LesionStudio: A Patient-Informed Platform for Task-Specific Optimization of Emission Tomography Reconstructions
Purpose: For task-specific PET and SPECT reconstruction optimization, we developed LesionStudio, an open-source platform incorporating patient background heterogeneity, realistic lesion morphology and texture, and lesion-background interactions beyond conventional phantoms. Methods: LesionStudio, a Python framework built on PyTomography, inserts lesions into PET/SPECT data using modality-specific forward projection and stochastic background ablation. Clinical realism was evaluated by physician review of 30 inserted lesions in eight DLBCL patients from a 1,579-lesion FDG lymphoma library. Quantification and detectability were assessed in FDG PET using spherical and patient-derived lesions varying in size, LBR, and site, reconstructed with OSEM and BSREM. Results: Inserted lesions were visually indistinguishable from true disease (AUC = 0.49). OSEM improved the patient-derived quantification limit from ~3.7 mL at iteration 4 to ~2.0 mL at iteration 10, but with increased noise and edge artifacts. Weakly regularized BSREM (beta = 5) achieved a similar ~2.0 mL limit with stability, whereas stronger regularization increased under-recovery. Detectability was contrast-dependent: no lesions were detected at LBR 2:1, whereas detectable volume ranged from 0.70 to 3.63 mL at LBR 4:1 and decreased to 0.70 mL at LBR 6:1-8:1 for both OSEM and weakly regularized BSREM. Conclusion: Reconstruction settings can shift SUV metrics and detectability enough to affect interpretation, so no single protocol suits every task. OSEM required early stopping for detection and feature stability but more iterations for mean-SUV quantification, whereas weakly regularized BSREM matched both at one setting. LesionStudio (https://github.com/qurit/LesionStudio), with our public library of realistic lesions, provides a reproducible framework for task-specific optimization across radiopharmaceuticals and scanners.