Statistical Positron Emission Tomography Image Reconstruction : System Geometric Models and Iterative Algorithms
نویسندگان
چکیده
Quantitative positron emission tomography (PET) using statistical techniques requires: d̂flk..»;„-.IK:. system geometric model that represents the probability of detecting an emission from each impgf. , pixel at each detector-pair, and (b) an iterative algorithm that reconstructs image as quaatitativfi , tt. measurements of radiotracer distribution in vivo. Conventional implementations of iterative[recm* ^ 't struction use system geometric models based either on linear interpolation or on computing jjuiAtof,i.^:): ume of intersection of detection tubes with each voxel, but these simple models ignore many hnpor^.,. .1^ tant physical system factors, like depth dependent geometric sensitivity and spatially variant Ofte£tefH; -i, pair resolution. In this paper, we evaluate a more accurate system geometric model that mqhjdgfir,;^v these physical factors. In addition, implementation variation among different, iterative /pfg<»itfunji^l/t^~ another factor that limits the performance. Here, we compare performance of filtered ba&prqjeA$QHt Y;"a (FBP) with the ordered subsets expectation maximization (OSEM) algorithm and a maximum agQfr,.R teriori (MAP) method using a Gibbt prior with convex potential functions. Using the contrtfit,■&£*$:;{; ;-.,. ery coefficient (CRC) as a performance measurement, we conducted various phantom experiments^ to: -<^H investigate how the choices of algorithm and system matrix affect reconstruction accuracy, .pie r*~ ,: . y,5 suits of these studies show that all of the iterative methods tested produce superior CRCs fhanR%P <#•,,„. «> matched background variance. And the combination of the accurate system geometric, moa]fd.,q^:-;^J-^ MAP reconstruction algorithm outperforms the other statistical methods. ./).-:,"•:■ BiomcdEngApplBasisComm,2002(April): 14:47-54. .•■ K >.^*<
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تاریخ انتشار 2008