Characterization of pinhole SPECT acquisition geometry

Abstract

A method is presented to estimate the acquisition geometry of a pinhole single photon emission computed tomography (SPECT) camera with a circular detector orbit. This information is needed for the reconstruction of tomographic images. The calibration uses the point source projection locations of a tomographic acquisition of three point sources located at known distances from each other. It is shown that this simple phantom provides the necessary and sufficient information for the proposed calibration method. The knowledge of two of the distances between the point sources proves to be essential. The geometry is estimated by fitting analytically calculated projections to the measured ones, using a simple least squares Powell algorithm. Some mild a priori knowledge is used to constrain the solutions of the fit. Several of the geometrical parameters are however highly correlated. The effect of these correlations on the reconstructed images is evaluated in simulation studies and related to the estimation accuracy. The highly correlated detector tilt and electrical shift are shown to be the critical parameters for accurate image reconstruction. The performance of the algorithm is finally demonstrated by phantom measurements. The method is based on a single SPECT scan of a simple calibration phantom, executed immediately after the actual SPECT acquisition. The method is also applicable to cone-beam SPECT and X-ray CT.

High-Resolution CT for Small-Animal Imaging Research

“…Displacements are continuously added in the system by many factors, including mechanical instability, vibrations, and thermal drifts of mechanical parts. Many methods have been proposed for the measurement of the misalignment parameters from the analysis of the acquisition data in cone beam geometry (Beque et al, 2003;Defrise et al, 2008;Noo et al, 2000;von Smekal et al, 2004;Yang et al, 2006). Such parameters can be embedded into the reconstruction process in order to produce misalignment-free reconstructed images (Karolczak et al, 2001).…”

Development of fully 3D image reconstruction techniques for pinhole SPECT imaging

“…We have developed an effective optimization me- thod to estimate the complete seven parameters (α, β, x c, y c , x p , y p , z p ) in the geometric calibration for 3D image reconstruction of single-pinhole SPECT [23]. To estimate these geometric parameters, at least three point objects (or sources) phantom with a priori knowledge of relative point position information such as the known distance of each point is necessary and sufficient to constrain the optimization procedure to converge to a unique solution with a general scheme [24][25][26]. The cost function is structured as the least-squares about the residual error of measured and estimated projections of point object [the first term in eq.…”

“…The cost function is structured as the least-squares about the residual error of measured and estimated projections of point object [the first term in eq. (9)], in which m is the projection view (m=1, 2,…, M), n is the nth point object (n=1, [24]. The second term could be constructed as a weight coefficient W multiplied by the sum of least-squares about the error between the known and estimated distance of each point.…”

Tomographie sténopéïque au 99mTc-MIBI dans l’hyperparathyroïdie primaire

“…L'algorithme permettait un choix indépendant de tous les paramètres d'acquisition relatifs à la géométrie de détection. Une calibration pour corriger les imperfections liées à la rotation de la tête, comme le suggère Bequé [13][14], n'a pas été …”