Purpose: To assess the performance of two approaches to the system response matrix (SRM) calculation in pinhole single photon emission computed tomography (SPECT) reconstruction.
Methods: Evaluation was performed using experimental data from a low magnification pinhole SPECT system that consisted of a rotating flat detector with a monolithic scintillator crystal. The SRM was computed following two approaches, which were based on Monte Carlo simulations (MC-SRM) and analytical techniques in combination with an experimental characterization (AE-SRM). The spatial response of the system, obtained by using the two approaches, was compared with experimental data. The effect of the MC-SRM and AE-SRM approaches on the reconstructed image was assessed in terms of image contrast, signal-to-noise ratio, image quality, and spatial resolution. To this end, acquisitions were carried out using a hot cylinder phantom (consisting of five fillable rods with diameters of 5, 4, 3, 2, and 1 mm and a uniform cylindrical chamber) and a custom-made Derenzo phantom, with center-to-center distances between adjacent rods of 1.5, 2.0, and 3.0 mm.
Results: Good agreement was found for the spatial response of the system between measured data and results derived from MC-SRM and AE-SRM. Only minor differences for point sources at distances smaller than the radius of rotation and large incidence angles were found. Assessment of the effect on the reconstructed image showed a similar contrast for both approaches, with values higher than 0.9 for rod diameters greater than 1 mm and higher than 0.8 for rod diameter of 1 mm. The comparison in terms of image quality showed that all rods in the different sections of a custom-made Derenzo phantom could be distinguished. The spatial resolution (FWHM) was 0.7 mm at iteration 100 using both approaches. The SNR was lower for reconstructed images using MC-SRM than for those reconstructed using AE-SRM, indicating that AE-SRM deals better with the projection noise than MC-SRM.
Conclusions: The authors' findings show that both approaches provide good solutions to the problem of calculating the SRM in pinhole SPECT reconstruction. The AE-SRM was faster to create and handle the projection noise better than MC-SRM. Nevertheless, the AE-SRM required a tedious experimental characterization of the intrinsic detector response. Creation of the MC-SRM required longer computation time and handled the projection noise worse than the AE-SRM.
Nevertheless, the MC-SRM inherently incorporates extensive modeling of the system and therefore experimental characterization was not required.
This work was supported in part by Q-PET project (PI11/01806 - Fondo de Investigaciones Sanitarias del Instituto de Salud Carlos III), the Spanish Ministry of Science and Innovation (SAF2009-08076), and IQ-BRAIN project (10CSA918001PR - Xunta de Galicia). P. Aguiar was awarded a “Sara Borrell” fellowship (CD09/00291) by Fondo de Investigaciones Sanitarias del Instituto de Salud Carlos III. The authors also acknowledge the support of IPHC in providing computing resources on the Regional Grid Infrastructure (http://www.grand-est.fr).
II. MATERIALS AND METHODS
II.A. Pinhole SPECT scanner
II.B. System response matrix
II.C. Monte Carlo SRM
II.D. Analytical and experimental SRM
II.E. Image reconstruction
II.F. Mechanical misalignments and calibration
II.F.1. Definition of mechanical misalignments
II.F.2. Calibration acquisition
II.G. MC-SRM versus AE-SRM
II.G.1. System PSFs
II.G.2. Contrast and signal-to-noise ratio (SNR)
II.G.3. Derenzo phantom
II.G.4. Spatial resolution
II.G.5. Matrix file size and computing time
III.A. System PSFs
III.B. Contrast and signal-to-noise-ratio
III.C. Derenzo phantom
III.D. Spatial resolution
III.E. Matrix file size and computing time
IV. DISCUSSION AND CONCLUSIONS
- Image reconstruction
- Spatial resolution
- Single photon emission computed tomography
- Image sensors
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