What is he best known for?
The fields of study he is best known for:
- Optics
- Internal medicine
- Statistics
The scientist’s investigation covers issues in Acoustic radiation force, Biomedical engineering, Ultrasound, Elastography and Acoustic radiation force impulse imaging.
His Acoustic radiation force study incorporates themes from Impulse, Ultrasonic sensor, Acoustic radiation and Optics.
His research investigates the connection between Biomedical engineering and topics such as Elasticity Imaging Techniques that intersect with issues in Image registration.
His work focuses on many connections between Ultrasound and other disciplines, such as Soft tissue, that overlap with his field of interest in Tissue stiffness.
His research in Elastography intersects with topics in Chronic liver disease and Breast imaging.
Mark L. Palmeri has researched Radiology in several fields, including Liver fibrosis and Medical physics.
His most cited work include:
- On the feasibility of remote palpation using acoustic radiation force. (625 citations)
- Quantifying hepatic shear modulus in vivo using acoustic radiation force. (538 citations)
- WFUMB Guidelines and Recommendations for Clinical Use of Ultrasound Elastography: Part 2: Breast (472 citations)
What are the main themes of his work throughout his whole career to date?
Acoustic radiation force, Acoustics, Biomedical engineering, Ultrasound and Ultrasonic sensor are his primary areas of study.
His study in Acoustic radiation force is interdisciplinary in nature, drawing from both Impulse, Elasticity Imaging Techniques, Transducer and Optics.
His Acoustics research includes elements of Wave propagation, Imaging phantom, Shear waves and Iterative reconstruction.
The concepts of his Biomedical engineering study are interwoven with issues in Diagnostic ultrasound, Radiology, Wave speed and Biomechanics.
Mark L. Palmeri works in the field of Ultrasound, namely Elastography.
His Elastography research incorporates themes from Acoustic attenuation and Liver fibrosis.
He most often published in these fields:
- Acoustic radiation force (46.41%)
- Acoustics (30.80%)
- Biomedical engineering (30.80%)
What were the highlights of his more recent work (between 2018-2021)?
- Ultrasound (26.58%)
- Acoustic radiation force (46.41%)
- Artificial intelligence (5.91%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Ultrasound, Acoustic radiation force, Artificial intelligence, Elasticity and Ultrasonic sensor.
His Ultrasound research integrates issues from Image quality and Prostate cancer.
Acoustic radiation force is a primary field of his research addressed under Acoustics.
His work in the fields of Elastography overlaps with other areas such as Geology.
His Ultrasonic sensor research focuses on Transducer and how it relates to Noise reduction, Noise and Feature.
In his study, which falls under the umbrella issue of Biomedical engineering, Shear wave elastography is strongly linked to Acoustic radiation force impulse imaging.
Between 2018 and 2021, his most popular works were:
- MimickNet, Mimicking Clinical Image Post- Processing Under Black-Box Constraints (8 citations)
- MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints (3 citations)
- Tractable calculation of the Green's tensor for shear wave propagation in an incompressible, transversely isotropic material. (3 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Optics
- Cancer
His main research concerns Ultrasound, Computer vision, Artificial intelligence, Acoustic radiation force impulse imaging and Deep learning.
His biological study spans a wide range of topics, including Displacement, Prostate cancer and Consistency.
His Computer vision research is multidisciplinary, incorporating perspectives in Prostate, Artificial neural network, Imaging phantom, Soft tissue and Virtual displacement.
His Acoustic radiation force impulse imaging research incorporates elements of Healthy volunteers, Shear wave elastography and Biomedical engineering.
His work deals with themes such as Image quality, Black box, Speckle noise, Matching and Test set, which intersect with Deep learning.
His Test set study integrates concerns from other disciplines, such as Black box and Similarity.
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