Joint optimization of collimator and reconstruction parameters in SPECT imaging for lesion quantification

Abstract

Obtaining the best possible task performance using reconstructed SPECT images requires optimization of both the collimator and reconstruction parameters. The goal of this study is to determine how to perform this optimization, namely whether the collimator parameters can be optimized solely from projection data, or whether reconstruction parameters should also be considered. In order to answer this question, and to determine the optimal collimation, a digital phantom representing a human torso with 16-mm-diameter hot lesions (activity ratio 8:1) was generated and used to simulate clinical SPECT studies with parallel-hole collimation. Two approaches to optimizing the SPECT system were then compared in a lesion quantification task: sequential-optimization, where collimation was optimized on projection data using the Cramer-Rao bound, and joint-optimization, which simultaneously optimized collimator and reconstruction parameters. For every condition, quantification performance in reconstructed images was evaluated using the root-mean-squared-error of 400 estimates of lesion activity. Compared to the joint-optimization approach, the sequential-optimization approach favoured a poorer resolution collimator, which, under some conditions, resulted in sub-optimal estimation performance. This implies that inclusion of the reconstruction parameters in the optimization procedure is important in obtaining the best possible task performance; in this study, this was achieved with a collimator resolution similar to that of a general-purpose (LEGP) collimator. This collimator was found to outperform the more commonly used high-resolution (LEHR) collimator, in agreement with other task-based studies, using both quantification and detection tasks.

Collimator optimization in myocardial perfusion SPECT using the ideal observer and realistic background variability for lesion detection and joint detection and localization tasks

“…We note that a given reconstruction method may or may not make optimal use of the sinogram information, and thus affect observer performance and the choice of the optimal collimator. It has been advocated that joint (simultaneous) optimization of collimator and reconstruction methods yields better detection/estimation task performance than the sequential approach, in which the collimator is optimized using the projection data, for various tasks such as defect detection, and estimation tasks (Zhou et al 2009, McQuaid et al 2011). In the cited studies, it was reported that the optimal collimators had higher resolution than those identified when optimizing using only the raw projection data (i.e.…”

“…There are, however, important limitations to this approach regarding how to optimize the resolution–sensitivity trade-off for a given task, such as estimation, i.e. quantifying one or more parameters of interest using the given image data (Lau et al 2001, Inoue et al 2004, Moore et al 2005, McQuaid et al 2011), or classification, i.e. deciding to which class an image belongs (Tsui 1978, Tsui et al 1983, Myers et al 1990, Moore et al 1995, 2005, Narayanan et al 2002, Zeng and Gullberg 2002, Gross et al 2003, Zhou and Gindi 2009).…”

Review of SPECT collimator selection, optimization, and fabrication for clinical and preclinical imaging

“…Similar results were obtained by Kamphuis et al, 140 who showed that a better contrast-to-noise ratio could be achieved for 2-cm cold lesions in a uniform background when using a GP collimator, rather than a medium-, high-, or ultra-highresolution collimator. Likewise, McQuaid et al 141 showed that better quantification of 16-mm hot lesions distributed throughout a human torso sized digital phantom could be obtained by using a GP collimator than a HR collimator. Interestingly, Zhou and Gindi found similar results for lesion detectability in an ideal observer study on sinogram data.…”

Scanning linear estimation: improvements over region of interest (ROI) methods

“…Community-adopted protocols which standardize associated imaging study variables are also widely discussed [16–21]. When study variables are highly controlled, SPECT/CT imaging studies have reported that error of activity estimates calculated from ROI statistics is in the neighborhood of 10% and sometimes lower [22–25]. See [26–29] for other examples in medical imaging of estimation tasks performed directly on projection data.…”