Effect of Point Spread Function Deconvolution in Reconstruction of Brain 18F-FDG PET Images on the Diagnostic Thinking Efficacy in Alzheimer's Disease

Impact of PET Reconstruction on Amyloid-β Quantitation in Cross-Sectional and Longitudinal Analyses

Amyloid-β (Aβ) accumulation in Alzheimer disease (AD) is typically measured using SUV ratio and the centiloid (CL) scale. The low spatial resolution of PET images is known to degrade quantitative metrics because of the partial-volume effect. This article examines the impact of spatial resolution, as determined by the reconstruction configuration, on the Aβ PET quantitation in both cross-sectional and longitudinal data. Methods: The cross-sectional study involved 89 subjects with 20-min [18F]florbetapir scans generated on an mCT (44 Aβ-negative [Aβ-], 45 Aβ-positive [Aβ+]) using 69 reconstruction configurations, which varied in number of iteration updates, point-spread function, time-of-flight, and postreconstruction smoothing. The subjects were classified as Aβ- or Aβ+ visually. For each reconstruction, Aβ CL was calculated using CapAIBL, and the spatial resolution was calculated as full width at half maximum (FWHM) using the barrel phantom method. The change in CLs and the effect size of the difference in CLs between Aβ- and Aβ+ groups with FWHM were examined. The longitudinal study involved 79 subjects (46 Aβ-, 33 Aβ+) with three 20-min [18F]flutemetamol scans generated on an mCT. The subjects were classified as Aβ- or Aβ+ using a cutoff CL of 20. All scans were reconstructed using low-, medium-, and high-resolution configurations, and Aβ CLs were calculated using CapAIBL. Since linear Aβ accumulation was assumed over a 10-y interval, for each reconstruction configuration, Aβ accumulation rate differences (ARDs) between the second and first periods were calculated for all subjects. Zero ARD was used as a consistency metric. The number of Aβ accumulators was also used to compare the sensitivity of CL across reconstruction configurations. Results: In the cross-sectional study, CLs in both the Aβ- and the Aβ+ groups were impacted by the FWHM of the reconstruction method. Without postreconstruction smoothing, Aβ- CLs increased for a FWHM of 4.5 mm or more, whereas Aβ+ CLs decreased across the FWHM range. High-resolution reconstructions provided the best statistical separation between groups. In the longitudinal study, the median ARD of low-resolution reconstructed data for the Aβ- group was greater than zero whereas the ARDs of higher-resolution reconstructions were not significantly different from zero, indicating more consistent rate estimates in the higher-resolution reconstructions. Higher-resolution reconstructions identified 10 additional Aβ accumulators in the Aβ- group, resulting in a 22% increased group size compared with the low-resolution reconstructions. Higher-resolution reconstructions reduced the average CLs of the negative group by 12 points. Conclusion: High-resolution PET reconstructions, inherently less impacted by partial-volume effect, may improve Aβ PET quantitation in both cross-sectional and longitudinal data. In the cross-sectional analysis, separation of CLs between Aβ- and Aβ+ cohorts increased with spatial resolution. Higher-resolution reconstructions also exhibited both improved consistency and improved sensitivity in measures of Aβ accumulation. These features suggest that higher-resolution reconstructions may be advantageous in early-stage AD therapies.

Interactions Between Brain 18F-FDG PET Metabolism and Hemodynamic Parameters at Different Ages of Life: Results From a Prospective Cross-Sectional Study

Brain 18F-FDG PET imaging is useful to characterize accelerated brain aging at a pre-symptomatic stage. This study aims to examine the interactions between brain glycolytic metabolism and hemodynamic parameters in different age groups.

Methods: A total of 72 patients (from 23 to 88 years of age, 38 women) without any cerebral diseases but with available cardiac, arterial peripheral, and central blood pressure measurements as well as arterial stiffness parameters obtained from brachial pressure and applanation tonometry and a brain 18F-FDG PET scan were prospectively included into this study. Quantitative voxel-to-voxel analyses were carried out to test for negative associations between brain glycolytic metabolism and individual hemodynamic parameters (p-voxel of <0.001 for the whole population and <0.005 for age groups).

Results: The heart rate parameter of the whole population showed the most extensive associations with brain metabolism (15,857 mm3, T-score: 5.1), predominantly affecting the frontal and temporal regions (69% of the volume). Heart rate for the younger age group, systolic and pulse pressure for the 41–60-year-old group, and diastolic pressure for the older group were most extensively associated with brain metabolism and mainly involved the fronto-temporal lobes (respective involvement of 52.8%, 60.9%, and 65.5%) which are also the regions implicated in accelerated brain aging.

Conclusion: This cross-sectional prospective study identified extensive associations between cerebral metabolism and hemodynamic parameters, indicating common aging mechanisms. Heart rate throughout adult life, systolic and pulse pressure parameters around middle age, and diastolic pressure parameters in older patients, suggest the existence of potentially therapeutic targets to prevent accelerated brain aging.

Dynamic PET Imaging Using Dual Texture Features

Purpose: This study aims to explore the impact of adding texture features in dynamic positron emission tomography (PET) reconstruction of imaging results. Methods: We have improved a reconstruction method that combines radiological dual texture features. In this method, multiple short time frames are added to obtain composite frames, and the image reconstructed by composite frames is used as the prior image. We extract texture features from prior images by using the gray level-gradient cooccurrence matrix (GGCM) and gray-level run length matrix (GLRLM). The prior information contains the intensity of the prior image, the inverse difference moment of the GGCM and the long-run low gray-level emphasis of the GLRLM. Results: The computer simulation results show that, compared with the traditional maximum likelihood, the proposed method obtains a higher signal-to-noise ratio (SNR) in the image obtained by dynamic PET reconstruction. Compared with similar methods, the proposed algorithm has a better normalized mean squared error (NMSE) and contrast recovery coefficient (CRC) at the tumor in the reconstructed image. Simulation studies on clinical patient images show that this method is also more accurate for reconstructing high-uptake lesions. Conclusion: By adding texture features to dynamic PET reconstruction, the reconstructed images are more accurate at the tumor.