What is she best known for?
The fields of study she is best known for:
- Magnetic resonance imaging
- Artificial intelligence
- Computer vision
Her main research concerns Undersampling, Image quality, Artificial intelligence, Computer vision and Compressed sensing.
Her Image quality research is multidisciplinary, relying on both Sampling, Imaging phantom, Coronary arteries, Cartesian coordinate system and Biomedical engineering.
Her Artificial intelligence research integrates issues from Magnetic resonance imaging and Affine transformation.
Her work in Computer vision addresses subjects such as k-space, which are connected to disciplines such as Structure from motion, Encoding and Motion compensation.
Her work in Compressed sensing covers topics such as Sparse approximation which are related to areas like Resolution and Reduction.
Her study in Golden angle is interdisciplinary in nature, drawing from both Cine mri and Free breathing.
Her most cited work include:
- Motion corrected compressed sensing for free-breathing dynamic cardiac MRI. (115 citations)
- Motion corrected compressed sensing for free-breathing dynamic cardiac MRI. (115 citations)
- Whole-heart coronary MR angiography with 2D self-navigated image reconstruction. (101 citations)
What are the main themes of her work throughout her whole career to date?
Claudia Prieto mainly investigates Artificial intelligence, Computer vision, Image quality, Magnetic resonance imaging and Imaging phantom.
Claudia Prieto usually deals with Artificial intelligence and limits it to topics linked to Pattern recognition and Regularization and Matching.
Her research in Computer vision focuses on subjects like Cartesian coordinate system, which are connected to Affine transformation and Motion.
Her Image quality research incorporates elements of Image resolution, Sampling, Nuclear medicine, Motion estimation and Coronary arteries.
In the subject of general Magnetic resonance imaging, her work in Magnetization transfer and Magnetic resonance angiography is often linked to Parametric statistics and High resolution, thereby combining diverse domains of study.
Her studies in Imaging phantom integrate themes in fields like Spin echo, T2 mapping, Healthy subjects and Biomedical engineering.
She most often published in these fields:
- Artificial intelligence (64.38%)
- Computer vision (50.68%)
- Image quality (44.29%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (64.38%)
- Imaging phantom (33.79%)
- Magnetic resonance imaging (36.07%)
In recent papers she was focusing on the following fields of study:
Claudia Prieto spends much of her time researching Artificial intelligence, Imaging phantom, Magnetic resonance imaging, Image quality and Biomedical engineering.
Her Artificial intelligence research includes elements of Computer vision and Pattern recognition.
She has included themes like Spin echo, T2 mapping, Fat fraction and Nuclear magnetic resonance in her Imaging phantom study.
Her Magnetic resonance imaging research focuses on Angiology and how it relates to Coronary arteries, Image resolution, Rigid motion and Resolution.
The various areas that Claudia Prieto examines in her Image quality study include Segmentation, Image segmentation, Scan time, Nuclear medicine and Subpixel rendering.
In her research, Wavelet is intimately related to Artificial neural network, which falls under the overarching field of Undersampling.
Between 2019 and 2021, her most popular works were:
- From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction. (15 citations)
- From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction. (15 citations)
- Water-fat Dixon cardiac magnetic resonance fingerprinting. (15 citations)
In her most recent research, the most cited papers focused on:
- Magnetic resonance imaging
- Artificial intelligence
- Internal medicine
Claudia Prieto focuses on Imaging phantom, T2 mapping, Biomedical engineering, In patient and Golden angle.
The concepts of her T2 mapping study are interwoven with issues in Cardiac magnetic resonance, Nuclear magnetic resonance and Free breathing.
Within one scientific family, she focuses on topics pertaining to Sampling under Biomedical engineering, and may sometimes address concerns connected to Image resolution, Cardiomyopathy and Respiratory motion.
Her research in Golden angle tackles topics such as Fat fraction which are related to areas like Liver tissue, Liver disease and Partial volume.
Her work deals with themes such as Deep learning and Computer vision, which intersect with Magnetic resonance imaging.
Her work carried out in the field of Cine mri brings together such families of science as Image quality and Artificial intelligence.
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