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Proceedings Paper

Presenting 3D MRI data in visual perspective
Author(s): Haiying Liu; Chialei Chin
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Paper Abstract

To improve depth perception a projection scheme was developed, which incorporates visual perspective transformation in the MR image reconstruction, in which an observation point (OP) was selected in space relative to the image volume. The image formation process was modeled as a camera, whose lens center (LC) coincide with the OP, and a picture plane (PP) was placed at the focal plane. The finite sized PP corresponded to what the observer saw. Each pixel in the PP defined a ray projection trajectory (PT) from LC. The intensity values for a set of discrete points along the PT were interpolated from the image volume. From these projection profiles, various projection images can be derived depending how the intensity profile is manipulated. To demonstrate the effectiveness of the method, we obtained three dimensional image data sets, which included anatomical (TR/TE/flip=13/6/9) and angiographical data sets (TR/TE/Flip=30/5.4/15) in transverse orientation from a patient head at 1.5T. Both quasi-surface rendering and MaxIP algorithms were used along the viewing trace of perspective projection than rather the parallel projection. For multiple Ops with different distances from the 3D image volume, consecutive views were obtained at a constant interval azimuthally. When displayed in cine-mode, the MaxIP images appeared realistic with improved depth perception, different portions of the image moved naturally according to the expectation of human visual system.

Paper Details

Date Published: 28 May 2001
PDF: 6 pages
Proc. SPIE 4319, Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures, (28 May 2001); doi: 10.1117/12.428077
Show Author Affiliations
Haiying Liu, Univ. of Minnesota/Twin Cities (United States)
Chialei Chin, Univ. of Minnesota/Twin Cities (United States)

Published in SPIE Proceedings Vol. 4319:
Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures
Seong Ki Mun, Editor(s)

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