Abstract
PurposePET and SPECT voxel kinetics are highly noised. To our knowledge, no study has determined the effect of denoising on the ability to detect differences in binding at the voxel level using Statistical Parametric Mapping (SPM).MethodsIn the present study, groups of subject-images with a 10%- and 20%- difference in binding of [123I]iomazenil (IMZ) were simulated. They were denoised with Factor Analysis (FA). Parametric images of binding potential (BPND) were produced with the simplified reference tissue model (SRTM) and the Logan non-invasive graphical analysis (LNIGA) and analyzed using SPM to detect group differences. FA was also applied to [123I]IMZ and [11C]flumazenil (FMZ) clinical images (n = 4) and the variance of BPND was evaluated.ResultsEstimations from FA-denoised simulated images provided a more favorable bias-precision profile in SRTM and LNIGA quantification. Simulated differences were detected in a higher number of voxels when denoised simulated images were used for voxel-wise estimations, compared to quantification on raw simulated images. Variability of voxel-wise binding estimations on denoised clinical SPECT and PET images was also significantly diminished.ConclusionIn conclusion, noise removal from dynamic brain SPECT and PET images may optimize voxel-wise BPND estimations and detection of biological differences using SPM.
Highlights
Molecular imaging using Positron Emission Tomography (PET) and Single Photon Emission Tomography (SPECT) is a powerful tool in the in vivo study of neuroreceptor systems in human and small-animal research
Quantification is most often performed on dynamic images that permit the extraction of the temporal kinetic pattern of the radiotracer
Given the advances in the domain of instrumentation and image reconstruction, kinetic analysis may be performed on tissue-activity curves (TACs) from individual brain voxels to create binding parameter images that can be used for statistical analysis of differences at the voxel level [1,2,3]
Summary
Molecular imaging using Positron Emission Tomography (PET) and Single Photon Emission Tomography (SPECT) is a powerful tool in the in vivo study of neuroreceptor systems in human and small-animal research. Quantitative analysis of dynamic PET and SPECT images is performed either at the regional level or at the voxel level: regional analysis of radiotracer kinetics implies an a priori definition of volumes-of-interest (VOI) in which radioactivity across voxels is averaged and examined as a whole. Dynamic images, being composed of serial images of a very short duration, naturally suffer from high noise This is a considerably limiting factor for the application of kinetic analysis at the voxel level, inducing bias and augmenting the variance of parameter estimates. The statistical power to detect group differences in binding in the context of biological studies is seriously compromised and this is probably the main reason why VOI analysis still remains in the first line of neuroreceptor quantitative imaging. Functional and structural Magnetic Resonance Imaging (MRI) have had a major contribution in the understanding of brain function and pathology exactly because statistical inferences became possible at the voxel level
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