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FocusDET, a new toolbox for SISCOM analysis. Evaluation of the registration accuracy using Monte Carlo simulation.
- Source :
-
Neuroinformatics [Neuroinformatics] 2013 Jan; Vol. 11 (1), pp. 77-89. - Publication Year :
- 2013
-
Abstract
- Subtraction of Ictal SPECT Co-registered to MRI (SISCOM) is an imaging technique used to localize the epileptogenic focus in patients with intractable partial epilepsy. The aim of this study was to determine the accuracy of registration algorithms involved in SISCOM analysis using FocusDET, a new user-friendly application. To this end, Monte Carlo simulation was employed to generate realistic SPECT studies. Simulated sinograms were reconstructed by using the Filtered BackProjection (FBP) algorithm and an Ordered Subsets Expectation Maximization (OSEM) reconstruction method that included compensation for all degradations. Registration errors in SPECT-SPECT and SPECT-MRI registration were evaluated by comparing the theoretical and actual transforms. Patient studies with well-localized epilepsy were also included in the registration assessment. Global registration errors including SPECT-SPECT and SPECT-MRI registration errors were less than 1.2 mm on average, exceeding the voxel size (3.32 mm) of SPECT studies in no case. Although images reconstructed using OSEM led to lower registration errors than images reconstructed with FBP, differences after using OSEM or FBP in reconstruction were less than 0.2 mm on average. This indicates that correction for degradations does not play a major role in the SISCOM process, thereby facilitating the application of the methodology in centers where OSEM is not implemented with correction of all degradations. These findings together with those obtained by clinicians from patients via MRI, interictal and ictal SPECT and video-EEG, show that FocusDET is a robust application for performing SISCOM analysis in clinical practice.
- Subjects :
- Algorithms
Electroencephalography
Humans
Image Processing, Computer-Assisted methods
Magnetic Resonance Imaging
Monte Carlo Method
Subtraction Technique
Tomography, Emission-Computed, Single-Photon
Brain diagnostic imaging
Diagnostic Errors statistics & numerical data
Epilepsies, Partial diagnostic imaging
Image Interpretation, Computer-Assisted methods
Image Processing, Computer-Assisted statistics & numerical data
Subjects
Details
- Language :
- English
- ISSN :
- 1559-0089
- Volume :
- 11
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Neuroinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 22903439
- Full Text :
- https://doi.org/10.1007/s12021-012-9158-x