Proof-of-Concept Study of Task-Based Adaptive SPECT Imaging via System Sampling Strategies on a 12-Head Robotic SPECT Platform.

In this work, we explored a computational framework for task-based adaptive SPECT imaging based on a robotic platform. The framework has two key components. First, we utilized the modified uniform Cramér-Rao bound (MUCRB) to approximate the imaging variance attainable with a given system configuration characterized by the imaging time distribution (ITD) over a discretized system configuration space. Second, we developed a computational approach to enable direct optimization of the ITD (and the associated system configuration) to achieve a minimum imaging variance. To enable efficient system optimization, a convex surrogate of the MUCRB-based variance metric was derived, and a non-uniform object-space pixelation (NUOP) strategy was introduced to reduce computational load, while preserving resolution within regions of interest (ROIs). This framework was evaluated using simulation studies with a 12-camera-head robotic SPECT system. We used several phantoms and a series of performance measures to demonstrate the effectiveness of the adaptive imaging strategy. For the brain phantom, the adapted ITD reduced RMSE from 859.0 to 734.3. For the hot-rod phantom, RMSE was reduced from 1411.9/1316.5 to 1191.2/1138.0 across the two ROIs, while CRC increased from 0.092/0.351 to 0.181/0.604. For the cold-rod phantom, RMSE was reduced from 720.0/691.1 to 603.5/587.4 across the two ROIs relative to whole-view ITD, while CRC increased from 0.069/0.150 to 0.373/0.499. These results demonstrated that the proposed framework enables efficient, task-specific optimization of acquisition strategies and significantly improved ROI image quality.