Bone single-photon emission computed tomography (SPECT)/computed tomography (CT) imaging suffers from poor spatial resolution, but the image quality can be improved during SPECT reconstruction by using anatomical information derived from CT imaging. The purpose of this work was to compare two different anatomically guided SPECT reconstruction methods to ordered subsets expectation maximization (OSEM) which is the most commonly used reconstruction method in nuclear medicine. The comparison was done in terms of lesion quantitation and lesion detectability.
View Article and Find Full Text PDFBackground: Bone SPECT/CT has been shown to offer superior sensitivity and specificity compared to conventional whole-body planar scanning. Furthermore, bone SPECT/CT allows quantitative imaging, which is challenging with planar methods. In order to gain better quantitative accuracy, Bayesian reconstruction algorithms, including both image derived and anatomically guided priors, have been utilized in reconstruction in PET/CT scanning, but they have not been widely used in SPECT/CT studies.
View Article and Find Full Text PDFObjective: Quantitative I and I single-photon emission computed tomography (SPECT) is hampered by down-scatter from the high-energy peaks. This paper presents a down-scatter compensation method, where down-scatter generated in the patient and gamma camera collimator and detector is modelled using Monte Carlo simulation in the ordered subsets expectation maximization SPECT reconstruction algorithm.
Materials And Methods: The new down-scatter compensation method was compared with conventional triple energy window (TEW) scatter compensation and Gaussian convolution-based forced detection Monte Carlo methods.
Objective: The aim of this work is to validate a software package called Hermes Internal Radiation Dosimetry (HIRD) for internal dose assessment tailored for clinical practice. The software includes all the necessary steps to perform voxel-level absorbed dose calculations including quantitative reconstruction, image coregistration and volume of interest tools.
Methods: The basics of voxel-level dosimetry methods and implementations to HIRD software are reviewed.
Reliable and reproducible quantification is essential in many clinical situations. Previously, single-photon emission computed tomography (SPECT) has not been considered a quantitative imaging modality, but recent advances in reconstruction algorithm development have made SPECT quantitative. In this study, we investigate the reproducibility of SPECT quantification with phantoms in a multicenter setting using novel third-party reconstruction software.
View Article and Find Full Text PDFObjective: Cardiac motion is a challenging cause of image artefacts in myocardial perfusion SPECT. A wide range of motion correction methods have been developed over the years, and so far automatic algorithms based on the reconstruction--reprojection principle have proved to be the most effective. However, these methods have not been fully optimised in terms of their free parameters and implementational details.
View Article and Find Full Text PDFObjective: Monte Carlo (MC)-simulations have proved to be a valuable tool in studying SPECT-reconstruction algorithms. Despite their popularity, the use of Monte Carlo-simulations is still often limited by their large computation demand. This is especially true in situations where full collimator and detector modelling with septal penetration, scatter and X-ray fluorescence needs to be included.
View Article and Find Full Text PDFPurpose: To test the potential of a new reconstruction algorithm with Monte Carlo-based scatter correction in half-time myocardial perfusion single-photon emission computed tomography (SPECT).
Materials And Methods: The mathematical four-dimensional NURBS-based Cardiac-Torso phantom and the SIMIND Monte Carlo simulation package were used to simulate full-time and half-time SPECT projection data. The data were reconstructed using the standard ordered subset expectation maximization-based algorithm and the new Monte Carlo-based algorithm.