Quantitative and Clinical Analysis of SPECT Image Registration for Epilepsy Studies
This study reports quantitative measurements of the accuracy of two popular voxel-based registration algorithms--Woods' automated image registration algorithm and mutual information correlation--and compares these with conventional surface matching (SM) registration. The registration algorithms...
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Published in | The Journal of nuclear medicine (1978) Vol. 40; no. 7; pp. 1098 - 1105 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Reston, VA
Soc Nuclear Med
01.07.1999
Society of Nuclear Medicine |
Subjects | |
Online Access | Get full text |
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Summary: | This study reports quantitative measurements of the accuracy of two popular voxel-based registration algorithms--Woods' automated image registration algorithm and mutual information correlation--and compares these with conventional surface matching (SM) registration.
The registration algorithms were compared (15 different matches each) for (a) three-dimensional brain phantom images, (b) an ictal SPECT image from a patient with partial epilepsy matched to itself after modification to simulate changes in the cerebral blood flow pattern and (c) ictal/interictal SPECT images from 15 patients with partial epilepsy. Blinded visual ranking and localization of the subtraction images derived from the patient images were also performed.
Both voxel-based registration methods were more accurate than SM registration (P < 0.0005). Automated image registration algorithm was more accurate than mutual information correlation for the computer-simulated ictal/interictal images and the patient ictal/interictal studies (P < 0.05). The subtraction SPECTs from SM were poorer in visual ranking more often than the voxel-based methods (P < 0.05).
Voxel intensity-based registration algorithms provide significant improvement in ictal/interictal SPECT registration accuracy and result in a clinically detectable improvement in the subtraction SPECT images. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0161-5505 1535-5667 |