A valid quantitative imaging method for the measurement of amyloid deposition in humans could improve Alzheimer's disease (AD) diagnosis and antiamyloid therapy assessment. Our group developed Pittsburgh Compound-B (PIB), an amyloid-binding radiotracer, for positron emission tomography (PET). The current study was aimed to further validate PIB PET through quantitative imaging (arterial input) and inclusion of subjects with mild cognitive impairment (MCI). Pittsburgh Compound-B studies were performed in five AD, five MCI, and five control subjects and five subjects were retested within 20 days. Magnetic resonance images were acquired for partial volume correction and region-of-interest definition (e.g., posterior cingulate: PCG; cerebellum: CER). Data were analyzed using compartmental and graphical approaches. Regional distribution volume (DV) values were normalized to the reference region (CER) to yield DV ratios (DVRs). Good agreement was observed between compartmental and Logan DVR values (e.g., PCG: r=0.89, slope=0.91); the Logan results were less variable. Nonspecific PIB retention was similar across subjects (n=15, Logan CER DV: 3.63+/-0.48). Greater retention was observed in AD cortical areas, relative to controls (P<0.05). The PIB retention in MCI subjects appeared either 'AD-like' or 'control-like'. The mean test/retest variation was approximately 6% in primary areas-of-interest. The Logan analysis was the method-of-choice for the PIB PET data as it proved stable, valid, and promising for future larger studies and voxel-based statistical analyses. This study also showed that it is feasible to perform quantitative PIB PET imaging studies that are needed to validate simpler methods for routine use across the AD disease spectrum.

https://journals.sagepub.com/doi/pdf/10.1038/sj.jcbfm.9600146

Kinetic Modeling of Amyloid Binding in Humans using PET Imaging and Pittsburgh Compound-B:

PET studies have been performed using the amyloid binding radiotracer Pittsburgh Compound B (PIB). Previous quantitative analyses using arterial blood showed that the Logan graphical analysis using 90 min of emission data (ART90) provided a reliable measure of PIB retention. This work reports on simplified methods of analysis for human PIB imaging. Methods: PIB PET scans were conducted in 24 subjects (6 Alzheimer’s disease [AD], 10 mild cognitive impairment [MCI], 8 controls) with arterial blood sampling. Retest scans were performed on 8 subjects (3 AD, 1 MCI, 4 controls) within 28 d. Data were analyzed over 60 and 90 min using the Logan analysis and (a) metabolite-corrected input functions based on arterial plasma (ART60, ART90), (b) carotid artery time–activity data with a population average metabolite correction (CAR60, CAR90); and (c) cerebellar reference tissue (CER60, CER90). Data also were analyzed using the simplified reference tissue method (SRTM60, SRTM90) and a single-scan method based on late-scan ratios of standardized uptake values (SUVR60, SUVR90). Results: All methods of analysis examined effectively discerned regional differences between AD and control subjects in amyloid-laden cortical regions, although the performance of the simplified methods varied in terms of bias, test−retest variability, intersubject variability, and effect size. CAR90 best agreed with ART90 distribution volume ratio (DVR) measures across brain regions and subject groups and demonstrated satisfactory test−retest variability (±7.1% across regions). CER90 and CER60 showed negative biases relative to ART90 in high-DVR subjects but had the lowest test−retest variability. The single-scan SUV-based methods showed the largest effect sizes for AD and control group differences and performed well in terms of intersubject and test−retest variability. Conclusion: Of the simplified methods for PIB analysis examined, CAR90 provided DVR measures that were most comparable to ART90; CER90 was the most reproducible and SUVR90 produced the largest effect size. All simplified methods were effective at distinguishing AD and control differences and may be effectively used in the analysis of PIB. SUVR60 data can be obtained with as little as 20 min of PET emission data collection. The relative strengths and limitations of each method must be considered for each experimental design.

https://jnm.snmjournals.org/content/jnumed/46/12/1959.full.pdf

Simplified quantification of Pittsburgh Compound B amyloid imaging PET studies: a comparative analysis.

The quality of images reconstructed by statistical iterative methods depends on an accurate model of the relationship between image space and projection space through the system matrix. The elements of the system matrix for the clinical Hi-Rez scanner were derived by processing the data measured for a point source at different positions in a portion of the field of view. These measured data included axial compression and azimuthal interleaving of adjacent projections. Measured data were corrected for crystal and geometrical efficiency. Then, a whole system matrix was derived by processing the responses in projection space. Such responses included both geometrical and detection physics components of the system matrix. The response was parameterized to correct for point source location and to smooth for projection noise. The model also accounts for axial compression (span) used on the scanner. The forward projector for iterative reconstruction was constructed using the estimated response parameters. This paper extends our previous work to fully three-dimensional. Experimental data were used to compare images reconstructed by the standard iterative reconstruction software and the one modeling the response function. The results showed that the modeling of the response function improves both spatial resolution and noise properties

Fully 3-D PET reconstruction with system matrix derived from point source measurements

We present a new version of STIR (Software for Tomographic Image Reconstruction), an open source object-oriented library implemented in C++ for 3D positron emission tomography reconstruction. This library has been designed such that it can be used for many algorithms and scanner geometries, while being portable to various computing platforms. This second release enhances its flexibility and modular design and includes additional features such as Compton scatter simulation, an additional iterative reconstruction algorithm and parametric image reconstruction (both indirect and direct). We discuss the new features in this release and present example results. STIR can be downloaded from http://stir.sourceforge.net.

/pdf/stir-software-for-tomographic-image-reconstruction-release-2-3c32slwfkt.pdf

STIR: software for tomographic image reconstruction release 2

In emission tomography statistically based iterative methods can improve image quality relative to analytic image reconstruction through more accurate physical and statistical modelling of high-energy photon production and detection processes. Continued exponential improvements in computing power, coupled with the development of fast algorithms, have made routine use of iterative techniques practical, resulting in their increasing popularity in both clinical and research environments. Here we review recent progress in developing statistically based iterative techniques for emission computed tomography. We describe the different formulations of the emission image reconstruction problem and their properties. We then describe the numerical algorithms that are used for optimizing these functions and illustrate their behaviour using small scale simulations.

https://iopscience.iop.org/article/10.1088/0031-9155/51/15/R01/pdf

Iterative reconstruction techniques in emission computed tomography.

The authors report on a new numerical implementation of the single-scatter simulation scatter correction algorithm for 3D PET. Its primary advantage over the original implementation is that it is a much faster calculation, currently requiring less than 30 sec execution time per bed position for an adult thorax, thus making clinical whole-body scatter correction more practical. The new code runs on a single processor workstation CPU instead of a vector processor array, making it highly portable. It is modular and independent of any particular reconstruction code. The computed scatter contribution is now intrinsically scaled relative to the emission image and no longer requires normalization to the scatter tails in the sinogram when all activity is contained within the field of view, making it more robust against noise. The new algorithm has been verified against the original code on both phantom and human thorax studies. Initial results indicate that scatter correction may be accurately performed following, instead of prior to, either 3D reprojection or Fourier rebinning. Some evidence is presented that the single-scatter operator, when applied to an uncorrected emission image provides reasonable compensation for multiple scatter.

New, faster, image-based scatter correction for 3D PET

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New, faster, image-based scatter correction for 3D PET