Automated Coronary Artery Tracking with a Voronoi-Based 3D Centerline Extraction Algorithm.

Coronary artery disease is one of the leading causes of death worldwide, and medical imaging methods such as coronary artery computed tomography are vitally important in its detection. More recently, various computational approaches have been proposed to automatically extract important artery coronary features (e.g., vessel centerlines, cross-sectional areas along vessel branches, etc.) that may ultimately be able to assist with more accurate and timely diagnoses. The current study therefore validated and benchmarked a recently developed automated 3D centerline extraction method for coronary artery centerline tracking using synthetically segmented coronary artery models based on the widely used Rotterdam Coronary Artery Algorithm Evaluation Framework (RCAAEF) training dataset. Based on standard accuracy metrics and the ground truth centerlines of all 32 coronary vessel branches in the RCAAEF training dataset, this 3D divide and conquer Voronoi diagram method performed exceptionally well, achieving an average overlap accuracy (OV) of 99.97%, overlap until first error (OF) of 100%, overlap of the clinically relevant portion of the vessel (OT) of 99.98%, and an average error distance inside the vessels (AI) of only 0.13 mm. Accuracy was also found to be exceptionally for all four coronary artery sub-types, with average OV values of 99.99% for right coronary arteries, 100% for left anterior descending arteries, 99.96% for left circumflex arteries, and 100% for large side-branch vessels. These results validate that the proposed method can be employed to quickly, accurately, and automatically extract 3D centerlines from segmented coronary arteries, and indicate that it is likely worthy of further exploration given the importance of this topic.

A closer look at yoga nidra- early randomized sleep lab investigations.

OBJECTIVES: We aimed to examine trial feasibility plus physiological and psychological effects of a guided meditation practice, Yoga Nidra, in adults with self-reported insomnia. METHODS: Twenty-two adults with self-reported insomnia were recruited to attend two visits at our research center. At Visit 1 (V1), participants were asked to lie quietly for ninety minutes. The primary outcome was change in electroencephalography (EEG). Heart rate variability (HRV), respiratory rate and self-reported mood and anxiety were also measured. At Visit 2 (V2), the same protocol was followed, except half of participants were randomized to practice Yoga Nidra for the first 30-min. RESULTS: There were no between-group changes (V1-V2) in alpha EEG power at O1 (Intervention: 13 ± 70%; Control: -20 ± 40%), HRV or sleep onset latency in response to Yoga Nidra. Respiratory rate, however, showed statistically significant difference between groups (Yoga Nidra -1.4 breaths per minute (bpm) change during and - 2.1 bpm afterwards vs. Control +0.2 bpm during and + 0.4 bpm after; p = .03 for both during and after). The intervention displayed good acceptability (well-tolerated) and credibility (perceived benefit ratings) with implementation success (target sample size reached; 5% dropout rate). CONCLUSIONS: This preliminary clinical trial provides early evidence that Yoga Nidra is a well-tolerated, feasible intervention for adults reporting insomnia. Decreased respiratory rate in response to Yoga Nidra needs to be confirmed in more definitive studies. TRIAL REGISTRATION INFORMATION: This trial was registered on ClinicalTrials.gov as "A Closer Look at Yoga Nidra: Sleep Lab Analyses" (NCT#03685227).

Investigation of transcutaneous electrical nerve stimulation improvements with microneedle array electrodes based on multiphysics simulation.

This paper investigates microneedle array electrodes for transcutaneous electrical nerve stimulation, and compares their performance with conventional surface electrodes. A three-dimensional model of tissue was developed for finite element multiphysics simulations. Investigations included current density in different depths of a tissue, space constant under electrodes, specific absorption ratio of tissue, selectivity of stimulation, temperature rise, and blood flow. Results showed that microneedle electrodes have up to 10% higher selectivity than the surface electrodes. Furthermore, it was found that stimulation using microneedle electrodes provides more robust current density at different tissue depths compared to the surface electrode stimulation. Microneedle electrodes showed enhanced stimulation parameters, particularly for targeting a specific nerve in a specific depth of a tissue.

Tracheal Sounds Features Changes in Different Sleep Stages Based on Complex Wavelet Analysis.

Breathing sounds analysis during sleep is an informative method to study the upper airway. Different sleep stages may affect the breathing sound features. In this study, the tracheal breathing sounds were recorded from 5 individuals and the complex Gaussian wavelet of the deceleration phase of about 3000 successive breath cycles were calculated. The segmented portions were divided into 30 seconds episodes and the appropriate sleep stage of each segment were labeled. The results showed that the Mahalanobis distance between the real parts of the complex Gaussian wavelet coefficients and the reference distribution of each stage is changing consistently in different sleep stages.

Molybdenum coated SU-8 microneedle electrodes for transcutaneous electrical nerve stimulation.

Electrophysiological devices are connected to the body through electrodes. In some applications, such as nerve stimulation, it is needed to minimally pierce the skin and reach the underneath layers to bypass the impedance of the first layer called stratum corneum. In this study, we have designed and fabricated surface microneedle electrodes for applications such as electrical peripheral nerve stimulation. We used molybdenum for microneedle fabrication, which is a biocompatible metal; it was used for the conductive layer of the needle array. To evaluate the performance of the fabricated electrodes, they were compared with the conventional surface electrodes in nerve conduction velocity experiment. The recorded signals showed a much lower contact resistance and higher bandwidth in low frequencies for the fabricated microneedle electrodes compared to those of the conventional electrodes. These results indicate the electrode-tissue interface capacitance and charge transfer resistance have been increased in our designed electrodes, while the contact resistance decreased. These changes will lead to less harmful Faradaic current passing through the tissue during stimulation in different frequencies. We also compared the designed microneedle electrodes with conventional ones by a 3-dimensional finite element simulation. The results demonstrated that the current density in the deep layers of the skin and the directivity toward a target nerve for microneedle electrodes were much more than those for the conventional ones. Therefore, the designed electrodes are much more efficient than the conventional electrodes for superficial transcutaneous nerve stimulation purposes.

Sleep Stage Detection Using Tracheal Breathing Sounds: A Pilot Study.

Sleep stage detection is needed in many sleep studies and clinical assessments. Generally, sleep stages are identified using spectral analysis of electrocephologram (EEG) and electrooculogram (EOG) signals. This study, for the first time, has investigated the feasibility of detecting sleep stages using tracheal breathing sounds, and whether the change of breathing sounds due to sleeping stage differs at different periods of sleeping time; the motivation was seeking an alternative technique for sleep stage identification. The tracheal breathing sounds of 12 individuals, who were referred for full overnight polysomnography (PSG) assessment, were recorded using a microphone placed over the suprasternal notch, and analyzed using higher order statistical analysis. Five noise-and-snore-free breathing cycles from wakefulness, REM and Stage II of sleep were selected from each subject for analysis. Data of the REM and Stage II were selected from beginning, middle and close to end of sleeping time. Hurst exponent was calculated from the bispectra of the inspiratory sounds of each subject at each sleeping stage in different periods of sleeping time. The participants' sleep stage were determined by sleep lab technologists during the PSG study using EEG and EOG signals. The results show separate and non-overlapping clusters for wakefulness, REM and Stage II for each subject. Thus, using a simple linear classifier, we were able to classify REM and Stage II of each subject with 100% accuracy. In addition, the results show that the same pattern existed as long as the REM and Stage II segments were close (less than 3 h) to each other in terms of time.

Designing and implementing bioimpedance spectroscopy device by measuring impedance in a mouse tissue.

Studies show that any complications including hemorrhage, lack of blood supply, lack of oxygen supply and death of cells in a tissue, will have a clear effect on electrical properties of that tissue. Thus, by measuring impedance of a set of tissues, potential problems of the damaged tissue may be found. Since electrical impedance is closely related to the measuring frequency, obviously, every tissue exhibits its own specific impedance according to its electrical properties at each frequency. This research project investigates design and manufacture method of a device for measuring tissue impedance at different frequencies. To this end, design of a multi frequency sinusoidal current source is required. This current source is built using a single harmonic Generator sample (Direct Digital Synthesizer AD9835) with working frequency (design-point frequency) between 1 Hz and 10 MHz and accuracy of 1 Hz and microcontroller (PIC16F628) capability. For measurement and display of tissue impedance, ARM AT91SAMs256 microcontroller was used. Thus, with this hardware created, it shows that there are significant impedance changes between mouse tissues.

Extraction of nucleolus candidate zone in white blood cells of peripheral blood smear images using curvelet transform.

The main part of each white blood cell (WBC) is its nucleus which contains chromosomes. Although white blood cells (WBCs) with giant nuclei are the main symptom of leukemia, they are not sufficient to prove this disease and other symptoms must be investigated. For example another important symptom of leukemia is the existence of nucleolus in nucleus. The nucleus contains chromatin and a structure called the nucleolus. Chromatin is DNA in its active form while nucleolus is composed of protein and RNA, which are usually inactive. In this paper, to diagnose this symptom and in order to discriminate between nucleoli and chromatins, we employ curvelet transform, which is a multiresolution transform for detecting 2D singularities in images. For this reason, at first nuclei are extracted by means of K-means method, then curvelet transform is applied on extracted nuclei and the coefficients are modified, and finally reconstructed image is used to extract the candidate locations of chromatins and nucleoli. This method is applied on 100 microscopic images and succeeds with specificity of 80.2% and sensitivity of 84.3% to detect the nucleolus candidate zone. After nucleolus candidate zone detection, new features that can be used to classify atypical and blast cells such as gradient of saturation channel are extracted.