Artificial count enhancement in lung scintigraphy for acquisition acceleration and pseudo-planar generation

OBJECTIVE: Ventilation-perfusion (V/Q) planar scintigraphy remains widely used for diagnosing pulmonary embolism (PE), despite the reported superior diagnostic performance of V/Q SPECT. In part, SPECT adoption is limited by the differences in clinical interpretation. Pseudo-planar from SPECT data can facilitate transition to SPECT, but existing methods, such as summed angular projections or forward reprojection, fail to reliably reproduce true planar image characteristics. Approach: We propose a novel artificial intelligence-based count enhancement framework to generate high-fidelity pseudo-planar perfusion images (pQ) from low-count inputs. Two conditional generative adversarial networks (cGANs) were trained using (i) 10% Poisson-resampled planar perfusion images and (ii) matched single-projection SPECT views as inputs, with corresponding full-count planar perfusion images as ground truth. The models were trained and evaluated on 829 paired planar and SPECT studies from a multi-scanner clinical cohort. Performance was assessed using quantitative image similarity metrics (SSIM, PSNR, MSE) and a clinically meaningful task-based evaluation using a previously validated deep learning computer-assisted diagnostic (CAD) model for vascular defect detection via free-response receiver operating characteristic (FROC) analysis. Main results: The cGAN trained on resampled planar images achieved the highest image fidelity (test SSIM: 0.70 ± 0.05; PSNR: 22.3 ± 1.2 dB), significantly outperforming SPECT-trained models and the conventional summed angular method (p < 0.001). In downstream clinical evaluation, this model also yielded superior defect detection performance (sensitivity: 0.800 ± 0.035 at 1.0 false positives per projection), surpassing current clinical practice of the summed angular method. Significance: This study introduces the first AI-based framework for generating pseudo-planar lung scintigraphy from low-count data. The proposed method demonstrates improved image realism and clinically relevant defect preservation, supporting its potential for hybrid planar-SPECT workflows in V/Q imaging and for accelerating acquisition of planar images. .