Using multimodal PET+MR data as conditional generative adversarial network inputs improves pseudo-CT and attenuation correction estimates for brain PET/MR.
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| Title: | Using multimodal PET+MR data as conditional generative adversarial network inputs improves pseudo-CT and attenuation correction estimates for brain PET/MR. |
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| Authors: | Fisher J; Department of Electrical Engineering, Stanford University Stanford, CA, USA.; Department of Radiology, Stanford University Stanford, CA, USA., Anaya E; Department of Electrical Engineering, Stanford University Stanford, CA, USA.; Department of Radiology, Stanford University Stanford, CA, USA., Chinn G; Department of Radiology, Stanford University Stanford, CA, USA., Levin CS; Department of Electrical Engineering, Stanford University Stanford, CA, USA.; Department of Radiology, Stanford University Stanford, CA, USA.; Department of Physics, Stanford University Stanford, CA, USA.; Department of Bioengineering, Stanford University Stanford, CA, USA. |
| Source: | American journal of nuclear medicine and molecular imaging [Am J Nucl Med Mol Imaging] 2026 Feb 15; Vol. 16 (1), pp. 44-54. Date of Electronic Publication: 2026 Feb 15 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: e-Century Pub. Corp Country of Publication: United States NLM ID: 101564121 Publication Model: eCollection Cited Medium: Print ISSN: 2160-8407 (Print) NLM ISO Abbreviation: Am J Nucl Med Mol Imaging Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 2160-8407 |
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| DOI: | 10.62347/BONK5634 |
