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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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Radiosensitization and Stromal Imaging Response Correlates for the HIF-1 Inhibitor PX-478 Given with or without Chemotherapy in Pancreatic Cancer

TL;DR: PX-478 is a mechanistically appealing and potentially clinically relevant enhancer of pancreatic cancer radiosensitivity, inhibiting tumor and stromal HIF-1 proangiogenic signaling and reducing the innate radiation resistance of hypoxic tumor cells.
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Axial-shear strain elastography for breast lesion classification: further results from in vivo data

TL;DR: The results demonstrate that the NASSA feature derived from ASSE has the potential to improve BIRADS breast lesion classification of fibroadenoma and malignant tumors.
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Axial–Shear Strain Distributions in an Elliptical Inclusion Model: Experimental Validation and in vivo Examples With Implications to Breast Tumor Classification

TL;DR: Axial-shear strain fill-in inside an inclusion may be a unique signature of stiff, loosely bonded, ellipsoidal or elongated inclusions at non-normal orientations that may be useful as a marker of benignity of benign breast lesions that are generally stiff, elongated and loosely bonded to the host tissues.
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Elastography: A decade of progress (2000-2010)

TL;DR: The specific purpose of this review is to describe the progress of the work on elastography at the University of Texas Medical School-Houston in the past decade, and to relate it to earlier work on this topic in the pre- ceding decade.
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Visualization of HIFU-induced lesion boundaries by axial-shear strain elastography: a feasibility study.

TL;DR: It is demonstrated that it is feasible to reliably visualize HIFU lesion boundaries using ASSE, and it was shown that ASSE enables high-contrast visualization of a "thin" untreated region in between multiple fully-treated HifU-lesions.