IEEE Transactions on Biomedical Engineering最新文献

Objective: Preterm infants are particularly exposed to severe cardio-respiratory events, associating apnea with bradycardia and oxygen desaturation. A patient-specific and event-specific model-based approach is proposed in this work to analyze the acute heart rate response to apnea-bradycardia events in preterm newborn.

Methods: A novel model integrating the main cardio-respiratory interactions which are specific to the neonatal period is proposed. An evolutionary algorithm is applied to estimate patient-specific model parameters from a database of 37 apnea-bradycardia episodes acquired from 10 preterm newborns. Unsupervised clustering (K-means) was applied to the identified parameters to define phenogroups of cardio-respiratory responses to apnea.

Results: A significant correspondence was observed between simulated and experimental heart rate series in all identifications (median RMSE = 8.85 bpm). Three clusters of parameters were found and were associated to three different pathophysiological dynamics related to apnea-bradycardia.

Conclusion and significance: The proposed method, based on patient and event-specific model parameter identification, provides a novel approach to characterize bradycardia dynamics in response to apnea, opening the way to the proposal of new personalized diagnosis and treatment possibilities in this particularly sensitive population.

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.

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 postdrug 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-MK6884 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.

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.

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.

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.

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.