Sub-minute acquisition with deep learning-based image filter in the diagnosis of colorectal cancers using total-body (18)F-FDG PET/CT

BACKGROUND: This study aimed to retrospectively evaluate the feasibility of total-body (18)F-FDG PET/CT ultrafast acquisition combined with a deep learning (DL) image filter in the diagnosis of colorectal cancers (CRCs). METHODS: The clinical and preoperative imaging data of patients with CRCs were collected. All patients underwent a 300-s list-mode total-body (18)F-FDG PET/CT scan. The dataset was divided into groups with acquisition durations of 10, 20, 30, 60, and 120 s. PET images were reconstructed using ordered subset expectation maximisation, and post-processing filters, including a Gaussian smoothing filter with 3 mm full width at half maximum (3 mm FWHM) and a DL image filter. The effects of the Gaussian and DL image filters on image quality, detection rate, and uptake value of primary and liver metastases of CRCs at different acquisition durations were compared using a 5-point Likert scale and semi-quantitative analysis, with the 300-s image with a Gaussian filter as the standard. RESULTS: All 34 recruited patients with CRCs had single colorectal lesions, and the diagnosis was verified pathologically. Of the total patients, 11 had liver metastases, and 113 liver metastases were detected. The 10-s dataset could not be evaluated due to high noise, regardless of whether it was filtered by Gaussian or DL image filters. The signal-to-noise ratio (SNR) of the liver and mediastinal blood pool in the images acquired for 10, 20, 30, and 60 s with a Gaussian filter was lower than that of the 300-s images (P < 0.01). The DL filter significantly improved the SNR and visual image quality score compared to the Gaussian filter (P < 0.01). There was no statistical difference in the SNR of the liver and mediastinal blood pool, SUVmax and TBR of CRCs and liver metastases, and the number of detectable liver metastases between the 20- and 30-s DL image filter and 300-s images with the Gaussian filter (P > 0.05). CONCLUSIONS: The DL filter can significantly improve the image quality of total-body (18)F-FDG PET/CT ultrafast acquisition. Deep learning-based image filtering methods can significantly reduce the noise of ultrafast acquisition, making them suitable for clinical diagnosis possible. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13550-023-01015-z.