IEEE Transactions on

Biomedical Engineering

IEEE Transactions on Biomedical Engineering contains basic and applied papers dealing with biomedical engineering. Papers range from engineering development in methods and techniques with biomedical applications to experimental and clinical investigations with engineering contributions.

Editor-in-Chief

Xiaochuan Pan

Xiaochuan Pan

Xiaochuan Pan is currently Professor of Radiology, Radiation & Cellular Oncology, Committee in Medical Physics, the College, and the University of Chicago Medicine Comprehensive Cancer Center at The University of Chicago. He received the BS (1982) and MS (1985) degrees in physics from Beijing University and the Institute of Physics, Science Academy of China and the MS (1988) and PhD (1991) degrees in physics from The University of Chicago. Following post-doc training in medical imaging from 1992-1994 in the Department of Radiology at The University of Chicago, he was appointed as an Assistant Professor of Radiology before being promoted to Associate Professor and Professor of Radiology in 2001 and 2006.

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Featured Articles

Acoustic Force Elastography Microscopy
Acoustic Force Elastography Microscopy 789 444 IEEE Transactions on Biomedical Engineering (TBME)

Acoustic Force Elastography Microscopy

Acoustic force elastography microscopy (AFEM) can be used to observe the spatial variation of mechanical properties of hydrogels in a non-contact manner and address the limitations of destructive mechanical testing measurements.

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Experimental Investigation of Microfluidic Device for Platelet Activation
Experimental Investigation of Microfluidic Device for Platelet Activation 789 444 IEEE Transactions on Biomedical Engineering (TBME)

Experimental Investigation of Microfluidic Device for Platelet Activation

We have developed microfluidic devices to activate platelets by using shear-induced platelet activation approach. These biomedical devices can be used in Platelet-rich plasma treatments without any adverse immune reaction.

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Minimally Invasive Implant Type Electromagnetic Biosensor for Continuous Glucose Monitoring System: In vivo Evaluation
Minimally Invasive Implant Type Electromagnetic Biosensor for Continuous Glucose Monitoring System: In vivo Evaluation 780 444 IEEE Transactions on Biomedical Engineering (TBME)

Minimally Invasive Implant Type Electromagnetic Biosensor for Continuous Glucose Monitoring System: In vivo Evaluation

A fundamentally new approach for the development of EM-based implantable glucose sensor and initial in vivo experiments with C57BL/6J mouse and Sprague Dawley rat for continuous glucose monitoring is presented.

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3-D Path-Following Control for Steerable Needles with Fiber Bragg Gratings in Multi-Core Fibers
3-D Path-Following Control for Steerable Needles with Fiber Bragg Gratings in Multi-Core Fibers 789 444 IEEE Transactions on Biomedical Engineering (TBME)

3-D Path-Following Control for Steerable Needles with Fiber Bragg Gratings in Multi-Core Fibers

This is the first study to demonstrate the safety and performance benefits of fiber Bragg gratings in multi-core fibers for autonomous steerable needle navigation, implemented here within a biologically inspired neurosurgical system.

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Lens Free Holographic Imaging for Urinary Tract Infection Screening
Lens Free Holographic Imaging for Urinary Tract Infection Screening 789 444 IEEE Transactions on Biomedical Engineering (TBME)

Lens Free Holographic Imaging for Urinary Tract Infection Screening

Holographic imaging can count red blood cells, white blood cells, crystals, casts, and bacteria suspended in urine. Particulate images can be reconstructed through the entire depth of a 2-mm channel.

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Feature Identification Framework for Back Injury Risk in Repetitive Work with Application in Sheep Shearing
Feature Identification Framework for Back Injury Risk in Repetitive Work with Application in Sheep Shearing 789 444 IEEE Transactions on Biomedical Engineering (TBME)

Feature Identification Framework for Back Injury Risk in Repetitive Work with Application in Sheep Shearing

We developed an unsupervised feature selection framework for back injury risk using data collected from Australian sheep shearers. Our framework identifies features in line with literature and outperforms general methods.

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