Biomedical Engineering / Biomedizinische Technik

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Volume 70, Issue 1

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Biomedical Engineering / Biomedizinische Technik

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Frontmatter

February 3, 2025

Page range: i-iii

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Editorial
Open Access
Change of publication model for Biomedical Engineering/Biomedizinische Technik

December 24, 2024

Katharina J. Appelt, Jens Haueisen

Page range: 1-2

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Research Articles
Open Access
Mechano-responses of quadriceps muscles evoked by transcranial magnetic stimulation

September 25, 2024

Zafirah Zakaria, Mazlina Mazlan, Tze Yang Chung, Victor S. Selvanayagam, John Temesi, Vhinoth Magenthran, Nur Azah Hamzaid

Page range: 3-10

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Mechanomyography (MMG) may be used to quantify very small motor responses resulting from muscle activation, voluntary or involuntary. The purpose of this study was to investigate the MMG mean peak amplitude (MPA) and area under the curve (AUC) and the corresponding mechanical responses following delivery of transcranial magnetic stimulation (TMS) to the knee extensors. Fourteen adults (23 ± 1 years) received single TMS pulses at intensities from 30–80 % maximum stimulator output to elicit muscle responses in the relaxed knee extensors while seated. An accelerometer-based sensor was placed on the rectus femoris (RF) and vastus lateralis (VL) muscle bellies to measure the MMG signal. Pearson correlation revealed a positive linear relationship between MMG MPA and TMS intensity for RF (r=0.569; p<0.001) and VL (r=0.618; p<0.001). TMS intensity of ≥60 % maximum stimulator output produced significantly higher MPA than at 30 % TMS intensity and evoked measurable movement at the knee joint. MMG MPA was positively correlated to AUC (r=0.957 for RF and r=0.603 for VL; both p<0.001) and knee extension angle (r=0.596 for RF and r=0.675 for VL; both p<0.001). In conclusion, MMG captured knee extensor mechanical responses at all TMS intensities with the response increasing with increasing TMS intensity. These findings suggest that MMG can be an additional tool for assessing muscle activation.
Open Access
A type-2 fuzzy inference-based approach enables walking speed estimation that adapts to inter-individual gait patterns

November 26, 2024

Linrong Li, Wenxiang Liao, Hongliu Yu

Page range: 11-20

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Objectives Individuals change walking speed by regulating step frequency (SF), stride length (SL), or a combination of both (FL combinations). However, existing methods of walking speed estimation ignore this regulatory mechanism. This paper aims to achieve accurate walking speed estimation while enabling adaptation to inter-individual speed regulation strategies. Methods We first extracted thigh features closely related to individual speed regulation based on a single thigh mounted IMU. Next, an interval type-2 fuzzy inference system was used to infer and quantify the individuals’ speed regulation intentions, enabling speed estimation independent of inter-individual gait patterns. Experiments with five subjects walking on a treadmill at different speeds and with different gait patterns validated our method. Results The overall root mean square error (RMSE) for speed estimation was 0.0704 ± 0.0087 m/s, and the RMSE for different gait patterns was no more than 0.074 ± 0.005 m/s. Conclusions The proposed method provides high-accuracy speed estimation. Moreover, our method can be adapted to different FL combinations without the need for individualised tuning or training of individuals with varying limb lengths and gait habits. We anticipate that the proposed method will help provide more intuitive speed adaptive control for rehabilitation robots, especially intelligent lower limb prostheses.
Open Access
DeepCOVIDNet-CXR: deep learning strategies for identifying COVID-19 on enhanced chest X-rays

October 8, 2024

Gokhan Altan, Süleyman Serhan Narli

Page range: 21-35

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Objectives COVID-19 is one of the recent major epidemics, which accelerates its mortality and prevalence worldwide. Most literature on chest X-ray-based COVID-19 analysis has focused on multi-case classification (COVID-19, pneumonia, and normal) by the advantages of Deep Learning. However, the limited number of chest X-rays with COVID-19 is a prominent deficiency for clinical relevance. This study aims at evaluating COVID-19 identification performances using adaptive histogram equalization (AHE) to feed the ConvNet architectures with reliable lung anatomy of airways. Methods We experimented with balanced small- and large-scale COVID-19 databases using left lung, right lung, and complete chest X-rays with various AHE parameters. On multiple strategies, we applied transfer learning on four ConvNet architectures (MobileNet, DarkNet19, VGG16, and AlexNet). Results Whereas DarkNet19 reached the highest multi-case identification performance with an accuracy rate of 98.26 % on the small-scale dataset, VGG16 achieved the best generalization performance with an accuracy rate of 95.04 % on the large-scale dataset. Conclusions Our study is one of the pioneering approaches that analyses 3615 COVID-19 cases and specifies the most responsible AHE parameters for ConvNet architectures in the multi-case classification.
Open Access
Prediction of muscular-invasive bladder cancer using multi-view fusion self-distillation model based on 3D T2-Weighted images

November 6, 2024

Yuan Zou, Jie Yu, Lingkai Cai, Chunxiao Chen, Ruoyu Meng, Yueyue Xiao, Xue Fu, Xiao Yang, Peikun Liu, Qiang Lu

Page range: 37-47

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Objectives Accurate preoperative differentiation between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is crucial for surgical decision-making in bladder cancer (BCa) patients. MIBC diagnosis relies on the Vesical Imaging-Reporting and Data System (VI-RADS) in clinical using multi-parametric MRI (mp-MRI). Given the absence of some sequences in practice, this study aims to optimize the existing T2-weighted imaging (T2WI) sequence to assess MIBC accurately. Methods We analyzed T2WI images from 615 BCa patients and developed a multi-view fusion self-distillation (MVSD) model that integrates transverse and sagittal views to classify MIBC and NMIBC. This 3D image classification method leverages z-axis information from 3D MRI volume, combining information from adjacent slices for comprehensive features extraction. Multi-view fusion enhances global information by mutually complementing and constraining information from the transverse and sagittal planes. Self-distillation allows shallow classifiers to learn valuable knowledge from deep layers, boosting feature extraction capability of the backbone and achieving better classification performance. Results Compared to the performance of MVSD with classical deep learning methods and the state-of-the-art MRI-based BCa classification approaches, the proposed MVSD model achieves the highest area under the curve (AUC) 0.927 and accuracy (Acc) 0.880, respectively. DeLong’s test shows that the AUC of the MVSD has statistically significant differences with the VGG16, Densenet, ResNet50, and 3D residual network. Furthermore, the Acc of the MVSD model is higher than that of the two urologists. Conclusions Our proposed MVSD model performs satisfactorily distinguishing between MIBC and NMIBC, indicating significant potential in facilitating preoperative BCa diagnosis for urologists.
Open Access
Evaluation of the RF depositions at 3T in routine clinical scans with respect to the SAR safety to improve efficiency of MRI utilization

September 18, 2024

Ruixin Li, Xinlian Chen, Yupeng Liao, Sherman Xuegang Xin

Page range: 49-59

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Objectives This study explores the potential for improving of 3T MRI utilization by assessing and tailoring RF exposure in routine clinical scans while complying to standard safety limit. Methods Using two generic human body models, we evaluated pbSAR 10g values at four landmark positions (knee, pelvis, thoracic spine, head) at different wbSAR levels. Specifically, we analyzed local SAR 10g in different operating modes and computed the maximum safety wbSAR, ensuring compliance with IEC limits. Results In normal operating mode, the RF power deposition reached wbSAR limit before the pbSAR 10g limit. In the first level controlled operating mode, pbSAR 10g limit is reached before the wbSAR limit in the knee, thoracic spine scanning scenarios, while the wbSAR limit is reached first in the pelvis scanning scenarios, making it the most potential-releasing (up to 33.33 %) scanning scenario. For head exposure, the head SAR 10g limit is reached before the wbSAR limit, highlighting the necessity for strict SAR control. Moreover, we calculated the minimum allowable TR for common imaging sequences for reference. Conclusions Different RF exposure setups are necessary to meet safety standards in various scenarios. By implementing careful RF exposure setups in routine clinical scans, the high potential capacity of 3T MRI can be fully released.
Open Access
A software tool for fabricating phantoms mimicking human tissues with designated dielectric properties and frequency

October 28, 2024

Xinyue Zhang, Guofang Xu, Qiaotian Zhang, Henghui Liu, Xiang Nan, Jijun Han

Page range: 61-70

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Objectives Dielectric materials play a crucial role in assessing and refining the measurement performance of dielectric properties for specific tasks. The availability of viable and standardized dielectric materials could greatly enhance medical applications related to dielectric properties. However, obtaining reliable phantoms with designated dielectric properties across a specified frequency range remains challenging. In this study, we propose software to easily determine the components of dielectric materials in the frequency range of 16 MHz to 3 GHz. Methods A total of 184 phantoms were fabricated and measured using open-ended coaxial probe method. The relationship among dielectric properties, frequency, and the components of dielectric materials was fitted through feedforward neural networks. Software was developed to quickly calculate the composition of dielectric materials. Results We performed validation experiments including blood, muscle, skin, and lung tissue phantoms at 128 MHz, 298 MHz, 915 MHz, and 2.45 GHz. Compared with literature values, the relative errors of dielectric properties are less than 15 %. Conclusions This study establishes a reliable method for fabricating dielectric materials with designated dielectric properties and frequency through the development of the software. This research holds significant importance in enhancing medical research and applications that rely on tissue simulation using dielectric phantoms.
Open Access
MedShapeNet – a large-scale dataset of 3D medical shapes for computer vision

December 30, 2024

Jianning Li, Zongwei Zhou, Jiancheng Yang, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Chongyu Qu, Tiezheng Zhang, Xiaoxi Chen, Wenxuan Li, Marek Wodzinski, Paul Friedrich, Kangxian Xie, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, Gian Marco Melito, Viet Duc Vu, Afaque R. Memon, Christopher Schlachta, Sandrine De Ribaupierre, Rajnikant Patel, Roy Eagleson, Xiaojun Chen, Heinrich Mächler, Jan Stefan Kirschke, Ezequiel de la Rosa, Patrick Ferdinand Christ, Hongwei Bran Li, David G. Ellis, Michele R. Aizenberg, Sergios Gatidis, Thomas Küstner, Nadya Shusharina, Nicholas Heller, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Anjany Sekuboyina, Maximilian T. Löffler, Hans Liebl, Reuben Dorent, Tom Vercauteren, Jonathan Shapey, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Achraf Ben-Hamadou, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Federico Bolelli, Costantino Grana, Luca Lumetti, Hamidreza Salehi, Jun Ma, Yao Zhang, Ramtin Gharleghi, Susann Beier, Arcot Sowmya, Eduardo A. Garza-Villarreal, Thania Balducci, Diego Angeles-Valdez, Roberto Souza, Leticia Rittner, Richard Frayne, Yuanfeng Ji, Vincenzo Ferrari, Soumick Chatterjee, Florian Dubost, Stefanie Schreiber, Hendrik Mattern, Oliver Speck, Daniel Haehn, Christoph John, Andreas Nürnberger, João Pedrosa, Carlos Ferreira, Guilherme Aresta, António Cunha, Aurélio Campilho, Yannick Suter, Jose Garcia, Alain Lalande, Vicky Vandenbossche, Aline Van Oevelen, Kate Duquesne, Hamza Mekhzoum, Jef Vandemeulebroucke, Emmanuel Audenaert, Claudia Krebs, Timo van Leeuwen, Evie Vereecke, Hauke Heidemeyer, Rainer Röhrig, Frank Hölzle, Vahid Badeli, Kathrin Krieger, Matthias Gunzer, Jianxu Chen, Timo van Meegdenburg, Amin Dada, Miriam Balzer, Jana Fragemann, Frederic Jonske, Moritz Rempe, Stanislav Malorodov, Fin H. Bahnsen, Constantin Seibold, Alexander Jaus, Zdravko Marinov, Paul F. Jaeger, Rainer Stiefelhagen, Ana Sofia Santos, Mariana Lindo, André Ferreira, Victor Alves, Michael Kamp, Amr Abourayya, Felix Nensa, Fabian Hörst, Alexander Brehmer, Lukas Heine, Yannik Hanusrichter, Martin Weßling, Marcel Dudda, Lars E. Podleska, Matthias A. Fink, Julius Keyl, Konstantinos Tserpes, Moon-Sung Kim, Shireen Elhabian, Hans Lamecker, Dženan Zukić, Beatriz Paniagua, Christian Wachinger, Martin Urschler, Luc Duong, Jakob Wasserthal, Peter F. Hoyer, Oliver Basu, Thomas Maal, Max J. H. Witjes, Gregor Schiele, Ti-chiun Chang, Seyed-Ahmad Ahmadi, Ping Luo, Bjoern Menze, Mauricio Reyes, Thomas M. Deserno, Christos Davatzikos, Behrus Puladi, Pascal Fua, Alan L. Yuille, Jens Kleesiek, Jan Egger

Page range: 71-90

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Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing. Methods We present MedShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. Results By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Conclusions MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://medshapenet.ikim.nrw/.
Open Access
Concept and development of a telemedical supervision system for anesthesiology in operating rooms using the interoperable communication standard ISO/IEEE 11073 SDC

October 25, 2024

Jonas Roth, Verena Voigt, Okan Yilmaz, Michael Schauwinhold, Michael Czaplik, Andreas Follmann, Carina B. Pereira

Page range: 91-101

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Objectives Discussion of a telemedical supervision system for anesthesiology in the operating room using the interoperable communication protocol SDC. Validation of a first conceptual demonstrator and highlight of strengths and weaknesses. Methods The system includes relevant medical devices, a central anesthesia workstation (AN-WS), and a remote supervision workstation (SV-WS) and the concept uses the interoperability standard ISO/IEEE 11073 SDC. The validation method involves a human patient simulator, and the system is tested in an intervention study with 16 resident anesthetists supervised by a senior anesthetist. Results This study presents a novel tele-supervision system that enables remote patient monitoring and communication between anesthesia providers and supervisors. It is composed of connected medical devices via SDC, a central AN-WS and a mobile remote SV-WS. The system is designed to handle multiple ORs and route the data to a single SV-WS. It enables audio/video connections and text chatting between the workstations and offers the supervisor to switch between cameras in the OR. Through a validation study the feasibility and usefulness of the system was assessed. Conclusions Validation results highlighted, that such system might not replace physically present supervisors but is able to provide supervision for scenarios where supervision is currently not available or only under adverse circumstances.

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