Global-two-stage filtering of clinical PET parametric maps: application to [(11)C]-(R)-PK11195

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.

A multi-task learning approach for the extraction of single-trial evoked potentials

Evoked potentials (EPs) are of great interest in neuroscience, but their measurement is difficult as they are embedded in background spontaneous electroencephalographic (EEG) activity which has a much larger amplitude. The widely used averaging technique requires the delivery of a large number of identical stimuli and yields only an "average" EP which does not allow the investigation of the possible variability of single-trial EPs. In the present paper, we propose the use of a multi-task learning method (MTL) for the simultaneous extraction of both the average and the N single-trial EPs from N recorded sweeps. The technique is developed within a Bayesian estimation framework and uses flexible stochastic models to describe the average response and the N shifts between the single-trial EPs and this average. Differently from other single-trial estimation approaches proposed in the literature, MTL can provide estimates of both the average and the N single-trial EPs in a single stage. In the present paper, MTL is successfully assessed on both synthetic (100 simulated recording sessions with N=20 sweeps) and real data (11 subjects with N=20 sweeps) relative to a cognitive task carried out for the investigation of the P300 component of the EP.

Importance of quantification for the analysis of PET data in oncology: review of current methods and trends for the future

In oncology, positron emission tomography (PET) is an important tool for tumour diagnosis and staging, assessment of response to treatment and evaluation of the pharmacokinetic properties and efficacy of new drugs. Despite its quantitative potential, however, in daily clinical practice PET is used almost exclusively with 2-deoxy-2-[(18)F]fluoro-D-glucose ([(18)F]FDG) and, in addition, [(18)F]FDG data are normally assessed visually or using simple indices as the standardised uptake value (SUV). After explaining why more sophisticated quantification methods can be useful in oncology, the paper reviews the approaches that are commonly used and those available but not routinely employed. Particular emphasis is addressed to the SUV, for its importance in clinical practice. Issues specific to PET quantification in oncology and related examples are then discussed. Finally, some ideas for the development of new quantitative methods for analysing PET data in oncology and for the application of approaches already existing but not commonly employed are presented.