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- 01HF5QKDS6RDZE6VM6QE88JFAZ classification C3.
- 01HF5QKDS6RDZE6VM6QE88JFAZ date "2023".
- 01HF5QKDS6RDZE6VM6QE88JFAZ language "eng".
- 01HF5QKDS6RDZE6VM6QE88JFAZ type conference.
- 01HF5QKDS6RDZE6VM6QE88JFAZ hasPart 01HF5QYZQ4A2AMR4Q04DNZ2GMM.pdf.
- 01HF5QKDS6RDZE6VM6QE88JFAZ subject "Technology and Engineering".
- 01HF5QKDS6RDZE6VM6QE88JFAZ doi "10.1109/NSSMICRTSD49126.2023.10338459".
- 01HF5QKDS6RDZE6VM6QE88JFAZ isbn "9798350338669".
- 01HF5QKDS6RDZE6VM6QE88JFAZ issn "2577-0829".
- 01HF5QKDS6RDZE6VM6QE88JFAZ presentedAt urn:uuid:22369f44-5131-4c6d-a97f-6aea802e8563.
- 01HF5QKDS6RDZE6VM6QE88JFAZ abstract "The increased sensitivity exhibited by long axial field-of-view (LAFOV) PET systems allows measurement of tracer kinetics across multiple organs and lesions at reduced doses and/or scan times. This flexibility has been utilized to perform dual-tracer imaging which in oncology offers complementary quantification about metabolic tumor processes. Yet to make dual-tracer studies more suitable for clinical practice, these scan protocols should be pushed towards lower injection doses and shorter scan times, but reduced count statistics inherently result in noisier PET images. Many have previously shown promising denoising performance of deep learning (DL) methods for low count PET images acquired at a single (static) late time point. However, only a few studies have investigated the use of DL to denoise dynamic PET data and the impact on kinetic modeling of parametric estimates. We first trained a DL algorithm (U-Net) with low-to-high count static 18F-FDG datasets and evaluated its performance using noise and clinical metrics. We next assessed the transferability of this pre-trained DL model for frame-by-frame image denoising of dynamic 18F-FGln and looked at the effect of such DL-based denoising on estimations of the volume of distribution of 18F-FGln (kinetic parameter to quantify glutamine pool size). This work provides the foundation for our next investigations into the use of DL for optimizing scan protocols to make dual-tracer studies more practical, and more accurate.".
- 01HF5QKDS6RDZE6VM6QE88JFAZ author 31BD9BF8-F0EE-11E1-A9DE-61C894A0A6B4.
- 01HF5QKDS6RDZE6VM6QE88JFAZ author F5BEDD1A-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HF5QKDS6RDZE6VM6QE88JFAZ author b8e57b18-e7e7-11ea-a281-d04867615d3e.
- 01HF5QKDS6RDZE6VM6QE88JFAZ author urn:uuid:1c0843f4-54ac-483b-94c4-fbb867f9c853.
- 01HF5QKDS6RDZE6VM6QE88JFAZ author urn:uuid:4549fb64-a8af-4d2d-bfb6-12a114c5e98a.
- 01HF5QKDS6RDZE6VM6QE88JFAZ author urn:uuid:5a02ec81-473d-4b72-81f4-b2d38e0d46a6.
- 01HF5QKDS6RDZE6VM6QE88JFAZ author urn:uuid:9e798700-4ba8-4e09-9bd2-3b0d80e68ccd.
- 01HF5QKDS6RDZE6VM6QE88JFAZ author urn:uuid:c657e957-1b8e-4bb2-ae4b-fe868b36fabb.
- 01HF5QKDS6RDZE6VM6QE88JFAZ author urn:uuid:c676c5e4-b9a1-4894-a19c-624d27a5937e.
- 01HF5QKDS6RDZE6VM6QE88JFAZ author urn:uuid:f850b140-608a-4862-8d43-21171ca3e626.
- 01HF5QKDS6RDZE6VM6QE88JFAZ dateCreated "2023-11-14T01:37:13Z".
- 01HF5QKDS6RDZE6VM6QE88JFAZ dateModified "2024-10-29T18:29:08Z".
- 01HF5QKDS6RDZE6VM6QE88JFAZ name "Deep learning image denoising in PET : quantitative impact on kinetic modeling and clinical metrics".
- 01HF5QKDS6RDZE6VM6QE88JFAZ pagination urn:uuid:8bbcfce9-d947-406d-b363-fae71fc71cbe.
- 01HF5QKDS6RDZE6VM6QE88JFAZ publisher urn:uuid:b4ea4de1-3824-4311-9ff5-812dc657acf8.
- 01HF5QKDS6RDZE6VM6QE88JFAZ sameAs LU-01HF5QKDS6RDZE6VM6QE88JFAZ.
- 01HF5QKDS6RDZE6VM6QE88JFAZ sourceOrganization urn:uuid:227a04c7-72bd-4a60-8251-0cc1a2a02a15.
- 01HF5QKDS6RDZE6VM6QE88JFAZ type C3.