What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Radiology
Jiang Hsieh spends much of his time researching Iterative reconstruction, Nuclear medicine, Artificial intelligence, Computer vision and Imaging phantom.
His studies in Iterative reconstruction integrate themes in fields like Image quality, Image resolution, Iterative method, Algorithm and Tomography.
His Nuclear medicine research incorporates elements of Cardiac imaging, Radiology and Radiography.
His work on Projection, Medical imaging, Scanner and Object as part of general Artificial intelligence study is frequently connected to Data acquisition, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Computer vision study integrates concerns from other disciplines, such as Maximum intensity and Computed tomography.
His work deals with themes such as Beam and Artifact, which intersect with Imaging phantom.
His most cited work include:
- A three-dimensional statistical approach to improved image quality for multislice helical CT. (777 citations)
- Computed Tomography: Principles, Design, Artifacts, and Recent Advances (735 citations)
- Prospectively Gated Transverse Coronary CT Angiography versus Retrospectively Gated Helical Technique : Improved Image Quality and Reduced Radiation Dose (502 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Iterative reconstruction, Projection and Optics.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Tomography and Computed tomography.
His Computer vision study combines topics in areas such as Computer graphics and Detector.
Jiang Hsieh combines subjects such as Image quality, Medical imaging, Imaging phantom, Nuclear medicine and Algorithm with his study of Iterative reconstruction.
His studies deal with areas such as Radiation dose and Radon transform as well as Image quality.
His work investigates the relationship between Projection and topics such as Reconstruction algorithm that intersect with problems in Cone beam reconstruction.
He most often published in these fields:
- Artificial intelligence (52.68%)
- Computer vision (51.27%)
- Iterative reconstruction (32.11%)
What were the highlights of his more recent work (between 2011-2021)?
- Iterative reconstruction (32.11%)
- Artificial intelligence (52.68%)
- Computer vision (51.27%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Iterative reconstruction, Artificial intelligence, Computer vision, Imaging phantom and Image quality.
His Iterative reconstruction research is multidisciplinary, relying on both Image processing, Radon transform, Nuclear medicine, Algorithm and Tomography.
His work on Deep learning, Reconstruction algorithm and Pixel as part of his general Artificial intelligence study is frequently connected to Component, thereby bridging the divide between different branches of science.
The various areas that Jiang Hsieh examines in his Computer vision study include Detector and Computed tomography.
Jiang Hsieh has included themes like Artifact, Image noise and Hounsfield scale in his Imaging phantom study.
His research integrates issues of Image resolution, Radiation dose, Image sensor, Dose reduction and Radiation in his study of Image quality.
Between 2011 and 2021, his most popular works were:
- Low-Dose X-ray CT Reconstruction via Dictionary Learning (385 citations)
- Model-Based Iterative Reconstruction for Dual-Energy X-Ray CT Using a Joint Quadratic Likelihood Model (89 citations)
- Recent Advances in CT Image Reconstruction (86 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Radiology
His main research concerns Artificial intelligence, Iterative reconstruction, Computer vision, Image and Tomography.
In general Artificial intelligence, his work in Deep learning is often linked to Component linking many areas of study.
His biological study spans a wide range of topics, including Image processing, Algorithm, Computer engineering and Image quality.
The concepts of his Computer vision study are interwoven with issues in Computed tomography and Pattern recognition.
His research in Image intersects with topics in Object, Signal and Artifact.
His Tomography research integrates issues from Iterative method, Radon transform, Noise and Hounsfield scale.
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.
