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Arun K. Thittai

Researcher at Indian Institute of Technology Madras

Publications -  63
Citations -  514

Arun K. Thittai is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Image quality & Imaging phantom. The author has an hindex of 10, co-authored 61 publications receiving 402 citations. Previous affiliations of Arun K. Thittai include University of Texas at Austin & University of Texas MD Anderson Cancer Center.

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Weighted non-linear beamformers for low cost 2-element receive ultrasound imaging system.

TL;DR: The image quality improved by both F-DwMAS and F-dewMAS compared to DAS andF-DMAS, and the later has the advantage of ready applicability to different acquisition schemes and settings compared to the former also having an additional advantage of better CNR compared to both F -DMAS and F -DewMAS.
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Rotation Elastogram Estimation Using Synthetic Transmit-aperture Technique: A Feasibility Study.

TL;DR: The results demonstrate that the contrast appeared in RE only in the case of loosely bonded inclusion, and the quality of RE improved considerably by employing the Synthetic Transmit Aperture technique.
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Strategies to Obtain Subpitch Precision in Lateral Motion Estimation in Ultrasound Elastography

TL;DR: This work describes a method wherein true RF A-lines are acquired and augmented at subpitch locations using CLA transducer, instead of interpolating the data, and using this new frame data for further image formation and/or processing to yield better lateral resolution and LDE.
Proceedings ArticleDOI

Simulation of photoacoustic tomography (PAT) system in COMSOL and comparison of two popular reconstruction techniques

TL;DR: This study attempts to describe how a commercially available Finite Element software (COMSOL(R), can serve as a single platform for simulating PAT that couples the electromagnetic, thermodynamic and acoustic pressure physics involved in PA phenomena.
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Small breast lesion classification performance using the normalized axial-shear strain area feature.

TL;DR: The results suggest that the ASSE feature can work equally well, even on small lesions, to improve the standard ultrasound BIRADS-based breast lesion classification of fibroadenoma and malignant tumors.