M-C Asselin

The University of Manchester, Manchester, England, United Kingdom

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Publications (6)29.14 Total impact

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    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.
    Physics in Medicine and Biology 04/2012; 57(8):2251-71. DOI:10.1088/0031-9155/57/8/2251 · 2.76 Impact Factor
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    ABSTRACT: 1. Axial (1 st row) and sagittal (2 nd row) planes of 3D images of VPM-PET (left), K trans from ETM (middle), and EFPM (right). Choroid Plexus (thick arrow) and pituitary gland (arrow heads) are clearly depicted. The GCV shows high K trans on the maps of ETM, rendered with red color, but not EFPM. Note that GCV and other large vessels were not found on the PET image render blue representing lowest intensity. Figure 2. Axial (1 st row) and sagittal (2 nd row) planes of 3D images of V p from ETM (left), and EFPM (right). Both ETM and DFPM images show choroid plexus (arrow) and GCV (arrow head) with high (red) and intermittent (green) Vp values, whereas blue color represents low values. Table 1. Comparison between EFPM and ETM for kinetic parameters Fit Results* EFPM ETM p-val Choroid Plexus K trans (min -1) 0.003 ± 0.004 0.003 ± 0.004 0.980 V p (%) 0.06 ± 0.05 0.05 ± 0.03 0.165 Error (%) 0.19 ± 0.07 0.25 ± 0.56 0.084 Great Cerebral Vein
    The International Society For Magnetic Resonance in Medicine; 05/2011
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    ABSTRACT: Iterative image reconstruction methods such as ordered-subset expectation maximization (OSEM) are widely used in PET. Reconstructions via OSEM are however reported to be biased for low-count data. We investigated this and considered the impact for dynamic PET. Patient listmode data were acquired in [(11)C]DASB and [(15)O]H(2)O scans on the HRRT brain PET scanner. These data were subsampled to create many independent, low-count replicates. The data were reconstructed and the images from low-count data were compared to the high-count originals (from the same reconstruction method). This comparison enabled low-statistics bias to be calculated for the given reconstruction, as a function of the noise-equivalent counts (NEC). Two iterative reconstruction methods were tested, one with and one without an image-based resolution model (RM). Significant bias was observed when reconstructing data of low statistical quality, for both subsampled human and simulated data. For human data, this bias was substantially reduced by including a RM. For [(11)C]DASB the low-statistics bias in the caudate head at 1.7 M NEC (approx. 30 s) was -5.5% and -13% with and without RM, respectively. We predicted biases in the binding potential of -4% and -10%. For quantification of cerebral blood flow for the whole-brain grey- or white-matter, using [(15)O]H(2)O and the PET autoradiographic method, a low-statistics bias of <2.5% and <4% was predicted for reconstruction with and without the RM. The use of a resolution model reduces low-statistics bias and can hence be beneficial for quantitative dynamic PET.
    Physics in Medicine and Biology 02/2011; 56(4):931-49. DOI:10.1088/0031-9155/56/4/004 · 2.76 Impact Factor
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    ABSTRACT: The precision of biological parameter estimates derived from dynamic PET data can be limited by the number of acquired coincidence events (prompts and randoms). These numbers are affected by the injected activity (A(0)). The benefits of optimizing A(0) were assessed using a new model of data variance which is formulated as a function of A(0). Seven cancer patients underwent dynamic [(15)O]H(2)O PET scans (32 scans) using a Biograph PET-CT scanner (Siemens), with A(0) varied (142-839 MBq). These data were combined with simulations to (1) determine the accuracy of the new variance model, (2) estimate the improvements in parameter estimate precision gained by optimizing A(0), and (3) examine changes in precision for different size regions of interest (ROIs). The new variance model provided a good estimate of the relative variance in dynamic PET data across a wide range of A(0)s and time frames for FBP reconstruction. Patient data showed that relative changes in estimate precision with A(0) were in reasonable agreement with the changes predicted by the model: Pearson's correlation coefficients were 0.73 and 0.62 for perfusion (F) and the volume of distribution (V(T)), respectively. The between-scan variability in the parameter estimates agreed with the estimated precision for small ROIs (<5 mL). An A(0) of 500-700 MBq was near optimal for estimating F and V(T) from abdominal [(15)O]H(2)O scans on this scanner. This optimization improved the precision of parameter estimates for small ROIs (<5 mL), with an injection of 600 MBq reducing the standard error on F by a factor of 1.13 as compared to the injection of 250 MBq, but by the more modest factor of 1.03 as compared to A(0) = 400 MBq.
    Physics in Medicine and Biology 10/2010; 55(22):6655-72. DOI:10.1088/0031-9155/55/22/005 · 2.76 Impact Factor
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    ABSTRACT: Schizophrenia is characterized by altered prefrontal activity and elevated striatal dopaminergic function. To investigate the relationship between these abnormalities in the prodromal phase of the illness, we combined functional Magnetic Resonance Imaging and (18)F-Dopa Positron Emission Tomography. When performing a verbal fluency task, subjects with an At-Risk Mental State showed greater activation in the inferior frontal cortex than controls. Striatal dopamine function was greater in the At-Risk group than in controls. Within the At-Risk group, but not the control group, there was a direct correlation between the degree of left inferior frontal activation and the level of striatal dopamine function. Altered prefrontal activation in subjects with an At-Risk Mental State for psychosis is related to elevated striatal dopamine function. These changes reflect an increased vulnerability to psychosis and predate the first episode of frank psychosis.
    Molecular Psychiatry 12/2009; 16(1):67-75. DOI:10.1038/mp.2009.108 · 14.50 Impact Factor
  • F.E. Turkheimer · J.A.D. Aston · M-C Asselin · R Hinz
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    ABSTRACT: In the kinetic analysis of dynamic PET data, one usually posits that the variation of the data through one dimension, time, can be described by a mathematical model encapsulating the relevant physiological features of the radioactive tracer. In this work, we posit that the remaining dimension, space, can also be modeled as a physiological feature, and we introduce this concept into a new computational procedure for the production of parametric maps. An organ and, in the instance considered here, the brain presents similarities in the physiological properties of its elements across scales: computationally, this similarity can be implemented in two stages. Firstly, a multi-scale decomposition of the dynamic frames is created through the wavelet transform. Secondly, kinetic analysis is performed in wavelet space and the kinetic parameters estimated at low resolution are used as priors to inform estimates at higher resolutions. Kinetic analysis in the above scheme is achieved by extension of the Patlak analysis through Bayesian linear regression that retains the simplicity and speed of the original procedure. Application to artificial and real data (FDG and FDOPA) demonstrates the ability of the procedure to reduce remarkably the variance of parametric maps (up to 4-fold reduction) without introducing sizeable bias. Significance of the methodology and extension of the procedure to other data (fMRI) and models are discussed.
    NeuroImage 09/2006; 32(1):111-21. DOI:10.1016/j.neuroimage.2006.03.002 · 6.36 Impact Factor

Publication Stats

165 Citations
29.14 Total Impact Points


  • 2010–2012
    • The University of Manchester
      Manchester, England, United Kingdom
  • 2011
    • University College London
      • Institute of Neurology
      Londinium, England, United Kingdom
  • 2009
    • Imperial College London
      Londinium, England, United Kingdom