Experimental Evaluation of Maximum-Likelihood-Based Data Preconditioning for DE-SPECT: A Clinical SPECT System Constructed With CZT Imaging Detectors

This study introduces a novel maximum-likelihood-based data preconditioning method for a 3-D position sensitive cadmium zinc telluride (CZT) detector used in the dynamic extremity-single photon emission computed tomography imaging system, an organ-dedicated Single-Photon Emission computed tomography system optimized for imaging peripheral vascular diseases in lower extremities. The 3-D CZT detectors offer subpixel resolution of ~0.5 mm FWHM in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>X</mml:mi> <mml:mo>-</mml:mo> <mml:mi>Y</mml:mi> <mml:mo>-</mml:mo> <mml:mi>Z</mml:mi></mml:math> directions and an ultrahigh energy resolution of 3 keV at 200 keV, 4.5 keV at 450 keV, and 5.4 keV at 511 keV. Given the intrinsic challenges posed by pixel boundary issues, spatial distortions, and nonuniformity inherent in large-volume, high-resolution CZT detectors, we proposed a Maximum-Likelihood-based preconditioning technique to reconstruct the projection, which effectively mitigates the pixel boundary issue and deconvolves the distortions and nonuniformity in detector responses. To facilitate the preconditioning step, we used sheet-beam scanning to measure the distortion map of the CZT detectors. We have evaluated our data preconditioning technique through extensive experimental evaluations, including Tc-99m sheet-beam scanning and image reconstruction of an image quality phantom. These results not only demonstrated the efficacy of the technique in reducing the impact of pixel boundary issues and correcting for spatial distortions. The proposed data preconditioning technique could potentially be applied across various types of imaging sensors.