Abstract
PurposeThis study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images.MethodsFifty-two oncological patients undergoing an 18F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-noise ratio (SNR) and target-to-background ratio (TBR) values were compared between the methods. Subgroup analyses compared TBRs after categorizing lesions based on parameters like lesion diameter, uptake or patient habitus.ResultsThe DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver.ConclusionsThe developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25–50% under various conditions.
Highlights
Positron emission tomography and computed tomography (PET/CT) is a non-invasive imaging modality widely used in oncology, providing both anatomical and functional information
More than 96.8% of the lesions were successfully identified in deep learning (DL) images
The DL method significantly improved the liver signal-to-noise ratio (SNR) for count reduction down to 33.3% (p < 0.001)
Summary
Positron emission tomography and computed tomography (PET/CT) is a non-invasive imaging modality widely used in oncology, providing both anatomical and functional information. To provide diagnostic PET images with sufficient image quality, a certain activity administered to the patient combined with an adequate acquisition time is recommended in the guideline for oncological 18F-FDG PET imaging [1]. The reduction on the administered activity is expected in the clinical management on the patients including those who need multiple PET examinations to monitor the therapy response [7,8,9]. With the advent of advanced PET/CT scanners and image reconstruction algorithms such as time of flight (TOF), activity reduction is possible in pre-clinical studies and clinical practice [10,11,12]. The reduced injected activity/acquisition time always causes increased noise, lower signal-to-noise ratio (SNR), and potentially unnecessary artefacts in PET images
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