Graphic plot analysis for estimating binding potential of translocator protein (TSPO) in positron emission tomography studies with [¹⁸F]FEDAA1106

Dynamic image denoising for voxel-wise quantification with Statistical Parametric Mapping in molecular neuroimaging

Purpose

PET 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).

Methods

In 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 (BP_ND) 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 BP_ND was evaluated.

Results

Estimations 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.

Conclusion

In conclusion, noise removal from dynamic brain SPECT and PET images may optimize voxel-wise BP_ND estimations and detection of biological differences using SPM.

Recent advances in parametric neuroreceptor mapping with dynamic PET: basic concepts and graphical analyses

Tracer kinetic modeling in dynamic positron emission tomography (PET) has been widely used to investigate the characteristic distribution patterns or dysfunctions of neuroreceptors in brain diseases. Its practical goal has progressed from regional data quantification to parametric mapping that produces images of kinetic-model parameters by fully exploiting the spatiotemporal information in dynamic PET data. Graphical analysis (GA) is a major parametric mapping technique that is independent on any compartmental model configuration, robust to noise, and computationally efficient. In this paper, we provide an overview of recent advances in the parametric mapping of neuroreceptor binding based on GA methods. The associated basic concepts in tracer kinetic modeling are presented, including commonly-used compartment models and major parameters of interest. Technical details of GA approaches for reversible and irreversible radioligands are described, considering both plasma input and reference tissue input models. Their statistical properties are discussed in view of parametric imaging.