Accurate MRI-free PET Quantification and Reliability Assessment via CT-to-MRI Synthesis and Conformal Prediction
Year: 2026
Type: preprint
Abstract: Standardized uptake value ratio (SUVR) quantification of amyloid PET commonly relies on a subject-specific T1-weighted MRI, yet a usable MRI is not always available. We therefore combined CT-to-MRI synthesis with conformal prediction for MRI-free PET quantification with reliability assessment. A slice-consistent Brownian Bridge Diffusion Model was strengthened with multi-dataset training and data-source conditioning to enrich the anatomical prior learned from MRI, and with expanded multi-slice CT context to exploit the through-plane continuity of subject-specific anatomy in CT. Synthetic MRI was used for FreeSurfer-based anatomical parcellation and PET quantification, while deep-ensemble uncertainty was calibrated with conformal prediction to estimate a plausible range for the corresponding real MRI-based SUVR. On $^{11}$C-PiB PET from OASIS-3, the proposed method achieved the highest synthesis and parcellation fidelity among the compared methods. Mean cortical SUVR from the proposed synthetic MRI agreed closely with the real MRI-based reference (MAE $0.016 \pm 0.015$, Pearson $r = 0.9987$) and yielded the same amyloid-positivity classification as the real MRI in all 87 test subjects at the prespecified cut-off. Conformal prediction achieved empirical coverage close to the prespecified levels, and at 90% target coverage, 85 of 87 subjects had prediction intervals that supported an unambiguous amyloid-positivity decision. Zero-shot evaluation on an independent external cohort showed that the proposed CT-to-MRI model could support PET quantification without retraining, although conformal calibration required target-cohort recalibration to recover the intended coverage. These findings support CT-to-MRI-based MRI-free PET quantification as a promising approach for providing accurate SUVR measurements and an explicit assessment of their reliability.
Source: arXiv (Cornell University)
Institutions: Seoul National University, Chosun University, Yonsei University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: U.S. Department of Energy, National Energy Research Scientific Computing Center, Korea Brain Research Institute, Seoul National University, Korea Health Industry Development Institute, Korea Basic Science Institute, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Office of Science, Advanced Research Projects Agency, Institute for Information and Communications Technology Promotion, National Supercomputing Center, Korea Institute of Science and Technology Information, Advanced Scientific Computing Research, Argonne National Laboratory, Oak Ridge National Laboratory
Awards: G120387841, G1371859516, G1610201404, G2806721493, G2947324332, G298074822, G3031715232, G347960030, G3812690899, G4286615259, G4475359334, G4700831490, G5027116804, G5287361696, G5334485699, G6052186190, G6120464043, G6246450888, G6521983585, G658211837, G67931008, G7050094077, G7358857777, G786726864, G8029776622, G8978267545
OpenAlex ID: W7224864330