ArticleProceedings of SPIE--the International Society for Optical Engineering2019
Evaluating the Impact of Intensity Normalization on MR Image Synthesis.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 1 of them a synthesis that pooled it.
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Who cites it
105 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Radiomics in Medical Imaging: Methods, Applications, and Challenges.Journal of imaging · 2026Review
- Influence of image preprocessing on reproducibility and longitudinal repeatability analysis of radiomics features in magnetic resonance image-guided accelerator imaging.Quantitative imaging in medicine and surgery · 2026Article
- Deep grey nuclei automated assessment in acute-subacute phase of middle cerebral artery cortical stroke helps predict the 3-month outcome.Brain imaging and behavior · 2026Article
- A deep representation learning model to predict response to vagus nerve stimulation.Nature communications · 2026Article
- Automated assessment of Knosp grade from pituitary adenoma MRI: Experimental comparison of a rule-based and a machine learning-based approach.Brain & spine · 2026Article
- Automated segmentation of pituitary adenomas, pituitary gland, and internal carotid arteries on routine coronal contrast-enhanced T1-weighted MRI: a single-sequence feasibility study.Frontiers in endocrinology · 2026Article
- Radiomics-based models to predict IDH mutation status and prognosis in gliomas using MRI: a multicenter study.Frontiers in oncology · 2026Article
- Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Adaptive Normalization Enhances the Generalization of Deep Learning Model in Chest X-Ray Classification.Journal of imaging · 2025Article
- Article
- Combined magnetic resonance imaging and serum analysis reveals distinct multiple sclerosis types.Brain : a journal of neurology · 2025Article
- Prediction of hemorrhagic transformation in acute ischemic stroke patients using clinico-radiomics models.Scientific reports · 2025Article
- Quantitative cardiac magnetic resonance standardized signal intensity comparison in dilated cardiomyopathy vs. cardiac sarcoidosis.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2025Article
- MRI markers of cerebrospinal fluid dynamics predict dementia and mediate the impact of cardiovascular risk.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Enhancing pix2pix with Swin Transformer for Cross Modal Brain CT-MR synthesis.Research square · 2025Article
45 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image. This process has been shown to have application in many medical image analysis tasks including imputation, registration, and segmentation. To carry out synthesis, the intensities of the input images are typically scaled-i.e., normalized-both in training to learn the transformation and in testing when applying the transformation, but it is not presently known what type of input scaling is optimal. In this paper, we consider seven different intensity normalization algorithms and three different synthesis methods to evaluate the impact of normalization. Our experiments demonstrate that intensity normalization as a preprocessing step improves the synthesis results across all investigated synthesis algorithms. Furthermore, we show evidence that suggests intensity normalization is vital for successful deep learning-based MR image synthesis.
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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.