UNLABELLED: A wide range of techniques for registration of medical images has been devised in recent years. The aim of this study is to quantify the overall spatial registration error of 3 different methods for image registration: interactive matching, surface matching, and uniformity index matching as described by Woods. METHODS: MRI and ethylcysteinate dimer-SPECT images of the brain were registered for 15 patients. The matching error was assessed by determining intra- and interobserver variability of registrations. Quantification of the registration error was based on the mean spatial distance of 5000 voxels between 2 image positions. The mean position after repeated registrations in each patient was used as the gold standard. To evaluate the coherence of the 3 different registration methods, intermethod variability was determined. RESULTS: Interactive matching showed an intraobserver/interobserver variability of 1.5+/-0.3 mm/1.6+/-0.3 mm (mean +/- SD). The time demand for this method was 11+/-5 min. Surface matching revealed a variability of 2.6+/-1.1 mm/3.8+/-1.0 mm and a time demand of 26+/-12 min. Reproducibility of Woods' algorithm was 2.2+/-0.8 mm with a time demand of 9+/-3 min. In 4 of the 15 cases, Woods' method failed. The mean deviation between all 3 methods was 2.3+/-0.8 mm. CONCLUSION: With a suitable user interface, interactive matching had the lowest registration error. The influence of subjectivity was shown to be negligible. Therefore, interactive matching is our preferred technique for image fusion of the brain.
UNLABELLED: A wide range of techniques for registration of medical images has been devised in recent years. The aim of this study is to quantify the overall spatial registration error of 3 different methods for image registration: interactive matching, surface matching, and uniformity index matching as described by Woods. METHODS: MRI and ethylcysteinate dimer-SPECT images of the brain were registered for 15 patients. The matching error was assessed by determining intra- and interobserver variability of registrations. Quantification of the registration error was based on the mean spatial distance of 5000 voxels between 2 image positions. The mean position after repeated registrations in each patient was used as the gold standard. To evaluate the coherence of the 3 different registration methods, intermethod variability was determined. RESULTS: Interactive matching showed an intraobserver/interobserver variability of 1.5+/-0.3 mm/1.6+/-0.3 mm (mean +/- SD). The time demand for this method was 11+/-5 min. Surface matching revealed a variability of 2.6+/-1.1 mm/3.8+/-1.0 mm and a time demand of 26+/-12 min. Reproducibility of Woods' algorithm was 2.2+/-0.8 mm with a time demand of 9+/-3 min. In 4 of the 15 cases, Woods' method failed. The mean deviation between all 3 methods was 2.3+/-0.8 mm. CONCLUSION: With a suitable user interface, interactive matching had the lowest registration error. The influence of subjectivity was shown to be negligible. Therefore, interactive matching is our preferred technique for image fusion of the brain.
Authors: Richard Beare; Joseph Yuan-Mou Yang; Wirginia J Maixner; A Simon Harvey; Michael J Kean; Vicki A Anderson; Marc L Seal Journal: Hum Brain Mapp Date: 2016-05-16 Impact factor: 5.038
Authors: S Dresel; J Grammerstorff; K Schwenzer; K Brinkbäumer; R Schmid; T Pfluger; K Hahn Journal: Eur J Nucl Med Mol Imaging Date: 2003-05-09 Impact factor: 9.236
Authors: André Manook; Behrooz H Yousefi; Antje Willuweit; Stefan Platzer; Sybille Reder; Andreas Voss; Marc Huisman; Markus Settles; Frauke Neff; Joachim Velden; Michael Schoor; Heinz von der Kammer; Hans-Jürgen Wester; Markus Schwaiger; Gjermund Henriksen; Alexander Drzezga Journal: PLoS One Date: 2012-03-09 Impact factor: 3.240