Combining Clinical LAFOV PET/CT with a Digital Twin Providing Motion-Free Ground Truth Reveals Quantitative Trade-offs in Respiratory Motion Correction

<jats:p> Purpose: Respiratory motion remains a major source of quantitative bias in PET and becomes increasingly relevant for high-sensitivity long axial field-of-view (LAFOV) PET/CT. Although numerous respiratory motion correction (MoCo) methods have been proposed, their quantitative accuracy cannot be established clinically because a patient-specific motion-free reference is fundamentally unavailable in vivo. This study combined clinical PET imaging with a digital twin, a realistic representation of both the PET/CT system and the patient, to objectively validate respiratory MoCo against a corresponding motion-free reference. Methods: Twenty patients (10 [ <jats:sup>18</jats:sup> F]FDG with predominantly pulmonary lesions and 10 [ <jats:sup>18</jats:sup> F]SiFAlin-TATE with predominantly hepatic lesions; total 135 lesions) were analyzed. The digital twin combined a validated LAFOV PET/CT simulation model with an anatomically realistic phantom containing 14 lung and liver lesions, two patient-derived respiratory patterns, and respiratory motion amplitudes of 2 and 3 cm, generating patient-like datasets with corresponding motion-free references. Data-driven and image-based MoCo were evaluated using lesion morphology, SUV <jats:sub>mean</jats:sub> , SUV <jats:sub>max</jats:sub> , and metabolic tumor volume (MTV). Results: In patients, data-driven MoCo produced larger SUV <jats:sub>mean</jats:sub> increases than image-based MoCo for liver (48.1±18.9% vs. 17.0 ± 12.0%; p&lt;0.01), lower-lung (32.5±21.2% vs. 16.3±15.6%, p=0.06), and upper-lung lesions (28.4±32.0% vs. 10.4 ± 17.2%; p&lt;0.01), with similar findings for SUV <jats:sub>max</jats:sub> and larger MTV reductions. Simulation revealed marked motion-induced SUV <jats:sub>mean</jats:sub> underestimation before correction, particularly in liver (-31.2±6.8%) and lower lung (-15.5±13.9%). Relative to the motion-free reference, data-driven MoCo most accurately recovered hepatic uptake (4.3±11.7% vs. -10.0 ± 9.2%; p=0.01) but overestimated pulmonary uptake (lower lung: 19.8±16.3% vs. -1.6 ± 10.2%; p=0.02). SUV <jats:sub>max</jats:sub> showed the same regional behavior, whereas image-based MoCo yielded MTV estimates closer to the reference. Quantitative recovery was largely independent of respiratory pattern, while larger motion amplitudes mainly affected image-based MoCo. Conclusion: Combining clinical PET with a realistic digital twin and corresponding motion-free ground truth enabled objective validation of respiratory MoCo beyond conventional clinical evaluation. Larger correction-induced quantitative changes should not be equated with greater quantitative accuracy. Instead, MoCo performance was region- and metric-dependent, highlighting the value of ground-truth-based validation for developing and benchmarking respiratory motion correction and quantitative PET on LAFOV PET/CT systems. </jats:p>