SPECT Imaging With Resolution Recovery

Abstract

Single-photon emission computed tomography (SPECT) is a method of choice for imaging spatial distributions of radioisotopes. Applications of this method are found in medicine, biomedical research and nuclear industry. This paper deals with improving spatial resolution in SPECT by applying correction for the point-spread function (PSF) in the reconstruction algorithm and optimizing the collimator. Several approaches are considered: the use of a depth-dependent PSF model for a parallel-beam collimator derived from experimental data, the extension of this model to a fan-beam collimator, a triangular approximation of the PSF for reconstruction acceleration, and a method for optimal fan-beam collimator design. An unmatched projector/backprojector ordered subsets expectation maximization (OSEM) algorithm is used for image reconstruction. Experimental results with simulated and physical phantom data of a micro-SPECT system show a significant improvement of spatial resolution with the proposed methods.Index Terms-Image quality, nuclear medicine, single photon emission computed tomography (SPECT).

Influence of void on image quality of industrial SPECT

“…where N is the number of pixels and M is the number of detectors. The PSF h is the probabilities that photon emitted from certain location are registered by a detector [12]. When calculating the PSFs, the material in the object was simulated as water (without void) for both the single-phase condition and the liquid-air phase condition.…”

Effects of ray profile modeling on resolution recovery in clinical CT

“…9 RM has its origin in nuclear medicine, where it has been proven to effectively recover resolution. [10][11][12] Publications using RM in the context of iterative reconstruction in transmission tomography exist, 1,5,6,[13][14][15] but none of them investigates RM separately. They all combine regularization or other a priori knowledge.…”

Shape and appearance priors for level set‐based left ventricle segmentation

“…In addition, the image data very often contains hot structures in the proximity of the LV, such as the liver, considered as perfusion artefacts that impose similar difficulties for correct LV segmentation. Furthermore, although resolution recovery techniques, such as [2], can be applied to increase SPECT spatial resolution, SPECT images have far lower resolution, as well as exhibit fewer anatomical landmarks (since the images suffer degradation because of various types of attenuation), in comparison with other modalities, such as Computed tomography (CT) and magnetic resonance imaging (MRI). Methods that are purely data-driven may only work well on SPECT images that do contain those image artefacts and ambiguities caused by perfusion at surrounding tissues, for example, paper [3] which uses a conventional edge based active contour method.…”