IEEE Transactions on Biomedical Engineering

Magnetic resonance imaging (#MRI) can extract tissue conductivity values from in vivo data using phase-based magnetic resonance electrical properties tomography (#MREPT). This process is prone to noise amplification due to the use of the Laplacian operator. Therefore, suppressing noise amplification is a crucial step in EPT reconstruction. In this paper, Chuanjiang Cui et al. addressed the aforementioned problem by developing the first zero-shot, self-supervised RF phase denoiser, which uses a randomly initialized CNN prior optimized with Stein’s Unbiased Risk Estimator (SURE) to inherently avoid network overfitting. Their approach works directly on single noisy images and has been successfully tested on simulated, in vivo, and clinical data, demonstrating its generalizability, robustness, and versatility across different resolutions, SNR levels, and data from different vendors. This study is the first to develop a self-supervised denoiser for featureless RF phase corrupted by non-Gaussian noise, utilizing a randomly initialized CNN prior that is optimized with an unbiased estimator. To learn more, explore the article published in #IEEE Transactions on Biomedical Engineering (#TBME): https://lnkd.in/d-TZqccD #MRI #MREPT #EPT #CNN #DeepLearning #SelfSupervisedLearning #NoiseReduction #PhaseDenoising #SURE #BiomedicalImaging #MedicalImaging #ElectricalPropertiesTomography #SignalProcessing #ZeroShotLearning #NeuralNetworks

Deep learning models can decode surface electromyographic signals into proportional hand movements, accurately controlling both individual finger and compound movements, with potential to enhance intuitive interfaces for assistive hand devices. Raul S?mpetru et al. presented a deep learning model that decodes forearm muscle activity into intended hand movements, achieving performance on par with computer vision. Using 320 sensors, they recorded surface electromyography (sEMG) signals from 13 participants performing various movements at different speeds, decoding a 22-joint hand skeleton. The model generated distinct neural embeddings aligned with hand anatomy, mapping muscle activity to complex movements without relying on anatomical priors — learning directly from muscle activity patterns. By leveraging?full-bandwidth?sEMG data, this approach enables precise, adaptive control, paving the way for more intuitive and responsive assistive devices. To learn more, explore the article published in #IEEE Transactions on Biomedical Engineering (#TBME): https://lnkd.in/dbd2zcrh #DeepLearning #EMG #sEMG #AssistiveTechnology #NeuralNetworks #BiomedicalEngineering #HumanMachineInterface #Prosthetics #AI #MachineLearning #SensorTechnology #Neuroprosthetics #MotorControl IEEE Engineering Medicine and Biology Society

Concentric tube robots (CTRs) are well-suited for minimally invasive surgery but face challenges transitioning from simulated designs to physical prototypes due to manufacturing uncertainties, particularly in tube curvature and diameter. Fred Lin et al. proposed an end-to-end design workflow for CTRs that incorporates these uncertainties into the design process, bridging the gap between simulation and real-world performance. Experiments confirm that the physical robot’s performance aligns with the simulated probability distribution from the optimization and demonstrate the feasibility of the overall system for micro-laryngeal surgical tasks. To learn more, explore the article published in #IEEE Transactions on Biomedical Engineering (#TBME): https://lnkd.in/dXEJd_rG #ConcentricTubeRobots #MinimallyInvasiveSurgery #SurgicalRobotics #MedicalRobotics #RobotDesign #BiomedicalEngineering #MedicalDevices #RoboticsResearch #PrecisionMedicine #Bioengineering #IEEE #TBME IEEE Engineering Medicine and Biology Society

Finger and fingertip loss is the most common form of upper-limb amputation. With a focus on amputations involving the loss of distal and/or partial middle finger segments, this paper outlines the design and development of a novel soft body-powered hydraulically driven actuation system for a prosthetic finger, while offering an in-depth examination of its subsystems. This study published in IEEE TBME, demonstrates that the selected soft wearable hydraulic mechanism transferred generated pressure from the participant’s PIP joint effectively, enabling movement of the prosthetic digit. This research contributes to current developments in versatile body-powered prosthetic devices, laying the foundations for broad applications in affordable healthcare devices. To learn more, explore the article published in #IEEE Transactions on Biomedical Engineering (#TBME): https://lnkd.in/dAayqjUR #Prosthetics #BiomedicalEngineering #SoftRobotics #HydraulicActuation #BodyPoweredProsthetics #MedicalDevices #RehabilitationEngineering #Amputation #WearableTechnology #AssistiveTechnology #Bionics IEEE Engineering Medicine and Biology Society

??We are thrilled to welcome renowned scholars to the IEEE Transactions on Biomedical Engineering (TBME) Editorial Board as our new Associate Editors?? ??Dr. Elena De Momi, Politecnico di Milano, Italy Expertise: #Robotics, #Surgery, #ComputerVision https://lnkd.in/drY_fWsr ??Dr. Javier Escudero, University of Edinburgh, UK Expertise: #EEG / #MEG, #SignalProcessing, #MultiwayAnalysis / #TensorDecompositions, #NetworkAnalysis, #BrainConnectivity https://lnkd.in/dQHF3ZQX ??Dr. Wei Gao, California Institute of Technology, USA Expertise: #Biosensors, #FlexibleElectronics, #WearableDevices, #Bioelectronics, #Microrobotics https://lnkd.in/dfqGsz9Q ??Dr. Ivana Isgum, University of Amsterdam, Netherlands Expertise: #MedicalImageAnalysis, #CardiovascularImaging https://lnkd.in/dbTnKGvR ??Dr. Chulhong Kim, Pohang University of Science and Technology, Republic of Korea Expertise: #PhotoacousticImaging, #UltrasoundImaging, #HighPerformanceComputing https://lnkd.in/dUkZD-Pi ??Dr. Amir Manbachi, Johns Hopkins University, USA Expertise: #Ultrasound, #UltrasoundImaging, #UltrasoundTransducers, #DopplerImaging, #TherapeuticUltrasound, #UltrasoundNeuromodulation. https://lnkd.in/d_UCZAef ??Dr. Li Wang, University of North Carolina at Chapel Hill, USA Expertise: #MedicalImageAnalysis, #InfantBrainDevelopment https://lnkd.in/dfM_jACG ?? Each of these experts brings a wealth of knowledge and experience in their respective fields, further strengthening our commitment to advancing biomedical engineering research and innovation. ?? Their dedication and expertise will be invaluable in guiding the journal's vision, enhancing the quality of our publication, and fostering cutting-edge research within the biomedical engineering community. Please join us in extending a warm welcome to our new Associate Editors! ?? We look forward to their contributions and the continued growth and success of IEEE TBME.

This paper, published in IEEE TBME, presents the design of an electrically small half-wavelength resonant circular-shaped triple-band annular ring-based strip antenna for an intraocular unit in both flat and conformal forms for retinal prosthesis application. The performance validation for the reported single-layer antenna is carried out by simulating the antenna structure using a detailed multilayer canonical and anatomical (Zubal phantom) eye model, which renders a good and satisfactory performance. The other performance metrics, such as axial ratio, average SAR, radiation pattern, and gain, have also been analyzed for the proposed antenna's flat and conformal form. The average SAR study over different tissue layers of the human eye has also been performed, conforming to the 1g and 10g tissue standards. The low ASAR values confirm the authenticity of the proposed antenna to be used in both flat and conformal forms at different locations inside an eyeball. Finally, the antenna is fabricated, and its performance metrics are verified by comparing them with the simulation. To learn more, explore the article published in #IEEE Transactions on Biomedical Engineering (#TBME): https://lnkd.in/dnjcbeM2 #AnnularRing #IntraocularAntenna #Biocompatible #ConformalCircularShape #ElectricallySmall #HalfWavelengthResonant #Multiband #RetinalProsthesis #StripAntenna IEEE Engineering Medicine and Biology Society

?????????? ?????? ???????????? ?????????????? At IEEE Transactions on Biomedical Engineering (#TBME), we welcome proposals for high-quality review papers that provide in-depth insights into emerging and significant topics in biomedical engineering. As the flagship journal of the IEEE Engineering in Medicine and Biology Society (#EMBS), TBME receives over 2,900 submissions annually and has a long-standing reputation for excellence in the field. It is also the most visited and downloaded journal in the EMBS portfolio on IEEE Xplore, ensuring broad visibility and impact for your work. All submitted papers are handled by one of our 90 editorial board experts, covering all aspects of biomedical engineering, ensuring a rigorous and expert-driven review process. ?????????????????????? ?????? ???????????? ?????????????? Review Papers are expected to be 15 or fewer pages and may not exceed a maximum length of 15 pages without explicit permission from the Editor-in-Chief. Overlength page charges for pages between 8 and 15 will be waived. However, overlength charges will apply for any pages beyond the 15th, as outlined below. Authors must obtain prior approval from the Editor-in-Chief before submitting a Review Paper that exceeds the 15-page limit. ?? ???????????????????? ?????????????????? Review paper proposals will be assessed based on: 1-The review should cover an emerging and important area of research activity within the biomedical engineering community. 2-The authors should have already made important contributions to the topic of the review article. ?????????????????? ?????????????????????? All proposals must be submitted via email to [email protected] and should include: ? A tentative title ? A list of authors with brief biographies ? A description (max 2 pages) with relevant references We look forward to receiving your proposals! #IEEE #BiomedicalEngineering #ReviewPapers #CallForPapers IEEE Engineering Medicine and Biology Society

TBME welcomes proposals from the EMB community for special issues or sections. Proposed topics should focus on emerging and significant areas of interest and activity in biomedical engineering. We expect proposers to be leading contributors in the proposed area, with a track record of significant high impact prior publications. Special issues typically consist of 25-40 articles, while special sections generally include about 10 articles. We invite you to complete the proposal form and submit via email to?[email protected]?with the subject line “Proposal for Special Issue/Section.” The required template and additional information are available on the TBME website: https://lnkd.in/dWezBAeC

In clinical ultrasound, conventional 2-D strain imaging often struggles to accurately quantify the heart's three orthogonal normal strain components. This limitation arises from the need for separate image acquisitions tailored to a pixel-dependent cardiac coordinate system, which requires additional computational resources and introduces estimation errors due to probe orientation variability. Furthermore, most systems omit shear strain information due to the complexity of displaying all components clearly. To overcome these challenges, a new study in IEEE TBME introduces an innovative 3-D high-spatial-resolution, coordinate-independent strain imaging approach that utilizes principal stretch and axis estimation. This method transforms all strain components into three principal stretches along three orthogonal principal axes, facilitating direct visualization of primary deformations and simplifying interpretation. To learn more, explore the article published in #IEEE Transactions on Biomedical Engineering (#TBME): https://lnkd.in/dMJnRvyG #ClinicalUltrasound #3DStrainImaging #CardiacImaging #UltrasoundInnovation #SpeckleTracking #PrincipalStrain #BiomedicalEngineering #MedicalImaging #StrainEstimation #HeartHealth #Cardiology #TissueIncompressibility IEEE Engineering Medicine and Biology Society

The compound action potential (CAP) of the auditory nerve is the collective response of the auditory nerve fibers to a brief stimulus. This study introduces a computational modeling framework to simulate both acoustically and electrically evoked CAPs in cochlear implant users with residual acoustic hearing. By incorporating the 3D cochlear anatomy and the hearing status (audiogram) of a virtual subject, the framework predicts realistic morphologies and amplitude growth of human CAP responses and replicates the suppression of electrically evoked CAPs by acoustic stimuli. It aims to support the development of objective methods for assessing patient status in the future. For further information, read the article in #IEEE Transactions on Biomedical Engineering (#TBME): https://lnkd.in/eJTyFrCQ #AuditoryNerve #CochlearImplants #HearingResearch #ComputationalModeling #AcousticStimulation #AuditoryCAP #HearingHealth #NeuralModeling #Audiology #HearingScience #AuditorySystem #ElectroacousticStimulation #PatientCare #HearingLoss #3DAnatomy