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Septimiu E. Salcudean

Researcher at University of British Columbia

Publications -  440
Citations -  15689

Septimiu E. Salcudean is an academic researcher from University of British Columbia. The author has contributed to research in topics: Imaging phantom & Elastography. The author has an hindex of 64, co-authored 399 publications receiving 14100 citations. Previous affiliations of Septimiu E. Salcudean include University of California, Berkeley & IBM.

Papers
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Quasi-Real Time Multi-Frequency 3D Shear Wave Absolute Vibro-Elastography (S-WAVE) System for Prostate

TL;DR: A novel quasi-real time system for quantitative and volumetric measurement of tissue elasticity in the prostate is described and numerical and visual correlations between stiffness measurements and cancer likelihood as determined from pathology results are shown.
Proceedings ArticleDOI

Fast Graph Refinement and Implicit Neural Representation for Tissue Tracking

TL;DR: KINFlow as mentioned in this paper uses a graph-based point refinement scheme to refine matched keypoints, updating their features and movement instead of discarding possible outliers, and then uses these refined matches to learn a novel neural implicit representation for estimating movement of any location given its k-NN keypoints.

A Coarse-Fine Approach to Force-Reflecting Hand

TL;DR: A coarse-fine approach to the design of high fidelity haptic interfaces is proposed based on prior work and new psychophysics studies and a novel twin-elbow manipulator with all but one of the drive motors in the base is proposed for its simplicity and large workspace size.
Journal ArticleDOI

A multiparametric volumetric quantitative ultrasound imaging technique for soft tissue characterization.

TL;DR: In this paper, a multiparametric 3D weighted QUS (3D QUS) method was proposed, involving the reconstruction of three QUS parameters: attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC), and effective scatterer diameter (ESD).
Journal ArticleDOI

Centre-specific autonomous treatment plans for prostate brachytherapy using cGANs.

TL;DR: In this paper, a conditional generative adversarial network (GAN) is used to learn a center's planning strategy and automatically reproduce rapid clinically acceptable plans for low-dose-rate prostate brachytherapy (LDR-PB).