What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Surgery
- Radiology
His primary areas of study are Optics, Cone beam computed tomography, Medical imaging, Nuclear medicine and Image quality.
His biological study spans a wide range of topics, including Computed radiography and Signal.
His Cone beam computed tomography research includes themes of Isocenter, Imaging phantom, Fluoroscopy and Iterative reconstruction.
Jeffrey H. Siewerdsen interconnects Image sensor, Image processing, Flat panel detector, Signal-to-noise ratio and Dosimetry in the investigation of issues within Medical imaging.
The various areas that Jeffrey H. Siewerdsen examines in his Nuclear medicine study include Tomography, Radiation therapy and Radiography.
His work carried out in the field of Image quality brings together such families of science as Geometry and Scanner.
His most cited work include:
- Flat-panel cone-beam computed tomography for image-guided radiation therapy (1113 citations)
- Cone‐beam computed tomography with a flat‐panel imager: Magnitude and effects of x‐ray scatter (488 citations)
- Cone‐beam computed tomography with a flat‐panel imager: Initial performance characterization (456 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Image quality, Artificial intelligence, Computer vision, Imaging phantom and Cone beam computed tomography.
The study incorporates disciplines such as Image resolution, Detector, Optics, Algorithm and Iterative reconstruction in addition to Image quality.
His Optics research is multidisciplinary, incorporating elements of Noise and Detective quantum efficiency.
His Artificial intelligence research focuses on subjects like Radiography, which are linked to Flat panel detector.
His studies in Imaging phantom integrate themes in fields like Tomosynthesis, Scanner and Biomedical engineering.
His Cone beam computed tomography research includes elements of Computed radiography, Medical imaging, Image-guided radiation therapy and Nuclear medicine.
He most often published in these fields:
- Image quality (43.36%)
- Artificial intelligence (37.65%)
- Computer vision (35.19%)
What were the highlights of his more recent work (between 2018-2021)?
- Image quality (43.36%)
- Artificial intelligence (37.65%)
- Computer vision (35.19%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Image quality, Artificial intelligence, Computer vision, Imaging phantom and Image-guided surgery.
Jeffrey H. Siewerdsen has researched Image quality in several fields, including Image resolution, Algorithm, Motion compensation, Projection and Iterative reconstruction.
His Artificial intelligence research incorporates themes from Calibration and Pattern recognition.
His work in Computer vision addresses issues such as Calibration, which are connected to fields such as Modulation.
His Imaging phantom study necessitates a more in-depth grasp of Nuclear medicine.
The Image-guided surgery study combines topics in areas such as Segmentation, Radiography, Fluoroscopy, Image registration and Cadaver.
Between 2018 and 2021, his most popular works were:
- Task-driven source–detector trajectories in cone-beam computed tomography: II. Application to neuroradiology (12 citations)
- Task-driven source-detector trajectories in cone-beam computed tomography: I. Theory and methods. (11 citations)
- Automatic pedicle screw planning using atlas-based registration of anatomy and reference trajectories. (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Surgery
- Radiology
Jeffrey H. Siewerdsen spends much of his time researching Image quality, Iterative reconstruction, Image-guided surgery, Algorithm and Artificial intelligence.
His Image quality study combines topics in areas such as Image resolution, Imaging phantom, Cone beam computed tomography and Projection.
His Imaging phantom study is concerned with the field of Optics as a whole.
As part of the same scientific family, Jeffrey H. Siewerdsen usually focuses on Iterative reconstruction, concentrating on Optical transfer function and intersecting with Medical imaging and Flat panel detector.
His Image-guided surgery research incorporates themes from Radiation dose, Nuclear medicine, Centroid, Cone beam ct and Atlas.
His Artificial intelligence study integrates concerns from other disciplines, such as Computer vision and Pattern recognition.
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