces streaks () caused by mild artifacts by applying an adaptive filter (). The filter () is perpendicularly oriented toward the center of the metal object (). The weight of the filter () is a function of the local structure tensor and the vector pointing to the metal object (). If it is determined that the strong artifacts are present in the image, a second processor () applies a sinogram completed image algorithm to correct for severe artifacts in the image. The sinogram completed image and adaptively filtered image are fused to a final corrected image. In the fusion process, highly corrupted tomographic regions are replaced by the result of the sinogram completed image and the remainder is replaced by the adaptively filtered image.

Radial adaptive filter for metal artifact correction

An apparatus for measuring parameters preparatory to a stent replacement of an aneurytic blood vessel in a patient ( ) includes a computed tomography (CT) scanner ( ) that acquires image data ( ) corresponding to multiple two-dimensional image slices. A reconstruction processor ( ) reconstructs a three-dimensional image representation ( ) from the image data ( ). A tracking processor ( ) produces a tracked vessel ( ) including at least a centerline ( ) and selected vessel boundaries ( ). A user interface ( ) displays a rendering ( ) of the image representation to an associated user ( ), measures selected vascular parameters corresponding to the stent parameters ( ), and graphically superimposes the measured parameters on the rendering of the image representation ( ).

Method and apparatus for semi-automatic aneurysm measurement and stent planning using volume image data

Computed tomography (CT) data ( ) is collected for a plurality of slices by a CT scanner ( ). At least a portion of the CT data is reconstructed ( ) to form a volume image ( ) defined by a plurality of two-dimensional image slices. At least one starting point is identified ( ) within a blood vessel imaged in the three-dimensional image volume ( ). The blood vessel is recursively tracked ( ) to form a blood vessel representation ( ).

Vessel tracking and tree extraction method and apparatus

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Solon, OH, United States of America

Krishna Subramanyan