- Preparing for the Artificial Intelligence Revolution in Nuclear Cardiology
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Garcia E, Piccinelli M
- KMID: 2541612
- Nucl Med Mol Imaging.
- 2023 Apr;57(2):51-60.
- doi: 10.1007/s13139-021-00733-3
A major opportunity in nuclear cardiology is the many significant artificial intelligence (AI) applications that have recently been reported. These developments include using deep learning (DL) for reducing the needed... -
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- Applying Pix2pix to Translate Hyperemia in Blood Pool Image into Corresponding Increased Bone Uptake in Delayed Image in Three‑Phase Bone Scintigraphy
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Park K, Moon J, Cho S, Kim J, Song H
- KMID: 2541609
- Nucl Med Mol Imaging.
- 2023 Apr;57(2):103-109.
- doi: 10.1007/s13139-022-00786-y
Purpose Delayed images may not be acquired due to severe pain, drowsiness, or worsening vital signs while waiting after blood pool imaging in three-phase bone scintigraphy. If the hyperemia in... -
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- Artificial Intelligence Increases the Agreement among Physicians Classifying Focal Skeleton/Bone Marrow Uptake in Hodgkin’s Lymphoma Patients Staged with [ 18 F]FDG PET/CT—a Retrospective Study
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Sadik M, López‑Urdaneta J, Ulén J, Enqvist O, Andersson P, Kumar R, Trägårdh E
- KMID: 2541611
- Nucl Med Mol Imaging.
- 2023 Apr;57(2):110-116.
- doi: 10.1007/s13139-022-00765-3
Purpose Classification of focal skeleton/bone marrow uptake (BMU) can be challenging. The aim is to investigate whether an artificial intelligence–based method (AI), which highlights suspicious focal BMU, increases interobserver agreement... -
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- Alzheimer’s Disease Prediction Using Attention Mechanism with Dual‑Phase 18 F‑Florbetaben Images
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Kang H, Kang D
- KMID: 2541614
- Nucl Med Mol Imaging.
- 2023 Apr;57(2):61-72.
- doi: 10.1007/s13139-022-00767-1
Introduction Amyloid-beta (Aβ) imaging test plays an important role in the early diagnosis and research of biomarkers of Alzheimer’s disease (AD) but a single test may produce Aβ-negative AD or... -
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- Automatic Lung Cancer Segmentation in [ 18 F]FDG PET/CT Using a Two-Stage Deep Learning Approach
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Park J, Kang SK, Hwang D, Choi H, Ha S, Seo JM, Eo JS, Lee JS
- KMID: 2541618
- Nucl Med Mol Imaging.
- 2023 Apr;57(2):86-93.
- doi: 10.1007/s13139-022-00745-7
Purpose Since accurate lung cancer segmentation is required to determine the functional volume of a tumor in [ 18 F]FDG PET/CT, we propose a two-stage U-Net architecture to enhance the... -
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- Voxel‑Based Internal Dosimetry for 177 Lu‑Labeled Radiopharmaceutical Therapy Using Deep Residual Learning
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Kim K, Lee M, Suh M, Cheon G, Lee J
- KMID: 2541620
- Nucl Med Mol Imaging.
- 2023 Apr;57(2):94-102.
- doi: 10.1007/s13139-022-00769-z
Purpose In this study, we propose a deep learning (DL)–based voxel-based dosimetry method in which dose maps acquired using the multiple voxel S-value (VSV) approach were used for residual learning. Methods... -
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- MR Template‑Based Individual Brain PET Volumes‑of‑Interest Generation Neither Using MR nor Using Spatial Normalization
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Seo S, Oh J, Chung J, Kim S, Kim J
- KMID: 2541616
- Nucl Med Mol Imaging.
- 2023 Apr;57(2):73-85.
- doi: 10.1007/s13139-022-00772-4
For more anatomically precise quantitation of mouse brain PET, spatial normalization (SN) of PET onto MR template and subsequent template volumes-of-interest (VOIs)-based analysis are commonly used. Although this leads to... -
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