Signal-to-noise Ratio Values Research Articles

Third generation dual-source CT coronary angiography with high-pitch spiral mode versus prospectively-gated sequential mode: comparison of radiation exposure and image quality

Objectives: To compare high-pitch spiral (HPS) and prospectively-gated step-and-shoot (SAS) coronary CT angiography (CCTA) using third generation dual-source CT regarding objective and subjective image quality parameters and radiation exposure. Methods: Eighty pairs of patients matched for gender, age, heart rate and BMI were enrolled in this retrospective study. High-pitch spiral and prospectively ECG-gated sequential CCTA were performed using third generation dual-source CT. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the left ventricle were calculated for each group. Image quality were also scored using four-point scale. Student t-test was used to compare SNR, CNR and mean effective dose values (ED) and Wilcoxon test was used to compare image quality scores. Interrater aggreement were evaluated using Cohen’s kappa statistics. Results: Between-group differences in terms of age, gender, BMI, heart rate, and Agatston score were statistically not significant. Mean SNR and CNR was higher in prospective SAS protocol (16.5 ± 6.2 vs. 14.7 ± 4.9, p = 0.047 and 13.0 ± 5.2 vs. 11.2 ± 4.3, p = 0.02). Image quality scores showed no significant difference between two scan protocols (p > 0.05). Regarding radiation exposure, CT dose index (CTDIvol), dose length product (DLP) and ED was significantly lower for high-pitch group (p < 0.0001). Conclusions: HPS CCTA using DSCT enables > 70% dose reduction while maintaining the image quality compared to prospectively ECG-gated SAS protocol. Therefore, HPS CCTA protocol can be preferred in patients appropriate for prospective ECG-triggered protocol.

Detection of Simultaneous Bearing Faults Fusing Cross Correlation With Multikernel SVM

Detection of simultaneous bearing faults for condition monitoring (CM) of bearings using time-domain analysis is quite challenging and open area particularly in noisy environment. This work presents a new scheme for simultaneous bearing fault detection using vibration signal, in cases where single-point localized bearing fault and multiple-point compound fault coexist. Bearings of a 415 V, 3kW, 3-phase Squirrel Cage Induction Motor (SCIM) have been used for data collection, while the loading arrangement is done using a 110V, 4kW DC generator connected with a load box and coupled to the motor. A cross-correlation-based time-domain feature extraction approach has been introduced. The neighborhood component analysis (NCA) technique has been applied to the cross-correlation-based features to reduce the complexity of the proposed model. Furthermore, the selected features have been fed into a multi-kernel support vector machine (MKSVM) to classify simultaneous bearing faults. This method has also been tested on signals contaminated with white Gaussian noise to verify reliability in the industrial environment. It is found that with only five features, the proposed model yields 100% classification performance metrics for raw signal and under noisy environments with signal-to-noise ratio (SNR) of 20 dB to 50 dB for both full load and no-load conditions. Whereas at 10 dB SNR value, performance decreases slightly, still an overall classification performance metrics of more than 99% is achieved by this method. Furthermore, this method has enhanced performance when compared to earlier studies with publicly available databases for localized bearing failure identification.

Transmit Antenna Selection Based on SNR prediction in TDD Systems Using Convolutional Neural Network

This paper proposes a method for predicting future signal-to-noise ratio (SNR) in a time-division-duplexing (TDD) mobile communication environments using a convolutional neural network (CNN). The communication system uses multiple receive antennas and transmit using only one or two antennas among them. A CNN model is proposed to predict the SNR at a future transmission time based on past SNRs received from multiple antennas. The probability of reception at a certain is set to 10-100%. In case that SNR cannot be measured due to the absence of reception, linear interpolation is performed using two adjacent recorded SNRs. If even two adjacent SNRs do not exist, the SNR is set to 0dB. Comparing the predicted SNRs at multiple antennas, the antenna with the highest SNR value is selected for future transmission. To verify antenna selection accuracy, computer simulation is conducted. The simulation results substantiate the superiority of the proposed method over conventional method in single antenna selection. Regarding multi-antenna selection, the proposed method demonstrates diminished accuracy relative to conventional methods at lower speeds. Nevertheless, a comprehensive evaluation considering the root mean square error (RMSE) demonstrates the overall superiority of the proposed method across all speeds.

Operational modal analysis for damage detection in a rotating wind turbine blade in the presence of measurement noise

The present paper demonstrates the feasibility of detecting and quantifying crack-type damage features in rotating wind turbine blades by Operational Modal Analysis (OMA) under noisy measurement conditions. The excitation of blade vibrations is achieved through the rotationally sampled turbulence spectrum of the inflowing wind field. Accelerations are sampled at 30 locations along the blade. Given the high sensitivity of OMA-reconstructed mode shapes to the presence of noise a six-part methodology has been developed. It is demonstrated that small cracks can be detected reliably down to signal-to-noise ratios (SNR) at the blade tip of 40 dB, well below the SNR values achievable with commercial accelerometers and wind-excited acceleration amplitudes. Calibration curves for crack lengths and center positions have been constructed. The proposed methodology holds the potential for the implementation of robust and low-cost structural health monitoring systems for wind turbines at locations with difficult access, such as in an offshore environment.

A signal conditioning module for denoising Electrocardiogram signals

In this work, we propose to use an optimal multiband filter with least mean square algorithm to design a signal conditioning module for denoising Electrocardiogram (ECG) signals contaminated with predominant noises. The module is implemented on a Field Programmable Gate Array (FPGA) hardware. The experimental results of the proposed module are investigated and compared using an ECGID database available on Physionet. Quantitative and qualitative analysis is performed using Signal to Noise Ratio (SNR), Mean Square Error (MSE), and quality indexes to assess the effectiveness of the module. The average values of SNR are 10.90124, and MSE is 0.001761, indicating the successful elimination of noises in the filtered ECG signal using the proposed module. The signal quality indexes also demonstrate that the relevant information for diagnosing cardiac functionality is preserved. Furthermore, the performance of the designed module is tested on ECG signals obtained from electrodes placed on the human body. The Spartan 3s500efg320-5 FPGA device is employed to implement the filter design module using the partial serial architecture.

Rethinking the clinical utility of distortion-product otoacoustic emission (DPOAE) signal-to-noise ratio

Objective Distortion-product otoacoustic emission (DPOAE) levels are repeatable over time in normal-hearing individuals making DPOAE levels an ideal measurement for monitoring cochlear status in clinic and research applications. However, if DPOAE signal-to-noise ratio (SNR) values instead of levels are used for monitoring, the repeatability of this value needs to be established. This retrospective, cross-sectional study sought to determine DPOAE SNR repeatability in younger children, older children, young adults and a patient population with normal hearing. Design Each participant attended four sessions where DPOAE discrete frequency sweeps were collected at conventional (≤ 8 kHz) and/or extended-high frequencies (> 8 kHz). To examine the extent of variability to be expected for DPOAE SNR, average absolute SNR differences-between-trials were determined and compared to average absolute DPOAE level differences-between-trials. Study samples One hundred forty-five participants, incorporating four different groups from three different studies. Ages ranged from 3 to 55 years. Results Average SNR differences-between-trials across all frequencies are greater than differences for average DPOAE levels. Improved calibration methods result in SNR differences-between-trials that are similar across all frequencies Conclusions When monitoring cochlear health over an extended bandwidth, DPOAE levels are less variable across trials than SNR values, thus allowing earlier indicators of cochlear damage.

Performance evaluation of DFT based speckle reduction framework for synthetic aperture radar (SAR) images at different frequencies and image regions

Polarimetric Synthetic Aperture Radar (PolSAR) images are widely used for remote sensing and geoscience applications. However, the coherent processing of radar signals in PolSAR imaging leads to the presence of speckle noise, which can significantly degrade image quality and limit the accuracy of subsequent analyses. To address this issue, speckle reduction frameworks are often applied to PolSAR images to reduce the noise level and enhance image quality. In this paper, the performance of Discrete Fourier Transform (DFT) based speckle reduction framework is evaluated on different bands (L, C, and P band) against various evaluation metrics like CV, SD, SNR, ENL, SSI and SMPI. The proposed framework is evaluated by comparing filtered and unfiltered images across different parameters, such as mean, standard deviation, coefficient of variation, equivalent number of looks (ENL), variance, and signal-to-noise ratio (SNR) on all three bands for the diagonal elements (T11, T22, and T33) of T3 matrix. These metrics provide a comprehensive evaluation of the proposed framework’s ability to (i) smoothen homogeneous regions, (ii) preserve contours, and (iii) retain polarimetric information. The framework’s ability to reduce speckle noise and improve image quality is demonstrated through a significant reduction in standard deviation, coefficient of variation, and improvement in SNR and ENL values. The proposed framework successfully preserved polarimetric information while effectively suppressing speckle noise. These results suggest that the proposed framework could be a valuable tool for improving PolSAR image quality and enhancing subsequent processing of PolSAR data.

A Novel ML-Based Symbol Detection Pipeline for Molecular Communication

Molecular Communication (MC) is the process of sending information by the use of particles instead of electromagnetic (EM) waves. This change in paradigm allows the use of MC in areas where EM transmission is undesirable. These include underground, underwater and even intra-body communications. While this novel paradigm promises new areas for communication, one of the major setbacks is its relatively low throughput caused by the propagation speed. This can be improved by decreasing the symbol duration; however, this can be a detriment to the correct decoding of symbols. This paper proposes a novel symbol detection pipeline to increase the possible throughput without increasing the error rate of the communication. This is based on a machine-learning algorithm for classification tasks using an L-point discrete time moving average filter and a wide range of features. Extensive simulations with long sequences at different signal-to-noise ratio (SNR) values were performed to determine how well the proposed method detects symbols. The results show that our method can detect symbols received when On-Off Keying (OOK) modulations are used with a 10 dB gain, even when transmissions with untrained SNR values occur.

Increasing the power and spectral efficiencies of an OFDM-based VLC system through multi-objective optimization.

The nonlinearity induced by light-emitting diodes in visible light communication (VLC) systems presents a challenge to the parametrization of orthogonal frequency division multiplexing (OFDM). The goal of the multi-objective optimization problem presented in this study is to maximize the transmitted power (superimposed LED bias-current and signal amplification) for both conventional and constant envelope (CE) OFDM while also maximizing spectral efficiency. The bit error rate (BER) metric is used to evaluate the optimization using the non-dominated sorting genetic algorithm II. Simulation results show that for a BER of 1×10-3, the signal-to-noise ratio (SNR) required decreases with the guard band due to intermodulation distortions. In contrast to SNR values of approximately 13 and 25dB achieved by traditional OFDM-based systems, the VLC system with CE signals achieves a guard band of 6% of the signal bandwidth with required SNR values of approximately 10.8 and 24dB for 4-quadrature amplitude modulation (QAM) and 16-QAM modulation orders, respectively.

Deep Learning-Accelerated Liver Diffusion-Weighted Imaging: Intraindividual Comparison and Additional Phantom Study of Free-Breathing and Respiratory-Triggering Acquisitions.

Deep learning-reconstructed diffusion-weighted imaging (DL-DWI) is an emerging promising time-efficient method for liver evaluation, but analyses regarding different motion compensation strategies are lacking. This study evaluated the qualitative and quantitative features, sensitivity for focal lesion detection, and scan time of free-breathing (FB) DL-DWI and respiratory-triggered (RT) DL-DWI compared with RT conventional DWI (C-DWI) in the liver and a phantom. Eighty-six patients indicated for liver MRI underwent RT C-DWI, FB DL-DWI, and RT DL-DWI with matching imaging parameters other than the parallel imaging factor and number of averages. Two abdominal radiologists independently assessed qualitative features (structural sharpness, image noise, artifacts, and overall image quality) using a 5-point scale. The signal-to-noise ratio (SNR) along with the apparent diffusion coefficient (ADC) value and its standard deviation (SD) were measured in the liver parenchyma and a dedicated diffusion phantom. For focal lesions, per-lesion sensitivity, conspicuity score, SNR, and ADC value were evaluated. Wilcoxon signed rank test and repeated-measures analysis of variance with post hoc test revealed the difference in DWI sequences. Compared with RT C-DWI, the scan times for FB DL-DWI and RT DL-DWI were reduced by 61.5% and 23.9%, respectively, with statistically significant differences between all 3 pairs (all P's < 0.001). Respiratory-triggered DL-DWI showed a significantly sharper liver margin, less image noise, and more minor cardiac motion artifact compared with RT C-DWI (all P's < 0.001), whereas FB DL-DWI showed more blurred liver margins and poorer intrahepatic vessels demarcation than RT C-DWI. Both FB- and RT DL-DWI showed significantly higher SNRs than RT C-DWI in all liver segments (all P's < 0.001). There was no significant difference in overall ADC values across DWI sequences in the patient or phantom, with the highest value recorded in the left liver dome by RT C-DWI. The overall SD was significantly lower with FB DL-DWI and RT DL-DWI than RT C-DWI (all P's ≤ 0.003). Respiratory-triggered DL-DWI showed a similar per-lesion sensitivity (0.96; 95% confidence interval, 0.90-0.99) and conspicuity score to those of RT C-DWI and significantly higher SNR and contrast-to-noise ratio values (P ≤ 0.006). The per-lesion sensitivity of FB DL-DWI (0.91; 95% confidence interval, 0.85-0.95) was significantly lower than that of RT C-DWI (P = 0.001), with a significantly lower conspicuity score. Compared with RT C-DWI, RT DL-DWI demonstrated superior SNR, comparable sensitivity for focal hepatic lesions, and reduced acquisition time, making it a suitable alternative to RT C-DWI. Despite FB DL-DWI's weakness in motion-related challenges, further refinement could potentiate FB DL-DWI in the context of abbreviated screening protocols, where time efficiency is a high priority.

DATA INTEGRITY AND QUALITY ANALYSIS OF LOW COST ZED-F9P U-BLOX GNSS RECEIVER

Thanks to the rapidly emerging low-cost dual-frequency GNSS receivers, a feasible alternative for geodetic-grade GNSS receivers became available for some GNSS applications. In this study, the performance of data integrity and quality of a low-cost ZED-F9P u-blox GNSS receiver was investigated by comparing it with a geodetic-grade GNSS receiver. Availability of the epoch and phase/code signal channels, signal-to-noise ratio (SNR), code multipath, and cycle slips were analyzed for the geodetic-grade and low-cost ZED-F9P u-blox GNSS receivers. One month’s data of GPS, GLONASS, and Galileo constellations were analysed using the RINEX files of the receivers. The results showed that the epoch availability of the geodetic-grade and u-blox GNSS receiver is comparable to each other, while the availability of phase/code signal channels of the geodetic-grade receiver is higher than the u-blox receiver. In terms of data quality, SNR values from both receivers are comparable, while the multipath level of the u-blox GNSS receiver is significantly higher than the geodetic-grade one. The results also showed that the number of cycle slips of the u-blox receiver is significantly higher than the geodetic-one.

Improved Radar Detection of Small Drones Using Doppler Signal-to-Clutter Ratio (DSCR) Detector

The detection of drones using radar presents challenges due to their small radar cross-section (RCS) values, slow velocities, and low altitudes. Traditional signal-to-noise ratio (SNR) detectors often fail to detect weak radar signals from small drones, resulting in high “Missed Target” rates due to the dependence of SNR values on RCS and detection range. To overcome this issue, we propose the use of a Doppler signal-to-clutter ratio (DSCR) detector that can extract both amplitude and Doppler information from drone signals. Theoretical calculations suggest that the DSCR of a target is less dependent on the detection range than the SNR. Experimental results using a Ku-band pulsed-Doppler surface surveillance radar and an X-band marine surveillance radar demonstrate that the DSCR detector can effectively extract radar signals from small drones, even when the signals are similar to clutter levels. Compared to the SNR detector, the DSCR detector reduces missed target rates by utilizing a lower detection threshold. Our tests include quad-rotor, fixed-wing, and hybrid vertical take-off and landing (VTOL) drones, with mean SNR values comparable to the surrounding clutter but with DSCR values above 10 dB, significantly higher than the clutter. The simplicity and low radar requirements of the DSCR detector make it a promising solution for drone detection in radar engineering applications.

Performance Improvement of CDMA Wireless Sensor Networks in Low SNR Channels Based on Raptor Codes

Low signal to noise ratio (SNR) channels require careful design of communication systems in order to operate reliably in such extreme channels. Special measures and techniques must be considered when designing communication systems for such environments to counteract the effect of high noise power. Channel coding is a powerful tool to aid against adverse channel conditions including low SNR. Coupling channel coding with a spreading technique can further strengthen the performance of communication systems. Hence, we studied the performance of a system that combines Raptor codes with direct sequence code division multiple access (DS-CDMA) in low SNR scenarios. The studied design can operate in a wide range of low SNR values. Simulation results show that the system can offer satisfactory BER performance at different code rates. Also, combining Raptor codes with DS-CDMA showed an improvement of approximately 6 dB when compared with other schemes using DS-CDMA in low SNR conditions.

Pre-Processing Based ECG Signal Analysis Using Emerging Tools

The objective of the paper is to count the clear beats (R-peaks or QRS complexes) in the Electrocardiogram (ECG) signal after the pre-processing step. Among the three selected techniques, Independent Principal Component Analysis (IPCA), Fractional Fourier Transform (FrFT) and Fractional Wavelet Transform (FrWT) have Sensitivity (Se) of 99.92%, 99.95% and 99.98%, Accuracy (Acc) of 99.88%, 99.93% and 99.96%, Detection Error Rate (DER) of 0.110%, 0.066% and 0.036% along with the disturbance of 2.3%, 2% and 1.7% respectively. The lowest DER in FrWT indicates that it outperforms the IPCA and FrFT. The False Negative plus False Positive (FN + FP) by using the three techniques was 32, 25 and 13 while the output Signal-to-Noise Ratio (SNR) was found to be 24.42, 26.19 and 34.37 dB respectively. These results were figured out on Real Time Database (RT DB). The values of output SNR, Se and Acc were recorded to be 34.37 dB, 99.98% and 99.96% while using FrWT based pre-processing on the data from RT DB. The same technique when used on the data from MIT-BIH arrhythmia Database (MB DB) gave the values of 24.81 dB, 99.96% and 99.93% respectively. It has been observed that all the attributes of the components of ECG signals were preserved when filtered with FrFT. On the other hand, the FrWT segregates the motion artefacts and ECG signal in a better way than FrFT. This is due to the fact that in FrWT decomposition takes place on the signal rotated at a selected angle resulting in less computationally complex decompositions at various levels. It reveals that FrWT retains important biology-related information as the originally recorded ECG signal has.

5T magnetic resonance imaging: radio frequency hardware and initial brain imaging.

We aimed to demonstrate the feasibility of generating high-resolution human brain magnetic resonance imaging (MRI) at 5 Tesla (T) using a quadrature birdcage transmit/48-channel receiver coil assembly. A quadrature birdcage transmit/48-channel receiver coil assembly was designed for human brain imaging at 5T. The radio frequency (RF) coil assembly was validated by electromagnetic (EM) simulations and phantom imaging experimental studies. The simulated B1+ field inside a human head phantom and inside a human head model generated by the birdcage coils driven in circularly polarized (CP) mode at 3T, 5T and 7T was compared. Signal-to-noise ratio (SNR) maps, the inverse g-factor maps for evaluation of parallel imaging performance, anatomic images, angiography images, vessel wall images and susceptibility weighted images (SWI) were acquired using the RF coil assembly at 5T and compared to those acquired using a 32-channel head coil on a 3T MRI scanner. For the EM simulations, 5T MRI provided less RF inhomogeneity compared to that of 7T. In the phantom imaging study, the distributions of the measured B1+ field were consistent with the distributions of the simulated B1+ field. In the human brain imaging study, the average SNR value of the brain in the transversal plane at 5T was 1.6 times of that at 3T. The 48-channel head coil at 5T had higher parallel acceleration capability than the 32-channel head coil at 3T. The anatomic images at 5T also showed higher SNR than those at 3T. Improved delineation of the hippocampus, lenticulostriate arteries, and basilar arteries was observed at 5T compared to 3T. SWI with a higher resolution of 0.3 mm ×0.3 mm ×1.2 mm could be acquired at 5T, which enabled better visualization of small blood vessels compared to that at 3T. 5T MRI can provide significant SNR improvement compared to that of 3T with less RF inhomogeneity than that of 7T. The ability to obtain high quality in vivo human brain images at 5T using the quadrature birdcage transmit/48-channel receiver coil assembly has significant in clinical and scientific research applications.

Correlation-Based Maximal Invariant Statistic for Spectrum Sensing

In this letter, spectrum sensing for wide-sense stationary signals with temporal correlation is studied. In order to perform uniformly most powerful invariant test (UMPIT) or locally most powerful invariant test (LMPIT), the ratio of the distributions of the maximum invariant statistics is derived in closed-form using Wijsman’s theorem. This ratio shows that UMPIT can be obtained when the signal-to-noise ratio (SNR) and the normalized auto-correlation matrix of the primary user signal are both known. In addition, the LMPIT with nominal SNR (LMPIT-NSNR) and the LMPIT with improved sample auto-correlation matrix (LMPIT-ISAC) are proposed based on this ratio. LMPIT-NSNR and LMPIT-ISAC are suitable for the case where the normalized auto-correlation matrix is known and the SNR is unknown, and for the case where both the normalized auto-correlation matrix and the SNR are unknown, respectively. Simulation results show that the performance of LMPIT-NSNR is robust to the fixed nominal SNR value and can approach the performance of UMPIT, and the LMPIT-ISAC performs better than the traditional covariance-based test.

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters-An Experimental Study.

In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model. Input images were acquired with varying combinations of exposure factors, such as kilovoltage, milli-ampere, and exposure time, which altered the resultant radiographic image quality. The data were sorted into five different datasets according to their measured SNR and CNR values. The deep-learning model was trained five distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection-over-union (IoU) metric of 0.9594 on test data of the same category but dropped to 0.5875 when tested on lower CNR test data. The result of this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters in order to enhance the performance of deep-learning segmentation models for NDT digital X-ray radiography applications.

Ultra-short echo time MR imaging in assessing cartilage endplate damage and relationship between its lesion and disc degeneration for chronic low back pain patients

ObjectiveTo investigate the feasibility of ultra-short echo time (UTE) magnetic resonance imaging (MRI) in the assessment of cartilage endplate (CEP) damage and further evaluate the relationship between total endplate score (TEPS) and lumbar intervertebral disc (IVD) degeneration for chronic low back pain patients.Materials and methodsIVD were measured in 35 patients using UTE imaging at 3T MR. Subtracted UTE images between short and long TEs were obtained to depict anatomy of CEP. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated to assess the image quality quantitatively. A new grading criterion for endplate damage evaluation was developed based on Rajasekaran.S grading system in this study. Two radiologists were employed to evaluate CEP and bony vertebral endplates (VEP) using this new grading criterion and assess TEPS, independently. Cohen’s kappa analysis was applied to evaluate the inter-observer agreement of endplate damage assessment between two radiologists, and the Kendall’s TAU-B analysis was employed to determine the relationship between TEPS and IVD degeneration evaluated with Pfirrmann grading.ResultsWell structural CEP was depicted on subtracted UTE images and confirmed by high SNR (33.06±2.92) and CNR values (9.4±2.08). Qualified subtracted UTE images were used by two radiologists to evaluate the degree of CEP and VEP damage. Excellent inter-observer agreement was confirmed by high value in Cohen’s kappa test (0.839, P < 0.001). Ensured by this, 138 endplates from 69 IVDs of 35 patients were classified into six grades based on the new grading criterion and TEPS of each endplate was calculated. In addition, the degeneration degree of IVDs were classified into five grades. Finally, using Kendall’s TAU-B analysis, significant relationship was obtained between endplate damage related TEPS and IVD degeneration (r = 0.864, P < 0.001).ConclusionEnsured by high image quality, UTE imaging might be considered an effective tool to assess CEP damage. Additionally, further calculated TEPS has shown strong positive association with IVD degeneration, suggesting that the severity of endplate damage is highly linked with the degree of IVD degeneration.

Single-Pulse X-ray Acoustic Computed Tomographic Imaging for Precision Radiation Therapy

High-precision radiation therapy is crucial for cancer treatment. Currently, the delivered dose can only be verified via simulations with phantoms, and an in-tumor, online dose verification is still unavailable. An innovative detection method called x-ray-induced acoustic computed tomography (XACT) has recently shown the potential for imaging the delivered radiation dose within the tumor. Prior XACT imaging systems have required tens to hundreds of signal averages to achieve high-quality dose images within the patient, which reduces its real-time capability. Here, we demonstrate that XACT dose images can be reproduced from a single x-ray pulse (4 µs) with sub-mGy sensitivity from a clinical linear accelerator. By immersing an acoustic transducer in a homogeneous medium, it is possible to detect pressure waves generated by the pulsed radiation from a clinical linear accelerator. After rotating the collimator, signals of different angles are obtained to perform a tomographic reconstruction of the dose field. Using 2-stage amplification with further bandpass filtering increases the signal-to-noise ratio (SNR). Acoustic peak SNR and voltage values were recorded for singular and dual-amplifying stages. The SNR for single-pulse mode was able to satisfy the Rose criterion, and the collected signals were able to reconstruct 2-dimensional images from the 2 homogeneous media. By overcoming the low SNR and requirement of signal averaging, single-pulse XACT imaging holds great potential for personalized dose monitoring from each individual pulse during radiation therapy.

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer's disease imaging.

Magnetic Resonance Imaging (MR Imaging) is routinely employed in diagnosing Alzheimer's Disease (AD), which accounts for up to 60-80% of dementia cases. However, it is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for contrast, acquisition time, and signal-to-noise ratio (SNR). The lack of this expertise contributes to the highly inefficient utilization of MRI services diminishing their clinical value. In this work, we extend our previous effort and demonstrate accelerated MRI via intelligent protocolling of the modified brain screen protocol, referred to as the Gold Standard (GS) protocol. We leverage deep learning-based contrast-specific image-denoising to improve the image quality of data acquired using the accelerated protocol. Since the SNR of MR acquisitions depends on the volume of the object being imaged, we demonstrate subject-specific (SS) image-denoising. The accelerated protocol resulted in a 1.94 × gain in imaging throughput. This translated to a 72.51% increase in MR Value-defined in this work as the ratio of the sum of median object-masked local SNR values across all contrasts to the protocol's acquisition duration. We also computed PSNR, local SNR, MS-SSIM, and variance of the Laplacian values for image quality evaluation on 25 retrospective datasets. The minimum/maximum PSNR gains (measured in dB) were 1.18/11.68 and 1.04/13.15, from the baseline and SS image-denoising models, respectively. MS-SSIM gains were: 0.003/0.065 and 0.01/0.066; variance of the Laplacian (lower is better): 0.104/-0.135 and 0.13/-0.143. The GS protocol constitutes 44.44% of the comprehensive AD imaging protocol defined by the European Prevention of Alzheimer's Disease project. Therefore, we also demonstrate the potential for AD-imaging via automated volumetry of relevant brain anatomies. We performed statistical analysis on these volumetric measurements of the hippocampus and amygdala from the GS and accelerated protocols, and found that 27 locations were in excellent agreement. In conclusion, accelerated brain imaging with the potential for AD imaging was demonstrated, and image quality was recovered post-acquisition using DL-based image denoising models.