Managing patients with hydrocephalus and cerebrospinal fluid disorders requires repeated head imaging. In adults, this is typically done with computed tomography (CT) or less commonly magnetic resonance imaging (MRI). However, CT poses cumulative radiation risks and MRI is costly. Transcranial ultrasound is a radiation-free, relatively inexpensive, and optionally point-of-care alternative. The initial use of this modality has involved measuring gross brain ventricle size by manual annotation. In this work, we explore the use of deep learning to automate the segmentation of brain right ventricle from transcranial ultrasound images. We found that the vanilla U-Net architecture encountered difficulties in accurately identifying the right ventricle, which can be attributed to challenges such as limited resolution, artifacts, and noise inherent in ultrasound images. We further explore the use of coordinate convolution to augment the U-Net model, which allows us to take advantage of the established acquisition protocol. This enhancement yielded a statistically significant improvement in performance, as measured by the Dice similarity coefficient. This study presents, for the first time, the potential capabilities of deep learning in automating hydrocephalus assessment from ultrasound imaging.
Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-processing. The first contribution of this study is to model tag fading by considering the interplay between T1 relaxation and the repeated application of radio frequency (RF) pulses during serial imaging sequences. This is a factor that has been overlooked in prior research on tMRI post-processing. Further, we have observed an emerging trend of utilizing raw tagged MRI within a deep learning-based (DL) registration framework for motion estimation. In this work, we evaluate and analyze the impact of commonly used image similarity objectives in training DL registrations on raw tMRI. This is then compared with the Harmonic Phase-based approach, a traditional approach which is claimed to be robust to tag fading. Our findings, derived from both simulated images and an actual phantom scan, reveal the limitations of various similarity losses in raw tMRI and emphasize caution in registration tasks where image intensity changes over time.
This work reports a deep-learning based registration algorithm that aligns scanning laser ophthalmoscopy (SLO) retinal images collected from a longitudinal pre-clinical animal study. We address the problem of determining correspondences between two retinal images in agreement with a geometric model such as an homography or thin-plate spline (TPS) transformation, and estimating its parameters. The contributions of this work are two-fold. First, we propose a convolutional neural network architecture for retinal image registration based on geometric models. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never-seen-before images. Overall, for mono-modality longitudinal registration, the deep-learning registration method achieved mean error in the range of 18.93 ± 0.51 µm (Hom), 26.01 ± 0.84 µm (TPS) and 39.30 ± 2.04 µm (TPS+Hom).
Normal Pressure Hydrocephalus (NPH) is a brain disorder associated with ventriculomegaly. Accurate segmentation of the ventricle system into its sub-compartments from magnetic resonance images (MRIs) could help evaluate NPH patients for surgical intervention. In this paper, we modify a 3D U-net utilizing probability maps to perform accurate ventricle parcellation, even with grossly enlarged ventricles and post-surgery shunt artifacts, from MRIs. Our method achieves a mean dice similarity coefficient (DSC) on whole ventricles for healthy controls of 0.864 ± 0.047 and 0.961 ± 0.024 for NPH patients. Furthermore, with the benefit of probability maps, the proposed method provides superior performance on MRI with grossly enlarged ventricles (mean DSC value of 0.965 ± 0.027) or post-surgery shunt artifacts (mean DSC value of 0.964 ± 0.031). Results indicate that our method provides a high robust parcellation tool on the ventricular systems which is comparable to other state-of-the-art methods.
Cadmium zinc telluride (CZT) radiation detectors are suitable for various applications, due to good energy performance at room temperature and simple pixilation to achieve high spatial resolution. Our group is developing a two-panel head-and-neck dedicated positron emission tomography (PET) system with CZT detectors. In this work, we present the back-end readout electronic design and the initial electronic noise results of our system. The back-end readout electronic incorporates RENA boards (a total 150 RENA boards for each panel), 30:1 fan-in boards connecting 30 RENA boards to the PicoZed 7010/7020 board (a total 5 fan-in boards for each panel). In each panel, 5:1 intermediate boards are used for biasing CZT detectors. The RENA board and the Picozed board are capable of data transmission of 50 Mbps and 6.6 Gbps, respectively. Electronic noise was also quantified using a square wave test pulse that provides charge injection into each channel of the RENA chip in the amount of 75fC/Volt. The pulse amplitude was chosen to generate approximately the same amount of charges as 511 keV photon would provide for each channels. The FWHM electronic noise at 511 keV was measured to be less than 1% (FWHM of 7.80 ± 1.47 ADC units or 4.89 ± 0.92 keV after conversion).
A new method for the detection of ionizing radiation with the potential to improve the coincidence time resolution in positron emission tomography (PET) was investigated. This method is based on Pockels effect (i.e., linear electro-optic modulation effect) and pump-probe measurement with cadmium telluride (CdTe) and lithium niobate (LiNbO3). In this work, the performance of the two detector materials were compared experimentally. CdTe detector material demonstrated a repeatable change in modulation signal level after laser diode illumination, while LiNbO3 crystal gave no response to laser diode as the radiation source, suggesting the shorter carrier lifetime and lower carrier mobility found of LiNbO3 material. The modulation signal induced by 511 KeV photons in LiNbO3 and CdTe both can be detected through the new method. We found that the CdTe crystal could provide a higher sensitivity to 511 KeV photons than the LiNbO3 crystal under the same bias voltage. In addition, the amplitude of modulation signal increased linearly with the bias voltage before saturation. The modulation signal strength in LiNbO3 crystal was continued to increase after 2200 V due to its high resistivity which could reduce the dark current in detector and thus reduce the noise level during experiment, while the modulation signal of CdTe with low resistivity tended to be saturated at the bias voltage of higher than 1400 V. Therefore, further increasing the bias voltage for both detector crystals may hypothetically enhance the modulation strength and detection sensitivity of PET.
The natural force of scouring has become one of the most critical risk endangering the endurance of bridges, thus leading to the necessity of deploying underwater monitoring sensors to actively detect potential scour holes under bridges. Due to the difficulty in re-charging batteries for underwater sensors, super capacitors with energy harvesting (EH) means are exploited to prolong the sustainability of underwater sensors. In this paper, an energy harvesting power supply based on a helical turbine is proposed to power underwater monitoring sensors. A small helical turbine is designed to convert water flow energy to electrical energy with favorable environmental robustness. A 3-inch diameter, 2.5-inch length and 3-bladed helical turbine was designed with two types of waterproof coupling with the sensor housing. Both designs were prototyped and tested under different flow conditions and we get valid voltage around 0.91 V which is enough to power monitoring sensor. The alternating current (AC) electrical energy generated by the helical turbine is then rectified and boosted to drive a DC charger for efficiently charging one super capacitor. The charging circuit was designed, prototyped and tested thoroughly with the helical turbine harvester. The results were promising, that the overall power supply can power an underwater sensor node with wireless transceivers for long-term operations
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