TY - JOUR
T1 - Acceleration of high resolution temperature based optimization for hyperthermia treatment planning using element grouping
AU - Kok, H. P.
AU - de Greef, M.
AU - Bel, A.
AU - Crezee, J.
PY - 2009
Y1 - 2009
N2 - In regional hyperthermia, optimization is useful to obtain adequate applicator settings. A speed-up of the previously published method for high resolution temperature based optimization is proposed. Element grouping as described in literature uses selected voxel sets instead of single voxels to reduce computation time. Elements which achieve their maximum heating potential for approximately the same phase/amplitude setting are grouped. To form groups, eigenvalues and eigenvectors of precomputed temperature matrices are used. At high resolution temperature matrices are unknown and temperatures are estimated using low resolution (1 cm) computations and the high resolution (2 mm) temperature distribution computed for low resolution optimized settings using zooming. This technique can be applied to estimate an upper bound for high resolution eigenvalues. The heating potential of elements was estimated using these upper bounds. Correlations between elements were estimated with low resolution eigenvalues and eigenvectors, since high resolution eigenvectors remain unknown. Four different grouping criteria were applied. Constraints were set to the average group temperatures. Element grouping was applied for five patients and optimal settings for the AMC-8 system were determined. Without element grouping the average computation times for five and ten runs were 7.1 and 14.4 h, respectively. Strict grouping criteria were necessary to prevent an unacceptable exceeding of the normal tissue constraints (up to approximately 2 degrees C), caused by constraining average instead of maximum temperatures. When strict criteria were applied, speed-up factors of 1.8-2.1 and 2.6-3.5 were achieved for five and ten runs, respectively, depending on the grouping criteria. When many runs are performed, the speed-up factor will converge to 4.3-8.5, which is the average reduction factor of the constraints and depends on the grouping criteria. Tumor temperatures were comparable. Maximum exceeding of the constraint in a hot spot was 0.24-0.34 degree C; average maximum exceeding over all five patients was 0.09-0.21 degree C, which is acceptable. High resolution temperature based optimization using element grouping can achieve a speed-up factor of 4-8, without large deviations from the conventional method
AB - In regional hyperthermia, optimization is useful to obtain adequate applicator settings. A speed-up of the previously published method for high resolution temperature based optimization is proposed. Element grouping as described in literature uses selected voxel sets instead of single voxels to reduce computation time. Elements which achieve their maximum heating potential for approximately the same phase/amplitude setting are grouped. To form groups, eigenvalues and eigenvectors of precomputed temperature matrices are used. At high resolution temperature matrices are unknown and temperatures are estimated using low resolution (1 cm) computations and the high resolution (2 mm) temperature distribution computed for low resolution optimized settings using zooming. This technique can be applied to estimate an upper bound for high resolution eigenvalues. The heating potential of elements was estimated using these upper bounds. Correlations between elements were estimated with low resolution eigenvalues and eigenvectors, since high resolution eigenvectors remain unknown. Four different grouping criteria were applied. Constraints were set to the average group temperatures. Element grouping was applied for five patients and optimal settings for the AMC-8 system were determined. Without element grouping the average computation times for five and ten runs were 7.1 and 14.4 h, respectively. Strict grouping criteria were necessary to prevent an unacceptable exceeding of the normal tissue constraints (up to approximately 2 degrees C), caused by constraining average instead of maximum temperatures. When strict criteria were applied, speed-up factors of 1.8-2.1 and 2.6-3.5 were achieved for five and ten runs, respectively, depending on the grouping criteria. When many runs are performed, the speed-up factor will converge to 4.3-8.5, which is the average reduction factor of the constraints and depends on the grouping criteria. Tumor temperatures were comparable. Maximum exceeding of the constraint in a hot spot was 0.24-0.34 degree C; average maximum exceeding over all five patients was 0.09-0.21 degree C, which is acceptable. High resolution temperature based optimization using element grouping can achieve a speed-up factor of 4-8, without large deviations from the conventional method
U2 - https://doi.org/10.1118/1.3168973
DO - https://doi.org/10.1118/1.3168973
M3 - Article
C2 - 19746813
SN - 0094-2405
VL - 36
SP - 3795
EP - 3805
JO - Medical physics
JF - Medical physics
IS - 8
ER -