Frontiers in Biomedical Technologies
Volume:9 Issue: 4, Autumn 2022

Brain-Computer Interface (BCI) Speller systems help people with mobility impairments improve their cognitive and physical abilities. Steady-State Visual Evoked Potential (SSVEP) signals have been used to build high-speed BCI speller systems. SSVEP signals are a subtype of Visual Evoked Potential (VEP), a form of co-frequency, and the harmonics response elicited by a specific frequency stimulus. Noise and artifacts are critical issues for target detection in SSVEP-based BCI systems.

Materials and Methods:

Thus, it is essential to provide target detection techniques that operate well in the presence of noises. One solution for overcoming the noise impact is to employ approaches that automatically extract the appropriate features for target detection from the training data. Deep Convolutional Neural Network (DCNN) was utilized in this study to automatically extract features from SSVEP data in noisy conditions. Moreover, the BETA database, which contains SSVEP data from 70 individuals collected outside of the electromagnetic shielding room, was used. In this regard, a suitable DCNN structure for target stimulus frequency identification was first designed. The network was pre-trained with part of the data from the BETA database. Finally, at the single-subject level, this pre-trained network was retrained and evaluated.

Results:

The results showed that after retraining, the accuracy and Information Transfer Rate (ITR) increased (p-value < 0.01) for all participants.

Conclusion:

The enhancement in accuracy and ITR are 25.72% and 43.10 bpm, respectively.

Determining the borders of brain tumors, seizure foci, and their elimination in patients with brain tumors is very important in preventing cancer recurrence. Multispectral Optical Intrinsic Signal Imaging (MS-OISI) image-guided neurosurgery using visible-Near-Infrared (NIR) wavelengths have shown great potential for image-guided neurosurgery. One of the main challenges is the need for low-power consumption, high-speed and above 100dB dynamic range to capture both visible and NIR photons. The overarching goal of this work is to create a Digital Pixel Sensor (DPS) as a Complementary Metal-Oxide-Semiconductor (CMOS) camera for MS-OISI of the brain in image-guided neurosurgery that has a wide dynamic range, low power consumption, and high speed.

The general view of neurosurgical system, DPS, and circuit operation of conventional Pulsed Frequency Modulation (PFM) DPS are given first. The proposed PFM DPS and circuit implementation are shown, as well as simulation results obtained using a circuit simulator. Finally, a comparison with other similar works is given.

The proposed pixel simulation results show that the performance parameters such as dynamic range and power consumption has improved in comparison to similar works. However, due to its complicated circuitry, it has a low spatial resolution, which can be compensated.

The image sensor is post-layout simulated in 0.18μm CMOS technology with a 1.3V supply voltage, resulting in 140dB dynamic range and 7.69μW power dissipation with a 11% fill factor. The key novelty for the proposed PFM DPS is: in-pixel Analog-to-Digital Conversion (ADC), using a low voltage and high-speed dynamic comparator. Furthermore, this work uses a self-reset mechanism, which eliminates the need for an external pulse source, as well as variable reference voltage, which eliminates the necessity for a global constant reference voltage. All of these features demonstrate the excellent potential of the proposed pixel for MS-OISI image-guided neurosurgery.

Cognitive dysfunction is common in individuals with depression and these cognitive deficits may be associated with a risk of suicide. Therefore, the identification of the cognitive functions of depressed patients and the introduction of effective interventions on these factors are highly important. This study aimed to compare the effectiveness of repetitive Transcranial Magnetic Stimulation (rTMS) and Theta Burst Stimulation (TBS) to improve selective attention, working memory, and response time of depressed individuals with and without a history of suicide.

This applied quasi-experimental study was conducted based on a pretest-posttest design. The population included 40 depressed patients referring to the clinics of Mashhad, Iran, in 2020. The samples were divided into four groups, namely individuals with a history of suicide subjected to treatment with rTMS, without a history of suicide receiving treatment with rTMS, with a history of suicide undergoing treatment with TBS, and without a history of suicide administered with TBS (n = 10 each). The data were collected using the Stroop Color and Word Test, Corsi block test, and reaction time tests and statistically analyzed using multivariate analysis of covariance.

The results confirmed the effectiveness of the intervention on the congruent reaction time, incongruent reaction time, working memory, simple reaction time, and selective reaction time in all four study groups (P < 0.05). The results of multivariate analysis of covariance showed that the group had a significant effect on the variables of congruent reaction time, simple reaction time, and selective reaction time (P < 0.05); however, it had no significant effect on the variables of incongruent reaction time and working memory (P > 0.05).

Compared to the rTMS method, the TBS had a greater effect on the variables of congruent reaction time, simple reaction time, and selective reaction time.

The purpose of this study is to use linear and non-linear features extracted from Electroencephalography (EEG) signal to predict the Mini-Mental State Examination (MMSE) test score by machine learning algorithms.

First, the MMSE test was taken from 20 subjects that were referred with the initial diagnosis of dementia. Then, the brain activity of subjects was recorded via EEG signal. After preprocessing this signal, various linear and non-linear features are extracted from it that are used as input to machine learning algorithms to predict MMSE test scores in three levels.

Based on the experiments, the best classification result is related to the Long Short-Term Memory (LSTM) network with 68% accuracy.

Findings show that by using machine learning algorithms and features extracted from EEG signal the MMSE scores are predicted in three levels. Although deep neural networks require a lot of data for training, the LSTM network has been able to achieve the best performance. By increasing the number of subjects, it is expected that the classification results will also increase.

At Magnetic Resonance Imaging (MRI), artifacts arising from metal implants are an obstacle to obtaining optimal images. This study aimed to evaluate the impact of View-Angle Tilting (VAT) and Slice Encoding for Metal Artifact Correction (SEMAC) techniques for the artifact reduction of patients during knee MRI with metal implants.

The MR images without any intervention of the knee from 20 patients with knee prostheses were used. The VAT and SEMAC metal artifact reduction techniques were applied to all the MR images. Volume and mass of the metal prosthesis were quantified using the MATLAB program and compared with the real measurements using nonparametric Wilcoxon tests in SPSS software. The qualitative analysis was performed by two blinded observers regarding the score of artifact size, distortions, image quality, and visualization of bone marrow and soft tissues adjacent to metal implants. In addition, Cohen’s kappa values were used for inter-observer agreement.

The average volume of the platinum based on the conventional, VAT, and SEMAC methods was estimated at 14.22 ± 0.43, 14.05 ± 0.4, and 13.3 ± 0.45 cm3, respectively. The statistical analysis showed no significant difference (P > 0.05) between the mean value of the platinum volume for the SEMAC method and the real measurement (13.6 ± 0.33 cm3). Furthermore, regarding the conventional, VAT, and SEMAC sequences, the mean mass of the platinum was obtained at 305.02 ± 9.22, 301.37 ± 8.58, and 285.28 ± 9.65 g, respectively, with the P-Value of 0.005, 0.009, and 0.268, compared to the real measurements (286.81±8.75 g). Notably, the blinded readers demonstrated that the SEMAC method was remarkably superior quality compared with VAT and conventional acquisitions (P-Value< 0.05).

The knee prosthesis metal artifact was reduced using the VAT and SEMAC techniques, in a way that, the reduction was significant by the SEMAC method. In addition, concerning the qualitative observer analysis, the application of the SEMAC technique provides improved visualization of tissue structures adjacent to metal implants.

Gold Nanoparticles (GNPs) with high density and an atomic number have lately been proposed as an alternative contrast agent for Computed Tomography (CT).

In the present study, the Contrast-to-Noise Ratio (CNR) of GNPs from various shapes, sizes, concentrations, and surface chemistry was compared with an iodine contrast agent using CT at different X-ray tube voltages and concentrations.

Our findings showed that GNPs in various concentrations, shapes, sizes, and X-ray tube energies from 80 to 140 kVp revealed greater image CNR than iodinated contrast media (Omnipaque). Smaller spherical GNPs (13 nm) had greater CNR than larger ones (60 nm) and Gold Nanorods )GNRs( with a larger Aspect Ratio (AR) represented excellent effect on CNR. In addition, Polyethylene Glycol (PEG) covering on GNRs decreased CNR. We observed image CNR was increased using increasing in kVp and concentration.

Smaller spherical GNPs can be proposed as a potential candidate as a future contrast agent alternative to iodinated contrast media.

A powerful imaging method for evaluating brain patches is resting-state functional magnetic resonance imaging, in which the subject is at rest. Artificial neural networks are one of the several Alzheimer's disease analysis and diagnosis methods which is used in this study. We investigate artificial neural networks' ability to diagnose Alzheimer's disease using resting-state functional magnetic resonance imaging data.

Functional and structural magnetic resonance imaging data acquisition was applied for 15 Alzheimer’s disease, 17 mild cognitive impaired, and ten normal healthy participants. Time series of blood oxygen level dependent extracted from the multi subject dictionary learning brain atlas after pre-processing. This study develops a one-dimensional convolutional neural network using extracted signals of the functional atlas for differential diagnosis of Alzheimer's disease.

Applying the proposed method to resting-state functional magnetic resonance imaging signals for classifying three classes of Alzheimer's disease patients resulted in overall accuracy, F1-score, and precision of 0.685 ,0.663, and 0.681 respectively. Using 39 regions in the brain and proposing a quiet simple network than most of the available deep learning-based methods are the main advantages of this model.

Resting-state functional magnetic resonance imaging signal recognition based on a functional atlas with the application of a deep neural network has a pattern recognition capability that can make a differential diagnosis with an acceptable level of accuracy and precision. Therefore, deep neural networks can be considered as a tool for the early diagnosis of Alzheimer’s disease.

Micro-SPECT system has recently been introduced on nuclear medicine in the preclinical and research in which NaI (Tl) and Cadmium Telluride (CdTe) are used as the gamma-ray detectors with more generally use of NaI (Tl). The present study aimed to evaluate different thicknesses of the NaI (Tl) and (CdTe) detectors on functional parameters of a micro-SPECT system.

A Micro-SPECT system with CdTe semiconductor detector and a hexagonal parallel hole collimator with a hole diameter of 0.11 mm, high of 24.05 mm, and septal thickness of 0.016 mm was simulated by SIMIND Monte Carlo program. The system performance was assessed by comparing the functional parameters, including system efficiency, sensitivity, energy and spatial resolution with the NaI (Tl) detector. The simulated scans of a 99mTc point source, a digital micro-Jacszack phantom, and a voxelized MOBY mouse phantom with the system were prepared to evaluate image quality.

The functional parameters; sensitivity, efficiency, planar spatial resolution, and image contrast of CdTe detector were determined 1.4, 1.2, 1.7, and 1.8 times higher than those of NaI (Tl), respectively. Moreover, the calculated energy resolution of CdTe and NaI (Tl) detectors was obtained 6.2% and 10.2% at 141 KeV, respectively. In the filtered back projection (FBP) reconstructed images of the micro-Jacszack phantom, minimum detectable size of the cold rods with CdTe and NaI (Tl) detectors were obtained 0.79 mm and 0.95 mm, respectively.

The imaging system with a 5.5 mm thickness CdTe detector provided better image quality and showed considerable efficiency.

With the widespread application of ionizing radiation in medical practice, concerns have been increased regarding the hazardous effects of radiation. Studies have demonstrated that some variables such as body dimensions affect the absorbed radiation dose. In this study, the association between Body Mass Index (BMI) and absorbed dose in Computed Tomography (CT) is investigated.

A total of 550 adult patients (age ≥ 15 years) were included in the study. The height and weight of the patients were recorded for BMI calculation. Dosimetry data were acquired from digital imaging and communications in medicine dose reports. The patients were categorized into five groups according to their BMI, the categorized information was then imported into ImPACT Dose software for calculation of Size-Specific Dose Estimate (SSDE) and organ and effective doses. The relationship between patient BMI and the effective dose was also determined.

A higher BMI contributed to increased radiation dose and SSDE in patients who had undergone chest or abdomen-pelvis CT examination (p < 0.05). 

The radiation dose is related to a patient’s BMI and rises with an increase in BMI. Accordingly, it is suggested that BMI and other variables, such as the type of scan and other body dimensions, which affect the radiation dose, can be used to estimate the radiation dose before performing CT. This estimation can be considered for the justification and optimization of CT examinations.

Drowsy driving accounts for many accidents and has attracted substantial research attention in recent years. Electroencephalography (EEG) signals are shown to be a reliable measure for the early detection of drowsiness. Unfortunately, there is no comprehensive study showing the applicability of drowsiness detection systems with EEG signals. In this research, we targeted the studies under the category of drowsiness detection, which adopted an EEG-based approach, to inspect the applicability of these systems from different aspects.

We included documented studies that utilized clinical devices and consumer-grade EEG headsets for detection of drowsiness and investigated the selected studies from different aspects such as the number of EEG channels, sampling frequency, extracted features, type of classifiers, and accuracy of detection. Among available headsets, we focused on the most popular ones, namely Muse, NeuroSky, and EMOTIV brands.

Considerable number of studies have used EEG headsets, and their reports showed that the highest average accuracy belongs to EMOTIV, and the highest maximum detection accuracy, 98.8%, was achieved by the Muse headset. Spectral features extracted from short periods of 1, 2, or 10 secs are the most popular features, and the support vector machine is the most commonly used classifier in drowsiness detection systems. Therefore, implementing a reliable detection system does not necessarily include complicated features and classifiers.

It is shown that, despite their few electrodes, commercial headsets have gained decent detection accuracy. This study sheds light on the current status of drowsiness detection systems and paves the way for future industrial designs of such systems.

Prostate cancer is one of the most common malignant cancers. Several radiotherapy planning methods have been suggested for the treatment of prostate cancer. In this study, four-field, and Field-In-Field (FIF) planning methods were compared based on dosimetric parameters.

In the radiotherapy Treatment Planning System (TPS) for 10 patients who were treated with the common four-field method, the planning was also performed by the FIF method. Dosimetric parameters were measured for Planning Target Volume (PTV), rectum, and bladder. These parameters included maximum dose, minimum dose, mean dose, V15%, V25%, V30%, and V35%, as well as Homogeneity Index (HI) and Conformity Index (CI). Two treatment planning methods based on dosimetric parameters were compared using paired t-test.

Maximum, minimum and mean dose in PTV, rectum, and bladder were significantly different for the two techniques.  There was no significant difference between the two planning techniques in dosimetric parameters of V15%, V25%, V30%, and V35% for rectum and bladder. The FIF technique delivers more doses to the tumor. HI was better in the FIF method than in the four-field method, but CI was not significantly different. In both techniques, the rectum and bladder did not receive doses above 60 Gy.

In the treatment of prostate cancer in both Four-field and FIF planning methods, the dose to the rectum and bladder is less than the tolerance dose. FIF technique is recommended to better control the tumor. Based on dosimetric parameters, no significant findings were obtained that prove the superiority of FIF over the four-field technique in the treatment of prostate cancer.