Abstract
PurposeA critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-dose scans on different scanners and tracers.MethodsBrain [18F]FDG PET imaging of 237 patients scanned with one scanner was used for the development of AI technology. The developed algorithm was then tested on [18F]FDG PET images of 45 patients scanned with three different scanners, [18F]FET PET images of 18 patients scanned with two different scanners, as well as [18F]Florbetapir images of 10 patients. A conditional generative adversarial network (GAN) was customized for cross-scanner and cross-tracer optimization. Three nuclear medicine physicians independently assessed the utility of the results in a clinical setting.ResultsThe improvement achieved by AI recovery significantly correlated with the baseline image quality indicated by structural similarity index measurement (SSIM) (r = −0.71, p < 0.05) and normalized dose acquisition (r = −0.60, p < 0.05). Our cross-scanner and cross-tracer AI methodology showed utility based on both physical and clinical image assessment (p < 0.05).ConclusionThe deep learning development for extensible application on unknown scanners and tracers may improve the trustworthiness and clinical acceptability of AI-based dose reduction.
Highlights
Positron emission tomography (PET) is one of the main imaging modalities in clinical routine procedures of oncology [1, 2], neurology [3], and cardiology [4]
The customized c-generative adversarial network (GAN) trained on [18F]FDG images from DMI was tested on [18F]FDG images on three different scanners
Additional results of peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) on [18F] FDG imaging on the three different scanners showed the same tendency as the normalized root mean squared error (NRMSE) results (Supplementary Figure S2)
Summary
Positron emission tomography (PET) is one of the main imaging modalities in clinical routine procedures of oncology [1, 2], neurology [3], and cardiology [4]. One of the critical bottlenecks for the wide application of PET is the ionizing radiation dose [5]. The imaging quality of PET is directly influenced by the activity of the injected tracer and the consequent radiation dose. A reduction of the radiation dose in PET protocols leads to the degradation of imaging quality. The technical advancement of PET scanners in recent decades has steadily reduced the radiation burden while preserving the imaging quality [7]. Breakthroughs have been made in signal measurement and imaging generation,
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