An Investigation of Lesion Detection Accuracy for Artificial Intelligence–Based Denoising of Low-Dose⁶⁴Cu-DOTATATE PET Imaging in Patients with Neuroendocrine Neoplasms

Abstract

Frequent somatostatin receptor PET, for example, ⁶⁴Cu-DOTATATE PET, is part of the diagnostic work-up of patients with neuroendocrine neoplasms (NENs), resulting in high accumulated radiation doses. Scan-related radiation exposure should be minimized in accordance with the as-low-as-reasonably achievable principle, for example, by reducing injected radiotracer activity. Previous investigations found that reducing ⁶⁴Cu-DOTATATE activity to below 50 MBq results in inadequate image quality and lesion detection. We therefore investigated whether image quality and lesion detection of less than 50 MBq of ⁶⁴Cu-DOTATATE PET could be restored using artificial intelligence (AI). <b>Methods:</b> We implemented a parameter-transferred Wasserstein generative adversarial network for patients with NENs on simulated low-dose ⁶⁴Cu-DOTATATE PET images corresponding to 25% (PET_25%), or about 48 MBq, of the injected activity of the reference full dose (PET_100%), or about 191 MBq, to generate denoised PET images (PET_AI). We included 38 patients in the training sets for network optimization. We analyzed PET intensity correlation, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean-square error (MSE) of PET_AI/PET_100% versus PET_25%/PET_100%. Two readers assessed Likert scale–defined image quality (1, very poor; 2, poor; 3, moderate; 4, good; 5, excellent) and identified lesion-suspicious foci on PET_AI and PET_100% in a subset of the patients with no more than 20 lesions per organ (<i>n</i> = 33) to allow comparison of all foci on a 1:1 basis. Detected foci were scored (C₁, definite lesion; C₀, lesion-suspicious focus) and matched with PET_100% as the reference. True-positive (TP), false-positive (FP), and false-negative (FN) lesions were assessed. <b>Results:</b> For PET_AI/PET_100% versus PET_25%/PET_100%, PET intensity correlation had a goodness-of-fit value of 0.94 versus 0.81, PSNR was 58.1 versus 53.0, SSIM was 0.908 versus 0.899, and MSE was 2.6 versus 4.7. Likert scale–defined image quality was rated good or excellent in 33 of 33 and 32 of 33 patients on PET_100% and PET_AI, respectively_. Total number of detected lesions was 118 on PET_100% and 115 on PET_AI. Only 78 PET_AI lesions were TP, 40 were FN, and 37 were FP, yielding detection sensitivity (TP/(TP+FN)) and a false discovery rate (FP/(TP+FP)) of 66% (78/118) and 32% (37/115), respectively. In 62% (23/37) of cases, the FP lesion was scored C₁, suggesting a definite lesion. <b>Conclusion:</b> PET_AI improved visual similarity with PET_100% compared with PET_25%, and PET_AI and PET_100% had similar Likert scale–defined image quality. However, lesion detection analysis performed by physicians showed high proportions of FP and FN lesions on PET_AI, highlighting the need for clinical validation of AI algorithms.

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