Dataset Information

Artificial intelligence guided enhancement of digital PET: scans as fast as CT?

ABSTRACT:

Purpose

Both digital positron emission tomography (PET) detector technologies and artificial intelligence based image post-reconstruction methods allow to reduce the PET acquisition time while maintaining diagnostic quality. The aim of this study was to acquire ultra-low-count fluorodeoxyglucose (FDG) ExtremePET images on a digital PET/computed tomography (CT) scanner at an acquisition time comparable to a CT scan and to generate synthetic full-dose PET images using an artificial neural network.

Methods

This is a prospective, single-arm, single-center phase I/II imaging study. A total of 587 patients were included. For each patient, a standard and an ultra-low-count FDG PET/CT scan (whole-body acquisition time about 30 s) were acquired. A modified pix2pixHD deep-learning network was trained employing 387 data sets as training and 200 as test cohort. Three models (PET-only and PET/CT with or without group convolution) were compared. Detectability and quantification were evaluated.

Results

The PET/CT input model with group convolution performed best regarding lesion signal recovery and was selected for detailed evaluation. Synthetic PET images were of high visual image quality; mean absolute lesion SUV_max (maximum standardized uptake value) difference was 1.5. Patient-based sensitivity and specificity for lesion detection were 79% and 100%, respectively. Not-detected lesions were of lower tracer uptake and lesion volume. In a matched-pair comparison, patient-based (lesion-based) detection rate was 89% (78%) for PERCIST (PET response criteria in solid tumors)-measurable and 36% (22%) for non PERCIST-measurable lesions.

Conclusion

Lesion detectability and lesion quantification were promising in the context of extremely fast acquisition times. Possible application scenarios might include re-staging of late-stage cancer patients, in whom assessment of total tumor burden can be of higher relevance than detailed evaluation of small and low-uptake lesions.

SUBMITTER: Hosch R

PROVIDER: S-EPMC9606065 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Artificial intelligence guided enhancement of digital PET: scans as fast as CT?

Hosch René R Weber Manuel M Sraieb Miriam M Flaschel Nils N Haubold Johannes J Kim Moon-Sung MS Umutlu Lale L Kleesiek Jens J Herrmann Ken K Nensa Felix F Rischpler Christoph C Koitka Sven S Seifert Robert R Kersting David D

European journal of nuclear medicine and molecular imaging 20220729 13

<h4>Purpose</h4>Both digital positron emission tomography (PET) detector technologies and artificial intelligence based image post-reconstruction methods allow to reduce the PET acquisition time while maintaining diagnostic quality. The aim of this study was to acquire ultra-low-count fluorodeoxyglucose (FDG) ExtremePET images on a digital PET/computed tomography (CT) scanner at an acquisition time comparable to a CT scan and to generate synthetic full-dose PET images using an artificial neural ...[more]

PMID: 35904589

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Dataset Information

Artificial intelligence guided enhancement of digital PET: scans as fast as CT?

Purpose

Methods

Results

Conclusion

Publications

Artificial intelligence guided enhancement of digital PET: scans as fast as CT?

Similar Datasets

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