Reducing between scanner differences in multi-center PET studies
Introduction
This work is part of the ongoing multi-center Alzheimer's Disease Neuroimaging Initiative (ADNI) project, a longitudinal, multi-site observational study of healthy controls, patients with mild cognitive impairment (MCI), and mild probable Alzheimer's disease (AD) patients. This five-year research project aims to study the rate of change of cognition, brain structure and function in 200 elderly controls, 400 subjects with mild cognitive impairment, and 200 with probable Alzheimer's disease. Data is being acquired longitudinally using magnetic resonance imaging (MRI), [18F]FDG PET, [11C]PiB PET, urine serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical and psychometric assessments. PET scans are being performed on half of the subjects in each of the three groups. The Division of Nuclear Medicine PET Center at the University of Michigan is the coordinating center for quality control and pre-processing of all PET studies, while several groups are responsible for the analysis of the PET data.
The objective of this work is the development of a framework for reduction of inter-scanner differences in static FDG scans acquired in ADNI. The scans are being obtained from 50 participating PET centers having different hardware and software. In all there were 15 different scanner-types in this project. In spite of using a standardized imaging protocol, systematic inter-scanner variability in PET images from various sites has been observed due to differences in scanner resolution, reconstruction techniques, and different implementations of scatter and attenuation corrections on the different scanner models. It is an important step to try to minimize these differences before the data across centers is pooled for analysis.
The differences in the human PET scans can be classified into two broad categories: 1) actual inter-subject variability, which includes both anatomic and functional differences and 2) systematic differences related to scanner hardware and software. The goal of PET is to determine the functional differences between individuals or groups of individuals, and hence removal of both the anatomic differences that exist between subjects as well as the systematic differences across scanner models is of interest. While much work has been done in reducing anatomic differences across subjects by the use of standardized atlases (Mazziotta et al., 1995, Minoshima et al., 1994a) and non-linear warping techniques (Minoshima et al., 1994b), the focus of the present work is the reduction of the systematic differences between the different scanner models.
The correction factors to reduce systematic inter-scanner variability were obtained from 3-D Hoffman brain phantom (Hoffman et al., 1990) scans acquired at the participating sites. The 3-D Hoffman brain phantom is a cylindrically shaped phantom that simulates the radiotracer distribution in a normal human brain for tracers aimed at measuring cerebral glucose metabolism or blood flow. The relative concentrations of radioactivity in “gray matter”, “white matter”, and all other structures are 4:1:0, respectively. The correction factors for each scanner type were obtained by comparison of the phantom scans with a ‘gold standard’ digital representation of the true Hoffman brain phantom (i.e. representing the actual radioactivity distribution).
The systematic differences in the reconstructed images across the different scanners were classified into two general types: high frequency differences, related primarily to image resolution; and low frequency differences, related to image uniformity and the more subtle aspects of image formation such as corrections for attenuation and scatter. Resolution differences are due primarily to differences in crystal sizes, and to a lesser extent due to detector material (LSO, BGO, GSO and LYSO), detector crystal axial depths, energy windows, as well as the number of rings, crystals per ring and axial field-of-view. The low frequency uniformity differences may manifest as differences in contrast (grey-to-white matter ratios) as well as superior-to-inferior, anterior-to-posterior, and/or midline-to-lateral gradients. These non-uniformities between scanners are likely to be caused primarily by disparity in the software routines that handle attenuation and scatter. The high frequency correction proposed in this work involves smoothing the data from different scanner models to a common resolution, whereas, the low frequency correction involves application of smooth affine correction factors following the high frequency correction. Both the high and low frequency correction factors were obtained by comparison of phantom scan data with the digital phantom. The phantom-based correction factors were applied to phantom scans to determine the maximum recovery possible using this approach. Subsequently, the phantom-based corrections were applied to 95 normal control scans to test their utility in human PET studies.
Section snippets
Methods
Hoffman brain phantom scans were obtained from all participating sites using a standard protocol. There were in all fifteen different scanner models among the participating sites (7 PET-only and 8 PET/CT scanners). The key features of the protocol include the following.
- 1.
The Hoffman phantom is filled with 0.5–0.6 mCi of 18F solution and placed in the scanner.
- 2.
The chest phantom is filled with 2.0–2.4 mCi of 18F solution and placed close to the Hoffman phantom to simulate the effects of out-of-field
Phantom data
As mentioned earlier, human studies vary due both to inter-subject as well as to inter-scanner differences. Since the same phantom was imaged at all participating sites, the phantom studies did not have any variability comparable to the “inter-subject” differences seen in humans. Thus, the differences in phantom scans are primarily due to scanner differences, though differences due to technical factors in performing the scan (e.g. proper mixing) could still exist. Since the correction factors
Results
The high frequency correction factors (FWHM of the smoothing kernels) for smoothing the images from the fifteen scanner models to 8 mm resolution are listed in the Table 1. Fig. 1 shows visually the reduction in resolution differences after application of the smoothing kernels to five of the 15 scanner models.
Fig. 2 shows image slices of the additive and multiplicative factors obtained from the simulation study where the reconstructed image contains residual attenuation alone. The
Discussion
This paper develops a framework for reducing the variability in PET scans obtained across different scanner models in a large multi-center study; an important step prior to pooling the data for analysis. The correction factors were derived from PET image data obtained by scanning the same object (the 3-D Hoffman brain phantom) at all 50 participating sites. Human PET scans from different centers are different not only because of the functional and anatomical differences between subjects but
Acknowledgment
This work was supported by the Alzheimer's Disease Neuroimaging Initiative AG024904.
References (6)
- et al.
A probabilistic atlas of the human brain: theory and rationale for its development. The International Consortium for Brain Mapping (ICBM)
NeuroImage
(1995) - Fessler J.A. 1995. ASPIRE 3.0 user's guide: A sparse iterative reconstruction library. Technical Report 293. Comm. and...
- et al.
3-D phantom to simulate cerebral blood flow and metabolic images for PET
IEEE Trans Nucl. Sci.
(1990)
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