What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Optics
- Artificial intelligence
His primary scientific interests are in Transducer, Optics, Acoustics, Ultrasound and Apodization.
Jørgen Arendt Jensen integrates Transducer and Field in his research.
His research in Optics intersects with topics in Synthetic aperture radar and Ultrasonic sensor.
His study in Acoustics is interdisciplinary in nature, drawing from both Estimator, Flow velocity and Vortex.
His studies in Ultrasound integrate themes in fields like Algorithm and Speckle pattern, Computer vision, Artificial intelligence.
His Apodization study integrates concerns from other disciplines, such as Sparse array and Window function.
His most cited work include:
- Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers (1796 citations)
- Synthetic Aperture Ultrasound Imaging (480 citations)
- A new method for estimation of velocity vectors (374 citations)
What are the main themes of his work throughout his whole career to date?
Jørgen Arendt Jensen mostly deals with Optics, Acoustics, Transducer, Ultrasound and Synthetic aperture radar.
Jørgen Arendt Jensen interconnects Transverse plane, Flow and Standard deviation in the investigation of issues within Optics.
His work on Capacitive micromachined ultrasonic transducers as part of general Acoustics research is frequently linked to Field, thereby connecting diverse disciplines of science.
His research integrates issues of Image quality, Apodization, Aperture, Scanner and Ultrasonic sensor in his study of Transducer.
His Ultrasound research includes elements of Biomedical engineering, Artificial intelligence and Computer vision.
Jørgen Arendt Jensen combines subjects such as Frame rate, Focus and Beamforming with his study of Synthetic aperture radar.
He most often published in these fields:
- Optics (28.83%)
- Acoustics (27.31%)
- Transducer (26.86%)
What were the highlights of his more recent work (between 2016-2021)?
- Vector flow (13.66%)
- Ultrasound (23.07%)
- Internal medicine (14.87%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Vector flow, Ultrasound, Internal medicine, Endocrinology and Optics.
His Vector flow research is multidisciplinary, incorporating perspectives in Flow, Blood flow, Standard deviation, Transverse plane and Nuclear medicine.
His Ultrasound research includes themes of Magnetic resonance imaging, Transducer and Artificial intelligence.
His Transducer research is multidisciplinary, relying on both Ultrasonic sensor, Capacitive sensing, Optoelectronics and Center frequency.
The study incorporates disciplines such as Synthetic aperture radar, Beamforming and Signal in addition to Optics.
His Beamforming research focuses on Acoustics and how it connects with Echo.
Between 2016 and 2021, his most popular works were:
- Ultrasound Open Platforms for Next-Generation Imaging Technique Development (52 citations)
- Global mRNA sequencing of human skeletal muscle: Search for novel exercise-regulated myokines. (37 citations)
- Hypoxia in Combination With Muscle Contraction Improves Insulin Action and Glucose Metabolism in Human Skeletal Muscle via the HIF-1α Pathway (25 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Optics
- Artificial intelligence
His primary areas of investigation include Vector flow, Internal medicine, Endocrinology, Ultrasound and Beamforming.
The Vector flow study combines topics in areas such as Flow, Blood flow, Plane wave, Standard deviation and Transverse plane.
His studies deal with areas such as Acoustics, Estimator, Pulse sequence and Oscillation as well as Transverse plane.
His work on Transducer as part of general Acoustics research is often related to Coaxial cable, thus linking different fields of science.
His Ultrasound study incorporates themes from Lung scan, Healthy subjects, Pulmonary edema and Nuclear medicine.
His Beamforming study combines topics in areas such as Image resolution, Optics, Synthetic aperture radar, Signal-to-noise ratio and Row and column spaces.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
