IEEE Transactions on Biomedical Engineering最新文献

Objective: To develop an effective method for phase correction of magnetic resonance spectroscopic imaging (MRSI) data.

Methods: In many MRSI applications, it is desirable to generate absorption-mode spectra, which requires correction of phase errors in the measured MRSI data. Conventional phase correction methods are sensitive to measurement noise and baseline distortion, often resulting in distorted absorption-mode spectra from MRSI data with low-SNR and long acquisition dead time. This paper proposed a novel model-based method for improved phase correction of MRSI data. The proposed method determined the zeroth-order phase and acquisition dead time using a Lorentzian-based spectral model and performed signal extrapolation using a generalized series model. Absorption-mode spectra were then generated from the phase-corrected and extrapolated MRSI data.

Results: The proposed method was evaluated using both simulated data and experimental data acquired from human subjects in multi-nuclei (³¹P, ²H, and ¹H) MRSI experiments. Simulation results demonstrated improved parameter estimation accuracy by the proposed method under various noise levels and dead times. The proposed method also consistently generated high-quality absorption-mode spectra with minimal spectral distortions from experimental data. The proposed method was compared with state-of-the-art methods (including the entropy method and LCModel method) and showed more robust phase correction performance with less spectral distortions.

Conclusion: This paper introduced a novel method for phase correction of MRSI data. Results from simulated and in vivo data demonstrated that high-quality absorption-mode spectra could be obtained using the proposed method.

Significance: This method will provide a useful tool for processing MRSI data.

Objective: To develop a transceiver radio frequency (RF) coil optimized for high resolution small-animal imaging at 14.1 T, aimed at enhancing signal-to-noise ratio (SNR) performance.

Methods: A hybrid distributed capacitance (HDC) birdcage coil was designed, combining conventional endring lumped capacitors with distributed capacitance along the legs, implemented using double-layer copper-clad substrates. Electromagnetic (EM) simulations were employed to optimize the coil's structural parameters and capacitance values for maximum RF performance. The HDC birdcage coil's performance was evaluated against a conventional bandpass (BP) design through electromagnetic simulations, bench tests, and phantom imaging. In vivo validation was performed using mouse imaging.

Results: EM simulations demonstrated that the HDC design enhances mean $text{B}_{1}^{+}$ and $text{B}_{1}^{-}$ field strengths by 11.8% and 11.7%, respectively, relative to the conventional BP design. The HDC design also showed reduced electric field (E-field) value in phantom, with 4.2% lower mean and 11.4% lower maximum E-field value. Bench measurements revealed a superior quality factor (Q factor) for the HDC coil, with a 34.2% higher unloaded Q value compared to the conventional design. Phantom imaging confirmed a 41% SNR improvement with the HDC design. The optimized HDC coil enabled mouse brain imaging at 50 $ !!mu !!text{ m}$ resolution.

Conclusion: The proposed HDC birdcage coil demonstrated superior receiver sensitivity and Q factor compared to conventional designs, yielding significant SNR improvements in 14.1 T imaging.

Significance: The results demonstrated the feasibility of achieving enhanced coil performance through HDC design at ultra-high field strength, providing a promising approach for improving image quality in small-animal MRI applications.

Objective: Accurate, non-invasive methods for estimating joint and muscle physiological states have the potential to greatly enhance control of wearable devices during real-world ambulation. Traditional modeling approaches and current estimation methods used to predict muscle dynamics often rely on complex equipment or computationally intensive simulations and have difficulty estimating across a broad spectrum of tasks or subjects.

Methods: Our approach used deep learning (DL) models trained on kinematic inputs to estimate internal physiological states at the knee, including moment, power, velocity, and force. We assessed each model's performance against ground truth labels from both a commonly used, standard OpenSim musculoskeletal model without EMG (static optimization) and an EMG-informed method (CEINMS), across 28 different cyclic and noncyclic tasks.

Results: EMG provided no benefit for joint moment/power estimation (e.g., biological moment), but was critical for estimating muscle states. Models trained with EMG-informed labels but without EMG as an input to the DL system significantly outperformed models trained without EMG (e.g., 33.7% improvement for muscle moment estimation) (p < 0.05). Models that included EMG-informed labels and EMG as a model input demonstrated even higher performance (49.7% improvement for muscle moment estimation) (p < 0.05), but require the availability of EMG during model deployment, which may be impractical.

Conclusion/significance: While EMG information is not necessary for estimating joint level states, there is a clear benefit during muscle level state estimation. Our results demonstrate excellent tracking of these states with EMG included only during training, highlighting the practicality of real-time deployment of this approach.

Objective: To design and fabricate a band-pass birdcage volume resonator and eight channel, conformal receive array coil for MRI of both awake and anesthetized marmoset brain at 7T. The coil is compatible with a whole body 7T clinical MRI scanner running in single channel transmit (sTx) mode.

Methods: The marmoset head coil included a shielded, band-pass birdcage transmit coil with 24 legs, as well as 8 overlapped receive elements. Electromagnetic (EM) field simulation was performed for the 24 leg band pass birdcage Tx coil to calculate the B₁⁺ efficiency. The efficacy of both transmit and receive coil designs were evaluated by measuring standard coil performance metrics. This was done while imaging a marmoset head phantom, as well as by acquiring in vivo, anesthetized and awake marmoset images.

Results: The transmit coil along with the optimized receive array produced high resolution (0.8 mm isotropic for EPI images; 0.36 mm isotropic for structural images) and high SNR (between 50 and 80) images of the marmoset brain. The simulated B₁⁺ efficiency of the birdcage at the center of the phantom was 2.6 µT/sqrt (W).

Conclusion and significance: A shielded, band-pass birdcage transmit coil was designed and fabricated for marmoset brain imaging at 7T. An 8-channel receive array consisting of eight overlapped loops, covering the whole brain of the marmoset, was also constructed and applied for signal reception. The system successfully allowed scanning of both young and older marmosets. It is well-suited for longitudinal studies of marmoset brain structure. The coil advantageously allows the study of neurodevelopment and primate brain function.

Muscle coordination pattern can be disrupted by neural disorders and perceptual disturbance, leading to abnormal gaits. However, it is still unclear how neuro-muscular control regulates walking gaits through training and exercising due to lengthy rehabilitation periods and complicated musculoskeletal redundancies. This paper proposes a gait rehabilitation monitoring method based on muscle deformation to elucidate the evolution of muscle coordination during the full-course vertigo-gait rehabilitation. The proposed method is verified by musculoskeletal dynamics simulation and experimentally validated through immediate applications to five healthy subjects and five vertigo patients. The vertigo gaits were assessed by comparing with the normal gait of healthy subjects. For the first time, this paper reports experimental results and muscle coordination analysis throughout the full course of vertigo-gait rehabilitation. The findings reveal that the vertigo patient adjusts phase difference between the rectus femoris (RF) and medial gastrocnemius (MG) at toe-off, driving the knee and ankle joints to regulate foot-to-ground angle, thereby enhancing gait stability and walking efficiency. These results indicate that muscle deformations serve as an alternative quantity besides traditionally employed kinematic features, and the proposed wearable sensing method is expected to provide an effective tool for clinical gait assessment.

Objective: Frequency selection is a crucial step for calibration-based magnetic particle imaging (MPI) reconstruction, enabling improvement in computational efficiency and noise suppression. Current methods combine signal-to-noise ratio (SNR) feature with a selection threshold. However, the selection threshold determination is experience-dependent, and the utilization of the system matrix (SM) and the imaging phantom signal is insufficient.

Method: To suppress these issues, an adaptive and robust frequency selection framework (AR-FSF) is proposed, including three modules: (i) Velocity-corrected feature calculation, which limits feature calculation to the calibration points with high field-free-region velocity, (ii) Adaptive threshold calculation, which adaptively calculates the noise level using the feature spectrum, (iii) Forward-backward selection, which selects high-SNR frequency components for both SM and imaging phantom for reconstruction.

Results: Signal experiments validate the effectiveness and the robustness of the introduced modules respectively. Reconstruction experiments further validate that the AR-FSF method can provide a simple and robust frequency selection process for reconstruction. In experiments using in-house data, the AR-FSF method provides suitable frequency components for fast and high-quality imaging, requiring a minimum reconstruction time of 4.5% compare to current methods.

Conclusion: The proposed AR-FSF method effectively simplifies the frequency selection process, enabling adaptive selection of frequency component for different phantoms, thereby achieving fast and high-quality reconstruction.

Significance: The AR-FSF method simplifies the frequency component selection process and can be widely applied in calibration-based MPI reconstruction, laying a methodological foundation for future biomedical applications.

Objective: This study aimed to develop a seizure detection algorithm using surface electromyography (sEMG) and accelerometry (ACC) signals recorded with miniaturized wearable sensors.

Methods: Continuous sEMG-ACC signals were acquired from patients wearing eight sensors positioned bilaterally on the upper trapezius, anterior deltoid, biceps brachii, and tibialis anterior muscles. We trained an extreme gradient boosting classifier to identify seizure epochs using setups with eight, two, and one sensor(s). Performance was evaluated via patient-wise nested cross-validation, and specificity was further assessed on an independent patient cohort without seizures.

Results: Eleven generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) were recorded from nine patients over 1359.6 h. The best results were obtained with a dual-sensor setup combining data from the right biceps brachii and the left tibialis anterior, achieving 100% sensitivity, 0.12 FAR/24h, and median detection latency of 22 s. On 1744.18 h of data from 19 patients without seizures, FAR/24h was 0.06.

Conclusion: The developed algorithm effectively detected GTCS and FBTCS in an epilepsy monitoring unit, even with a reduced number of sensors.

Significance: This approach could enable timely interventions in outpatient settings, potentially improving safety and independence for people with epilepsy.

Objective: The development of brain-computer interface (BCI) technology has enabled animals to execute movements in accordance with human intent. The rat robot represents a novel robotic system based on BCI technology. However, due to limitations in electrode fabrication techniques and the use of simplistic control strategies, current rat robots are restricted to limited movement patterns, which hinder their applicability in real-world scenarios. To address these challenges, we have developed a portable wireless neural stimulator and a novel 3D integrated stimulating electrode. By refining the locomotion control strategy, we aim to achieve complex, high-degree-of-freedom movement in rat robot systems.

Methods: 3D integrated electrodes were implanted into the rats' head, with no reward-based training required. By utilizing a wearable wireless stimulation backpack to connect the electrodes and deliver electrical stimulation to multiple brain regions, thereby enabling the rat to perform forward movement, turning, and stopping behaviors.

Results: The experimental results demonstrate that under optimized stimulation parameters, the forward speed of the rat robot can be controlled to achieve 31.06 ± 1.21 m/min, the turning angle can reach up to 150 ± 1.22°, and the stopping duration can be flexibly adjusted. Furthermore, we presented a practical scenario in which the rat robot successfully executed a predefined navigation task in a real-world environment, thereby validating its high degree of movement flexibility and control precision.

Conclusion: This study achieved high-degree-of-freedom motion control of rat robots without the need for reward-based training, which was previously unattainable.

Significance: This research establishes a crucial foundation and provides valuable technical references for the application of animal robots in fields such as information reconnaissance and wreckage search and rescue operations.

A novel 3D-printed microwave probe operating in the 25-45 GHz frequency range is designed and fabricated for early skin tumor detection using signal processing. Due to the highly lossy nature of the skin, electromagnetic wave penetration is difficult. To overcome this limitation, a multi-section probe design was developed to enhance wave penetration into the skin layer. This design effectively mitigates the effects of high-loss tangents in tissues and compensates for the small size of tumors, aiding in early detection. The probe's performance is validated through simulations and experimental measurements, showing excellent agreement. For imaging evaluation, a phantom model composed of pork skin, measuring 30 mm × 30 mm with a skin thickness of 4 mm, is utilized. A total of 215 scanning points were analyzed, and time-domain reflection waves were extracted, demonstrating the probe's ability to detect variations in tissue properties accurately. These signals were then processed using an entropy-based method. The reconstructed images across various scenarios highlight the effectiveness of the proposed probe in achieving high-resolution microwave imaging, indicating its strong potential for non-invasive, early-stage tumor detection.