Jose Anton-Rodriguez

18F-Florbetapir PET in Patients with Frontotemporal Dementia and Alzheimer Disease

Pathologic deposition of amyloid β (Aβ) protein is a key component in the pathogenesis of Alzheimer disease (AD) but not a feature of frontotemporal dementia (FTD). PET ligands for Aβ protein are increasingly used in diagnosis and research of dementia syndromes. Here, we report a PET study using 18F-florbetapir in healthy controls and patients with AD and FTD. Methods: Ten healthy controls (mean age ± SD, 62.5 ± 5.2 y), 10 AD patients (mean age ± SD, 62.6 ± 4.5), and 8 FTD patients (mean age ± SD, 62.5 ± 9.6) were recruited to the study. All patients underwent detailed clinical and neuropsychologic assessment and T1-weighted MR imaging and were genotyped for apolipoprotein E status. All participants underwent dynamic 18F-florbetapir PET on a high-resolution research tomograph, and FTD patients also underwent 18F-FDG PET scans. Standardized uptake value ratios (SUVRs) were extracted for predefined gray and white matter regions of interest using cerebellar gray matter as a reference region. Static PET images were evaluated by trained raters masked to clinical status and regional analysis. Results: Total cortical gray matter 18F-florbetapir uptake values were significantly higher in AD patients (median SUVR, 1.73) than FTD patients (SUVR, 1.13, P = 0.002) and controls (SUVR, 1.26, P = 0.04). 18F-Florbetapir uptake was also higher in AD patients than FTD patients and controls in the frontal, parietal, occipital, and cingulate cortices and in the central subcortical regions. Only 1 FTD patient (homozygous for apolipoprotein E ε4) displayed high cortical 18F-florbetapir retention, whereas 18F-FDG PET demonstrated mesiofrontal hypometabolism consistent with the clinical diagnosis of FTD. Most visual raters classified 1 control (10%) and 8 AD (80%) and 2 FTD (25%) patients as amyloid-positive, whereas ratings were tied in another 2 FTD patients and 1 healthy control. Conclusion: Cortical 18F-florbetapir uptake is low in most FTD patients, providing good discrimination from AD. However, visual rating of FTD scans was challenging, with a higher rate of discordance between interpreters than in AD and control subjects.

Investigation of motion induced errors in scatter correction for the HRRT brain scanner

Patient motion during PET scans introduces errors in the attenuation correction and image blurring leading to false changes in regional radioactivity concentrations. However, the potential effect that motion has on simulation-based scatter correction is not fully appreciated. Specifically for tracers with high uptake close to the edge of head (e.g. scalp and nose) as observed with [11C]Verapamil, mismatches between transmission and emission data can lead to significant quantification errors and image artefacts due to over scatter correction. These errors are linked with unusually high values in the scatter scaling factors (SSF) returned during the single scatter simulation process implemented in the HRRT image reconstruction. Reconstruction of μ-map with TXTV (an alternative μ-map reconstruction using non-linear filtering rather than brain segmentation and scatter correction of the transmission data) was found to improve the scatter simulation results for [11C]Verapamil and [18F]FDG. The errors from patient motion were characterised and quantified through simulations by applying realistic transformations to the attenuation map (μ-map). This generated inconsistencies between the emission and transmission data, and introduced large over-corrections of scatter similar to some cases observed with [11C]Verapamil. Automated Image Registration (AIR) based motion correction was also implemented, and found to remove the artifact and recover quantification in dynamic studies after aligning all the PET images to a common reference space.

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

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.

Isotope specific resolution recovery image reconstruction in high resolution PET imaging

PURPOSE Measuring and incorporating a scanner-specific point spread function (PSF) within image reconstruction has been shown to improve spatial resolution in PET. However, due to the short half-life of clinically used isotopes, other long-lived isotopes not used in clinical practice are used to perform the PSF measurements. As such, non-optimal PSF models that do not correspond to those needed for the data to be reconstructed are used within resolution modeling (RM) image reconstruction, usually underestimating the true PSF owing to the difference in positron range. In high resolution brain and preclinical imaging, this effect is of particular importance since the PSFs become more positron range limited and isotope-specific PSFs can help maximize the performance benefit from using resolution recovery image reconstruction algorithms. METHODS In this work, the authors used a printing technique to simultaneously measure multiple point sources on the High Resolution Research Tomograph (HRRT), and the authors demonstrated the feasibility of deriving isotope-dependent system matrices from fluorine-18 and carbon-11 point sources. Furthermore, the authors evaluated the impact of incorporating them within RM image reconstruction, using carbon-11 phantom and clinical datasets on the HRRT. RESULTS The results obtained using these two isotopes illustrate that even small differences in positron range can result in different PSF maps, leading to further improvements in contrast recovery when used in image reconstruction. The difference is more pronounced in the centre of the field-of-view where the full width at half maximum (FWHM) from the positron range has a larger contribution to the overall FWHM compared to the edge where the parallax error dominates the overall FWHM. CONCLUSIONS Based on the proposed methodology, measured isotope-specific and spatially variant PSFs can be reliably derived and used for improved spatial resolution and variance performance in resolution recovery image reconstruction. The benefits are expected to be more substantial for more energetic positron emitting isotopes such as Oxygen-15 and Rubidium-82.

Evaluation of image based spatially variant and count rate dependant point spread functions on the HRRT PET scanner

Spatial resolution is of high importance especially in preclinical and brain PET imaging and characterization of the scanners resolution properties requires measuring its point spread function (PSF). In our previous work we measured the PSF in 2 PET/CT scanners using a printed point source array. Here we extend the work to accurately measure the spatial variation of the PSF on the High Resolution Research Tomograph (HRRT) and assess the impact of the scanners depth of interaction (DOI) capability on the spatial resolution. Furthermore we characterize the dependency of the PSF to count rate. An array of 15×11 printed sources was scanned twice, initially on its own (10min scan) and subsequently with an extension line containing ∼300MBq of carbon-11 at the scan starts (15 × 10min contiguous frames). Data were reconstructed with OP-OSEM and invariant PSF OP-OSEM. The PSF was found to be radially dependent in all directions but importantly radially symmetric and almost axially independent. The FWHM improves by ∼1.3mm using the PSF-OP-OSEM but is still radially variable with almost 1 mm degradation within the boundaries within which the brain is typically located. When DOI was not taken into account, a degradation up to 0.7mm was seen in the radial FWHM. In terms of count rate dependency, a clear resolution deterioration was seen, for count rates above 5–10 kcps with such count rates observed during many clinical scans.

Image-Based Spatially Variant and Count Rate Dependent Point Spread Function on the HRRT

Spatial resolution on the High Resolution Research Tomograph (HRRT) is of high importance, due to the need for accurate quantification of small brain structures. Thus accurate characterization of the scanners resolution properties and subsequent inclusion of such information within image reconstruction, requires measuring its point spread function (PSF). In this study we measured in detail the spatial variation of the image space PSF and assessed the impact of the scanners depth of interaction (DOI) capability on the spatial resolution. Furthermore, we characterized the dependency of the PSF on progressively increasing count rate statistics. An array of 15 × 11 printed point sources was scanned twice, initially on its own to measure the PSF spatial dependency, and subsequently with an extension line containing ~ 300 MBq of carbon-11, to assess the count rate dependency. PSF data were reconstructed with the scanners default OP-OSEM and invariant PSF OP-OSEM algorithms, followed by image space model fitting. The axial, radial and tangential components of the PSF were found to vary under radial and angular transformations, but being radially symmetric and almost axially independent. The FWHM improves by ~ 1.3 mm using the PSF-OP-OSEM but is still radially variable with ~ 1 mm degradation within the FOV boundaries. When DOI was not taken into account, an additional degradation up to 0.7 mm was seen in the radial FWHM using OP-OSEM. Using the invariant PSF OP-OSEM, this degradation was less notable ( ~ 0.5 mm), despite the fact an invariant kernel is used. In terms of count rate dependency, a clear resolution degradation was seen for mean count rates above 5-10 kcps. However, this degradation was found to vary within the FOV. The spatially variant PSF at low count rates could be incorporated within a resolution modelling image reconstruction. However, including a count rate dependent PSF model is less straightforward.

Multiple target marker tracking for real-time, accurate, and robust rigid body motion tracking of the head for brain PET

Although motion correction in medical imaging is well established and has attracted much interest and research funding, a gap still exists in that there is a lack of reliable, low-cost hardware to enable such techniques to be widely adopted in healthcare. For PET, motion during scanning causes image blur which degrades image quality and quantifiability. In most marker based motion tracking systems used for brain imaging, a single tracking tool is fixed to the subject, however it is crucially important to ensure that the tool is rigidly fixed to the subjects head otherwise the tool may slip and the tracking data becomes unreliable. A tracking system has been developed using open source code and a single low cost digital camera that tracks multiple, small (≤10 mm2) target markers printed onto adhesive paper which are attached to the subjects forehead. The system can track the 6 degree of freedom motion of the head to sub mm precision and in real-time while being robust against facial deformations that may move the target markers non-rigidly. In this study a standard, consumer grade, visible light webcam was used with a resolution of 1600×1200, operated at 30 Hz, and achieved simultaneous tracking of multiple markers with 0.2 mm and 0.3° mean error. Calibration to PET space was performed using simultaneous tracking of a 18F point source doped marker with mean positional error of 0.7 mm.

Isotope specific resolution modelling image reconstruction for high resolution PET imaging

Measuring the spatially variant point spread function (PSF) on a PET scanner involves using a point source to sample the field of view (FOV) at multiple locations. However, since most clinically used isotopes have short half-lives, usually other non-clinically used long-lived isotopes are employed in practice. As such, due to the difference in positron range, non-optimal PSF models that do not correspond to those needed for the data to be reconstructed, are used within resolution modelling (RM) image reconstruction, usually underestimating the true PSF. In our previous work, the spatially variant PSF was measured on the HRRT based on clinically used isotopes. Here we extend the work by evaluating the impact of using isotope-specific PSF maps within RM image reconstruction. Evaluation is performed using point source, phantom and clinical datasets. Results suggest that further improvements in spatial resolution and contrast can be obtained by using an isotope-specific PSF.

Isotope dependent system matrices for high resolution PET imaging

Measuring and incorporating scanner specific point spread functions (PSFs) within image reconstruction has been shown to improve spatial resolution in reconstructed PET images. However, due to the short half-life of the clinically used isotopes, other long-lived isotopes not used in clinical practice are chosen to perform the PSF measurements, consequently leading to over or under estimation of the true PSF width during reconstruction, due to the difference in positron range. In high resolution brain and preclinical imaging, this effect is of particular importance since the resolution becomes more positron range limited and isotope-specific PSFs can help maximizing the performance benefit from using resolution recovery image reconstruction algorithms. In this work, we use a printing technique to simultaneously measure multiple point sources and demonstrate the feasibility of deriving isotope-dependent system matrices on the High Resolution Research Tomograph (HRRT) by measuring spatially-variant and isotope-specific PSFs using Fluorine-18 and Carbon-11. Initial results based on these 2 isotopes illustrate that even small differences in positron range can result in different PSF maps. The difference is more distinct in the centre of the field of view (FOV) where the full width at half maximum (FWHM) from the positron range has a larger contribution in the overall FWHM compared to the edge of the FOV, where the parallax error dominates the overall FWHM. Further PSF measurements are underway to evaluate clinically used isotopes with larger positron ranges and significantly shorter half-lives. These measurements could be used to create a database of isotope-dependent system matrices to be used within image reconstruction.

Image space identification of a motion tracking tool in PET and PET/CT

Subject motion during PET data acquisition reduces the achievable resolution. Methods to correct PET data for motion often use external motion tracking devices, for example with commercial Infrared based cameras. Accurate spatial and temporal alignment between the Infrared camera and PET or PET/CT is essential. Here we propose a simple and robust method for direct identification in PET image space of the motion tracking tool that is attached to the head to calculate the ground truth transformation between PET and IR tracking space. This is achieved by identifying the set of reflective markers of the tracking tool in the attenuation correction image from either the conventional transmission scan of the PET only system or the CT scan of the combined PET/CT system. The tool detection process can be complicated however, with careful design of the tracking tool, it can be simple. The tool detection consists of applying a threshold to the attenuation images to select only the highest attenuating regions and using blob detection techniques to get coordinates. These regions can be designed to correspond with the positions of the reflective markers. Importantly, due to an extension of the small angles approximation, the accuracy of the tool detection seems to have little effect on the overall motion correction transformation. By mis-calculating the tool position by 5 mm the resultant effect changes a voxel position by 0.092 mm