What is he best known for?
The fields of study he is best known for:
- Optics
- Artificial intelligence
- Computer vision
His primary areas of investigation include Iterative reconstruction, Tomography, Optics, Artificial intelligence and Computer vision.
His Iterative reconstruction research incorporates elements of Image resolution, Image quality, Medical imaging, Algorithm and Image restoration.
The study incorporates disciplines such as Photoacoustic Techniques, Inverse problem and Ultrasound in addition to Tomography.
His Photoacoustic Techniques research is multidisciplinary, relying on both Whole body imaging, Photoacoustic spectroscopy, Optical phenomena and Biomedical engineering.
His Optics research is multidisciplinary, incorporating perspectives in Ultrasonic sensor and Radiography.
His Artificial intelligence study combines topics in areas such as Genetic algorithm, Mathematical optimization and Pattern recognition.
His most cited work include:
- Multiple-image radiography (206 citations)
- Imaging challenges in biomaterials and tissue engineering. (178 citations)
- Full-Wave Iterative Image Reconstruction in Photoacoustic Tomography With Acoustically Inhomogeneous Media (162 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Iterative reconstruction, Optics, Tomography, Artificial intelligence and Image quality.
His Iterative reconstruction research is multidisciplinary, incorporating elements of Algorithm, Image restoration and Medical imaging.
His Algorithm study integrates concerns from other disciplines, such as Image resolution and Inverse problem.
In his research on the topic of Tomography, Transducer is strongly related with Ultrasonic sensor.
His biological study spans a wide range of topics, including Computed tomography, Computer vision and Pattern recognition.
His Image quality research integrates issues from Image processing, Imaging phantom, Noise and Radiation therapy.
He most often published in these fields:
- Iterative reconstruction (51.40%)
- Optics (35.75%)
- Tomography (35.98%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (35.28%)
- Pattern recognition (14.02%)
- Image quality (21.73%)
In recent papers he was focusing on the following fields of study:
Mark A. Anastasio mainly focuses on Artificial intelligence, Pattern recognition, Image quality, Iterative reconstruction and Medical imaging.
His work focuses on many connections between Artificial intelligence and other disciplines, such as Detection theory, that overlap with his field of interest in Bayesian probability.
His studies deal with areas such as Magnetic resonance imaging and Noise as well as Image quality.
His Iterative reconstruction study incorporates themes from Acoustics, Regularization, Tomography, Computed tomography and Compressed sensing.
His study on Tomography also encompasses disciplines like
- Breast imaging which intersects with area such as Optics, Fluence and Attenuation,
- Image resolution together with Biomedical engineering and Fluorescence.
Mark A. Anastasio has included themes like Variation, Modality, Inverse problem and Photoacoustic imaging in biomedicine in his Medical imaging study.
Between 2018 and 2021, his most popular works were:
- A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography. (28 citations)
- Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods (14 citations)
- High-resolution transcriptional and morphogenetic profiling of cells from micropatterned human ESC gastruloid cultures. (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Optics
- Algorithm
His primary scientific interests are in Artificial intelligence, Pattern recognition, Deep learning, Image quality and Medical imaging.
His research in the fields of Supervised learning and Artificial neural network overlaps with other disciplines such as Observer and Test statistic.
The Deep learning study combines topics in areas such as Algorithm, Regularization and Prior probability.
His Algorithm study frequently links to related topics such as Iterative reconstruction.
His Medical imaging research includes elements of Image and Inverse problem.
Mark A. Anastasio focuses mostly in the field of Inverse problem, narrowing it down to matters related to Ultrasound and, in some cases, Photoacoustic imaging in biomedicine and Tomography.
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