journal: Biomedical Engineering / Biomedizinische Technik

Impact Factor: 1.3

Published since January 1, 1956

Biomedical Engineering / Biomedizinische Technik

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering and the German Society of Biomaterials

ISSN: 1862-278X

Editor-in-Chief: Jens Haueisen

Edited by: Peter Augat, Daniel Baumgarten, Thomas M. Deserno, Frédéric Heim, Steffen Leonhardt, Klaus M. Radermacher, Erik Schkommodau, Thomas Stieglitz, Gudrun Wagenknecht

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About this journal

Objective
Biomedical Engineering / Biomedizinische Technik (BMT) is a high-quality forum for the exchange of knowledge in the fields of biomedical engineering, medical information technology and biotechnology/bioengineering. As an established journal with a tradition of more than 60 years, BMT addresses engineers, natural scientists, and clinicians working in research, industry, or clinical practice. All articles are peer-reviewed. All manuscripts must be submitted in English.

Topics

diagnostic imaging
image processing
biosignal processing
modelling and simulation
biomechanics
information and communication in medicine
telemedicine and e-health
surgery
minimum invasive interventions
endoscopy and image-guided therapy
diagnostic and therapeutic instrumentation
clinical engineering
micro- and nanosystems in medicine
active implants
biosensors
neuro-engineering
neural systems
rehabilitation and prosthetics
biomaterials
cellular and tissue engineering
artificial organs
biomedical engineering for audiology, ophthalmology, emergency and dental medicine
general subjects (e.g., education and training, health technology assessment)
special topics (e.g., linking proteomics and genomics with biomedical engineering, emergency medicine)

Article formats
Reviews, Research Articles, Short Communications

Your Benefits

Your benefits:

Imperative topic
Applicative approach to engineering facilities and methods
New areas of application of cutting-edge technologies in modern medicine
State-of-the-art solutions for requirements in current medical practice
Ahead-of-print online publication

History

The Journal was founded in 1956 as the official journal of the German Society of Biomedical Engineering Biomedizinische Technik and published firstly in German. In a transition period as of 2004 publication in German and English was possible, but since 2010 the journal appears only in English. Consistently the title was changed to Biomedical Engineering – Biomedizinische Technik.

Other publications in the field

Journals:

Books, books series and multi-volume works:

„Biomedizinische Technik” (12 Volumes). (Overview)
S. Dickel, M. Franzen, Ch. Kehl, Herausforderung Biomedizin. Gesellschaftliche Deutung und soziale Praxis, “VerKörperungen/MatteRealities - Perspektiven empirischer Wissenschaftsforschung 14”, 2014.(Overview)
D. Hochmann, Prüf- und Bewertungsmethoden für Knieorthesen, “Forschung für die Rehabilitationstechnik 1”, 2012.(Overview)
K. König, A. Ostendorf (Eds.), Optically Induced Nanostructures. Biomedical and Technical Applications, 2015. (Overview)
W. Niederlag, H. U. Lemke, H. Lehrach, H.-O. Peitgen, Der virtuelle Patient, “Health Academy 1”, 2014.(Overview)
W. Niederlag, H. U. Lemke et al., Der digitale Operationssaal, “Health Academy 2”, 2014.(Overview)
I. Roterman-Konieczna (Ed.), Simulations in Medicine. Pre-Clinical and Clinical Applications, 2015.(Overview)
J. Werner, Kooperative und autonome Systeme der Medizintechnik. Funktionswiederherstellung und Organersatz, 2014.(Overview)

Previous Titles

2012: from Biomedizinische Technik – Biomedical Engineering to Biomedical Engineering – Biomedizinische Technik

Abstracting and Indexing

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Supplementary Materials

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Ahead of Print / Just Accepted

Ahead of Print / Just Accepted

Volume 69 (2024)

Volume 68 (2023)

Volume 67 (2022)

Volume 66 (2021)

Volume 65 (2020)

Volume 64 (2019)

Volume 63 (2018)

Volume 62 (2017)

Volume 61 (2016)

Volume 60 (2015)

Volume 59 (2014)

Volume 58 (2013)

Volume 57 (2012)

Volume 56 (2011)

Volume 55 (2010)

Volume 54 (2009)

Volume 53 (2008)

Volume 52 (2007)

Volume 51 (2006)

Volume 50 (2005)

Volume 49 (2004)

Volume 48 (2003)

Volume 47 (2002)

Volume 46 (2001)

Volume 45 (2000)

Volume 44 (1999)

Volume 43 (1998)

Volume 42 (1997)

Volume 41 (1996)

Volume 40 (1995)

Volume 39 (1994)

Volume 38 (1993)

Volume 37 (1992)

Volume 36 (1991)

Volume 35 (1990)

Volume 34 (1989)

Volume 33 (1988)

Volume 32 (1987)

Volume 31 (1986)

Volume 30 (1985)

Volume 29 (1984)

Volume 28 (1983)

Volume 27 (1982)

Volume 26 (1981)

Volume 25 (1980)

Volume 24 (1979)

Volume 23 (1978)

Volume 22 (1977)

Volume 21 (1976)

Volume 20 (1975)

Volume 19 (1974)

Volume 18 (1973)

Volume 17 (1972)

Volume 16 (1971)

Volume 15 (1970)

Volume 14 (1969)

Volume 13 (1968)

Issue 1-6

Volume 12 (1967)

Issue 1-6

Volume 11 (1966)

Volume 10 (1965)

Issue 1-4

Volume 9 (1964)

Issue 1-4

Volume 8 (1963)

Volume 7 (1962)

Volume 6 (1961)

Volume 5 (1960)

Volume 4 (1959)

Volume 3 (1958)

Volume 2 (1957)

Volume 1 (1956)

Biomedical Engineering / Biomedizinische Technik publishes special and thematic issues focussed on important and emerging topics in the field of study. The journal has established a rigorous process to ensure that any special issue manuscripts follow the same high-quality standards and peer review processes as regular manuscripts. For further information on the journal’s peer review policy please see the "Instructions for Authors".

Recent Special Issues

Special Issue: Medical Flow and Dosing Measurement Metrology in Drug Delivery; Guest Editors: Elsa Batista, Hugo Bissig and Stephan Klein [Issue 1, Volume 68 (2023)]

Journal Impact Factor	1.3	2023, Journal Citation Reports (Clarivate, 2024)
5-year Journal Impact Factor	1.5	2023, Journal Citation Reports (Clarivate, 2024)
Journal Citation Indicator	0.32	2023, Journal Citation Reports (Clarivate, 2024)
CiteScore	3.5	2023, Scopus (Elsevier B.V., 2024)
SCImago Journal Rank	0.353	2023, SJR (Scimago Lab, 2024; Data Source: Scopus)
Source Normalized Impact per Paper	0.520	2023, CWTS Journal Indicators (CWTS B.V., 2024; Data Source: Scopus)

More information about journal rankings

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Submission
You can easily submit your manuscript online. Simply go to http://mc.manuscriptcentral.com/bmt and you will be guided through the whole peer-reviewing and publishing process.

Your benefits of publishing with us

Rapid online publication ahead-of-print with short turnaround times
High quality manuscript processing through ScholarOne Manuscripts®
Optional open access publication
Each article published under an open access license is subject to an Article Processing Charge of €2,000.
Accepted papers will be published online first as DOI-citable, forward-linked articles for quickest possible visibility for the scientific community
Every article easily discoverable because of Search Engine Optimization (SEO) and comprehensive abstracting and indexing services
Secure archiving by De Gruyter and the independent archiving service Portico
Professional sales and marketing activities

Submission process

Before submitting a manuscript, please have a look at our Instructions for Authors
Submission of your paper via our submission management tool
Peer review process (you will be guided through every step)
Decision on your paper (6-8 weeks from submission to first decision)
When papers are accepted subject to revision, the revised manuscript must be returned within 12 weeks (research articles, reviews) or 6 weeks (short communications, case reports, letters to the editors) after the author has been notified of the decision
If accepted: you have the option to publish it open access

Please note

Manuscripts must be written in clear and concise English
Before submitting your article please have a look at our Ethical Guidelines and our Copyright Transfer Agreement
Biomedical Engineering follows Tier 2 of our Data Sharing Policy. Authors are encouraged to provide a data availability statement. Please refer to the Data Sharing Policy for guidance on how to write a data availability statement.　
As a condition of submission, the Template for Ethical and Legal Declarations must be customized by the submitting author on behalf of all others and uploaded as a separate Word file. Instructions on how to make declarations can be found here.
Our Repository Policy allows you to distribute 30 PDF copies of your published article to colleagues (the PDF has to include the information that it is an author's copy). Please also feel free to distribute the link to the online abstract
If you have any general questions please visit our FAQ page for authors or contact us at: bmt.editorial@degruyter.com

We look forward to receiving your manuscript!

Hybrid Open Access

In this journal, authors have the option to publish their article under an open access license. Open Access allows you as an author to retain copyright and share your findings with colleagues and interested parties worldwide without any restraints.
Please note that authors from institutions with which we have a transformative agreement can publish open access without paying an article processing charge (APC). More information on the eligible institutions and articles can be found under the "Funding and Support" tab here.

Editorial

Editor-in-Chief
Jens Haueisen (Ilmenau, Germany)

Contact person
Jens Haueisen, jens.haueisen@tu-ilmenau.de

Associate Editors
Peter Augat (Institut für Biomechanik, Germany)
Daniel Baumgarten (Institut für Elektrotechnik und Biomedizinische Technik, Austria)
Thomas Deserno (Peter L. Reichertz Instituts für Medizinische Informatik, Technische Universität Braunschweig, Germany)
Frédéric Heim (Laboratoire de Physique et Mécanique Textiles
Universite de Haute-Alsace, France)
Steffen Leonhardt (Helmholtz Institute of Biomedical Engineering, RWTH Aachen University, Germany)
Klaus Radermacher (Helmholtz-Institute for Biomedical Engineering,
RWTH Aachen University)
Erik Schkommodau (Institute for Medical Technologies, University of Applied Sciences Northwestern Switzerland, Switzerland)
Thomas Stieglitz (Department of Microsystems Engineering (IMTEK) University of Freiburg, Germany)
Gudrun Wagenknecht (Forschungszentrum Jülich GmbH, Germany)

Editorial Board
†Ulrich Boenick (Berlin, Germany)
Olaf Dössel (Karlsruhe, Germany)
Pamela Habibovic (Maastricht, Netherlands)
Branislav Jaramaz (Pittsburgh, PA, United States)
Marc Kraft (Berlin, Germany)
Thomas Lenarz (Hannover, Germany)
Harry van Lenthe (Leuven, Belgium)
Benny Lo (London, UK)
Luca Mainardi (Milano, Italy)
Silvestro Micera (Lausanne, Switzerland & Pisa, Italy)
Thomas Penzel (Berlin, Germany)
Andrea Robitzki (Karlsruhe, Germany)
Klaus-Peter Schmitz (Rostock, Germany)
Tobias Schaeffter (Berlin, Germany)
Jess G. Snedeker (Zurich, Switzerland)
Leif Sörnmo (Lund, Sweden)
Nobuhiko Sugano (Osaka, Japan)
Jürgen Werner (Bochum, Germany)
Werner Wolf (Neubiberg, Germany)

BMT Editorial Office
Walter de Gruyter GmbH
Genthiner Str. 13
10785 Berlin
Germany
Tel. +49-30-26005-176
Fax: +49-30-26005-352
E-mail: bmt.editorial@degruyter.com

Volume 69 Issue 5

October 2024

Publicly Available October 7, 2024

Frontmatter

Page range: i-iii

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Research Articles

April 11, 2024

Sparse-view CT reconstruction based on group-based sparse representation using weighted guided image filtering

Rong Xu, Yi Liu, Zhiyuan Li, Zhiguo Gui

Page range: 431-439

Abstract

Objectives In the past, guided image filtering (GIF)-based methods often utilized total variation (TV)-based methods to reconstruct guidance images. And they failed to reconstruct the intricate details of complex clinical images accurately. To address these problems, we propose a new sparse-view CT reconstruction method based on group-based sparse representation using weighted guided image filtering. Methods In each iteration of the proposed algorithm, the result constrained by the group-based sparse representation (GSR) is used as the guidance image. Then, the weighted guided image filtering (WGIF) was used to transfer the important features from the guidance image to the reconstruction of the SART method. Results Three representative slices were tested under 64 projection views, and the proposed method yielded the best visual effect. For the shoulder case, the PSNR can achieve 48.82, which is far superior to other methods. Conclusions The experimental results demonstrate that our method is more effective in preserving structures, suppressing noise, and reducing artifacts compared to other methods.

May 6, 2024

Comparative evaluation of volumetry estimation from plain and contrast enhanced computed tomography liver images

Lakshmipriya Balagourouchetty, Pragatheeswaran K. Jayanthi, Biju Pottakkat, Govindarajalou Ramkumar

Page range: 441-453

Abstract

Objectives Surgery planning for liver tumour is carried out using contrast enhanced computed tomography (CECT) images to determine the optimal resection strategy and to assess the volume of liver and tumour. Current surgery planning tools interpret even the functioning liver cells present within the tumour boundary as tumour. Plain CT images provide inadequate information for treatment planning. This work attempts to address two shortcomings of existing surgery planning tools: (i) to delineate functioning liver cells from the non-functioning tumourous tissues within the tumour boundary and (ii) to provide 3D visualization and actual tumour volume from the plain CT images. Methods All slices of plain CT images containing liver are enhanced by means of fuzzy histogram equalization in Non-Subsampled Contourlet Transform (NSCT) domain prior to 3D reconstruction to clearly delineate liver, non-functioning tumourous tissues and functioning liver cells within the tumour boundary. The 3D analysis from plain and CECT images was carried out on five types of liver lesions viz. HCC, metastasis, hemangioma, cyst, and abscess along with normal liver. Results The study resulted in clear delineation of functional liver tissues from non-functioning tumourous tissues within the tumour boundary from CECT as well as plain CT images. The volume of liver calculated using the proposed approach is found comparable with that obtained using Myrian-XP, a currently followed surgery planning tool in clinical practice. Conclusions The obtained results from plain CT images will undoubtedly provide valuable diagnostic assistance and surgery planning even for the subset of patients for whom CECT acquisition is not advisable.

April 24, 2024

Vein segmentation and visualization of upper and lower extremities using convolution neural network

Amit Laddi, Shivalika Goyal, Himani, Ajay Savlania

Page range: 455-464

Abstract

Objectives The study focused on developing a reliable real-time venous localization, identification, and visualization framework based upon deep learning (DL) self-parametrized Convolution Neural Network (CNN) algorithm for segmentation of the venous map for both lower and upper limb dataset acquired under unconstrained conditions using near-infrared (NIR) imaging setup, specifically to assist vascular surgeons during venipuncture, vascular surgeries, or Chronic Venous Disease (CVD) treatments. Methods A portable image acquisition setup has been designed to collect venous data (upper and lower extremities) from 72 subjects. A manually annotated image dataset was used to train and compare the performance of existing well-known CNN-based architectures such as ResNet and VGGNet with self-parameterized U-Net, improving automated vein segmentation and visualization. Results Experimental results indicated that self-parameterized U-Net performs better at segmenting the unconstrained dataset in comparison with conventional CNN feature-based learning models, with a Dice score of 0.58 and displaying 96.7 % accuracy for real-time vein visualization, making it appropriate to locate veins in real-time under unconstrained conditions. Conclusions Self-parameterized U-Net for vein segmentation and visualization has the potential to reduce risks associated with traditional venipuncture or CVD treatments by outperforming conventional CNN architectures, providing vascular assistance, and improving patient care and treatment outcomes.

May 8, 2024

STF-Net: sparsification transformer coding guided network for subcortical brain structure segmentation

Xiufeng Zhang, Lingzhuo Tian, Shengjin Guo, Yansong Liu

Page range: 465-480

Abstract

Subcortical brain structure segmentation plays an important role in the diagnosis of neuroimaging and has become the basis of computer-aided diagnosis. Due to the blurred boundaries and complex shapes of subcortical brain structures, labeling these structures by hand becomes a time-consuming and subjective task, greatly limiting their potential for clinical applications. Thus, this paper proposes the sparsification transformer (STF) module for accurate brain structure segmentation. The self-attention mechanism is used to establish global dependencies to efficiently extract the global information of the feature map with low computational complexity. Also, the shallow network is used to compensate for low-level detail information through the localization of convolutional operations to promote the representation capability of the network. In addition, a hybrid residual dilated convolution (HRDC) module is introduced at the bottom layer of the network to extend the receptive field and extract multi-scale contextual information. Meanwhile, the octave convolution edge feature extraction (OCT) module is applied at the skip connections of the network to pay more attention to the edge features of brain structures. The proposed network is trained with a hybrid loss function. The experimental evaluation on two public datasets: IBSR and MALC, shows outstanding performance in terms of objective and subjective quality.

March 18, 2024

Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery

Emine Esra Karaca, Feyza Dicle Işık, Reza Hassanpour, Kasım Oztoprak, Özlem Evren Kemer

Page range: 481-489

Abstract

Objectives In this study, we developed a machine learning approach for postoperative corneal endothelial cell images of patients who underwent Descemet’s membrane keratoplasty (DMEK). Methods An AlexNet model is proposed and validated throughout the study for endothelial cell segmentation and cell location determination. The 506 images of postoperative corneal endothelial cells were analyzed. Endothelial cell detection, segmentation, and determining of its polygonal structure were identified. The proposed model is based on the training of an R-CNN to locate endothelial cells. Next, by determining the ridges separating adjacent cells, the density and hexagonality rates of DMEK patients are calculated. Results The proposed method reached accuracy and F1 score rates of 86.15 % and 0.857, respectively, which indicates that it can reliably replace the manual detection of cells in vivo confocal microscopy (IVCM). The AUC score of 0.764 from the proposed segmentation method suggests a satisfactory outcome. Conclusions A model focused on segmenting endothelial cells can be employed to assess the health of the endothelium in DMEK patients.

March 29, 2024

AML leukocyte classification method for small samples based on ACGAN

Chenxuan Zhang, Junlin Zhu

Page range: 491-499

Abstract

Leukemia is a class of hematologic malignancies, of which acute myeloid leukemia (AML) is the most common. Screening and diagnosis of AML are performed by microscopic examination or chemical testing of images of the patient’s peripheral blood smear. In smear-microscopy, the ability to quickly identify, count, and differentiate different types of blood cells is critical for disease diagnosis. With the development of deep learning (DL), classification techniques based on neural networks have been applied to the recognition of blood cells. However, DL methods have high requirements for the number of valid datasets. This study aims to assess the applicability of the auxiliary classification generative adversarial network (ACGAN) in the classification task for small samples of white blood cells. The method is trained on the TCIA dataset, and the classification accuracy is compared with two classical classifiers and the current state-of-the-art methods. The results are evaluated using accuracy, precision, recall, and F1 score. The accuracy of the ACGAN on the validation set is 97.1 % and the precision, recall, and F1 scores on the validation set are 97.5 , 97.3, and 97.4 %, respectively. In addition, ACGAN received a higher score in comparison with other advanced methods, which can indicate that it is competitive in classification accuracy.

June 4, 2024

Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements

Sahar Taghi Zadeh Makouei, Caglar Uyulan

Page range: 501-513

Abstract

Objectives The primary objective of this research is to improve the average classification performance for specific movements in patients with cervical spinal cord injury (SCI). Methods The study utilizes a low-frequency multi-class electroencephalography (EEG) dataset from Graz University of Technology. The research combines convolutional neural network (CNN) and long-short-term memory (LSTM) architectures to uncover neural correlations between temporal and spatial aspects of the EEG signals associated with attempted arm and hand movements. To achieve this, three different methods are used to select relevant features, and the proposed model’s robustness against variations in the data is validated using 10-fold cross-validation (CV). The research also investigates subject-specific adaptation in an online paradigm, extending movement classification proof-of-concept. Results The combined CNN-LSTM model, enhanced by three feature selection methods, demonstrates robustness with a mean accuracy of 75.75 % and low standard deviation (+/− 0.74 %) in 10-fold cross-validation, confirming its reliability. Conclusions In summary, this research aims to make valuable contributions to the field of neuro-technology by developing EEG-controlled assistive devices using a generalized brain-computer interface (BCI) and deep learning (DL) framework. The focus is on capturing high-level spatiotemporal features and latent dependencies to enhance the performance and usability of EEG-based assistive technologies.

March 21, 2024

Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction

Nishi Shahnaj Haider, Ajoy K. Behera

Page range: 515-528

Abstract

Objectives Computerized breath sound based diagnostic methods are one of the emerging technologies gaining popularity in terms of detecting respiratory disorders. However, the breath sound signal used in such automated systems used to be too noisy, which affects the quality of the diagnostic interpretations. To address this problem, the proposed work presents the new hybrid approach to reject the noises from breath sound. Methods In this method, 80 chronic obstructive pulmonary disease (COPD), 75 asthmatics and 80 normal breath sounds were recorded from the participants of a hospital. Each of these breath sound data were decontaminated using hybrid method of Butterworth band-pass filter, transient artifact reduction algorithm and spectral subtraction algorithm. The study examined the algorithms noise rejection potential over each category of breath sound by estimating the noise rejection performance metrics, i.e., mean absolute error (MAE), mean square error (MSE), peak signal to noise ratio (PSNR), and signal to noise ratio (SNR). Results Using this algorithm, the study obtained a high value of SNR of 70 dB and that of PSNR of 72 dB. Conclusions The study could definitely a suitable one to suppress noises and to produce noise free breath sound signal.

June 12, 2024

Optimized Schlieren imaging for real-time visualization of high-intensity focused ultrasound waves

Helena Nawrath, Andreas Holl, Lennart Bedarf, Denis Mai, Heiko Alfke, Jens Gröbner

Page range: 529-533

Abstract

Objectives This article presents an low-cost experimental setup for visualizing refraction anomalies caused by high-intensity focused ultrasound (HIFU). The technique is based on Schlieren imaging, commonly used to visualize temperature and pressure differences in a medium. With this setup, double images of the Schlieren or their shadows to be investigated occur, so that the experimental setup is modified to avoid these double image artifacts. Methods The optical setup mainly consists of a point light source, a parabolic mirror, and a camera. Birefringence artifacts are avoided by placing the point light source at a certain vertical distance to the camera, so that the light beam passes through the medium only once. The soundfield is generated by a HIFU transducer in a water tank placed in the beam path of the optical setup. Results The experimental setup is capable of capturing Schlieren or shadow images. These images show the soundfield without disturbing double images and enable further analysis and qualitative assessment of the soundfield. Conclusions The presented setup provides a reliable and efficient method for visualizing refraction anomalies caused by the sonic field of a HIFU transducer and allows for accurate depiction of the refraction anomalies. The double images that usually occur are avoided.

(Deutsch)

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Online ISSN: 1862-278X
Print ISSN: 0013-5585
Type: Journal
Language: English
Publisher: De Gruyter
First published: January 1, 1956
Publication Frequency: 6 Issues per Year
Audience: researchers and decision-makers in the field of bioengineering, managers in development laboratories and in the entire sector of medical research, chief consultants in clinics, sanatoriums and hospitals, self-employed businesspeople and traders in the area of biomedical engineering, professionals in health insurance, libraries