TY - GEN
T1 - Voxel Stuffing
T2 - International Workshop on Medical Imaging and Augmented Reality, MIAR 2001
AU - Minamikawa-Tachino, R.
AU - Yamaguchi, Y.
AU - Fujishiro, I.
AU - Sakuraba, H.
N1 - Publisher Copyright:
© 2001 IEEE.
PY - 2001
Y1 - 2001
N2 - This paper proposes a new algorithm, called Voxel Stuffing, to reconstruct single high-quality volume data from multiple sparsely-spaced sequences of cross-sectional images acquired by magnetic resonance imaging (MRI). Although fine and isotropic cross-sectional images can be obtained by using the most advanced MRI facilities, sparse sampling is commonly performed in the clinical examination. Intensive feasibility study was performed with three regular grid volume data sets, whose sources include an analytic function; a numerical simulation; and measurements. In either case, the Voxel Stuffing algorithm generates a higher-quality volume data from triple sequences of cross-sectional images in comparison with any volume data reconstructed linearly from a single sequence of class-sectional images. The Voxel Stuffing algorithm is extended to reconstruct a rectilinearly structured volume data set from triple non-orthogonal sequences of cross-sectional images, which are taken commonly in the general MRI clinical examination. The effectiveness of the extended Voxel Stuffing algorithm is illustrated with an MRI data set for a human brain containing a tumor.
AB - This paper proposes a new algorithm, called Voxel Stuffing, to reconstruct single high-quality volume data from multiple sparsely-spaced sequences of cross-sectional images acquired by magnetic resonance imaging (MRI). Although fine and isotropic cross-sectional images can be obtained by using the most advanced MRI facilities, sparse sampling is commonly performed in the clinical examination. Intensive feasibility study was performed with three regular grid volume data sets, whose sources include an analytic function; a numerical simulation; and measurements. In either case, the Voxel Stuffing algorithm generates a higher-quality volume data from triple sequences of cross-sectional images in comparison with any volume data reconstructed linearly from a single sequence of class-sectional images. The Voxel Stuffing algorithm is extended to reconstruct a rectilinearly structured volume data set from triple non-orthogonal sequences of cross-sectional images, which are taken commonly in the general MRI clinical examination. The effectiveness of the extended Voxel Stuffing algorithm is illustrated with an MRI data set for a human brain containing a tumor.
KW - MRI
KW - Volume modeling
KW - cross-sectional images
KW - interpolation
KW - volume data
UR - https://www.scopus.com/pages/publications/84964557310
UR - https://www.scopus.com/pages/publications/84964557310#tab=citedBy
U2 - 10.1109/MIAR.2001.930293
DO - 10.1109/MIAR.2001.930293
M3 - Conference contribution
AN - SCOPUS:84964557310
T3 - Proceedings - International Workshop on Medical Imaging and Augmented Reality, MIAR 2001
SP - 235
EP - 240
BT - Proceedings - International Workshop on Medical Imaging and Augmented Reality, MIAR 2001
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 June 2001 through 12 June 2001
ER -