4D space-time techniques: a medical imaging case study

We present the problem of visualizing time-varying medical data. Two medical imaging modalities are compared-MRI and dynamic SPECT. For each modality, we examine several derived scalar and vector quantities such as the change in intensity over time, the spatial gradient, and the change of the gradient over time. We compare several methods for presenting the data, including isosurfaces, direct volume rendering, and vector visualization using glyphs. These techniques may provide more information and context than methods currently used in practice; thus it is easier to discover temporal changes and abnormalities in a data set.

[1]  Brian Cabral,et al.  Imaging vector fields using line integral convolution , 1993, SIGGRAPH.

[2]  A. Celler,et al.  The incorporation of organ uptake into dynamic SPECT (dSPECT) image reconstruction , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[3]  M. Stella Atkins,et al.  Visualization of time-varying MRI data for MS lesion analysis , 2001, SPIE Medical Imaging.

[4]  M. Stella Atkins,et al.  Fully automatic segmentation of the brain in MRI , 1998, IEEE Transactions on Medical Imaging.

[5]  William E. Lorensen,et al.  The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics , 1998 .

[6]  Isaac N. Bankman,et al.  Handbook of medical imaging , 2000 .

[7]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[8]  T. J. Atherton,et al.  4D Volume Rendering with the Shear Warp Factorisation , 2000, 2000 IEEE Symposium on Volume Visualization (VV 2000).

[9]  A. Celler,et al.  Performance of the dynamic single photon emission computed tomography (dSPECT) method for decreasing or increasing activity changes. , 2000, Physics in medicine and biology.

[10]  David Ellsworth,et al.  Accelerating Time-Varying Hardware Volume Rendering Using TSP Trees and Color-Based Error Metrics , 2000, 2000 IEEE Symposium on Volume Visualization (VV 2000).

[11]  David H. Laidlaw,et al.  Visualizing Diffusion Tensor MR Images Using Streamtubes and Streamsurfaces , 2003, IEEE Trans. Vis. Comput. Graph..

[12]  Gordon L. Kindlmann,et al.  Hue-balls and lit-tensors for direct volume rendering of diffusion tensor fields , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[13]  J. Mazziotta,et al.  Rapid Automated Algorithm for Aligning and Reslicing PET Images , 1992, Journal of computer assisted tomography.

[14]  Robert Allen,et al.  Handbook of Medical Imaging—Processing and Analysis , 2001 .

[15]  J. Hajnal,et al.  Detection of Subtle Brain Changes Using Subvoxel Registration and Subtraction of Serial MR Images , 1995, Journal of computer assisted tomography.

[16]  Paolo Sabella,et al.  A rendering algorithm for visualizing 3D scalar fields , 1988, SIGGRAPH.

[17]  David H. Laidlaw,et al.  Visualizing diffusion tensor images of the mouse spinal cord , 1998, Proceedings Visualization '98 (Cat. No.98CB36276).

[18]  Nelson Max,et al.  Texture splats for 3D scalar and vector field visualization , 1993, Proceedings Visualization '93.

[19]  M. Stella Atkins,et al.  Segmentation of multiple sclerosis lesions in MRI: an image analysis approach , 1998, Medical Imaging.