An adaptive generation of a digital mask to improve activity distribution in SPECT images

In this study, we consider the clinical situation where only the boundaries of the investigated region of interest (ROI) are available and the remaining part of the studied object (background) is inhomogeneous. Additionally, no information regarding activity concentrations in either ROI or background is available. Under such circumstances, which are typical for clinical SPECT and PET oncology studies, accurate recovery of the activity distribution inside the ROI represents a challenging task. Especially, in mask-based partial volume effect (PVE) corrections (PVEC), the digital mask should adequately reflect the true activity distribution. In this respect, the direct implementation of the mask values from the conventionally reconstructed (and, obviously, degraded by PVE) image may affect the accuracy of the resulting activity distribution. In this paper, we present a modification of the mask-based approach where the mask values in the ROI are neither a priori known nor taken from the conventional image, but are determined from the projection data by a special ROI-based algorithm. Because case-specific acquisition parameters, attenuation map, and the projection dataset are employed there, the created mask appears to be adapted to the analyzed SPECT or PET study. Our data processing begins with the segmentation step dividing the scanned object into an ROI and a background. Then, the regional system matrices reflecting the contributions of (i) the ROI and (ii) the background to the projection dataset are computed. These matrices allow us to generate the system of equations from which the average activity concentrations in the ROI and background are derived. We incorporate the average ROI activity concentration into our mask by assigning it to each voxel inside this ROI. At the same time, the mask values for the inhomogeneous background are copied directly from the conventional image. Finally, the mask-based PVEC is applied to recover the activity distribution in the ROI. We validated our method using both a physical phantom experiment and analytical simulations, which in both cases contained 21 active and cold inserts. The performance of the proposed adaptive method was compared with the image-based PVEC where the mask values inside the ROI were calculated based on the conventional image. In terms of recovery of the total activity, the adaptive method outperformed the image-based PVEC for 19 (simulations) and for 20 (physical experiments) out of 21 considered containers. In terms of recovery of the activity distribution, results of the adaptive method were better than ones provided by imagebased PVEC for 16 (simulations) and for 13 (experiments) out of 17 considered containers.

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