Image optimization for low-dose 18F-FDG breast PET/MRI using deep learning: a pilot study

Positron emission tomography with magnetic resonance imaging (PET/MRI) provides noninvasive molecular characterization of breast cancer and has the potential to improve diagnostic accuracy, staging, treatment response assessment, and guide personalized care. Reducing the radiation dose from 2-deoxy-2-[ 18 F]fluoro-D-glucose ( 18 F-FDG) to a level similar to digital mammography while maintaining image quality may facilitate clinical utilization. This study was performed to evaluate diagnostic image quality and lesion conspicuity of low-dose 18 F-FDG breast PET/MRI using denoising and to evaluate the effect of denoising on radiotracer uptake semi-quantification. This pilot study was a secondary analysis of a single-institution prospective study of 18 F-FDG breast PET/MRI for 23 women with primary invasive breast cancer. Random undersampling of the PET data from a 30-min simultaneous prone 18 F-FDG breast PET/MRI was used to produce simulated low-dose (SLD) images approximating 90% reduced injected activity (37 MBq). A deep learning-based, denoising (DN) algorithm was then applied. SLD and DN datasets were evaluated by three readers for image quality and lesion conspicuity and were compared to full-dose images to assess clinical acceptability. Image noise was quantified by liver SUV standard deviation . 18 F-FDG uptake (SUV max and SUV mean ) was measured in tumor and normal breast tissue and compared between datasets. Wilcoxon signed rank test, repeated measures analysis of variance, and Bland–Altman analyses were performed. Compared to SLD, DN datasets scored better for artifacts, perceived signal-to-noise ratio, image sharpness/resolution, and overall image quality ( p < 0.001). Compared to full-dose images, DN scored better than SLD for diagnostic image quality ( p < 0.001) and lesion conspicuity ( p = 0.002). For diagnostic image quality, DN was equivalent or slightly inferior to full-dose images in 89.9% (62/69) of reads. Readers preferred DN (76.8%; 53/69) to SLD (4.3%; 3/69), and both were preferred equally in 10.1% (7/69) of reads. Compared to SLD, quantitative image noise was less in DN images ( p < 0.0001). Agreement was excellent between all datasets for tumor SUV max and SUV mean . Acceptable image quality may be achieved with low-dose 18 F-FDG breast PET/MRI using denoising, which warrants further prospective validation.