Rochelle L Hatton | PubMed Health

PubMed2003

Optimisation of brain SPET and portability of normal databases.

Use of a normal database in quantitative regional analysis of brain single-photon emission tomography (SPET) facilitates the detection of functional defects in individual or group studies by accounting for inter-subject variability. Different reconstruction methods and suboptimal attenuation and scatter correction methods can introduce additional variance that will adversely affect such analysis. Similarly, processing differences across different instruments and/or institutions may invalidate the use of external normal databases. The object of this study was to minimise additional variance by comparing reconstructions of a physical phantom with its numerical template so as to optimise processing parameters. Age- and gender-matched normal scans acquired on two different systems were compared using SPM99 after processing with both standard and optimised parameters. For three SPET systems we have optimised parameters for attenuation correction, lower window scatter subtraction, reconstructed pixel size and fanbeam focal length for both filtered back-projection (FBP) and iterative (OSEM) reconstruction. Both attenuation and scatter correction improved accuracy for all systems. For single-iteration Chang attenuation correction the optimum attenuation coefficient (mu) was 0.45-0.85 of the narrow beam value (Nmu) before, and 0.75-0.85 Nmu after, scatter subtraction. For accurately modelled OSEM attenuation correction, optimum mu was 0.6-0.9 Nmu before and 0.9-1.1 Nmu after scatter subtraction. FBP appeared to change in-plane voxel dimensions by about 2% and this was confirmed by line phantom measurements. Improvement in accuracy with scatter subtraction was most marked for the highest spatial resolution system. Optimised processing reduced but did not remove highly significant regional differences between normal databases acquired on two different SPET systems.

Algorithms↗

PubMed2003

Practical aspects of a data-driven motion correction approach for brain SPECT.

UNLABELLED: Patient motion can cause image artifacts in single photon emission computed tomography despite restraining measures. Data-driven detection and correction of motion can be achieved by comparison of acquired data with the forward projections. This enables the brain locations to be estimated and data to be correctly incorporated in a three-dimensional (3-D) reconstruction algorithm. Digital and physical phantom experiments were performed to explore practical aspects of this approach. METHODS: Noisy simulation data modeling multiple 3-D patient head movements were constructed by projecting the digital Hoffman brain phantom at various orientations. Hoffman physical phantom data incorporating deliberate movements were also gathered. Motion correction was applied to these data using various regimes to determine the importance of attenuation and successive iterations. Studies were assessed visually for artifact reduction, and analyzed quantitatively via a mean registration error (MRE) and mean square difference measure (MSD). RESULTS: Artifacts and distortion in the motion corrupted data were reduced to a large extent by application of this algorithm. MRE values were mostly well within 1 pixel (4.4 mm) for the simulated data. Significant MSD improvements (>2) were common. Inclusion of attenuation was unnecessary to accurately estimate motion, doubling the efficiency and simplifying implementation. Moreover, most motion-related errors were removed using a single iteration. The improvement for the physical phantom data was smaller, though this may be due to object symmetry. CONCLUSION: These results provide the basis of an implementation protocol for clinical validation of the technique.

Algorithms↗