Deconvolution-based partial volume correction in Raclopride-PET and Monte Carlo comparison to MR-based method

In this work, we evaluated three iterative deconvolution algorithms and compared their performance to partial volume (PV) correction based on structural imaging in brain positron emission tomography (PET) using a database of Monte Carlo-simulated images. We limited our interest to quantitative radioligand PET imaging, particularly to (11)C-Raclopride and striatal imaging. The studied deconvolution methods included Richardson-Lucy, reblurred Van Cittert, and reblurred Van Cittert with the total variation regularization. We studied the bias and variance of the regional estimates of binding potential (BP) values and the accuracy of regional TACs as a function of the applied image processing. The resolution/noise tradeoff in parametric BP images was addressed as well. The regional BP values and TACs obtained by deconvolution were almost as accurate than those by structural imaging-based PV correction (GTM method) when the ideal volumes of interests (VOIs) were used to extract TACs from the images. For deconvolution methods, the ideal VOIs were slightly eroded from the exact anatomical VOI to limit the bias due to tissue fraction effect which is not corrected for by deconvolution-based methods. For the GTM method, the ideal VOIs were the exact anatomical VOIs. The BP values and TACs by deconvolution were less affected by segmentation and registration errors than those with the GTM-based PV correction. The BP estimates and TACs with deconvolution-based PV correction were more accurate than BPs and TACs derived without PV correction. The parametric images obtained by the deconvolution-based PV correction showed considerably improved resolution with only slightly increased noise level compared to the case with no PV correction. The reblurred Van Cittert method was the best of the studied deconvolution methods. We conclude that the deconvolution is an interesting alternative to structural imaging-based PV correction as it leads to quantification results of similar accuracy, and it is less prone to registration and segmentation errors than structural imaging-based PV correction. Moreover, PV-corrected parametric images can be readily computed based on deconvolved dynamic images.     

Improved quantification of amyloid burden and associated biomarker cut-off points: results from the first amyloid Singaporean cohort with overlapping cerebrovascular disease

European Journal of Nuclear Medicine and Molecular Imaging - The analysis of the [11C]PiB-PET amyloid images of a unique Asian cohort of 186 participants featuring overlapping vascular diseases...     

Motion correction of multi-frame PET data in neuroreceptor mapping: Simulation based validation

Patient motion during positron emission tomography scanning can affect the accuracy of the data analysis in two ways: 1) movement occurring during emission data acquisition alters the time activity curves (TACs), measured at a voxel or region of interest (ROI), and hence introduces errors in the parameter estimates derived from kinetic modeling; 2) emission–transmission mismatches introduce errors during attenuation and scatter correction, and hence in the radioactivity distribution estimates for each time frame of the scan. With the aim of designing an algorithm-based frame realignment method, we first conducted investigations that aimed at optimizing the parameters of a coregistration method, such as the choice of the target volume and the similarity criterion. Based on these results we designed a novel frame realignment strategy in a multi-step algorithm using uncorrected reconstructed images, cross-correlation similarity criteria for the determination of inter-frame motion parameters and emission-transmission mismatch for each frame. Features and validation results are reported here based on a multi-subject simulated [¹¹C]raclopride dynamic PET scan database incorporating intra-frame movements of various magnitudes and with various times of occurrence. Performances of the proposed algorithm were evaluated at regional and voxel-based level for binding potential parametric images.     

A PET imaging study of 5-HT(1A) receptors in cat brain after acute and chronic fluoxetine treatment

Immuno-electron microscopic and beta-microprobe studies have demonstrated that the internalization of serotonin 5-HT(1A) autoreceptors, after acute treatment with the selective 5-HT(1A) receptor agonist 8-OH-DPAT or with the specific serotonin reuptake inhibitor (SSRI) fluoxetine, is associated with a marked decrease in the in vivo binding of [(18)F]MPPF in the nucleus raphe dorsalis (NRD) of rat. To determine whether this event might be amenable to brain imaging, the present [(18)F]MPPF positron emission tomographic (PET) study was carried out in anesthetized cats given or not a single dose (5 mg/kg, i.v.) or chronically treated with fluoxetine (5 mg/kg, s.c. for 21 days). Compared to control, [(18)F]MPPF binding potential was considerably (and visibly) decreased in the cat NRD after acute fluoxetine treatment, while it remained unchanged in other brain regions. Unexpectedly, after chronic fluoxetine treatment, [(18)F]MPPF binding potential was not affected in any brain region. In parallel immuno-electron microscopic experiments carried out in rat, the density of 5-HT(1A) autoreceptors on the plasma membrane of NRD dendrites was comparable to control after chronic fluoxetine treatment. If the decrease in [(18)F]MPPF binding at the onset of SSRI treatment was detectable by PET imaging, it could potentially serve as a biological index of efficacy.     

Test-retest reproducibility of 18F-MPPF PET in healthy humans: a reliability study.

The aim of this study was to assess the reliability of 2'-methoxyphenyl-(N-2'-pyridinyl)-p-18F-fluoro-benzamidoethylpiperazine (18F-MPPF) PET binding parameter's quantification via a test-retest study over a long-term period. METHODS: Ten healthy volunteers underwent 2 dynamic 18F-MPPF PET scans in an interval of 6 mo. As a methodologic control, 10 simulated datasets, including interindividual functional and anatomic variabilities, were also used to assess the measurement variations in the absence of intraindividual variability. Indices of tracer binding were computed using 2 different models: (a) the simplified reference tissue model (SRTM) and (b) the Logan graphical model. The SRTM allows computing the binding potential (BP) index and plasma-to-brain transport constants (R1, k2). The Logan model evaluates the distribution volume (DV). For both methods, cerebellum was taken as the reference region. From both models, binding indices were calculated with time-activity curves extracted from regions of interest, on one hand, and for each voxel to perform parametric images on the other hand. RESULTS: Reliability indices--that is, bias, variability, and intraclass correlation (ICC)--indicated a good reproducibility: the BP percentage change in mean between test and retest is close to 1% in rich regions and 2% in poor regions. The typical error is around 7%. Mean ICC is over 0.70. The DV percentage change in the mean is +/-2.5%, with a typical error close to 6% and an ICC over 0.60. CONCLUSION: Our results show a good reliability, with a reasonable level of intraindividual biologic variability that allows crossover studies with 18F-MPPF in which small percentage changes are expected between test and retest measurements, in group studies and for single subject assessment.     

Simulation-based evaluation of OSEM iterative reconstruction methods in dynamic brain PET studies

The reconstruction of dynamic PET data is usually performed using filtered backprojection algorithms (FBP). This method is fast, robust, linear and yields reliable quantitative results. However, the use of FBP for low count data, such as dynamic PET data, generally results in poor visual image quality, exhibiting high noise, disturbing streak artifacts and low contrast. These signal-to-noise ratio and contrast in the reconstructed images may alter the quantification of physiological indexes, such as the regional Binding Potential (BP) obtained from kinetic modeling. Iterative reconstruction methods are often presented as viable alternatives to FBP reconstruction. In this study, we investigated the characteristics of the UW-OSEM and the ANW-OSEM iterative reconstruction methods in the context of ligand-receptor PET studies with low counts. The assessment was conducted using replicates of simulated [18F]MPPF acquisitions. The quantitative accuracy obtained with the iterative and analytical methods was compared. The results show that analytical methods are more robust to the low count data than iterative methods, and therefore enable a better estimate of the regional activity values and binding potential. The positivity constraint in MLEM-based algorithms leads to overestimations of the activity in regions with low activity concentration, typically the cerebellum. This overestimation results in significant bias in BP estimates with iterative reconstruction methods. The bias is confirmed from the reconstruction of real PET data.     

Longitudinal trajectory of Amyloid‐related hippocampal subfield atrophy in nondemented elderly

Hippocampal atrophy and abnormal β‐Amyloid (Aβ) deposition are established markers of Alzheimer's disease (AD). Nonetheless, longitudinal trajectory of Aβ‐associated hippocampal subfield atrophy prior to dementia remains unclear. We hypothesized that elevated Aβ correlated with longitudinal subfield atrophy selectively in no cognitive impairment (NCI), spreading to other subfields in mild cognitive impairment (MCI). We analyzed data from two independent longitudinal cohorts of nondemented elderly, including global PET‐Aβ in AD‐vulnerable cortical regions and longitudinal subfield volumes quantified with a novel auto‐segmentation method (FreeSurfer v.6.0). Moreover, we investigated associations of Aβ‐related progressive subfield atrophy with memory decline. Across both datasets, we found a converging pattern that higher Aβ correlated with faster CA1 volume decline in NCI. This pattern spread to other hippocampal subfields in MCI group, correlating with memory decline. Our results for the first time suggest a longitudinal focal‐to‐widespread trajectory of Aβ‐associated hippocampal subfield atrophy over disease progression in nondemented elderly.     

Correction of partial-volume effect for PET striatal imaging: fast implementation and study of robustness.

PET imaging of D(2) receptors or (18)F-L-dopa metabolism are reference protocols to follow and study neurodegenerative diseases, but the accuracy of striatal PET imaging is limited by the partial-volume effect (PVE). For such studies, the geometric transfer matrix (GTM) method has been proposed to correct the regional mean values for PVE and is now widely used. METHODS: The GTM method models the geometric interactions induced by the PET system between the anatomic regions in which PVE correction is performed. This implies estimation of the corresponding regional spread function (RSF). The literature describes 2 implementations for the RSF calculation; they differ in the way the point spread function (PSF) of the imaging system is modeled, but no comparison or discussion has been given. The first and reference implementation uses an accurate intrinsic detector PSF that is applied in the sinogram space. The second uses a global PSF that is applied in the image space. In this work, we compared the 2 GTM implementations for 3-dimensional (3D) PET striatal imaging using Monte Carlo simulations and a phantom study. We studied the robustness of the GTM correction with respect to residual registration errors between PET and anatomy and with respect to residual segmentation errors. RESULTS: Despite the differences in RSF calculation and computation cost between the 2 implementations, similar recovery results were obtained (between 95% and 100%). The study of robustness of the GTM correction yielded 2 results. A realistic residual misregistration between the anatomic and PET images did not modify the algorithm accuracy but decreased its precision. Conversely, a realistic residual missegmentation of the anatomic regions submitted to GTM correction decreased the correction accuracy. CONCLUSION: A simple but efficient implementation in the image space of the GTM method yields accurate PVE correction in striatal regions in studies with 3D PET and enables clinical use. The method is less sensitive to residual misregistration errors between PET and anatomy than to residual missegmentation of the anatomy. Special care should be taken with segmentation of the regions to correct for PVE.     

Optimization of dynamic measurement of receptor kinetics by wavelet denoising

The most important technical limitation affecting dynamic measurements with PET is low signal-to-noise ratio (SNR). Several reports have suggested that wavelet processing of receptor kinetic data in the human brain can improve the SNR of parametric images of binding potential (BP). However, it is difficult to fully assess these reports because objective standards have not been developed to measure the tradeoff between accuracy (e.g. degradation of resolution) and precision. This paper employs a realistic simulation method that includes all major elements affecting image formation. The simulation was used to derive an ensemble of dynamic PET ligand (11C-raclopride) experiments that was subjected to wavelet processing. A method for optimizing wavelet denoising is presented and used to analyze the simulated experiments. Using optimized wavelet denoising, SNR of the four-dimensional PET data increased by about a factor of two and SNR of three-dimensional BP maps increased by about a factor of 1.5. Analysis of the difference between the processed and unprocessed means for the 4D concentration data showed that more than 80% of voxels in the ensemble mean of the wavelet processed data deviated by less than 3%. These results show that a 1.5x increase in SNR can be achieved with little degradation of resolution. This corresponds to injecting about twice the radioactivity, a maneuver that is not possible in human studies without saturating the PET camera and/or exposing the subject to more than permitted radioactivity.     

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