Analyzing Data Incompleteness for MRI Data for Quality Enhancement
Magnetic resonance imaging (MRI) is a powerful medical imaging technique widely used for diagnosing various conditions because it provides detailed images of internal structures within the body. However, like any imaging modality, MRI images can be susceptible to artifacts that may arise from variou...
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| Veröffentlicht in: | IEEE access 2024, Vol.12, p.183542-183554 |
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| creator | Shanbhag, Sanjay Raju, Supreetha Gurupur, Varadraj P. Sowmya Kamath, S. Kandala, Rajesh N. V. P. S. Trader, Elizabeth A. Lal, Shyam |
| description | Magnetic resonance imaging (MRI) is a powerful medical imaging technique widely used for diagnosing various conditions because it provides detailed images of internal structures within the body. However, like any imaging modality, MRI images can be susceptible to artifacts that may arise from various sources, including hardware imperfections, patient motion, and image acquisition techniques. Detecting and mitigating these artifacts are crucial steps in ensuring MRI scans' reliability and clinical utility. In this paper, we present algorithms specifically designed to address the challenges of undersampling and motion artifacts in MR images. Our approach involves leveraging advanced image processing techniques, including line detection algorithms for undersampling detection and blur parameter estimation for motion artifact analysis. By accurately identifying and quantifying these artifacts, our algorithms aim to improve MRI data's overall quality and completeness, ultimately enhancing diagnostic accuracy and patient care. |
| doi_str_mv | 10.1109/ACCESS.2024.3511384 |
| format | Article |
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| subjects | Accuracy Algorithms Artifact identification Data analysis Data incompleteness Data models diagnostic image quality Encoding Euclidean distance Fast Fourier transforms Image acquisition Image processing Image quality Image reconstruction Imaging techniques Liver Magnetic resonance imaging Medical imaging Motion artifacts Motion perception Parameter estimation Parameter identification Transfer learning under-sampling detection |
| title | Analyzing Data Incompleteness for MRI Data for Quality Enhancement |
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