RT Journal Article
SR Electronic
T1 Automated Detection of Local Normalization Areas for Ictal-Interictal Subtraction Brain SPECT
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP 1419
OP 1425
VO 43
IS 11
A1 Nicolas Boussion
A1 Claire Houzard
A1 Karine Ostrowsky
A1 Philippe Ryvlin
A1 François Mauguière
A1 Luc Cinotti
YR 2002
UL http://jnm.snmjournals.org/content/43/11/1419.abstract
AB Whole-brain activity is often chosen to quantitatively normalize peri-ictal and interictal SPECT scans before their subtraction. This use is not justified, because significant and extended modification of the cerebral blood flow can occur during a seizure. We validated and compared 2 automatic methods able to determine the optimal reference region, using simulation and clinical data. Methods: In the first method, the selected reference region is the intersection of peri-ictal-interictal areas with no significantly different z values. The other method relies on a 3-dimensional iterative voxel aggregation. The increase of the selected volume is stopped by using 2 different variance tests (Levene and SE). These algorithms were tested on 39 epileptic patients and were validated using 1 interictal and 10 peri-ictal scans simulated from the mean image of 22 healthy subjects. Results: In the patient studies, the mean relative activity of the selected regions, compared with whole-brain activity (classic normalization), was 122.6%. Their average relative size (compared with the size of the whole brain) was 33.2% for the z map method, 22.8% for the SE test, and 11.8% for the Levene test. After application of our automatic processes, subtraction of the simulated images revealed a recovery of abnormal regions up to 45% larger than the region obtained with classic normalization. Conclusion: These results illustrate the role of normalization on the subtracted peri-ictal and interictal images. Our methods are automatic and objective and give good results on various simulated images. The z map construction is worth considering because it is simple, selects large parts of the brain, and requires little computation time.