Work

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

Year: 2026

Type: article

Abstract: Objective. Ventilation–perfusion ( V / Q ) planar scintigraphy remains widely used for diagnosing pulmonary embolism, 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 model for vascular defect detection via free-response receiver operating characteristic 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.

Source: Physics in Medicine and Biology

DOI: 10.1088/1361-6560/ae9d10

Authors: Amir Jabbarpour, Ghassel Siraj, Sanaz Kaviani, Jochen Lang, Eric Moulton, Ran Klein

Institutions: Carleton University, University of Ottawa, Ottawa Hospital

Language: English

Topic: Medical Imaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,325