Optimization of methods for quantification of rCBF using high-resolution [¹⁵O]H₂O PET images

Abstract

This study aimed to derive accurate estimates of regional cerebral blood flow (rCBF) from noisy dynamic [¹⁵O]H₂O PET images acquired on the high-resolution research tomograph, while retaining as much as possible the high spatial resolution of this brain scanner (2-3 mm) in parametric maps of rCBF. The PET autoradiographic method and generalized linear least-squares (GLLS), with fixed or extended to include spatially variable estimates of the dispersion of the measured input function, were compared to nonlinear least-squares (NLLS) for rCBF estimation. Six healthy volunteers underwent two [¹⁵O]H₂O PET scans with continuous arterial blood sampling. rCBF estimates were obtained from three image reconstruction methods (one analytic and two iterative, of which one includes a resolution model) to which a range of post-reconstruction filters (3D Gaussian: 2, 4 and 6 mm FWHM) were applied. The optimal injected activity was estimated to be around 11 MBq kg⁻¹ (800 MBq) by extrapolation of patient-specific noise equivalent count rates. Whole-brain rCBF values were found to be relatively insensitive to the method of reconstruction and rCBF quantification. The grey and white matter rCBF for analytic reconstruction and NLLS were 0.44 ± 0.03 and 0.15 ± 0.03 mL min⁻¹ cm⁻³, respectively, in agreement with literature values. Similar values were obtained from the other methods. For generation of parametric images using GLLS or the autoradiographic method, a filter of ≥ 4 mm was required in order to suppress noise in the PET images which otherwise produced large biases in the rCBF estimates.

Cerebral blood flow and glucose metabolism in healthy volunteers measured using a high-resolution PET scanner

“…These values are in line with the recent data acquired on an HRRT by Walker and co-workers [17], who found values of 0.44 and 0.15 mL/cm 3 per minute (Table 3). The good correlation between BFM and NLR results implies that this linearization is a valid approach.…”

“…Total gray and white matter CBFs were 0.35 ± 0.05 and 0.25 ± 0.04 mL/cm 3 per minute, respectively, (GM:WM = 1.4). Table 3 shows CBF values obtained from the manually drawn gray and white matter regions (ratio GM/WM = 3.3) as well as a comparison with literature data, recently obtained using another HRRT scanner [17]. Figure 4 shows the relationship between CBF values derived from BFM and NLR analyses on smoothed data.…”

“…These glucose measurements, however, were based on a static FDG protocol together with population average-based values for the rate constants. In a recent study, HRRT image quality was shown to be good enough to allow for quantitatively correct CBF measurements [17]. …”

Cerebral Blood Flow and Glucose Metabolism Measured With Positron Emission Tomography Are Decreased in Human Type 1 Diabetes

“…Next, fully corrected sinograms were reconstructed using the standard 3D OP-OSEM reconstruction algorithm (31–33), resulting in 207 image planes with 256 × 256 voxels and a voxel size of 1.22 × 1.22 × 1.22 mm 3 . The effective spatial resolution of the reconstructed images was 3 mm full-width at half maximum.…”

Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework

“…As the use of TOF PET receded in the late 1980s and as iterative approaches became routine clinical tools due to their improved contrast-vs.-noise trade-offs and better visual quality, analytic (non-TOF) algorithms have lately been used only for specialized quantitative studies and as a gold standard for comparative studies evaluating the biases of reconstruction approaches (Blinder et al , 2012; Reilhac et al , 2008; van Velden et al , 2008; Badawi et al , 1999; van Velden et al , 2009; Verhaeghe et al , 2010; Walker et al , 2011; Walker et al , 2012); analytic reconstructions are also used for intrinsic spatial resolution characterization of PET scanners (NEMA Standards Publication NU 2-2001NEMA Standards Publication NU 2-2001). With the renewed interest in TOF PET, analytic TOF methods have been revisited, and several algorithms have been studied (Cho et al , 2008; Conti et al , 2005; Kao, 2008; Watson, 2007), with improved noise characteristics reported (Watson, 2007, 2008).…”