Faculty, Staff and Student Publications

Language

English

Publication Date

9-30-2025

Journal

EJNMMI Physics

DOI

10.1186/s40658-025-00797-6

PMID

41026324

PMCID

PMC12484437

PubMedCentral® Posted Date

9-30-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Purpose: Misregistration between CT and PET can result in mis-localization and inaccurate quantification of the tracer uptake in PET. Data-driven gated (DDG) CT can correct registration and quantification but requires a radiation dose of 1.3 mSv and 1 min of acquisition time. AI registration (AIR) does not require an additional CT and has been validated to improve registration and reduce the 'banana' misregistration artifacts around the diaphragm. We aimed to compare a validated AIR and DDG CT in registration and quantification of avid thoracic lesions misregistered in DDG PET scans.

Methods: Thirty PET/CT patient data (23 with 18F-FDG, 4 with 68Ga-Dotatate, and 3 with 18F-PSMA piflufolastat) with at least one misregistered avid lesion in the thorax were recruited. Patient studies were conducted using DDG CT to correct misregistration with DDG PET data of the phases 30 to 80% on GE Discovery MI PET/CT scanners. Non-attenuation correction DDG PET and misregistered CT were input to AIR and the AIR-corrected CT data were output to register and quantify the DDG PET data. Registration and quantification of lesion SUVmax and signal-to-background ratio (SBR) of the lesion SUVmax to the 2-cm background mean SUV were compared for each of the 51 avid lesions.

Results: DDG CT outperformed AIR in misregistration correction and quantification of avid thoracic lesions (1.16 ± 0.45 cm). Most lesions (46/51, 90%) showed improved registration from DDG CT relative to AIR, with 10% (5/51) being similar between AIR and DDG CT. The lesions in the baseline CT were an average of 2.06 ± 1.0 cm from their corresponding lesions in the DDG CT, while those in the AIR CT were an average of 0.97 ± 0.54 cm away. AIR significantly improved lesion registration compared to the baseline CT (P < 0.0001). SUVmax increased by 18.1 ± 15.3% with AIR, but a statistically significantly larger increase of 34.4 ± 25.4% was observed with DDG CT (P < 0.0001). A statistically significant increase in SBR was also observed, rising from 10.5 ± 12.1% of AIR to 21.1 ± 20.5% of DDG CT (P < 0.0001). Many registration improvements by AIR were still left with misregistration. AIR could mis-localize a lymph node to the lung parenchyma or the ribs, and could also mis-localize a lung nodule to the left atrium. AIR could also distort the rib cage and the circular shape of the aorta cross section.

Conclusions: DDG CT outperformed AIR in both localization and quantification of the thoracic avid lesions. AIR improved registration of the misregistered PET/CT. Registered lymph nodes could be falsely misregistered by AIR. AIR-induced distortion of the rib cage can also negatively impact image quality. Further research on AIR's accuracy in modeling true patient respiratory motion without introducing new misregistration or anatomical distortion is warranted.

Keywords

Positron emission tomography computed tomography, Attenuation correction, Neural networks, Deeplearning, Artificial intelligence, Data driven gating, Misregistration correction, Respiratory motion

Published Open-Access

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