What is he best known for?
The fields of study he is best known for:
- Radiology
- Magnetic resonance imaging
- Computer vision
His primary areas of study are Computer vision, Artificial intelligence, Nuclear medicine, Radiology and Magnetic resonance imaging.
His study in Computer vision concentrates on Image processing, Projection, Composite image filter, Frame rate and Image quality.
Charles A. Mistretta combines subjects such as Image resolution, Streak and Undersampling with his study of Projection.
The various areas that he examines in his Artificial intelligence study include Digital video, Series and Spatial frequency.
His Nuclear medicine study incorporates themes from Radiography, Computed radiography, Noise, Energy and Computed tomography.
His research in Magnetic resonance imaging focuses on subjects like Angiography, which are connected to Blood pool agent, Aorta and Single injection.
His most cited work include:
- Time‐resolved contrast‐enhanced 3D MR angiography (822 citations)
- Undersampled projection reconstruction applied to MR angiography. (347 citations)
- Highly constrained backprojection for time-resolved MRI (306 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Nuclear medicine, Radiology and Optics.
His Artificial intelligence study which covers Fluoroscopy that intersects with Visualization and Absorption edge.
Computer vision is closely attributed to Pulse sequence in his work.
His study in Nuclear medicine is interdisciplinary in nature, drawing from both Image quality and Radiography.
His work in Projection covers topics such as Undersampling which are related to areas like Streak.
His Angiography research integrates issues from Magnetic resonance imaging and Biomedical engineering.
He most often published in these fields:
- Artificial intelligence (34.41%)
- Computer vision (33.82%)
- Nuclear medicine (20.59%)
What were the highlights of his more recent work (between 2010-2021)?
- Artificial intelligence (34.41%)
- Computer vision (33.82%)
- Digital subtraction angiography (10.88%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Computer vision, Digital subtraction angiography, Angiography and Nuclear medicine.
His work deals with themes such as Imaging phantom, Fluoroscopy and Volume, which intersect with Artificial intelligence.
His study focuses on the intersection of Computer vision and fields such as Medical imaging with connections in the field of Composite image filter.
His research in Digital subtraction angiography intersects with topics in Undersampling, Intracranial Arteriovenous Malformations, Rotation and Contrast.
Charles A. Mistretta interconnects Reconstruction algorithm, Magnetic resonance imaging, Biomedical engineering and Confidence interval in the investigation of issues within Angiography.
His research integrates issues of Image resolution, Image quality, Magnetic resonance angiography, Ultra low dose and Ct fluoroscopy in his study of Nuclear medicine.
Between 2010 and 2021, his most popular works were:
- Noise reduction in spectral CT: reducing dose and breaking the trade-off between image noise and energy bin selection. (81 citations)
- 4D digital subtraction angiography: implementation and demonstration of feasibility. (58 citations)
- Noncontrast-enhanced three-dimensional (3D) intracranial MR angiography using pseudocontinuous arterial spin labeling and accelerated 3D radial acquisition. (49 citations)
In his most recent research, the most cited papers focused on:
- Magnetic resonance imaging
- Optics
- Radiology
Charles A. Mistretta mostly deals with Nuclear medicine, Digital subtraction angiography, Image resolution, Artificial intelligence and Computer vision.
His studies deal with areas such as Image quality, Magnetic resonance angiography, Frame rate, Energy and Three dimensional imaging as well as Nuclear medicine.
His Digital subtraction angiography study integrates concerns from other disciplines, such as Undersampling, Linear regression and Perfusion.
His research in Image resolution tackles topics such as Iterative reconstruction which are related to areas like Algorithm, Image processing, Noise reduction, Image sensor and Dosimetry.
The study incorporates disciplines such as Angiography, Temporal resolution, Series and Computed tomography in addition to Artificial intelligence.
His biological study spans a wide range of topics, including Magnetic resonance imaging, Projection and Biomedical engineering.
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