ArticleNMR in biomedicine2022
Deep learning-based classification of preclinical breast cancer tumor models using chemical exchange saturation transfer magnetic resonance imaging.
Article in NMR in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A rapid and accurate guanidine CEST imaging in ischemic stroke using a machine learning approach.Physics in medicine and biology · 2026Article
- Chemical exchange saturation transfer MRI for neurodegenerative diseases: An update on clinical and preclinical studies.Neural regeneration research · 2026Article
- Chemical Exchange Saturation Transfer Imaging in Neuroinflammation: Methods, Challenges, and Recommendations.International journal of molecular sciences · 2025Review
- Improving quantification accuracy of a nuclear Overhauser enhancement signal at -1.6 ppm at 4.7 T using a machine learning approach.Physics in medicine and biology · 2025Article
- Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data.NMR in biomedicine · 2025Article
- Advancing In Vivo Molecular Bioimaging With Optimal Frequency Offset Selection and Deep Learning Reconstruction for CEST MRI.IEEE access : practical innovations, open solutions · 2025Article
- Development and Validation of Four Different Methods to Improve MRI-CEST Tumor pH Mapping in Presence of Fat.Journal of imaging · 2024Article
- Diagnostic and theranostic opportunities in Neuro-oncology.Advances in oncology · 2024Article
- A machine learning approach that measures pH using acidoCEST MRI of iopamidol.NMR in biomedicine · 2023Article
- Accelerated and quantitative three-dimensional molecular MRI using a generative adversarial network.Magnetic resonance in medicine · 2023Article
- Amide proton transfer imaging of Alzheimer's disease and Parkinson's disease.Magnetic resonance letters · 2023Review
- An end-to-end LSTM-Attention based framework for quasi-steady-state CEST prediction.Frontiers in neuroscience · 2023Article
- Theranostic Cancer Treatment Using Lentinan-Coated Selenium Nanoparticles and Label-Free CEST MRI.Pharmaceutics · 2022Article
- Label-Free Assessment of Mannitol Accumulation Following Osmotic Blood-Brain Barrier Opening Using Chemical Exchange Saturation Transfer Magnetic Resonance Imaging.Pharmaceutics · 2022Article
- The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review.Cancers · 2022Review
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8 authors.
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Abstract
Chemical exchange saturation transfer (CEST) magnetic resonance imaging has shown promise for classifying tumors based on their aggressiveness, but CEST contrast is complicated by multiple signal sources and thus prolonged acquisition times are often required to extract the signal of interest. We investigated whether deep learning could help identify pertinent Z-spectral features for distinguishing tumor aggressiveness as well as the possibility of acquiring only the pertinent spectral regions for more efficient CEST acquisition. Human breast cancer cells, MDA-MB-231 and MCF-7, were used to establish bi-lateral tumor xenografts in mice to represent higher and lower aggressive tumors, respectively. A convolutional neural network (CNN)-based classification model, trained on simulated data, utilized Z-spectral features as input to predict labels of different tissue types, including MDA-MB-231, MCF-7, and muscle tissue. Saliency maps reported the influence of Z-spectral regions on classifying tissue types. The model was robust to noise with an accuracy of more than 91.5% for low and moderate noise levels in simulated testing data (SD of noise less than 2.0%). For in vivo CEST data acquired with a saturation pulse amplitude of 2.0 μT, the model had a superior ability to delineate tissue types compared with Lorentzian difference (LD) and magnetization transfer ratio asymmetry (MTR
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