ArticleNuclear medicine and molecular imaging2023
MR Template-Based Individual Brain PET Volumes-of-Interest Generation Neither Using MR nor Using Spatial Normalization.
Article in Nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 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
- Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study.European journal of nuclear medicine and molecular imaging · 2025Article
- Deep learning-based measurement of split glomerular filtration rate withEJNMMI physics · 2024Article
- Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method.Nuclear medicine and molecular imaging · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 dependency on the corresponding MR and the process of SN, routine preclinical/clinical PET images cannot always afford corresponding MR and relevant VOIs. To resolve this issue, we propose a deep learning (DL)-based individual-brain-specific VOIs (i.e., cortex, hippocampus, striatum, thalamus, and cerebellum) directly generated from PET images using the inverse-spatial-normalization (iSN)-based VOI labels and deep convolutional neural network model (deep CNN). Our technique was applied to mutated amyloid precursor protein and presenilin-1 mouse model of Alzheimer's disease. Eighteen mice underwent T2-weighted MRI and Supplementary Information: The online version contains supplementary material available at 10.1007/s13139-022-00772-4.
Indexed as
Identifiers
What OpenQuestion holds
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.