Collimator optimization in SPECT based on a joint detection and localization task

Abstract

In SPECT the collimator is a crucial element of the imaging chain and controls the noiseresolution tradeoff of the collected data. Optimizing collimator design has been a long studied topic, with many different criteria used to evaluate the design. One class of criteria is taskedbased, in which the collimator is designed to optimize detection of a signal (lesion). Here we consider a new, more realistic, task, the joint detection and localization of a signal. Furthermore, we use an ideal observer -one that attains a theoretically maximum task performance -to optimize collimator design. The ideal observer operates on the sinogram data. We consider a family of parallel-hole low-energy collimators of varying resolution and efficiency and optimize over this set. We observe that for a 2-D object characterized by noise due to background variability and a sinogram with photon noise, the optimal collimator tends to be of lower resolution and higher efficiency than equivalent commercial collimators. Furthermore, this optimal design is insensitive to the tolerance radius within which the signal must be localized. So for this scenario, the addition of a localization task does not change the optimal collimator. Optimal collimator resolution gets worse as signal size grows, and improves as the level of background variability noise increases. These latter two trends are also observed when the detection task is signal-known-exactly and background variable.

Optimization of energy window and evaluation of scatter compensation methods in myocardial perfusion SPECT using the ideal observer with and without model mismatch and an anthropomorphic model observer

“…Thus, it is reasonable and has been proposed by other authors to optimize acquisition and instrumentation in terms of IO performance. 14-16 This is to ensure that there is maximum information in the data about the task. In this concept, the role of reconstruction is to put that information in a form that a human observer can best interpret.…”

“…Its application has been limited to cases where the background and signal models could be expressed analytically, which often do not capture the variability observed in clinical studies. 14-16 To overcome this limitation, methods based on Markov Chain Monte Carlo (MCMC) techniques have been developed and applied to estimate the IO test statistic, i.e., the likelihood ratio (LR) for realistic and general backgrounds and signal models for binary defect detection tasks. 17-19 …”

Optimization of energy window and evaluation of scatter compensation methods in MPS using the ideal observer with model mismatch

“…The IO is an observer that makes optimal use of all the information in the image data about the task. Thus it is reasonable, and has been proposed by other authors, to optimize acquisition and instrumentation in terms of IO performance [14][15][16]. This is to ensure that there is maximum information in the data about the task.…”

“…Its application has been limited to cases where the background and signal models could be expressed analytically, which often do not capture the variability observed in clinical studies [14][15][16]. To overcome this limitation, methods based on Markov Chain Monte Carlo (MCMC) techniques have been developed and applied to estimate the IO test statistic, i.e., the likelihood ratio (LR), for realistic and general background and signal models for binary defect detection tasks [17][18][19].…”

Design studies of a CZT-based detector combined with a pixel-geometry-matching collimator for SPECT imaging

“…To simplify the calculation, we only took the intrinsic and collimator resolution into consideration. Therefore, the theoretical spatial resolution can be calculated as [4]: $$R_{\mathit{spa}}=\sqrt{R_{\mathit{int}}^2+R_{\mathit{col}}^2}$$…”