What is he best known for?
The fields of study he is best known for:
- Optics
- Internal medicine
- Artificial intelligence
Tomography, Optics, Multispectral image, Iterative reconstruction and Molecular imaging are his primary areas of study.
His Tomography research includes themes of Image resolution, Computer vision, Artificial intelligence and Biomedical engineering.
His Optics study integrates concerns from other disciplines, such as Image quality and Medical imaging.
His Multispectral image study combines topics from a wide range of disciplines, such as Optical coherence tomography, Fluorescence-lifetime imaging microscopy, Scanner and Pathology.
His work deals with themes such as Image sensor, Transducer, Ultrasonic sensor and Ultrasound, which intersect with Iterative reconstruction.
His Molecular imaging research incorporates elements of Preclinical imaging, Nanotechnology and Optoacoustic imaging.
His most cited work include:
- Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo (508 citations)
- Molecular Imaging by Means of Multispectral Optoacoustic Tomography (MSOT) (490 citations)
- Multifunctional Nanocarriers for diagnostics, drug delivery and targeted treatment across blood-brain barrier: perspectives on tracking and neuroimaging. (306 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Tomography, Optics, Biomedical engineering, Multispectral image and Ultrasound.
The various areas that Daniel Razansky examines in his Tomography study include Image resolution, Iterative reconstruction, Computer vision, Artificial intelligence and Molecular imaging.
Daniel Razansky has included themes like Image quality and Algorithm in his Iterative reconstruction study.
The study incorporates disciplines such as Ultrasonic sensor and Transducer in addition to Optics.
While the research belongs to areas of Biomedical engineering, he spends his time largely on the problem of Optoacoustic imaging, intersecting his research to questions surrounding Photoacoustic imaging in biomedicine.
His research investigates the link between Multispectral image and topics such as Fluorescence that cross with problems in Biophysics.
He most often published in these fields:
- Tomography (51.23%)
- Optics (44.88%)
- Biomedical engineering (32.17%)
What were the highlights of his more recent work (between 2019-2021)?
- Biomedical engineering (32.17%)
- Tomography (51.23%)
- Ultrasound (18.65%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Biomedical engineering, Tomography, Ultrasound, Optics and Microscopy.
His Biomedical engineering research is multidisciplinary, relying on both Optoacoustic imaging, Tracking, Photon diffusion, Magnetic resonance imaging and Laser.
His Tomography research is multidisciplinary, incorporating perspectives in Image resolution, Computer vision, Ultrashort pulse, Temporal resolution and Visualization.
His research integrates issues of Molecular imaging and Optical coherence tomography in his study of Visualization.
When carried out as part of a general Ultrasound research project, his work on Microbubbles is frequently linked to work in Tumor detection, therefore connecting diverse disciplines of study.
His research in Microscopy tackles topics such as Intravital microscopy which are related to areas like Skull, Biophysics, Amyloidosis and Pathology.
Between 2019 and 2021, his most popular works were:
- High‐Speed Large‐Field Multifocal Illumination Fluorescence Microscopy (13 citations)
- Validity of machine learning in biology and medicine increased through collaborations across fields of expertise (11 citations)
- Monitoring of Stimulus Evoked Murine Somatosensory Cortex Hemodynamic Activity With Volumetric Multi-Spectral Optoacoustic Tomography. (5 citations)
In his most recent research, the most cited papers focused on:
- Optics
- Internal medicine
- Artificial intelligence
Daniel Razansky mainly focuses on Tomography, Biomedical engineering, Artificial intelligence, Fluorescence microscope and Optoacoustic imaging.
He has researched Tomography in several fields, including Magnetic resonance imaging, Translation, Functional imaging, Scanner and Iterative reconstruction.
His Biomedical engineering study combines topics in areas such as Imaging phantom, Wavelength, Photon diffusion and Ultrasound.
In his study, Convolutional neural network and Deep learning is strongly linked to Computer vision, which falls under the umbrella field of Artificial intelligence.
His Optoacoustic imaging research includes elements of Optical coherence tomography, Molecular imaging, Neuroimaging, Visualization and Rendering.
His study with Light scattering involves better knowledge in Optics.
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