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.
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Xiaochuan Pan
Editor-in-chief
Editor-in-chief

"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.

Professor Pan’s research centers on physics, algorithms, and engineering underpinning tomographic imaging and its biomedical and clinical applications. He and his laboratory have conducted research on advanced theory and algorithms for... Read more

"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.

Professor Pan’s research centers on physics, algorithms, and engineering underpinning tomographic imaging and its biomedical and clinical applications. He and his laboratory have conducted research on advanced theory and algorithms for conventional and spectral computed tomography (CT), positron emission tomography (PET), single-photo-emission computed tomography (SPECT), and tomosynthesis especially digital breast tomosynthesis (DBT) and digital lung tomosynthesis (DLT). In collaborating with leading researchers in the field, he and his team have worked on magnetic resonance imaging (MRI) and have also investigated emerging imaging techniques, including electron-paramagnetic resonance imaging (EPRI), phase-contrast CT, and photo-acoustic tomography (PAT), among others. In recent years, he and his team have developed vigorous interest/effort in translating theoretical concepts and methods to biomedical application work that includes developing innovative hardware systems and workflows, enabled by advanced algorithms, with a strong emphasis on the relevance and impact of imaging technological solutions tailored to specific applications of biomedical and/or clinical significance, and have established continuous, close clinical and industrial collaboration and developed robust translational projects to facilitate this effort. Dr. Pan is a Fellow of AAPM, AIMBE, IAMBE, IEEE, OSA, and SPIE."

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Featured Articles

IEEE Transactions on

Biomedical Engineering

DECEMBER 2020
VOLUME 67
NUMBER 12
IEBEAX
67
TBME, Volume 67, Issue 12, December 2020
A Machine Learning Enabled Wireless Intracranial Brain Deformation Sensing System
A leading cause of traumatic brain injury (TBI) is intracranial brain deformation from mechanical impact. This deformation is viscoelastic and differs from a traditional rigid transformation. Here, we present a machine learning enabled wireless sensing system, which can predict the trajectory of intracranial brain deformation by interpreting the magnetic sensor outputs created by the change in position of the implanted soft magnet. Both in vitro and in vivo experimental results showed an overall accuracy of over 92%, suggesting that this sensing scheme can be an effective tool for studying TBI due to in situ and real-time brain deformation prediction... Read more
Laparoscopic Renal Denervation System for Treating Resistant Hypertension: Overcoming Limitations of Catheter-based Approaches
The radical sympathectomy and percutaneous catheter-based renal denervation (RDN) are two techniques proposed to treat life-threatening resistant hypertension. However, sympathectomy has been abandoned due to the procedure being too invasive, and RDN resulted in variation in blood pressure reduction between patients due to suboptimal denervation. Thus, a method to effectively ablate renal nerves while not being very invasive is needed to treat the resistant hypertension patients. Here we propose a minimally invasive Laparoscopic Denervation System (LDS) to serve this unmet clinical need. The LDS employs a direct renal nerve ablation technique while not imparting thermal arterial damage... Read more
On the Safety of Human Body Communication
Human Body Communication (HBC) utilizes the human body as a physically secure, energy-efficient communication medium between devices on and around the body by sending electrical signals through the body. This paper provides a safety analysis of different modalities of HBC, for the first time, by comparing the current, electric field, magnetic field intensities from HBC with the established ICNIRP, IEEE, NIOSH safety standards through theory, analytical models and simulations. A study on a set of 7 subjects show that wearing an HBC enabled watch does not affect vital parameters including l heart rate, Mean Arterial Pressure, Respiration Rate, Peripheral Capillary Oxygen Saturation, Temperature... Read more
Brain-Computer Interface-based Soft Robotic Glove Rehabilitation for Stroke
This paper presents the results of a study involving the use of a Brain-Computer Interface-based Soft Robotic Glove as a novel strategy in stroke rehabilitation. The technology uses the electroencephalogram signals from stroke patients to drive the assistive actions of the soft robotic glove to assist them in physically carrying out activities of daily living. The two-arm study showed prolonged improvements in FMA and ARAT scores although no significant intergroup differences were observed during the study. In addition, all of the patients in the BCI-SRG group also experienced a vivid kinesthetic illusion lasting beyond the active intervention period... Read more
Mammography Image Quality Assurance Using Deep Learning
Image quality assurance is crucial in mammography to ensure reliable breast cancer diagnostics. Analyzing images of a technical phantom allows to routinely and reliably assess image quality. Current state-of-the-art analysis determines local image quality features by applying pre-processing and regression procedures for a set of repeatedly recorded images. This proof of concept paper demonstrates that mammography image quality assessment can benefit from deep learning. A neural network is trained on a large database of phantom images, and it is shown that the trained net retrieves the local image quality features already from single images without cumbersome pre-processing. This allows to maintain quality standards at significantly less labor... Read more