Photon attenuation during SPECT imaging significantly degrades the diagnostic outcome and the quantitative accuracy of
final reconstructed images. It is well known that attenuation correction can be done by using iterative reconstruction methods
if we access to attenuation map. Two methods have been used to calculate the attenuation map: transmission-based and
transmissionless techniques. In this phantom study, we evaluated the importance of attenuation correction by quantitative
evaluation of errors associated with each method. For transmissionless approach, the attenuation map was estimated from the
emission data only. An EM algorithm with attenuation model was developed and used for attenuation correction during
image reconstruction. Finally, a comparison was done between reconstructed images using our OSEM code and analytical
FBP method before and after attenuation correction. The results of measurements showed that: our programs are capable to
reconstruct SPECT images and correct the attenuation effects. Moreover, to evaluate reconstructed image quality before and
after attenuation correction we applied a novel approach using Image Quality Index. Attenuation correction increases the
quality and quantitative accuracy in both methods. This increase is independent of activity in quantity factor and decreases
with activity in quality factor. In EM algorithm, it is necessary to use regularization to obtain true distribution of attenuation
coefficients.
Early staging of prostate cancer (PC) is a significant challenge, in part because of the small tumor sizes in- volved. Our long-term goal is to determine realistic diagnostic task performance benchmarks for standard PC imaging with single photon emission computed tomography (SPECT). This paper reports on a localization receiver operator characteristic (LROC) validation study comparing human and model observers. The study made use of a digital anthropomorphic phantom and one-cm tumors within the prostate and pelvic lymph nodes. Uptake values were consistent with data obtained from clinical In-111 ProstaScint scans. The SPECT simulation modeled a parallel-hole imaging geometry with medium-energy collimators. Nonuniform attenua- tion and distance-dependent detector response were accounted for both in the imaging and the ordered-subset expectation-maximization (OSEM) iterative reconstruction. The observer study made use of 2D slices extracted from reconstructed volumes. All observers were informed about the prostate and nodal locations in an image. Iteration number and the level of postreconstruction smoothing were study parameters. The results show that a visual-search (VS) model observer correlates better with the average detection performance of human observers than does a scanning channelized nonprewhitening (CNPW) model observer.
SPECT imaging using In-111 ProstaScint is an FDA-approved method for diagnosing prostate cancer metastases within the pelvis. However, conventional medium-energy parallel-hole (MEPAR) collimators produce poor image quality and we are investigating the use of multipinhole (MPH) imaging as an alternative. This paper presents a method for evaluating MPH designs that makes use of sampling-sensitive (SS) mathematical model observers for tumor detectionlocalization tasks. Key to our approach is the redefinition of a normal (or background) reference image that is used with scanning model observers. We used this approach to compare different MPH configurations for the task of small-tumor detection in the prostate and surrounding lymph nodes. Four configurations used 10, 20, 30, and 60 pinholes evenly spaced over a complete circular orbit. A fixed-count acquisition protocol was assumed. Spherical tumors were placed within a digital anthropomorphic phantom having a realistic Prostascint biodistribution. Imaging data sets were generated with an analytical projector and reconstructed volumes were obtained with the OSEM algorithm. The MPH configurations were compared in a localization ROC (LROC) study with 2D pelvic images and both human and model observers. Regular and SS versions of the scanning channelized nonprewhitening (CNPW) and visual-search (VS) model observers were applied. The SS models demonstrated the highest correlations with the average human-observer results
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