High Resolution Research Tomograph Data Research Articles

Scanning rats on the high resolution research tomograph (HRRT): A comparison study with a dedicated micro‐PET

The Siemens ECAT high resolution research tomograph (HRRT) is a dedicated human brain PET camera with a 6% absolute sensitivity and a (2.3 mm)(3) spatial resolution, improving to (1.8 mm)(3) when point spread function (PSF) modeling algorithms are used. These values are very close to those of dedicated small animal PET cameras such as the Siemens microPET FOCUS 120 (F120). The larger axial and transaxial field of view of the HRRT compared to the F120 allows, in principle, for simultaneous imaging of several rodents thus potentially reducing scanning costs and time. This study investigates the feasibility of using the HRRT for quantitative small animal brain studies. We compare, in terms of magnitude, reproducibility, and asymmetry, the nondisplaceable tissue input binding potentials (BP(ND)) in the striata obtained from [(11)C]methylphenidate scans of the same rats imaged on both the F120 and the HRRT. The animal studies are complemented by a phantom study aimed at investigating noise properties relevant to the size of typical regions of interest used in rat brain image analysis. (i) The BP(ND) values obtained from HRRT data are lower than those obtained on the F120 by 38% when PSF modeling is not used, while they are 7% higher with PSF modeling. (ii) The within animal reproducibility on the HRRT is 18% without PSF modeling, worse than the 6% reproducibility on the F120, and is even further degraded to a value of 27% with the use of PSF modeling. (iii) The asymmetry between the left and right striatum in healthy rats worsens from 4.7% in the F120 images to 7.8% in the HRRT images reconstructed without PSF modeling, and is even worse with a value of 14.8% when PSF modeling is used. (iv) Overshooting artifacts and clumpiness in the noise structure of the HRRT images reconstructed with PSF modeling are clearly visible. The spatial resolution achieved on the HRRT without the use of resolution recovery techniques is not sufficient to allow for reliable quantitative small animal brain imaging. While PSF modeling in the reconstruction of the HRRT images in principle improves the resolution close to the level of the F120, it also introduces small scale nonuniformity artifacts and overshooting artifacts which preclude reliable quantitative small animal brain imaging on the HRRT.

Quantification of serotonin transporter availability with [11C]MADAM — A comparison between the ECAT HRRT and HR systems

The High Resolution Research Tomograph (HRRT) is the PET system providing the highest resolution for imaging of the human brain. In this study, the improved quantitative performance of the HRRT was evaluated in comparison with a previously developed lower resolution PET system, the ECAT HR. The radioligand [(11)C]MADAM was chosen for the purpose since it provides a signal for serotonin transporter (5-HTT) binding in cortical and sub-cortical brain regions of different sizes and expressing different 5-HTT densities. A secondary objective was to assess the effect of partial volume effect (PVE) correction on the cross-comparability between the two systems. Six male control subjects (ages 20-35 yr) were examined twice using the HRRT and the HR system, respectively. Regions of interest (ROIs) included cortical regions (frontal cortex, temporal cortex, insula, anterior cingulate cortex, and hippocampus), sub-cortical regions (caudate, putamen, thalamus, dorsal brainstem and ventral midbrain) and cerebellum. The ROIs were manually delineated on T1-weighted MRI-images and subsequently applied to both HRRT and HR images. Regional binding potential (BP(ND)) values were calculated with the simplified reference tissue model (SRTM) using cerebellum as the reference region. The percent difference in BP(ND) between the systems was calculated for each ROI. In addition, both HRRT and HR data were corrected for PVE using established MRI-based methods described by Meltzer and Müller-Gärtner. The effect of PVE correction (PVEc) on the agreement between the systems was assessed via percent difference calculation and linear regression analysis. Quantification with SRTM showed that regional BP(ND) values for [(11)C]MADAM were on average 23% higher for the HRRT than those obtained by the HR system. More specifically, BP(ND) measured with HRRT was 31.1±48.1% higher in neocortical/limbic regions and 14.6±20.9% higher in sub-cortical regions. The effect of PVEc varied between regions. After correction according to Müller-Gärtner, the agreement between systems was best in the neocortical/limbic regions (3.7±22.5%). With the exception of the caudate, in which the agreement was improved by approximately 17% using the Meltzer method, the effect of PVEc in sub-cortical regions was less pronounced. Linear regression analysis showed improved correlation between the two systems after PVEc, particularly in the neocortical/limbic regions. As expected, BP(ND) values measured with the HRRT were higher than those measured with the HR due to higher resolution and recovery. The difference in BP(ND) between the two systems was approximately 30% in the neocortical/limbic regions. PVEc improved the agreement between the systems in particular for the neocortical/limbic regions. In these regions, the best agreement was found after applying Müller-Gärtner's PVEc. The demonstrated agreement provides an opportunity for combining data between the two systems in clinical studies aimed at evaluating receptor/transporter availability in cortical brain regions.

In vivo Validation of Reconstruction-Based Resolution Recovery for Human Brain Studies

The aim of this study was to validate in vivo the accuracy of a reconstruction-based partial volume correction (PVC), which takes into account the point spread function of the imaging system. The NEMA NU2 Image Quality phantom and five healthy volunteers (using [(11)C]flumazenil) were scanned on both HR+ and high-resolution research tomograph (HRRT) scanners. HR+ data were reconstructed using normalization and attenuation-weighted ordered subsets expectation maximization (NAW-OSEM) and a PVC algorithm (PVC-NAW-OSEM). HRRT data were reconstructed using 3D ordinary Poisson OSEM (OP-OSEM) and a PVC algorithm (PVC-OP-OSEM). For clinical studies, parametric volume of distribution (V(T)) images were generated. For phantom data, good recovery was found for both OP-OSEM (0.84 to 0.97) and PVC-OP-OSEM (0.91 to 0.98) HRRT reconstructions. In addition, for the HR+, good recovery was found for PVC-NAW-OSEM (0.84 to 0.94), corresponding well with OP-OSEM. Finally, for clinical data, good correspondence was found between PVC-NAW-OSEM and OP-OSEM-derived V(T) values (slope: 1.02+/-0.08). This study showed that HR+ image resolution using PVC-NAW-OSEM was comparable to that of the HRRT scanner. As the HRRT has a higher intrinsic resolution, this agreement validates reconstruction-based PVC as a means of improving the spatial resolution of the HR+ scanner and thereby improving the quantitative accuracy of positron emission tomography.

HRRT Versus HR+ Human Brain PET Studies: An Interscanner Test–Retest Study

The high-resolution research tomograph (HRRT) is a dedicated human brain PET scanner. The purpose of this study was to compare the quantitative accuracy of the HRRT with that of the clinical HR+ PET scanner and to assess effects of differences in spatial resolution between both scanners ( approximately 2.7 mm and approximately 7.0 mm for HRRT and HR+, respectively). Paired (11)C-flumazenil scans of 7 healthy volunteers were assessed. For each volunteer, dynamic scans (including arterial sampling) were acquired on both scanners on the same day, thereby minimizing intersubject variability. Volume of distribution was generated using Logan plot analysis with plasma input. In addition, other plasma input, reference tissue (with pons as the reference tissue input), and parametric methods were included in the interscanner comparison. Logan volume-of-distribution analysis of HRRT data showed higher values than that of HR+ data (slope with the intercept fixed at the origin of 1.14 +/- 0.10 to 1.19 +/- 0.10, depending on the HRRT reconstruction method used). Smoothing HRRT reconstructions with a 6-mm full width at half maximum gaussian kernel reduced this slope toward the line of identity (1.04 +/- 0.11 to 1.07 +/- 0.11), retaining good correlation between HR+ and HRRT data (r, approximately 0.98). Similar trends were observed for other plasma input, reference tissue, and parametric methods. However, after reference matching the reference tissue models showed lower HRRT kinetic parameter values than HR+ values (slope with fixed intercept, 0.90 +/- 0.10 to 0.94 +/- 0.13). Higher values of pharmacokinetic parameter values, obtained from HRRT versus HR+ PET studies, indicate improved HRRT PET quantification primarily due to a reduction in partial-volume effects.

Image derived input functions for dynamic High Resolution Research Tomograph PET brain studies

The High Resolution Research Tomograph (HRRT) is a dedicated human brain positron emission tomography (PET) scanner. The aim of the present study was to validate the use of image derived input functions (IDIF) as an alternative for arterial sampling for HRRT human brain studies. To this end, IDIFs were extracted from 3D ordinary Poisson ordered subsets expectation maximization (OP-OSEM) and reconstruction based partial volume corrected (PVC) OP-OSEM images. IDIFs, either derived directly from regions of interest or further calibrated using manual samples taken during scans, were evaluated for dynamic [ 11C]flumazenil data ( n = 6). Results obtained with IDIFs were compared with those obtained using blood sampler input functions (BSIF). These comparisons included areas under the curve (AUC) for peak (0–3.3 min) and tail (3.3–55.0 min). In addition, slope, intercept and Pearson's correlation coefficient of tracer kinetic analysis results based on IDIF and BSIF were calculated for each subject. Good peak AUC ratios (0.83 ± 0.21) between IDIF and BSIF were found for calibrated IDIFs extracted from OP-OSEM images. This combination of IDIFs and images also provided good slope values (1.07 ± 0.11). Improved resolution, as obtained with PVC OP-OSEM, changed AUC ratios to 1.14 ± 0.35 and, for tracer kinetic analysis, slopes changed to 0.95 ± 0.13. For all reconstructions, non-calibrated IDIFs gave poorer results (> 61 ± 34% higher slopes) compared with calibrated IDIFs. The results of this study indicate that the use of IDIFs, extracted from OP-OSEM or PVC OP-OSEM images, is feasible for dynamic HRRT data, thereby obviating the need for online arterial sampling.

Gap Filling Strategies for 3-D-FBP Reconstructions of High-Resolution Research Tomograph Scans

The High-Resolution Research Tomograph (HRRT) is a dedicated human brain positron emission tomography scanner. Currently available iterative reconstruction algorithms show bias due to nonnegativity constraints. Consequently, implementation of 3-D filtered backprojection (3-D-FBP) is of interest. To apply 3-D-FBP all missing data including those due to gaps between detector heads need to be estimated. The aim of this study was to evaluate various gap filling strategies for 3-D-FBP reconstructions of HRRT data, such as linear and bilinear interpolation or constraint Fourier space gap filling (confosp). Furthermore, missing planes were estimated using segment 0 image data only (noniterative) or by using reconstructed images based on all previous segments (iterative method). Use of bilinear interpolation showed worst correspondence between reconstructed and true activity concentration, especially for small structures. Moreover, phantom data indicated that use of linear interpolation resulted in artifacts in planes located near the edge of the field-of-view. Use of confosp did not show these artifacts. Iterative estimations of the missing planes for |segments| 0 improved image quality at the cost of more computation time. Therefore, use of confosp for filling sinogram gaps with both iterative and noniterative estimation of missing planes are recommended for quantitative 3-D-FBP of HRRT studies.

HeinzelCluster: accelerated reconstruction for FORE and OSEM3D

Using iterative three-dimensional (3D) reconstruction techniques for reconstruction of positron emission tomography (PET) is not feasible on most single-processor machines due to the excessive computing time needed, especially so for the large sinogram sizes of our high-resolution research tomograph (HRRT).In our first approach to speed up reconstruction time we transform the 3D scan into the format of a two-dimensional (2D) scan with sinograms that can be reconstructed independently using Fourier rebinning (FORE) and a fast 2D reconstruction method. On our dedicated reconstruction cluster (seven four-processor systems, Intel PIII@700 MHz, switched fast ethernet and Myrinet, Windows NT Server), we process these 2D sinograms in parallel. We have achieved a speedup >23 using 26 processors and also compared results for different communication methods (RPC, Syngo, Myrinet GM).The other approach is to parallelize OSEM3D (implementation of C Michel), which has produced the best results for HRRT data so far and is more suitable for an adequate treatment of the sinogram gaps that result from the detector geometry of the HRRT. We have implemented two levels of parallelization for our dedicated cluster (a shared memory fine-grain level on each node utilizing all four processors and a coarse-grain level allowing for 15 nodes) reducing the time for one core iteration from over 7 h to about 35 min.