PDRP-Slovenia proves to be a robust and reproducible functional imaging biomarker independent of patient population. It accurately differentiates PD patients from NCs and AP and correlates well with the clinical measure of PD progression.

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PURPOSE To evaluate the reproducibility of the expression of Parkinson’s Disease Related Pattern (PDRP) across multiple sets of 18F-FDG-PET brain images reconstructed with different reconstruction algorithms. METHODS 18F-FDG-PET brain imaging was performed in two independent cohorts of Parkinson’s disease (PD) patients and normal controls (NC). Slovenian cohort (20 PD patients, 20 NC) was scanned with Siemens Biograph mCT camera and reconstructed using FBP, FBP+TOF, OSEM, OSEM+TOF, OSEM+PSF and OSEM+PSF+TOF. American Cohort (20 PD patients, 7 NC) was scanned with GE Advance camera and reconstructed using 3DRP, FORE-FBP and FORE-Iterative. Expressions of two previously-validated PDRP patterns (PDRP-Slovenia and PDRP-USA) were calculated. We compared the ability of PDRP to discriminate PD patients from NC, differences and correlation between the corresponding subject scores and ROC analysis results across the different reconstruction algorithms. RESULTS The expression of PDRP-Slovenia and PDRP-USA networks was significantly elevated in PD patients compared to NC (p<0.0001), regardless of reconstruction algorithms. PDRP expression strongly correlated between all studied algorithms and the reference algorithm (r≥0.993, p<0.0001). Average differences in the PDRP expression among different algorithms varied within 0.73 and 0.08 of the reference value for PDRP-Slovenia and PDRP-USA, respectively. ROC analysis confirmed high similarity in sensitivity, specificity and AUC among all studied reconstruction algorithms. CONCLUSIONS These results show that the expression of PDRP is reproducible across a variety of reconstruction algorithms of 18F-FDG-PET brain images. PDRP is capable of providing a robust metabolic biomarker of PD for multicenter 18F-FDG-PET images acquired in the context of differential diagnosis or clinical trials.

BackgroundThe aim of the study was to explore the influence of various time-of-flight (TOF) and non-TOF reconstruction algorithms on positron emission tomography/computer tomography (PET/CT) image quality.Materials and methods.Measurements were performed with a triple line source phantom, consisting of capillaries with internal diameter of ∼ 1 mm and standard Jaszczak phantom. Each of the data sets was reconstructed using analytical filtered back projection (FBP) algorithm, iterative ordered subsets expectation maximization (OSEM) algorithm (4 iterations, 24 subsets) and iterative True-X algorithm incorporating a specific point spread function (PSF) correction (4 iterations, 21 subsets). Baseline OSEM (2 iterations, 8 subsets) was included for comparison. Procedures were undertaken following the National Electrical Manufacturers Association (NEMA) NU-2-2001 protocol.ResultsMeasurement of spatial resolution in full width at half maximum (FWHM) was 5.2 mm, 4.5 mm and 2.9 mm for FBP, OSEM and True-X; and 5.1 mm, 4.5 mm and 2.9 mm for FBP+TOF, OSEM+TOF and True-X+TOF respectively. Assessment of reconstructed Jaszczak images at different concentration ratios showed that incorporation of TOF information improves cold contrast, while hot contrast only slightly, however the most prominent improvement could be seen in background variability - noise reduction.ConclusionsOn the basis of the results of investigation we concluded, that incorporation of TOF information in reconstruction algorithm mostly affects reduction of the background variability (levels of noise in the image), while the improvement of spatial resolution due to incorporation of TOF information is negligible. Comparison of traditional and modern reconstruction algorithms showed that analytical FBP yields comparable results in some parameter measurements, such as cold contrast and relative count error. Iterative methods show highest levels of hot contrast, when TOF and PSF corrections were applied simultaneously.

Background Alzheimer’s disease (AD) is marked by accumulation of Aβ and tau protein causing neurodegeneration in the brain. AD represents a pathological and clinical continuum, ranging from mild cognitive impairment (MCI) to dementia. A specific metabolic imaging biomarker of AD ‐ Alzheimer’s disease‐related pattern (ADRP) has been previously identified from 2‐[18F]fluorodeoxyglucose positron emission tomography (2‐[18F]FDG‐PET) scans of clinically diagnosed AD patients, using scaled subprofile model based on principal component analysis (SSM/PCA) 1–3. However the clinical diagnosis may be wrong in substantial number of cases 4. We aimed to identify ADRP on patients with Alzheimer’s pathological changes in cerebrospinal (CSF) fluid and to validate it in an independent group of AD and other dementia patients and compare it to the original ADRP 1. Method 2‐[18F]FDG‐PET scans from 20 AD1 and 20 normal controls (NC1) were used for pattern identification. Additional 110 scans were analyzed for validation: 68 from AD patients (37 AD2, 13 atypical clinical presentation AD (atAD), 18 MCI), 21 with frontotemporal dementia (FTD), 8 non‐AD MCI and 13 NC2 (Table 1). AD was defined with CSF biomarkers as: Aβ42 < 815 pg/ml, pTau > 60 pg/ml, tTau > 400 pg/ml. Topographic profile rating algorithm was used to prospectively calculate ADRP Z‐scores from subjects scans3. Mini‐mental state examination (MMSE) was performed in all patients. Results ADRP was identified and voxel weights were found to be stable by bootstrap resampling, Figure 1. Pattern’s expression was significantly higher in AD1 than NC1 (p<0.001) and it strongly correlated (r=‐0.70) with MMSE. Pattern’s expression was also higher in AD2 than NC2 (p<0.001), MCI vs. NC2 (p<0.001), MCI vs. non‐AD MCI (p=0.02) and AD2 vs. FTD (p=0.02), Figure 2. Newly identified pattern moderately correlated (r=0.52) with the original one 1. Conclusions We identified ADRP on a cohort of pathologically confirmed AD patients, which was not done before. ADRP has shown to be a reliable metabolic biomarker of AD related neurodegeneration. References: (1) Mattis,PJ. et al. Neurology 87,1925–33(2016); (2) Teune,LK. et al. Curr Alzheimer Res 11,725–32(2014); (3) Spetsieris,PG. & Eidelberg,D. Neuroimage 54,2899–914(2011); (4) Beach,TG. et al. J. Neuropathol. Exp. Neurol. 71,266–273(2012).