Pub Date : 2024-10-25DOI: 10.1109/TBME.2024.3486748
Zhiwei Li;Zhengxuan Zhou;Xiaoyu Zhang;Yulin Wang;Hanwen Wang;Yingwei Li;Xiaoli Li
Objective: Transcranial ultrasound stimulation (TUS) is a promising non-invasive neuromodulation method for brain disorders. Commonly-used TUS systems in research include custom-built and commercial devices. Custom-built devices typically consist of traditional function generator, power amplifier, and ultrasound transducer. Due to cumbersome wiring and absence of dedicated control software, the operation of these devices is inconvenient. Commercial devices often have limited waveform modes and cannot perform ultrasound modulation with complex waveforms. These limitations limit the application of TUS technology by ordinary users. Therefore, we propose a portable TUS system with multiple modes and high acoustic pressure. Methods: The proposed portable TUS system utilizes a high-power multi-mode stimulator, and an ultrasound transducer with impedance matching module to achieve multiple modes and high acoustic pressure ultrasound neuromodulation. Results: The stimulator can output four types of waveforms: continuous pulse continuous stimulus (CPCS), intermittent pulse continuous stimulus (IPCS), continuous pulse intermittent stimulus (CPIS), and intermittent pulse intermittent stimulus (IPIS). When using a same transducer, it generates a peak negative pressure that is nearly identical to one produced by a commercial device. And compared to commercial transducer, the peak negative pressure of our transducer is significantly higher, reaching a maximum of 0.95 MPa. Conclusion: In-vitro experiments were conducted using rat hippocampal brain slices. The experimental results demonstrated the effectiveness of the TUS system for neural stimulation. Significance: It offers a design method of a portable multi-mode, high pressure TUS system, which is used for complex neural modulation research.
目的：经颅超声刺激（TUS）是一种治疗脑部疾病的前景广阔的非侵入性神经调节方法。研究中常用的 TUS 系统包括定制设备和商用设备。定制设备通常由传统的函数发生器、功率放大器和超声换能器组成。由于接线繁琐且没有专用的控制软件，这些设备的操作很不方便。商用设备通常只有有限的波形模式，无法进行复杂波形的超声调制。这些局限性限制了普通用户对 TUS 技术的应用。因此，我们提出了一种具有多种模式和高声压的便携式 TUS 系统：方法：所提出的便携式 TUS 系统利用大功率多模式刺激器和带阻抗匹配模块的超声换能器来实现多模式和高声压超声神经调制：该刺激器可输出四种波形：连续脉冲连续刺激（CPCS）、间歇脉冲连续刺激（IPCS）、连续脉冲间歇刺激（CPIS）和间歇脉冲间歇刺激（IPIS）。当使用相同的传感器时，它产生的负压峰值与商用设备产生的负压峰值几乎相同。与商用传感器相比，我们的传感器产生的峰值负压明显更高，最大可达 0.95 兆帕：结论：我们使用大鼠海马脑片进行了体外实验。实验结果证明了 TUS 系统对神经刺激的有效性：意义：提供了一种用于复杂神经调控研究的便携式多模式高压 TUS 系统的设计方法。
{"title":"A New Multi-Mode, High Pressure Portable Transcranial Ultrasound Stimulation System","authors":"Zhiwei Li;Zhengxuan Zhou;Xiaoyu Zhang;Yulin Wang;Hanwen Wang;Yingwei Li;Xiaoli Li","doi":"10.1109/TBME.2024.3486748","DOIUrl":"10.1109/TBME.2024.3486748","url":null,"abstract":"<italic>Objective:</i> Transcranial ultrasound stimulation (TUS) is a promising non-invasive neuromodulation method for brain disorders. Commonly-used TUS systems in research include custom-built and commercial devices. Custom-built devices typically consist of traditional function generator, power amplifier, and ultrasound transducer. Due to cumbersome wiring and absence of dedicated control software, the operation of these devices is inconvenient. Commercial devices often have limited waveform modes and cannot perform ultrasound modulation with complex waveforms. These limitations limit the application of TUS technology by ordinary users. Therefore, we propose a portable TUS system with multiple modes and high acoustic pressure. <italic>Methods:</i> The proposed portable TUS system utilizes a high-power multi-mode stimulator, and an ultrasound transducer with impedance matching module to achieve multiple modes and high acoustic pressure ultrasound neuromodulation. <italic>Results:</i> The stimulator can output four types of waveforms: continuous pulse continuous stimulus (CPCS), intermittent pulse continuous stimulus (IPCS), continuous pulse intermittent stimulus (CPIS), and intermittent pulse intermittent stimulus (IPIS). When using a same transducer, it generates a peak negative pressure that is nearly identical to one produced by a commercial device. And compared to commercial transducer, the peak negative pressure of our transducer is significantly higher, reaching a maximum of 0.95 MPa. <italic>Conclusion:</i> In-vitro experiments were conducted using rat hippocampal brain slices. The experimental results demonstrated the effectiveness of the TUS system for neural stimulation. <italic>Significance:</i> It offers a design method of a portable multi-mode, high pressure TUS system, which is used for complex neural modulation research.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1078-1084"},"PeriodicalIF":4.4,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1109/TBME.2024.3486191
Thibault Marin;Vasily Belov;Yanis Chemli;Jinsong Ouyang;Yassir Najmaoui;Georges El Fakhri;Sridhar Duvvuri;Philip Iredale;Nicolas J. Guehl;Marc D. Normandin;Yoann Petibon
Receptor occupancy (RO) studies using PET neuroimaging play a critical role in the development of drugs targeting the central nervous system (CNS). The conventional approach to estimate drug receptor occupancy consists in estimation of binding potential changes between two PET scans (baseline and post-drug injection). This estimation is typically performed separately for each scan by first reconstructing dynamic PET scan data before fitting a kinetic model to time activity curves. This approach fails to properly model the noise in PET measurements, resulting in poor RO estimates, especially in low receptor density regions. Objective: In this work, we evaluate a novel joint direct parametric reconstruction framework to directly estimate distributions of RO and other kinetic parameters in the brain from a pair of baseline and post-drug injection dynamic PET scans. Methods: The proposed method combines the use of regularization on RO maps with alternating optimization to enable estimation of occupancy even in low binding regions. Results: Simulation results demonstrate the quantitative improvement of this method over conventional approaches in terms of accuracy and precision of occupancy. The proposed method is also evaluated in preclinical in-vivo experiments using 11C-MK-6884 and a muscarinic acetylcholine receptor 4 positive allosteric modulator drug, showing improved estimation of receptor occupancy as compared to traditional estimators. Conclusion: The proposed joint direct estimation framework improves RO estimation compared to conventional methods, especially in intermediate to low-binding regions. Significance: This work could potentially facilitate the evaluation of new drug candidates targeting the CNS.
利用 PET 神经成像技术进行的受体占位（RO）研究在开发针对中枢神经系统（CNS）的药物方面发挥着至关重要的作用。估算药物受体占据率的传统方法包括估算两次 PET 扫描（基线扫描和药物注射后扫描）之间的结合电位变化。这种估算通常是通过首先重建动态 PET 扫描数据，然后将动力学模型拟合到时间活动曲线上，从而分别对每次扫描进行估算。这种方法无法正确模拟 PET 测量中的噪声，导致 RO 估计结果不佳，尤其是在低受体密度区域：在这项工作中，我们评估了一种新颖的联合直接参数重建框架，该框架可直接从一对基线和药物注射后动态 PET 扫描中估算大脑中 RO 和其他动力学参数的分布：方法：所提出的方法将RO图的正则化与交替优化相结合，即使在低结合区域也能估计占据率：模拟结果表明，与传统方法相比，该方法在占据率的准确性和精确性方面有了定量改进。在使用 11C-MK6884 和毒蕈碱乙酰胆碱受体 4 阳性异位调节剂药物进行的临床前体内实验中，也对所提出的方法进行了评估，结果表明与传统估计方法相比，受体占据率的估计有所改进：结论：与传统方法相比，拟议的联合直接估算框架改进了受体占有率估算，尤其是在中低结合区域：意义：这项工作可能有助于评估以中枢神经系统为靶点的候选新药。
{"title":"PET Mapping of Receptor Occupancy Using Joint Direct Parametric Reconstruction","authors":"Thibault Marin;Vasily Belov;Yanis Chemli;Jinsong Ouyang;Yassir Najmaoui;Georges El Fakhri;Sridhar Duvvuri;Philip Iredale;Nicolas J. Guehl;Marc D. Normandin;Yoann Petibon","doi":"10.1109/TBME.2024.3486191","DOIUrl":"10.1109/TBME.2024.3486191","url":null,"abstract":"Receptor occupancy (RO) studies using PET neuroimaging play a critical role in the development of drugs targeting the central nervous system (CNS). The conventional approach to estimate drug receptor occupancy consists in estimation of binding potential changes between two PET scans (baseline and post-drug injection). This estimation is typically performed separately for each scan by first reconstructing dynamic PET scan data before fitting a kinetic model to time activity curves. This approach fails to properly model the noise in PET measurements, resulting in poor RO estimates, especially in low receptor density regions. <italic>Objective:</i> In this work, we evaluate a novel joint direct parametric reconstruction framework to directly estimate distributions of RO and other kinetic parameters in the brain from a pair of baseline and post-drug injection dynamic PET scans. <italic>Methods:</i> The proposed method combines the use of regularization on RO maps with alternating optimization to enable estimation of occupancy even in low binding regions. <italic>Results:</i> Simulation results demonstrate the quantitative improvement of this method over conventional approaches in terms of accuracy and precision of occupancy. The proposed method is also evaluated in preclinical in-vivo experiments using 11C-MK-6884 and a muscarinic acetylcholine receptor 4 positive allosteric modulator drug, showing improved estimation of receptor occupancy as compared to traditional estimators. <italic>Conclusion:</i> The proposed joint direct estimation framework improves RO estimation compared to conventional methods, especially in intermediate to low-binding regions. <italic>Significance:</i> This work could potentially facilitate the evaluation of new drug candidates targeting the CNS.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1057-1066"},"PeriodicalIF":4.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1109/TBME.2024.3486119
Chong Ma;Jiaojiao Pang;Ruizhe Wang;Dong Xu;Min Xiang;Zhuo Wang
As the number of sensors in magnetocardiography (MCG) arrays increases to capture detailed cardiac activity, some channels contribute minimally to task performance, resulting in data redundancy and resource consumption. Although existing methods can reduce the number of channels required to meet task demands, they often struggle to balance computational time and the accuracy of the selected channels and overlook the scalability of the selected channels. This limitation means that when environmental conditions change, or when sensors malfunction, redesigning channel configurations becomes necessary, which increases experimental uncertainties. This study introduces a task-driven adversarial channel selection method tailored for binary classification of MCG signals. The optimal channel combination is determined through a group-wise search using a heuristic algorithm, whose objective function is designed to maximize the difference between the classification accuracy and cosine similarity of the selected channel. In evaluations using an MCG dataset from Qilu Hospital of Shandong University, the proposed method successfully reduced the number of channels from 36 to 5 without compromising classification performance. Furthermore, it outperforms existing hybrid sequential forward search method by achieving comparable accuracy with fewer channels, while also demonstrating superior scalability compared to both hybrid sequential forward search and pearson-rank methods. This approach strikes a balance between computational consumption and accuracy, while improving the scalability of the selected channel combinations, enhancing the efficiency and practicality of the MCG system.
随着磁心动图（MCG）阵列中用于捕捉详细心脏活动的传感器数量的增加，一些通道对任务性能的贡献微乎其微，从而导致数据冗余和资源消耗。虽然现有方法可以减少所需的通道数量以满足任务需求，但它们往往难以在计算时间和所选通道的准确性之间取得平衡，并且忽略了所选通道的可扩展性。这种局限性意味着当环境条件发生变化或传感器出现故障时，必须重新设计通道配置，从而增加了实验的不确定性。本研究针对 MCG 信号的二进制分类，介绍了一种任务驱动的对抗信道选择方法。通过使用启发式算法进行分组搜索来确定最佳信道组合，其目标函数旨在最大化分类准确性与所选信道余弦相似度之间的差值。在使用山东大学齐鲁医院的 MCG 数据集进行的评估中，所提出的方法成功地将通道数从 36 个减少到 5 个，而不会影响分类性能。此外，该方法还优于现有的混合顺序前向搜索方法，用更少的通道获得了相当高的准确率，同时与混合顺序前向搜索方法和皮尔森秩方法相比，还表现出了更优越的可扩展性。这种方法在计算消耗和准确性之间取得了平衡，同时提高了所选信道组合的可扩展性，增强了 MCG 系统的效率和实用性。
{"title":"A Task-Driven Adversarial Channel Selection Method for Binary Classification Based on Magnetocardiography","authors":"Chong Ma;Jiaojiao Pang;Ruizhe Wang;Dong Xu;Min Xiang;Zhuo Wang","doi":"10.1109/TBME.2024.3486119","DOIUrl":"10.1109/TBME.2024.3486119","url":null,"abstract":"As the number of sensors in magnetocardiography (MCG) arrays increases to capture detailed cardiac activity, some channels contribute minimally to task performance, resulting in data redundancy and resource consumption. Although existing methods can reduce the number of channels required to meet task demands, they often struggle to balance computational time and the accuracy of the selected channels and overlook the scalability of the selected channels. This limitation means that when environmental conditions change, or when sensors malfunction, redesigning channel configurations becomes necessary, which increases experimental uncertainties. This study introduces a task-driven adversarial channel selection method tailored for binary classification of MCG signals. The optimal channel combination is determined through a group-wise search using a heuristic algorithm, whose objective function is designed to maximize the difference between the classification accuracy and cosine similarity of the selected channel. In evaluations using an MCG dataset from Qilu Hospital of Shandong University, the proposed method successfully reduced the number of channels from 36 to 5 without compromising classification performance. Furthermore, it outperforms existing hybrid sequential forward search method by achieving comparable accuracy with fewer channels, while also demonstrating superior scalability compared to both hybrid sequential forward search and pearson-rank methods. This approach strikes a balance between computational consumption and accuracy, while improving the scalability of the selected channel combinations, enhancing the efficiency and practicality of the MCG system.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1045-1056"},"PeriodicalIF":4.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1109/TBME.2024.3485233
Sherine Brahma;Andreas Kofler;Felix F. Zimmermann;Tobias Schaeffter;Amedeo Chiribiri;Christoph Kolbitsch
Stress perfusion cardiac magnetic resonance is an important technique for examining and assessing the blood supply of the myocardium. Currently, the majority of clinical perfusion scans are evaluated based on visual assessment by experienced clinicians. This makes the process subjective, and to this end, quantitative methods have been proposed to offer a more user-independent assessment of perfusion. These methods, however, rely on time-consuming deconvolution analysis and are susceptible to data outliers caused by artifacts due to cardiac or respiratory motion. In our work, we introduce a novel deep-learning method that integrates the commonly used Fermi function with a neural network architecture for fast, accurate, and robust myocardial perfusion quantification. This approach employs the Fermi model to ensure that the perfusion maps are consistent with measured data, while also utilizing a prior based on a 3D convolutional neural network to generalize spatio-temporal information across different patient data. Our network is trained within a self-supervised learning framework, which circumvents the need for ground-truth perfusion labels that are challenging to obtain. Furthermore, we extended this training methodology by adopting a technique that ensures estimations are resistant to data outliers, thereby improving robustness against motion artifacts. Our simulation experiments demonstrated an overall improvement in the accuracy and robustness of perfusion parameter estimation, consistently outperforming traditional deconvolution analysis algorithms across varying Signal-to-Noise Ratio scenarios in the presence of data outliers. For the in vivo studies, our method generated robust perfusion estimates that aligned with clinical diagnoses, while being approximately five times faster than conventional algorithms.
{"title":"Robust Myocardial Perfusion MRI Quantification With DeepFermi","authors":"Sherine Brahma;Andreas Kofler;Felix F. Zimmermann;Tobias Schaeffter;Amedeo Chiribiri;Christoph Kolbitsch","doi":"10.1109/TBME.2024.3485233","DOIUrl":"10.1109/TBME.2024.3485233","url":null,"abstract":"Stress perfusion cardiac magnetic resonance is an important technique for examining and assessing the blood supply of the myocardium. Currently, the majority of clinical perfusion scans are evaluated based on visual assessment by experienced clinicians. This makes the process subjective, and to this end, quantitative methods have been proposed to offer a more user-independent assessment of perfusion. These methods, however, rely on time-consuming deconvolution analysis and are susceptible to data outliers caused by artifacts due to cardiac or respiratory motion. In our work, we introduce a novel deep-learning method that integrates the commonly used Fermi function with a neural network architecture for fast, accurate, and robust myocardial perfusion quantification. This approach employs the Fermi model to ensure that the perfusion maps are consistent with measured data, while also utilizing a prior based on a 3D convolutional neural network to generalize spatio-temporal information across different patient data. Our network is trained within a self-supervised learning framework, which circumvents the need for ground-truth perfusion labels that are challenging to obtain. Furthermore, we extended this training methodology by adopting a technique that ensures estimations are resistant to data outliers, thereby improving robustness against motion artifacts. Our simulation experiments demonstrated an overall improvement in the accuracy and robustness of perfusion parameter estimation, consistently outperforming traditional deconvolution analysis algorithms across varying Signal-to-Noise Ratio scenarios in the presence of data outliers. For the in vivo studies, our method generated robust perfusion estimates that aligned with clinical diagnoses, while being approximately five times faster than conventional algorithms.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1031-1044"},"PeriodicalIF":4.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10731565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1109/TBME.2024.3485019
Alexander Robert Bateman;Jeannette Lechner-Scott;Grant Alexander Bateman;Saadallah Ramadan;Tracie Barber
Objective: An investigation was performed to determine the relevant hemodynamic parameters which could help assess vascular pathology in human diseases. Using these parameters, this study aims to assess if there are any hemodynamic differences in the cerebral veins of multiple sclerosis (MS) patients and controls which could impact the etiology of MS. Methods: 40 MS participants and 20 controls were recruited for this study. Magnetic resonance imaging (MRI) was performed to enable 3D geometries of the anatomy and the blood flow rates at the boundaries to be computed. Computational fluid dynamics (CFD) models were created for each participant and simulated using patient-specific boundary conditions. Results: The pressure drop and vascular resistance did not significantly differ between the groups. The internal jugular vein (IJV) cross-sectional area was larger in the MS group (Right IJV: p = 0.04, Left IJV: p = 0.02) and the straight sinus (ST) flow rate was higher in MS across all ages (p = 0.005) compared to controls. Vascular resistance was shown to indicate regions in the cerebral veins which could correspond to increased venous pressure. Conclusion & Significance: This study shows that the pressure and vascular resistance of the cerebral veins are unlikely to be directly related to the etiology of MS. The finding of higher ST flow could correspond to increased inflammation in the deep venous system. Resistance as a measure of vascular pathology shows promise and could be useful to holistically investigate blood flow hemodynamics in a variety of other diseases of the circulatory system.
目的一项调查旨在确定有助于评估人类疾病血管病理学的相关血液动力学参数。利用这些参数，本研究旨在评估多发性硬化症（MS）患者和对照组的脑静脉是否存在可能影响 MS 病因的血液动力学差异。进行磁共振成像（MRI），以计算解剖结构的三维几何图形和边界处的血流速度。为每位参与者创建了计算流体动力学（CFD）模型，并使用患者特定的边界条件进行模拟：结果：各组之间的压降和血管阻力没有明显差异。与对照组相比，多发性硬化症组的颈内静脉（IJV）横截面积较大（右颈内静脉：p = 0.04，左颈内静脉：p = 0.02），所有年龄段的多发性硬化症患者的直窦（ST）流速较高（p = 0.005）。血管阻力显示了脑静脉中可能与静脉压力增加相对应的区域。结论和意义：本研究表明，脑静脉的压力和血管阻力不太可能与多发性硬化症的病因直接相关。ST血流较高的发现可能与深静脉系统炎症加重相对应。阻力作为血管病理学的一种测量方法前景广阔，可用于全面研究循环系统各种其他疾病的血流血流动力学。
{"title":"Computational Fluid Dynamic Simulation of the Cerebral Venous System in Multiple Sclerosis and Control Patients: Are Hemodynamic Variances Evident in Multiple Sclerosis?","authors":"Alexander Robert Bateman;Jeannette Lechner-Scott;Grant Alexander Bateman;Saadallah Ramadan;Tracie Barber","doi":"10.1109/TBME.2024.3485019","DOIUrl":"10.1109/TBME.2024.3485019","url":null,"abstract":"<italic>Objective:</i> An investigation was performed to determine the relevant hemodynamic parameters which could help assess vascular pathology in human diseases. Using these parameters, this study aims to assess if there are any hemodynamic differences in the cerebral veins of multiple sclerosis (MS) patients and controls which could impact the etiology of MS. <italic>Methods:</i> 40 MS participants and 20 controls were recruited for this study. Magnetic resonance imaging (MRI) was performed to enable 3D geometries of the anatomy and the blood flow rates at the boundaries to be computed. Computational fluid dynamics (CFD) models were created for each participant and simulated using patient-specific boundary conditions. <italic>Results:</i> The pressure drop and vascular resistance did not significantly differ between the groups. The internal jugular vein (IJV) cross-sectional area was larger in the MS group (Right IJV: p = 0.04, Left IJV: p = 0.02) and the straight sinus (ST) flow rate was higher in MS across all ages (p = 0.005) compared to controls. Vascular resistance was shown to indicate regions in the cerebral veins which could correspond to increased venous pressure. <italic>Conclusion & Significance:</i> This study shows that the pressure and vascular resistance of the cerebral veins are unlikely to be directly related to the etiology of MS. The finding of higher ST flow could correspond to increased inflammation in the deep venous system. Resistance as a measure of vascular pathology shows promise and could be useful to holistically investigate blood flow hemodynamics in a variety of other diseases of the circulatory system.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1021-1030"},"PeriodicalIF":4.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10731983","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1109/TBME.2024.3484676
Nicolas Detrez;Sazgar Burhan;Katarina Rewerts;Jessica Kren;Steffen Buschschlüter;Dirk Theisen-Kunde;Matteo Mario Bonsanto;Robert Huber;Ralf Brinkmann
Objective: Optical coherence elastography (OCE) has been introduced for several medical applications to determine tissue mechanical parameters. However, in order to measure sensitive healthy tissue like brain in vivo, the excitation force needs to be carefully controlled and as low as possible (under 100 µN). Preferably, the excitation should be applied in a non-contact manner. Methods: In this work, an air-jet excitation source for this specific purpose has been developed and characterized. The design focus was set on the exact measurement and control of the generated excitation force to better comply with in vivo medical safety requirements during surgery. Results: Therefore, an excitation force control and measurement system based on the applied gas flow was developed. Conclusion: This system can generate short, high dynamic air-puffs lasting fewer than 5 ms, as well as quasi-static excitation forces lasting 700 ms. The force range covers 1µN to 40 mN with a force error margin between 0.1% and 16% in the relevant range. The excitation source, in conjunction with a 3.2 MHz optical coherence system, enables phase-based, dynamic, and quasi steady-state elastography, as well as robust non-contact classical indentation measurements. Significance: The presented system is a preliminary prototype intended for further development into a clinical version to be used in situ during brain tumor surgery.
{"title":"Flow-Controlled Air-Jet for In Vivo Quasi Steady-State and Dynamic Elastography With MHz Optical Coherence Tomography","authors":"Nicolas Detrez;Sazgar Burhan;Katarina Rewerts;Jessica Kren;Steffen Buschschlüter;Dirk Theisen-Kunde;Matteo Mario Bonsanto;Robert Huber;Ralf Brinkmann","doi":"10.1109/TBME.2024.3484676","DOIUrl":"10.1109/TBME.2024.3484676","url":null,"abstract":"<italic>Objective:</i> Optical coherence elastography (OCE) has been introduced for several medical applications to determine tissue mechanical parameters. However, in order to measure sensitive healthy tissue like brain <italic>in vivo</i>, the excitation force needs to be carefully controlled and as low as possible (under 100 µN). Preferably, the excitation should be applied in a non-contact manner. <italic>Methods:</i> In this work, an air-jet excitation source for this specific purpose has been developed and characterized. The design focus was set on the exact measurement and control of the generated excitation force to better comply with <italic>in vivo</i> medical safety requirements during surgery. <italic>Results:</i> Therefore, an excitation force control and measurement system based on the applied gas flow was developed. <italic>Conclusion</i>: This system can generate short, high dynamic air-puffs lasting fewer than 5 ms, as well as quasi-static excitation forces lasting 700 ms. The force range covers 1µN to 40 mN with a force error margin between 0.1% and 16% in the relevant range. The excitation source, in conjunction with a 3.2 MHz optical coherence system, enables phase-based, dynamic, and quasi steady-state elastography, as well as robust non-contact classical indentation measurements. <italic>Significance:</i> The presented system is a preliminary prototype intended for further development into a clinical version to be used <italic>in situ</i> during brain tumor surgery.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1008-1020"},"PeriodicalIF":4.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726870","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1109/TBME.2024.3482983
Yiming Han;Raymond G Stephany;Linran Zhao;Parvez Ahmmed;Alper Bozkurt;Yaoyao Jia
This paper introduces a wirelessly powered multimodal animal physiological monitoring application-specific integrated circuit (ASIC). Fabricated in the 180 nm process, the ASIC can be integrated into an injectable device to monitor subcutaneous body temperature, electrocardiography (ECG), and photoplethysmography (PPG). To minimize the device size, the ASIC employs a miniature receiver (Rx) coil for wireless power receiving and data communication through a single inductive link operating at 13.56 MHz. We propose a folded L-shape Rx coil with improved coupling to the transmitter (Tx) coil, even in the presence of misalignment, and enhanced quality factor. The ASIC functions alternatively between recording and sleeping modes, consuming 2.55 µW on average. For PPG measurements, a reflection-type PPG sensor illuminates an LED with tunable current pulses. A current-input analog frontend (AFE) amplifies the current of a photodiode (PD) with 30.8 pARMS current input-referred noise (IRN). The ECG AFE captures ECG signals with a configurable gain of 45-80 dB. The temperature AFE achieves 0.02 °C inaccuracy within a sensing range between 27-47 °C. The AFE outputs are sequentially digitized by a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with an effective number of bits (ENOB) of 8.79. To improve the reliability of data transmission, we propose a memory-assisted backscatter scheme that stores ADC data in an off-chip memory and transmits it when the coupling condition is stable. This scheme achieves a package loss rate (PLR) lower than 0.2% while allowing 24-hour data storage. The device's functionality has been evaluated by in vivo experiments.
{"title":"A Wireless Miniature Injectable Device With Memory-Assisted Backscatter for Multimodal Animal Physiological Monitoring","authors":"Yiming Han;Raymond G Stephany;Linran Zhao;Parvez Ahmmed;Alper Bozkurt;Yaoyao Jia","doi":"10.1109/TBME.2024.3482983","DOIUrl":"10.1109/TBME.2024.3482983","url":null,"abstract":"This paper introduces a wirelessly powered multimodal animal physiological monitoring application-specific integrated circuit (ASIC). Fabricated in the 180 nm process, the ASIC can be integrated into an injectable device to monitor subcutaneous body temperature, electrocardiography (ECG), and photoplethysmography (PPG). To minimize the device size, the ASIC employs a miniature receiver (Rx) coil for wireless power receiving and data communication through a single inductive link operating at 13.56 MHz. We propose a folded L-shape Rx coil with improved coupling to the transmitter (Tx) coil, even in the presence of misalignment, and enhanced quality factor. The ASIC functions alternatively between recording and sleeping modes, consuming 2.55 µW on average. For PPG measurements, a reflection-type PPG sensor illuminates an LED with tunable current pulses. A current-input analog frontend (AFE) amplifies the current of a photodiode (PD) with 30.8 pA<sub>RMS</sub> current input-referred noise (IRN). The ECG AFE captures ECG signals with a configurable gain of 45-80 dB. The temperature AFE achieves 0.02 °C inaccuracy within a sensing range between 27-47 °C. The AFE outputs are sequentially digitized by a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with an effective number of bits (ENOB) of 8.79. To improve the reliability of data transmission, we propose a memory-assisted backscatter scheme that stores ADC data in an off-chip memory and transmits it when the coupling condition is stable. This scheme achieves a package loss rate (PLR) lower than 0.2% while allowing 24-hour data storage. The device's functionality has been evaluated by in vivo experiments.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"988-999"},"PeriodicalIF":4.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1109/TBME.2024.3481897
Mercy Edoho;Omar Mamad;David C Henshall;Catherine Mooney;Lan Wei
Objective: Animal models of drug-resistant epilepsy represent an important resource for discovering new drug targets and testing experimental medicines. Intra-amygdala microinjection of kainic acid in mice is one of the most widely regarded models of drug-resistant epilepsy. Mice develop acute status epilepticus, which diminishes after a few hours and then, within a few days, mice display spontaneous seizures. The frequency of spontaneous seizures varies between mice, with some developing low or high seizure rates. Method: We developed a feature-based and transfer learning-based approach, for predicting the emergent spontaneous seizure rates in the intra-amygdala kainic acid model based on the acute EEGs recorded in mice during status epilepticus lasting 40 minutes. The method was trained on data from 28 mice and tested on data from 16 mice. Results: The feature-based and transfer learning-based models achieved accuracies of 69% and 75%, respectively on the test set in classifying emergent epilepsy as normal or outlier (i.e. low-frequency or high-frequency seizure rate). Conclusion: A limitation of the intra-amygdala kainic acid model has been the loss of time and resources from generating mice with low or high rates of spontaneous seizures. The feature-based and transfer learning-based models will assist researchers in identifying mice with a normal frequency of seizures before the onset of spontaneous seizures. Significance: We have implemented this approach as a web server, which can potentially reduce the time and resources spent analysing the EEGs of mice who develop low-frequency or high-frequency seizure rates.
{"title":"Classification System for Predicting Emergent Epilepsy Phenotype in the Intra-Amygdala Kainic Acid Mouse Model of Epilepsy","authors":"Mercy Edoho;Omar Mamad;David C Henshall;Catherine Mooney;Lan Wei","doi":"10.1109/TBME.2024.3481897","DOIUrl":"10.1109/TBME.2024.3481897","url":null,"abstract":"<italic>Objective:</i> Animal models of drug-resistant epilepsy represent an important resource for discovering new drug targets and testing experimental medicines. Intra-amygdala microinjection of kainic acid in mice is one of the most widely regarded models of drug-resistant epilepsy. Mice develop acute status epilepticus, which diminishes after a few hours and then, within a few days, mice display spontaneous seizures. The frequency of spontaneous seizures varies between mice, with some developing low or high seizure rates. <italic>Method:</i> We developed a feature-based and transfer learning-based approach, for predicting the emergent spontaneous seizure rates in the intra-amygdala kainic acid model based on the acute EEGs recorded in mice during status epilepticus lasting 40 minutes. The method was trained on data from 28 mice and tested on data from 16 mice. <italic>Results:</i> The feature-based and transfer learning-based models achieved accuracies of 69% and 75%, respectively on the test set in classifying emergent epilepsy as normal or outlier (i.e. low-frequency or high-frequency seizure rate). <italic>Conclusion:</i> A limitation of the intra-amygdala kainic acid model has been the loss of time and resources from generating mice with low or high rates of spontaneous seizures. The feature-based and transfer learning-based models will assist researchers in identifying mice with a normal frequency of seizures before the onset of spontaneous seizures. <italic>Significance:</i> We have implemented this approach as a web server, which can potentially reduce the time and resources spent analysing the EEGs of mice who develop low-frequency or high-frequency seizure rates.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"978-987"},"PeriodicalIF":4.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1109/TBME.2024.3479081
Mengyang Zhao;Xu Cao;Jinchao Feng;Mingwei Zhou;Chengpu Wei;Roberta diFlorio-Alexander;Brian W. Pogue;Shudong Jiang;Keith D. Paulsen
Objective: To develop a novel Magnetic Resonance Imaging (MRI)-guided Near-Infrared Spectroscopic Tomography (MRg-NIRST) imaging system with an MRI-compatible breast optical interface for breast imaging. Methods: The breast interface consists of eight flexible opto-electronic circuit strips, each equipped with six photodetectors and six side-firing fiber-probes. Concurrent MRI and NIRST data were acquired from a total of 2,304 source-detector positions at six wavelengths, enabling 3D MRg-NIRST image reconstruction of the entire breast. The system was validated through a series of phantom and normal subject studies. Results: Reconstructed images of phantoms with inclusions ranging 10–25 mm in diameter showed errors in the estimated inclusion diameter and contrast of total hemoglobin (HbT) within the inclusion relative to the background were ranged in [−7%, 12%] and [7%, 28%], respectively. HbT estimates from reconstructed images of nine normal subjects ranged between 8.0–l25.2 μM, align with previous imaging studies. Conclusion: Results from both phantom and normal subject studies indicate that this system has the potential to be easily integrated into clinical practice for acquiring 3D MRg-NIRST images of the entire breast. Significance: The flexibility of the wearable breast optical interface, along with the increased number of sources and detectors, has improved the optical accessibility for breasts of various sizes, shapes, and tumor locations. 3D MRg-NIRST image reconstruction, based on optical data collected from multiple source-detector layers across the entire breast, demonstrates that MRg-NIRST is ready to be tested clinically for its potential to enhance breast cancer detection alongside MRI.
{"title":"MRI-Guided Near-Infrared Spectroscopic Tomography (MRg-NIRST) Imaging System With Wearable Breast Optical Interface for Breast Cancer Imaging","authors":"Mengyang Zhao;Xu Cao;Jinchao Feng;Mingwei Zhou;Chengpu Wei;Roberta diFlorio-Alexander;Brian W. Pogue;Shudong Jiang;Keith D. Paulsen","doi":"10.1109/TBME.2024.3479081","DOIUrl":"10.1109/TBME.2024.3479081","url":null,"abstract":"<italic>Objective:</i> To develop a novel Magnetic Resonance Imaging (MRI)-guided Near-Infrared Spectroscopic Tomography (MRg-NIRST) imaging system with an MRI-compatible breast optical interface for breast imaging. <italic>Methods:</i> The breast interface consists of eight flexible opto-electronic circuit strips, each equipped with six photodetectors and six side-firing fiber-probes. Concurrent MRI and NIRST data were acquired from a total of 2,304 source-detector positions at six wavelengths, enabling 3D MRg-NIRST image reconstruction of the entire breast. The system was validated through a series of phantom and normal subject studies. <italic>Results:</i> Reconstructed images of phantoms with inclusions ranging 10–25 mm in diameter showed errors in the estimated inclusion diameter and contrast of total hemoglobin (HbT) within the inclusion relative to the background were ranged in [−7%, 12%] and [7%, 28%], respectively. HbT estimates from reconstructed images of nine normal subjects ranged between 8.0–l25.2 μM, align with previous imaging studies. <italic>Conclusion:</i> Results from both phantom and normal subject studies indicate that this system has the potential to be easily integrated into clinical practice for acquiring 3D MRg-NIRST images of the entire breast. <italic>Significance:</i> The flexibility of the wearable breast optical interface, along with the increased number of sources and detectors, has improved the optical accessibility for breasts of various sizes, shapes, and tumor locations. 3D MRg-NIRST image reconstruction, based on optical data collected from multiple source-detector layers across the entire breast, demonstrates that MRg-NIRST is ready to be tested clinically for its potential to enhance breast cancer detection alongside MRI.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"899-908"},"PeriodicalIF":4.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-15DOI: 10.1109/TBME.2024.3481010
Fan Yang, Zhihao Xue, Hongfei Lu, Jingjing Xu, Haiyang Chen, Zhuo Chen, Yixin Emu, Ahmed Aburas, Juan Gao, Chenhao Gao, Hang Jin, Shengxian Tu, Chenxi Hu
Objective: To propose a 3D nonrigid registration method that accurately estimates the 3D displacement field from artifact-corrupted Coronary Magnetic Resonance Angiography (CMRA) images.
Methods: We developed a novel registration framework for registration of artifact-corrupted images based on a 3D U-Net initializer and a deep unrolling network. By leveraging a supervised learning framework with training labels estimated from fully-sampled images, the unrolling network learns a task-specific motion prior which reduces motion estimation biases caused by undersampling artifacts in the source images. We evaluated the proposed method, UNROLL, against an iterative Free-Form Deformation (FFD) registration method and a recently proposed Respiratory Motion Estimation network (RespME-net) for 6-fold (in-distribution) and 11-fold (out-of-distribution) accelerated CMRA.
Results: Compared to the baseline methods, UNROLL improved both the accuracy of motion estimation and the quality of motion-compensated CMRA reconstruction at 6-fold acceleration. Furthermore, even at 11-fold acceleration, which was not included during training, UNROLL still generated more accurate displacement fields than the baseline methods. The computational time of UNROLL for the whole 3D volume was only 2 seconds.
Conclusion: By incorporating a learned respiratory motion prior, the proposed method achieves highly accurate motion estimation despite the large acceleration.
Significance: This work introduces a fast and accurate method to estimate the displacement field from low-quality source images. It has the potential to significantly improve the quality of motion-compensated reconstruction for highly accelerated 3D CMRA.
{"title":"Robust Fast Inter-Bin Image Registration for Undersampled Coronary MRI Based on a Learned Motion Prior.","authors":"Fan Yang, Zhihao Xue, Hongfei Lu, Jingjing Xu, Haiyang Chen, Zhuo Chen, Yixin Emu, Ahmed Aburas, Juan Gao, Chenhao Gao, Hang Jin, Shengxian Tu, Chenxi Hu","doi":"10.1109/TBME.2024.3481010","DOIUrl":"https://doi.org/10.1109/TBME.2024.3481010","url":null,"abstract":"<p><strong>Objective: </strong>To propose a 3D nonrigid registration method that accurately estimates the 3D displacement field from artifact-corrupted Coronary Magnetic Resonance Angiography (CMRA) images.</p><p><strong>Methods: </strong>We developed a novel registration framework for registration of artifact-corrupted images based on a 3D U-Net initializer and a deep unrolling network. By leveraging a supervised learning framework with training labels estimated from fully-sampled images, the unrolling network learns a task-specific motion prior which reduces motion estimation biases caused by undersampling artifacts in the source images. We evaluated the proposed method, UNROLL, against an iterative Free-Form Deformation (FFD) registration method and a recently proposed Respiratory Motion Estimation network (RespME-net) for 6-fold (in-distribution) and 11-fold (out-of-distribution) accelerated CMRA.</p><p><strong>Results: </strong>Compared to the baseline methods, UNROLL improved both the accuracy of motion estimation and the quality of motion-compensated CMRA reconstruction at 6-fold acceleration. Furthermore, even at 11-fold acceleration, which was not included during training, UNROLL still generated more accurate displacement fields than the baseline methods. The computational time of UNROLL for the whole 3D volume was only 2 seconds.</p><p><strong>Conclusion: </strong>By incorporating a learned respiratory motion prior, the proposed method achieves highly accurate motion estimation despite the large acceleration.</p><p><strong>Significance: </strong>This work introduces a fast and accurate method to estimate the displacement field from low-quality source images. It has the potential to significantly improve the quality of motion-compensated reconstruction for highly accelerated 3D CMRA.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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