Deep learning-based organ-wise dosimetry of Cu-DOTA-rituximab through only one scanning.
Journal:
Scientific reports
PMID:
39955298
Abstract
This study aimed to generate a delayed Cu-dotatate (DOTA)-rituximab positron emission tomography (PET) image from its early-scanned image by deep learning to mitigate the inconvenience and cost of estimating absorbed radiopharmaceutical doses. We acquired PET images from six patients with malignancies at 1, 24, and 48 h post-injection (p. i.) with 8 mCi Cu-DOTA-rituximab to fit a time-activity curve for dosimetry. We used a paired image-to-image translation (I2I) model based on a generative adversarial network to generate delayed images from early PET images. The image similarity function between the generated image and its ground truth was determined by comparing L1 and perceptual losses. We also applied organ-wise dosimetry to acquired and generated images using OLINDA/EXM. The quality of the generated images was good, even of tumors, when using the L1 loss function as an additional loss to the adversarial loss function. The organ-wise cumulative uptake and corresponding equivalent dose were estimated. Although the absorbed dose in some organs was accurately measured, predictions for organs associated with body clearance were relatively inaccurate. These results suggested that paired I2I can be used to alleviate burdensome dosimetry for radioimmunoconjugates.