Distribution Volume Ratio Images Research Articles

Impact of cerebral blood flow and amyloid load on SUVR bias

BackgroundDespite its widespread use, the semi-quantitative standardized uptake value ratio (SUVR) may be biased compared with the distribution volume ratio (DVR). This bias may be partially explained by changes in cerebral blood flow (CBF) and is likely to be also dependent on the extent of the underlying amyloid-β (Aβ) burden. This study aimed to compare SUVR with DVR and to evaluate the effects of underlying Aβ burden and CBF on bias in SUVR in mainly cognitively unimpaired participants. Participants were scanned according to a dual-time window protocol, with either [18F]flutemetamol (N = 90) or [18F]florbetaben (N = 31). The validated basisfunction-based implementation of the two-step simplified reference tissue model was used to derive DVR and R1 parametric images, and SUVR was calculated from 90 to 110 min post-injection, all with the cerebellar grey matter as reference tissue. First, linear regression and Bland–Altman analyses were used to compare (regional) SUVR with DVR. Then, generalized linear models were applied to evaluate whether (bias in) SUVR relative to DVR could be explained by R1 for the global cortical average (GCA), precuneus, posterior cingulate, and orbitofrontal region.ResultsDespite high correlations (GCA: R2 ≥ 0.85), large overestimation and proportional bias of SUVR relative to DVR was observed. Negative associations were observed between both SUVR or SUVRbias and R1, albeit non-significant.ConclusionThe present findings demonstrate that bias in SUVR relative to DVR is strongly related to underlying Aβ burden. Furthermore, in a cohort consisting mainly of cognitively unimpaired individuals, the effect of relative CBF on bias in SUVR appears limited. EudraCT Number: 2018-002277-22, registered on: 25-06-2018.

Noninvasive Measurement of [11C]PiB Distribution Volume Using Integrated PET/MRI.

A noninvasive image-derived input function (IDIF) method using PET/MRI was applied to quantitative measurements of [11C] Pittsburgh compound-B (PiB) distribution volume (DV) and compared with other metrics. Fifty-three patients suspected of early dementia (71 ± 11 y) underwent 70 min [11C]PiB PET/MRI. Nineteen of them (68 ± 11 y) without head motion during the scan were enrolled in this study and compared with 16 age-matched healthy controls (CTL: 68 ± 11 y). The dynamic frames reconstructed from listmode PET data were used for DV calculation. IDIF with metabolite correction was applied to the Logan plot method, and DV was normalized into DV ratio (DVR) images using the cerebellar reference (DVRL). DVR and standardized uptake value ratio (SUVR) images were also calculated using the reference tissue graphical method (DVRr) and the 50–70 min static data with cerebellar reference, respectively. Cortical values were compared using the 3D-T1WI MRI segmentation. All patients were assigned to the early Alzheimer’s disease (eAD) group because of positive [11C]PiB accumulation. The correlations of regional values were better for DVRL vs. DVRr (r2 = 0.97) than for SUVR vs. DVRr (r2 = 0.88). However, all metrics clearly differentiated eAD from CTL with appropriate thresholds. Noninvasive quantitative [11C]PiB PET/MRI measurement provided equivalent DVRs with the two methods. SUVR images showed acceptable results despite inferior variability and image quality to DVR images.

Voxelwise Relationships Between Distribution Volume Ratio and Cerebral Blood Flow: Implications for Analysis of β-Amyloid Images.

Quantification of β-amyloid (Aβ) in vivo is often accomplished using the distribution volume ratio (DVR), based on a simplified reference tissue model. We investigated the local relationships between DVR and cerebral blood flow (CBF), as well as relative CBF (R1), in nondemented older adults. Fifty-five nondemented participants (mean age, 78.5 y) in the Baltimore Longitudinal Study of Aging underwent (15)O-H2O PET CBF and dynamic (11)C-PiB PET. (15)O-H2O PET images were normalized and smoothed using SPM. A simplified reference tissue model with linear regression and spatial constraints was used to generate parametric DVR images. The DVR images were regressed on CBF images on a voxel-by-voxel basis using robust biologic parametric mapping, adjusting for age and sex (false discovery rate, P = 0.05; spatial extent, 50 voxels). DVR images were also regressed on R1 images, a measure of the transport rate constant from vascular space to tissue. All analyses were performed on the entire sample, and on high and low tertiles of mean cortical DVR. Voxel-based analyses showed that increased DVR is associated with increased CBF in the frontal, parietal, temporal, and occipital cortices. However, this association appears to spare regions that typically show early Aβ deposition. A more robust relationship between DVR and CBF was observed in the lower tertile of DVR, that is, negligible cortical Aβ load, compared with the upper tertile of cortical DVR and Aβ load. The spatial distributions of the DVR-CBF and DVR-R1 correlations showed similar patterns. No reliable negative voxelwise relationships between DVR and CBF or R1 were observed. Robust associations between DVR and CBF at negligible Aβ levels, together with similar spatial distributions of DVR-CBF and DVR-R1 correlations, suggest that regional distribution of DVR reflects blood flow and tracer influx rather than pattern of Aβ deposition in those with minimal Aβ load. DVR-CBF associations in individuals with a higher DVR are more likely to reflect true associations between patterns of Aβ deposition and CBF or neural activity. These findings have important implications for analysis and interpretation of voxelwise correlations with external variables in individuals with varying amounts of Aβ load.

CT-based attenuation correction in I-123-ioflupane SPECT.

PurposeAttenuation correction (AC) based on low-dose computed tomography (CT) could be more accurate in brain single-photon emission computed tomography (SPECT) than the widely used Chang method, and, therefore, has the potential to improve both semi-quantitative analysis and visual image interpretation. The present study evaluated CT-based AC for dopamine transporter SPECT with I-123-ioflupane.Materials and methodsSixty-two consecutive patients in whom I-123-ioflupane SPECT including low-dose CT had been performed were recruited retrospectively at 3 centres. For each patient, 3 different SPECT images were reconstructed: without AC, with Chang AC and with CT-based AC. Distribution volume ratio (DVR) images were obtained by scaling voxel intensities using the whole brain without striata as reference. For assessing the impact of AC on semi-quantitative analysis, specific-to-background ratios (SBR) in caudate and putamen were obtained by fully automated SPM8-based region of interest (ROI) analysis and tested for their diagnostic power using receiver-operator-characteristic (ROC) analysis. For assessing the impact of AC on visual image reading, screenshots of stereotactically normalized DVR images presented in randomized order were interpreted independently by two raters at each centre.ResultsCT-based AC resulted in intermediate SBRs about half way between no AC and Chang. Maximum area under the ROC curve was achieved by the putamen SBR, with negligible impact of AC (0.924, 0.935 and 0.938 for no, CT-based and Chang AC). Diagnostic accuracy of visual interpretation also did not depend on AC.ConclusionsThe impact of CT-based versus Chang AC on the interpretation of I-123-ioflupane SPECT is negligible. Therefore, CT-based AC cannot be recommended for routine use in clinical patient care, not least because of the additional radiation exposure.

Apathy correlates with prefrontal amyloid β deposition in Alzheimer's disease

ObjectiveNeuropsychiatric symptoms affect many patients with Alzheimer's disease (AD). (11C)Pittsburgh Compound-B (PIB) positron emission tomography (PET) has enabled the in vivo visualisation of brain amyloid-β (Aβ) deposition. This study exploratively...

3.5D dynamic PET image reconstruction incorporating kinetics-based clusters

Standard 3D dynamic positron emission tomographic (PET) imaging consists of independent image reconstructions of individual frames followed by application of appropriate kinetic model to the time activity curves at the voxel or region-of-interest (ROI). The emerging field of 4D PET reconstruction, by contrast, seeks to move beyond this scheme and incorporate information from multiple frames within the image reconstruction task. Here we propose a novel reconstruction framework aiming to enhance quantitative accuracy of parametric images via introduction of priors based on voxel kinetics, as generated via clustering of preliminary reconstructed dynamic images to define clustered neighborhoods of voxels with similar kinetics. This is then followed by straightforward maximum a posteriori (MAP) 3D PET reconstruction as applied to individual frames; and as such the method is labeled ‘3.5D’ image reconstruction. The use of cluster-based priors has the advantage of further enhancing quantitative performance in dynamic PET imaging, because: (a) there are typically more voxels in clusters than in conventional local neighborhoods, and (b) neighboring voxels with distinct kinetics are less likely to be clustered together. Using realistic simulated 11C-raclopride dynamic PET data, the quantitative performance of the proposed method was investigated. Parametric distribution-volume (DV) and DV ratio (DVR) images were estimated from dynamic image reconstructions using (a) maximum-likelihood expectation maximization (MLEM), and MAP reconstructions using (b) the quadratic prior (QP-MAP), (c) the Green prior (GP-MAP) and (d, e) two proposed cluster-based priors (CP-U-MAP and CP-W-MAP), followed by graphical modeling, and were qualitatively and quantitatively compared for 11 ROIs. Overall, the proposed dynamic PET reconstruction methodology resulted in substantial visual as well as quantitative accuracy improvements (in terms of noise versus bias performance) for parametric DV and DVR images. The method was also tested on a 90 min 11C-raclopride patient study performed on the high-resolution research tomography. The proposed method was shown to outperform the conventional method in visual as well as quantitative accuracy improvements (in terms of noise versus regional DVR value performance).

Prediction of cognitive decline based on hemispheric cortical surface maps of FDDNP PET

A cross-sectional study to establish whether a subject's cognitive state can be predicted based on regional values obtained from brain cortical maps of FDDNP Distribution Volume Ratio (DVR), which shows the pattern of beta amyloid and neurofibrillary binding, along with those of early summed FDDNP PET images (reflecting the pattern of perfusion) was performed. Dynamic FDDNP PET studies were performed in a group of 23 subjects (8 control (NL), 8 Mild Cognitive Impairment (MCI) and 7 Alzheimer's Disease (AD) subjects). FDDNP DVR images were mapped to the MR derived hemispheric cortical surface map warped into a common space. A set of Regions of Interest (ROI) values of FDDNP DVR and early summed FDDNP PET (0-6 min post tracer injection), were thus calculated for each subject which along with the MMSE score were used to construct a linear mathematical model relating ROI values to MMSE. After the MMSE prediction models were developed, the models' predictive ability was tested in a non-overlapping set of 8 additional individuals, whose cognitive status was unknown to the investigators who constructed the predictive models. Among all possible subsets of ROIs, we found that the standard deviation of the predicted MMSE was 1.8 by using only DVR values from medial and lateral temporal and prefrontal regions plus the early summed FDDNP value in the posterior cingulate gyrus. The root mean square prediction error for the eight new subjects was 1.6. FDDNP scans reflect progressive neuropathology accumulation and can potentially be used to predict the cognitive state of an individual.

Comparative evaluation of Logan and relative-equilibrium graphical methods for parametric imaging of dynamic [18F]FDDNP PET determinations

Logan graphical analysis with cerebellum as reference region has been widely used for the estimation of the distribution volume ratio (DVR) of [(18)F]FDDNP as a measure of amyloid burden and tau deposition in human brain because of its simplicity and computational ease. However, spurious parametric DVR images may be produced with shorter scanning times and when the noise level is high. In this work, we have characterized a relative-equilibrium-based (RE) graphical method against the Logan analysis for parametric imaging and region-of-interest (ROI) analysis. Dynamic [(18)F]FDDNP PET scans were performed on 9 control subjects and 12 patients diagnosed with Alzheimer's disease. Using the cerebellum as reference input, regional DVR estimates were derived using both the Logan analysis and the RE plot approach. Effects on DVR estimates obtained at voxel and ROI levels by both graphical approaches using data in different time windows were investigated and compared with the standard values derived using the Logan analysis on a voxel-by-voxel basis for the time window of 35-125 min used in previous studies. Larger bias and variability were observed for DVR estimates obtained by the Logan graphical analysis at the voxel level when short time windows (85-125 and 45-65 min) were used, because of high noise levels in voxel-wise parametric imaging. However, when the Logan graphical analysis was applied at the ROI level over those short time windows, the DVR estimates did not differ significantly from the standard values derived using the Logan analysis on the voxel level for the time window of 35-125 min, and their bias and variability were remarkably lower. Conversely, the RE plot approach was more robust in providing DVR estimates with less bias and variability even when short time windows were used. The DVR estimates obtained at voxel and ROI levels were consistent. No significant differences were observed in DVR estimates obtained by the RE plot approach for all paired comparisons with the standard values. The RE plot approach provides less noisy parametric images and gives consistent and reliable regional DVR estimates at both voxel and ROI levels, indicating that it is preferred over the Logan graphical analysis for analyzing [(18)F]FDDNP PET data.

Using a reference tissue model with spatial constraint to quantify [11C]Pittsburgh compound B PET for early diagnosis of Alzheimer's disease

Introduction:Reference tissue model (RTM) is a compartmental modeling approach that uses reference tissue time activity curve (TAC) as input for quantification of ligand–receptor dynamic PET without blood sampling. There are limitations in applying the RTM for kinetic analysis of PET studies using [11C]Pittsburgh compound B ([11C]PIB). For region of interest (ROI) based kinetic modeling, the low specific binding of [11C]PIB in a target ROI can result in a high linear relationship between the output and input. This condition may result in amplification of errors in estimates using RTM. For pixel-wise quantification, due to the high noise level of pixel kinetics, the parametric images generated by RTM with conventional linear or nonlinear regression may be too noisy for use in clinical studies. Methods:We applied RTM with parameter coupling and a simultaneous fitting method as a spatial constraint for ROI kinetic analysis. Three RTMs with parameter coupling were derived from a classical compartment model with plasma input: an RTM of 4 parameters (R1, k′2R, k4, BP) (RTM4P); an RTM of 5 parameters (R1, k2R, NS, k6, BP) (RTM5P); and a simplified RTM (SRTM) of 3 parameters (R1, k′2R, BP) (RTM3P). The parameter sets [k′2R, k4], [k2R, NS, k6], and k′2R are coupled among ROIs for RTM4P, RTM5P, and RTM3P, respectively. A linear regression with spatial constraint (LRSC) algorithm was applied to the SRTM for parametric imaging. Logan plots were used to estimate the distribution volume ratio (DVR) (=1+BP (binding potential)) in ROI and pixel levels. Ninety-minute [11C]PIB dynamic PET was performed in 28 controls and 6 individuals with mild cognitive impairment (MCI) on a GE Advance scanner. ROIs of cerebellum (reference tissue) and 15 other regions were defined on coregistered MRIs. Results:The coefficients of variation of DVR estimates from RTM3P obtained by the simultaneous fitting method were lower by 77–89% (in striatum, frontal, occipital, parietal, and cingulate cortex) as compared to that by conventional single ROI TAC fitting method. There were no significant differences in both TAC fitting and DVR estimates between the RTM3P and the RTM4P or RTM5P. The DVR in striatum, lateral temporal, frontal and cingulate cortex for MCI group was 25% to 38% higher compared to the control group (p≤0.05), even in this group of individuals with generally low PIB retention. The DVR images generated by the SRTM with LRSC algorithm had high linear correlations with those from the Logan plot (R2=0.99). Conclusion:In conclusion, the RTM3P with simultaneous fitting method is shown to be a robust compartmental modeling approach that may be useful in [11C]PIB PET studies to detect early markers of Alzheimer's disease where specific ROIs have been hypothesized. In addition, the SRTM with LRSC algorithm may be useful in generating R1 and DVR images for pixel-wise quantification of [11C]PIB dynamic PET.

Motion correction eliminates discontinuities in parametric PET images of neuroreceptor binding

Head movements during PET studies of cerebral neuroreceptors with positron emission tomography (PET), which are often recorded as dynamic studies over periods ranging from one to two hours, do not only lead to blurred images, but, by distorting pixel time-activity curves, may also seriously disturb the kinetic analysis. Here we report on the effect of head motion on parametric images of the distribution volume ratio (DVR) as well as on the elimination of artefacts, if the dynamic PET data are corrected for head movements. For this purpose we utilized six PET studies done with the 5HT2A-receptor ligand [18F]-altanserin. Prior to the tracer injection a transmission scan of 10 min was recorded for measured attenuation correction. During the PET scan, which was acquired in listmode for 1 h, the position of the head was monitored by a Polaris infrared motion tracking system. The listmode data were sorted into 42 time frames between 10 s and 2 min in duration. A time frame consists of 63 images of 128 128 voxels with a voxel size of 2 mm 2 mm 2.43 mm. The motion correction used the multiple acquisition frame (MAF) approach, which calculates individual attenuation files for each emission frame and its corresponding head position to avoid a misalignment between transmission and emission data. After reconstruction of attenuation corrected emission frames each image frame was realigned to match the head position of the first emission frame. Both the motion corrected and not corrected dynamic images were evaluated by the non-invasive Logan plot method to obtain parametric images of DVR. In addition, a dynamic [18F]-altanserin PET scan was simulated and affected by similar movements as seen in the human studies. In this way data without statistical noise could be analysed. DVR images of motion-affected [18F]-altanserin scans showed artefacts whose extent was dependent on the amount of movement. The artefacts were mainly located at the border of the cortical tissue, especially at the interior edge towards white matter. The artefacts exhibited as discontinuities and small spots, whose values exceeded the expected DVR values or were even negative. The discontinuities were found with movements of 4 mm and greater. Isolated spots were present even with movements of only 2 mm. The artefacts disappeared when the MAF based motion correction was applied. The observations obtained in human data could be confirmed in the simulated noise free [18F]-altanserin images confirming that the artefacts are due to motion and not to statistical noise. Whereas the native PET images look just blurred, if the patient has moved during the PET scan, parametric images of the Logan DVR, which are calculated by pixel-wise linear regression, contain severe discontinuities primarily at the cortical edge. At this location, the data used in the DVR calculation change between grey and white matter data because of the head motion. The MAF based head motion correction is able to avoid the described errors.

Distribution volume ratios without blood sampling from graphical analysis of PET data.

The distribution volume ratio (DVR), which is a linear function of receptor availability, is widely used as a model parameter in imaging studies. The DVR corresponds to the ratio of the DV of a receptor-containing region to a nonreceptor region and generally requires the measurement of an arterial input function. Here we propose a graphical method for determining the DVR that does not require blood sampling. This method uses data from a nonreceptor region with an average tissue-to-plasma efflux constant k2 to approximate the plasma integral. Data from positron emission tomography studies with [11C]raclopride (n = 20) and [11C]d-threo-methylphenidate ([11C]dMP) (n = 8) in which plasma data were taken and used to compare results from two graphical methods, one that uses plasma data and one that does not. k2 was 0.163 and 0.051 min-1 for [11C]raclopride and [11C]dMP, respectively. Results from both methods were very similar, and the average percentage difference between the methods was -0.11% for [11C]raclopride and 0.46% for [11C]dMP for DVR of basal ganglia (BG) to cerebellum (CB). Good agreement between the two methods was also achieved for DVR images created by both methods. This technique provides an alternative method of analysis not requiring blood sampling that gives equivalent results for the two ligands studied. It requires initial studies with blood sampling to determine the average kinetic constant and to test applicability. In some cases, it may be possible to neglect the k2 term if the BG/CB ratio becomes reasonably constant for a sufficiently long period of time over the course of the experiment.