ArticleComputer methods and programs in biomedicine2021
Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning.
Article in Computer methods and programs in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
- Complex Diagnostic Challenges in Glioblastoma: The Role ofPharmaceuticals (Basel, Switzerland) · 2024Review
- An automatic analysis framework for FDOPA PET neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2023Article
- Deep learning model for automatic image quality assessment in PET.BMC medical imaging · 2023Article
- Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies.Current cardiology reports · 2022Review
- Article
- Artificial Intelligence and Positron Emission Tomography Imaging Workflow:: Technologists' Perspective.PET clinics · 2022Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
introductionWith biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to perform automated quality control (QC) for both single-site and multi-site datasets have been explored in some neuroimaging techniques (e.g. EEG or MRI), although these methods struggle to find replication in other domains. The aim of this study is to test the feasibility of an automated QC pipeline for brain [
methodsTwo different Convolutional Neural Networks (CNNs) were used and combined to assess spatial misalignment to a standard template and the signal-to-noise ratio (SNR) relative to 200 static [
resultsThe CNN performance was excellent in the training dataset (accuracy for motion: 0.86 ± 0.01, accuracy for SNR: 0.69 ± 0.01), leading to 100% accurate QC classification when applied to the two out-of-sample datasets. Data dimensionality reduction affected the generalizability of the CNNs, especially when the classifiers were applied to the out-of-sample data from 3D to 1D datasets.
conclusionsThis feasibility study shows that it is possible to perform automatic QC of [
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Registered trials
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