ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2023
Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks.
Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Comparative Prognostic Performance of Hippocampal Volume and Plasma Biomarkers in Amyloid-Negative Subjective Cognitive Decline: A Four-Year Follow-up.Journal of clinical neurology (Seoul, Korea) · 2026Article
- First-in-Human Imaging Results of PHAROS: A Versatile High-Resolution TOF/DOI PET Scanner for Brain, Breast, and Extremity Imaging.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Impact of adjunctive quantitative analysis on visual interpretation of amyloid PET: a multiple tracer, multicentre study.European radiology · 2026Article
- Stratifying amyloid burden in early Alzheimer's disease using cascaded attention-guided vision transformer using [¹⁸F]Florbetapir PET.European radiology · 2026Article
- Stage-dependent reorganization of amyloid PET region-symptom bipartite networks in drug-naïve, amyloid-positive Alzheimer's disease.Translational psychiatry · 2026Article
- AI-enhanced Centiloid quantification of amyloid PET images.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- An interactive cascaded deep learning framework with expert refinement for accurate striatal subregion segmentation.Scientific reports · 2026Article
- Artificial Intelligence-Powered Quantification of Flortaucipir PET for Detecting Tau Pathology.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Does Q.Clear Processing Change PET Ratios? Quantitative Evidence Using BTXBrain-DAT.Brain sciences · 2025Article
- Reliability of Automated Amyloid PET Quantification: Real-World Validation of Commercial Tools Against Centiloid Project Method.Tomography (Ann Arbor, Mich.) · 2025Article
- Revisiting Centiloids using AI.Research square · 2025Article
- Consistency Analysis of Centiloid Values Across Three Commercial Software Platforms for Amyloid PET Quantification.Diagnostics (Basel, Switzerland) · 2025Article
- Alteration of prefrontal functional connectivity in preclinical Alzheimer's disease: an fNIRS study.Frontiers in aging neuroscience · 2025Article
- Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization.Nuclear medicine and molecular imaging · 2024Article
- ImprovingJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Age and gender effects on striatal dopamine transporter density and cerebral perfusion in individuals with non-degenerative parkinsonism: a dual-phaseEJNMMI research · 2024Article
- Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method.Nuclear medicine and molecular imaging · 2024Article
- Deep Learning-Driven Estimation of Centiloid Scales from Amyloid PET Images withBrain sciences · 2024Article
- Perspectives of the European Association of Nuclear Medicine on the role of artificial intelligence (AI) in molecular brain imaging.European journal of nuclear medicine and molecular imaging · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a novel method for automatic quantification of amyloid PET using deep learning-based spatial normalization (SN) of PET images, which does not require MRI or CT images of the same patient. The accuracy of the method was evaluated for 3 different amyloid PET radiotracers compared with MRI-parcellation-based PET quantification using FreeSurfer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.