Mean Signal-to-noise Ratio Values Research Articles

Clinical efficacy of motion-insensitive imaging technique with deep learning reconstruction to improve image quality in cervical spine MR imaging.

To demonstrate that a T2 periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique using deep learning reconstruction (DLR) will provide better image quality and decrease image noise. From December 2020 to March 2021, 35 patients examined cervical spine MRI were included in this study. Four sets of images including fast spin echo (FSE), original PROPELLER, PROPELLER DLR50%, and DLR75% were quantitatively and qualitatively reviewed. We calculated the signal-to-noise ratio (SNR) of the spinal cord and sternocleidomastoid (SCM) muscle and the contrast-to-noise ratio (CNR) of the spinal cord by applying region-of-interest at the spinal cord, SCM muscle, and background air. We evaluated image noise with regard to the spinal cord, SCM, and back muscles at each level from C2-3 to C6-7 in the 4 sets. At all disc levels, the mean SNR values for the spinal cord and SCM muscles were significantly higher in PROPELLER DLR50% and DLR75% compared to FSE and original PROPELLER images (P < .0083). The mean CNR values of the spinal cord were significantly higher in PROPELLER DLR50% and DLR75% compared to FSE at the C3-4 and 4-5 levels and PROPELLER DLR75% compared to FSE at the C6-7 level (P < .0083). Qualitative analysis of image noise on the spinal cord, SCM, and back muscles showed that PROPELLER DLR50% and PROPELLER DLR75% images showed a significant denoising effect compared to the FSE and original PROPELLER images. The combination of PROPELLER and DLR improved image quality with a high SNR and CNR and reduced noise. Motion-insensitive imaging technique (PROPELLER) increased the image quality compared to conventional FSE images. PROPELLER technique with a DLR reduced image noise and improved image quality.

Effects of Body Position on Cochlear Function in Infants: An Otoacoustic Emission Study.

Otoacoustic Emission (OAE) is frequently recorded in various body positions for infants. However, little is available about whether these deviations will produce non-pathological effects on the clinical results. The current study assessed body position's effect on infants' inner ear function. Sixty normally hearing infants participated in an analytical cross-sectional study. Distortion-product OAEs (DPOAEs) were measured in the supine, side-lying, and upright positions. The DPOAE amplitude and signal-to-noise ratio (SNR) were recorded across the 1500 to 6000Hz range. The mean DPOAE amplitude and SNR values were significantly greater in the upright position than supine and side-lying positions (p < 0.05). These differences were more pronounced in the 3000 to 6000Hz range. The effects of gender and ear asymmetry on DPOAEs were not statistically significant. Our findings suggested that the upright position could be regarded as the best position for assessing DPOAEs in infants.

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.

Effects of visual information on the face masked speech communication in Covid-secure classrooms

Acoustical measurements and speech intelligibility tests were carried out to investigate the effects of masks on speech communication experienced in real Covid-secure university classrooms during the pandemic. Face-masked speech levels and noise levels were measured to understand the acoustical effects of masks on speech sounds during 15 multiple lectures in 3 university classrooms. The speech intelligibility scores were also evaluated for lower and higher SNR (signal-to-noise ratio) conditions, and for with and without the presence of visual information conditions to investigate the effects of both the acoustic and visual signals in understanding speech communication in actual classroom situations. In the 3 active university classrooms the students experienced on average: speech levels of 55.1 dBA (σ = 5.5 dBA), noise levels of 42.3 dBA (σ = 3.9 dBA), and a speech-to-noise ratio of 12.8 dBA σ = 5.2 dBA). The mean SNR values at the listener’s position for the 15 lectures varied from 3.6 dBA to 20.0 dBA. The use of a portable sound amplification system increases the face-masked speech levels mostly at mid and high frequencies (500–4 kHz), thus it can be more useful for achieving higher SNR values in classrooms. The presence of visual cues have little effect on achieving more higher speech intelligibility scores in higher SNR conditions. The present results show that visual obstruction of the talker’s mouth decreases speech intelligibility scores by a maximum of 10% in lower SNR conditions, particularly at a SNR of 6 dBA or lower.

Preliminary results of abdominal simultaneous multi-slice accelerated diffusion-weighted imaging with motion-correction in patients with cystic fibrosis and impaired compliance

ObjectivesThe aim of this prospective study was to compare scan time, image quality, signal-to-noise Ratio (SNR), and apparent diffusion coefficient (ADC) values of simultaneous multi-slice accelerated diffusion-weighted imaging with motion-correction (DWI SMS Moco) to standard diffusion-weighted imaging (sDWI) in free-breathing abdominal magnetic resonance imaging (MRI) in pediatric and young adult patients with cystic fibrosis (CF).Material and methods16 patients (7 male and 9 female, 12–41 years old) with CF were examined prospectively in a single-center from November 2020 to March 2021 on a 1.5 Tesla clinical MR scanner. The characteristics of overall image quality and delimitability of mesenteric lymph nodes were evaluated using a 5-point Likert scale by two experienced pediatric radiologists independently from each other. Quantitative parameters with SNR and ADC values were assessed in 8 different locations and compared using a Wilcoxon signed-rank test.ResultsThe acquisition time for DWI SMS Moco was 32% shorter than for sDWI. Regarding quality comparison, overall image quality and delimitability of mesenteric lymph nodes were significant higher in DWI SMS Moco (p ≤ 0.05 for both readers). The readers preferred DWI SMS Moco to sDWI in all cases (16/16). Mean SNR values from DWI SMS Moco and sDWI were similar in 7 from 8 locations. The ADC values showed no significant difference between DWI SMS Moco and sDWI in any of the evaluated locations (p > 0.05).ConclusionsThe DWI SMS Moco improves overall image quality and delimitability of mesenteric lymph nodes compared to sDWI with similar SNR and ADC values and a distinguished reduction of scan time in free-breathing by one third. We conclude that MRI with DWI SMS Moco could be helpful in monitoring the effect of the high-efficiency modulator (HEM) therapy in cystic fibrosis (CF) patients homozygous or heterozygous for F508del in the abdomen.

Enhancing the accuracy of shock advisory algorithms in automated external defibrillators during ongoing cardiopulmonary resuscitation using a deep convolutional Encoder-Decoder filtering model

Survival from out-of-hospital cardiac arrests (OHCA) depends on an accurate defibrillatory shock decision during cardiopulmonary resuscitation (CPR). Since chest compressions induce severe motion artifact in the electrocardiogram (ECG), current automatic external defibrillators (AEDs) instruct the user not to perform CPR during the rhythm analysis period. However, performing continuous CPR is vital and dramatically increases the chance of survival. Hence, we demonstrate a novel application of a deep convolutional neural network encoder-decoder (CNNED) method to suppress CPR artifact in near real-time, using only ECG data. The encoder portion of the CNNED uses the magnitude and phase contents derived via time-varying spectral analysis to learn distinct features that are representative of both the ECG signal and CPR artifact. The decoder portion takes the results from the encoder and reconstructs what is perceived as the motion artifact-removed ECG data. These procedures are done via a multitude of training of the CNNED using many different arrhythmias contaminated with CPR. In this study, CPR-contaminated ECGs were generated by combining clean ECGs with 52 different CPR artifacts. ECG data from CUDB, VFDB, SDDB, and AFDB datasets which belong to the Physionet’s Physiobank archive were used to create the training set containing 14-second ECG segments. The performance of the proposed CNNED was evaluated on a separate test set comprised of 23,816 CPR-contaminated 14-second ECG segments from 458 subjects. The results were evaluated by three metrics: signal-to-noise ratio (SNR), correlation coefficient, and accuracy of Defibtech’s AED shock advisory algorithm (SAA). CNNED resulted in an increase of the mean SNR value from −3 dB to 7.5 dB and 7.1 dB for shockable and non-shockable rhythms, respectively. 80.85% of the filtered shockable and 74.13% of the filtered non-shockable ECG data were highly correlated (>0.7) with the artifact-free ECG; these values were only 13.30% and 12.71% for CPR-contaminated shockable and non-shockable, respectively, without our filtering approach. Comparing results of Defibtech’s AED SAA before and after applying CNNED on the CPR-contaminated ECG, the specificity improved from 96.21% to 99.14% for normal sinus rhythm, and from 88.5% to 96.45% for other non-shockable rhythms. The sensitivity of shockable detection also increased from 67.68% to 90.90% for ventricular fibrillation, and from 62.71% to 82.26% for ventricular tachycardia. These results indicate continuous and accurate AED rhythm analysis without stoppage of CPR, using only ECG data.

Federated Learning for 5G Radio Spectrum Sensing.

Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum without interfering with the licensed users’ (primary users’ (PUs’)) transmission. Deep learning (DL) has proven to be a good choice as an intelligent SS algorithm that considers radio environmental factors in the decision-making process. It is impossible though for SU to collect the required data and train complex DL models. In this paper, we propose to employ a Federated Learning (FL) algorithm in order to distribute data collection and model training processes over many devices. The proposed method categorizes FL devices into groups by their mean Signal-to-Noise ratio (SNR) and creates a common DL model for each group in the iterative process. The results show that detection accuracy obtained via the FL algorithm is similar to detection accuracy obtained by employing several DL models, namely convolutional neural networks (CNNs), specialized in spectrum detection for a PU signal with a given mean SNR value. At the same time, the main goal of simplification of the SS process in the network is achieved.

Abstract 12792: Enhancing the Accuracy of Cardiac Rhythm Analysis in Automated External Defibrillators During Ongoing Cardiopulmonary Resuscitation by Applying a Deep Encoder-Decoder Filtering Model

Survival from out-of-hospital cardiac arrests depends on an accurate defibrillatory shock decision during cardiopulmonary resuscitation (CPR). Since chest compressions induce severe motion artifact in the electrocardiogram (ECG), current automatic external defibrillators (AEDs) do not perform CPR during the rhythm analysis period. However, performing continuous CPR is vital and dramatically increases the chance of survival. Hence, we demonstrate a novel application of a deep convolutional neural network encoder-decoder (CNNED) method in suppressing CPR artifact in near real-time using only ECG data. The encoder portion of the CNNED uses the frequency and phase contents derived via time-varying spectral analysis to learn distinct features that are representative of both the ECG signal and CPR artifact. The decoder portion takes the results from the encoder and reconstructs what is perceived as the motion artifact removed ECG data. These procedures are done via multitude of training of CNNED using many different arrhythmia contaminated with CPR. In this study, CPR-contaminated ECGs were generated by combining clean ECG with CPR artifacts from 52 different performers. ECG data from CUDB, VFDB, and SDDB datasets which belong to the Physionet’s Physiobank archive were used to create the training set containing 89,984 14-second ECG segments. The performance of the proposed CNNED was evaluated on a separate test set comprising of 23,816 CPR-contaminated 14-second ECG segments from 458 subjects. The results were evaluated by two metrics: signal-to-noise ratio (SNR), and Defibtech’s AED rhythm analysis algorithm. CNNED resulted in the increase of the mean SNR value from -3 dB to 5.63 dB and 6.3 dB for shockable and non-shockable rhythms, respectively. Comparing Defibtech’s AED rhythm classifier before and after applying CNNED on the CPR-contaminated ECG, the specificity improved from 96.57% to 99.31% for normal sinus rhythm, and from 91.5% to 96.57% for other non-shockable rhythms. The sensitivity of shockable detection also increased from 67.68% to 87.76% for ventricular fibrillation, and from 62.71% to 81.27% for ventricular tachycardia. These results indicate continuous and accurate AED rhythm analysis without stoppage of CPR using only ECG data.

Characterizing the stimulation interference in electroencephalographic signals during brain-computer interface-controlled functional electrical stimulation therapy.

The integration of brain-computer interface (BCI) and functional electrical stimulation (FES) has brought about a new rehabilitation strategy: BCI-controlled FES therapy or BCI-FEST. During BCI-FEST, the stimulation is triggered by the patient's brain activity, often monitored using electroencephalography (EEG). Several studies have demonstrated that BCI-FEST can improve voluntary arm and hand function after an injury, but few studies have investigated the FES interference in EEG signals during BCI-FEST. In this study, we evaluated the effectiveness of band-pass filters, used to extract the BCI-relevant EEG components, in simultaneously reducing stimulation interference. We used EEG data from eight participants recorded during BCI-FEST. Additionally, we separately recorded the FES signal generated by the stimulator to estimate the spectral components of the FES interference, and extract the noise in time domain. Finally, we calculated signal-to-noise ratio (SNR) values before and after band-pass filtering, for two types of movements practiced during BCI-FEST: reaching and grasping. The SNR values were greater after filtering across all participants for both movement types. For reaching movements, mean SNR values increased between 1.31 dB and 36.3 dB. Similarly, for grasping movements, mean SNR values increased between 2.82 dB and 40.16 dB, after filtering. Band-pass filters, used to isolate EEG frequency bands for BCI application, were also effective in reducing stimulation interference. In addition, we provide a general algorithm that can be used in future studies to estimate the frequencies of FES interference as a function of the selected stimulation pulse frequency, FSTIM , and the EEG sampling rate, FS .

Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition.

.Significance: Photoacoustic imaging (PAI) can be used to infer molecular information about myocardial health non-invasively in vivo using optical excitation at ultrasonic spatial resolution. For clinical and preclinical linear array imaging systems, conventional delay-and-sum (DAS) beamforming is typically used. However, DAS cardiac PA images are prone to artifacts such as diffuse quasi-static clutter with temporally varying noise-reducing myocardial signal specificity. Typically, multiple frame averaging schemes are utilized to improve the quality of cardiac PAI, which affects the spatial and temporal resolution and reduces sensitivity to subtle PA signal variation. Furthermore, frame averaging might corrupt myocardial oxygen saturation quantification due to the presence of natural cardiac wall motion. In this paper, a spatiotemporal singular value decomposition (SVD) processing algorithm is proposed to reduce DAS PAI artifacts and subsequent enhancement of myocardial signal specificity.Aim: Demonstrate enhancement of PA signals from myocardial tissue compared to surrounding tissues and blood inside the left-ventricular (LV) chamber using spatiotemporal SVD processing with electrocardiogram (ECG) and respiratory signal (ECG-R) gated in vivo murine cardiac PAI.Approach: In vivo murine cardiac PAI was performed by collecting single wavelength (850 nm) photoacoustic channel data on eight healthy mice. A three-dimensional (3D) volume of complex PAI data over a cardiac cycle was reconstructed using a custom ECG-R gating algorithm and DAS beamforming. Spatiotemporal SVD was applied on a two-dimensional Casorati matrix generated using the 3D volume of PAI data. The singular value spectrum (SVS) was then filtered to remove contributions from diffuse quasi-static clutter and random noise. Finally, SVD processed beamformed images were derived using filtered SVS and inverse SVD computations.Results: Qualitative comparison with DAS and minimum variance (MV) beamforming shows that SVD processed images had better myocardial signal specificity, contrast, and target detectability. DAS, MV, and SVD images were quantitatively evaluated by calculating contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and signal-to-noise ratio (SNR). Quantitative evaluations were done at three cardiac time points (during systole, at end-systole (ES), and during diastole) identified from co-registered ultrasound M-Mode image. Mean CR, gCNR, and SNR values of SVD images at ES were 245, 115.15, and 258.17 times higher than DAS images with statistical significance evaluated with one-way analysis of variance.Conclusions: Our results suggest that significantly better-quality images can be realized using spatiotemporal SVD processing for in vivo murine cardiac PAI.

Efficiency of Fat Suppression in T1-Weighted Inner Ear Magnetic Resonance Imaging: Multipoint Dixon Method Versus Hybrid Techniques

Background Temporal bone is a region where fat suppression is difficult due to the inhomogeneity of various structures with different molecular properties. Introduction: We aimed to determine the most effective fat suppression sequence in order to increase the visibility of the inner ear region.Materials and Methods The hybrid techniques and T1-Weighted mDIXON images of 40 patients with Magnetic Resonance (MR) imaging of the inner ear were prospectively compared by two experienced radiologists in terms of fat suppression efficacy. In all fat-suppressed sequences, the Signal to Noise Ratio (SNR), the spinal cord signal intensity / mean fat signal intensity ratio and spinal cord signal to noise ratio were calculated. The suppression efficacy of MR techniques for fat areas in the inner ear was visually graded.ResultsQualitative assessment of image quality due to fat suppression in the inner ear was made; the Dixon technique performed significantly better than SPAIR and SPIR techniques (p<0.0001). The mean signal intensity of the inner ear fat and SNR for the Dixon technique were significantly lower than that for SPIR and SPAIR techniques (p<0.0001). Inter-observer agreement regarding the assessment of the inner ear fat, mean signal intensity values and mean SNR values for fat suppression techniques was significant.ConclusionThe Dixon technique exhibited higher image quality and fat suppression efficiency than the hybrid techniques in the MR imaging of the inner ear.

Utilization of virtual low-keV monoenergetic images generated using dual-layer spectral detector computed tomography for the assessment of peritoneal seeding from ovarian cancer.

This study aimed to compare the quality of virtual low-keV monoenergetic images vs conventional images reconstructed from dual-layer spectral detector computed tomography (SDCT) for the detection of peritoneal implants of ovarian cancer.Fifty ovarian cancer patients who underwent abdominopelvic SDCT scans were included in this retrospective study. Virtual monoenergetic images at 40 (VMI40) and 50 keV (VMI50), and two conventional images were reconstructed using filtered back projection (FBP) and iterative model reconstruction (IMR) protocols. The mean attenuation of the peritoneal implant, signal-to-noise ratio (SNR), contrast-to-noise ratio relative to ascites (CNRA) and adjacent reference tissues (e.g., bowel wall, hepatic, or splenic parenchyma [CNRB]) were calculated and compared using paired t tests. Qualitative image analysis regarding overall image quality, image noise, image blurring, lesion conspicuity, was performed by two radiologists. A subgroup analysis according to the peritoneal implant region was also conducted.VMI40 yielded significantly higher mean attenuation (183.35) of SNR and CNR values (SNR 11.69, CNRA 7.39, CNRB 2.68), compared to VMI50, IR, and FBP images (P < .001). The mean attenuation (129.65), SNR and CNR values (SNR 9.37, CNRA 5.72, CNRB 2.02) of VMI50 were also significantly higher than those of IR and FBP images (P < .001). In the subgroup analysis, all values were significantly higher on VMI40 regardless of the peritoneal implant region (P < .05). In both readers, overall image quality and image blurring showed highest score in VMI50, while image noise and lesion conspicuity showed best score in IMR and VMI40 respectively. Inter-reader agreements are moderate to almost perfect in every parameter.The low-keV VMIs improved both quantitative assessment and lesion conspicuity of peritoneal implants from ovarian cancer compared to conventional images.

A Direct Detection Fiber Optic Distributed Acoustic Sensor With a Mean SNR of 7.3 dB at 102.7 km

In this work, we present the experimental results of a direct detection $\varphi $ -OTDR based distributed acoustic sensor system. The system uses two cascaded acousto-optic modulators in order to generate optical pulses with very high extinction ratio and dual photodetector scheme for high dynamic range. The proposed schemes are investigated in detail and their performance enhancement is experimentally verified. Four piezoelectric based fiber stretchers are placed on a ∼104 km single-mode test fiber at the distances of 1 km, 10 km, 87 km and 102.7 km and used for perturbation tests. The stretchers generated vibration signals which are analyzed to quantify the system performance. The signal-to-noise ratio (SNR) of vibration signals at the monitored distances is measured over the 12-hour recorded data within 34-second time windows considering the multi-point random interference of scattered light and fading phenomena. Using the 12-hour data, SNR histograms at four different locations are generated and mean SNR values are obtained. The signals received from 102.7 km has a maximum SNR of 24.7 dB and a mean SNR of 7.3 dB with a spatial resolution of 15 m. To the best of our knowledge, this is the highest-range reported direct detection $\varphi $ -OTDR based distributed acoustic sensor system.

An investigation of quantitative accuracy for deep learning based denoising in oncological PET

Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SNR), subsequently affecting diagnostic and quantitative accuracies. Deep learning methods have shown a great potential to reduce the noise and improve the SNR in low dose PET data.In this work, we comprehensively investigated the quantitative accuracy of small lung nodules, in addition to visual image quality, using deep learning based denoising methods for oncological PET imaging. We applied and optimized an advanced deep learning method based on the U-net architecture to predict the standard dose PET image from 10% low-dose PET data. We also investigated the effect of different network architectures, image dimensions, labels and inputs for deep learning methods with respect to both noise reduction performance and quantitative accuracy. Normalized mean square error (NMSE), SNR, and standard uptake value (SUV) bias of different nodule regions of interest (ROIs) were used for evaluation.Our results showed that U-net and GAN are superior to CAE with smaller SUVmean and SUVmax bias at the expense of inferior SNR. A fully 3D U-net has optimal quantitative performance compared to 2D and 2.5D U-net with less than 15% SUVmean bias for all the ten patients. U-net outperforms Residual U-net (r-U-net) in general with smaller NMSE, higher SNR and lower SUVmax bias. Fully 3D U-net is superior to several existing denoising methods, including Gaussian filter, anatomical-guided non-local mean (NLM) filter, and MAP reconstruction with Quadratic prior and relative difference prior, in terms of superior image quality and trade-off between noise and bias. Furthermore, incorporating aligned CT images has the potential to further improve the quantitative accuracy in multi-channel U-net.We found the optimal architectures and parameters of deep learning based methods are different for absolute quantitative accuracy and visual image quality. Our quantitative results demonstrated that fully 3D U-net can both effectively reduce image noise and control bias even for sub-centimeter small lung nodules when generating standard dose PET using 10% low count down-sampled data.

Auditory and Histopathological Effects of Topical Mercurochrome Treatment in Rats with Tympanic Membrane Perforation.

Topical treatment is first choice in the treatment of uncomplicated chronic otitis media. It was intended to assess auditory and histopathological safety of ototopical use of mercurochrome solution in rats with induced tympanic membrane perforation. The study was conducted on 21 female Wistar-Albino rats which were randomly assigned into 3 groups. In all rats, perforation was performed at right tympanic membrane. Distortion product otoacoustic emissions (DPOAEs) measurements were performed at frequencies of 2000, 3000 and 4000 Hz (with L1/L2: 70 /70 dB at 2f1-f2 frequency; f2/f1 ratio: 1:22) before recovery from anesthesia and signal-to-noise ratio (SNR) were recorded. Normal saline, 2% mercurochrome and gentamicin were given to group 1, 2 and 3 twice daily over a week, respectively. Rats were sacrificed after DPOAE measurements on day 14. Right temporal bone specimens were examined under light microscope after processing. Based on DPOAE results, there was no significant difference among groups before treatment. On day 14, significant differences were found in DPOAE measurements at 3000 and 4000 Hz, and in mean SNR values in 2% mercurochrome and gentamicin groups when compared to normal saline group while no significant difference was detected at 2000 Hz among groups. In addition, significant degeneration was detected in Corti organs, spiral ganglions and stria vascularis in groups 2 and 3. In this study, it was observed that mercurochrome use in external otitis and otitis media with tympanic membrane perforation could cause ototoxicity and concluded that the solution should be used cautiously.

Histogram-based comparison between dynamic and static lung perfused blood volume images using dual energy CT

ObjectiveThe purpose of this study was to compare the results of a histogram-based analysis of static and dynamic lung perfused blood volume (LPBV) images. MethodsSixty-five patients (mean age: 61.3 years, 36 male) underwent dynamic and static LPBV for evaluation of pulmonary vascular diseases (n = 11), lung carcinoma (n = 27) or pulmonary thromboembolism (PTE: n = 27). Seven sets of dynamic sequential scans were performed at the pulmonary trunk using dual-energy technique before the static LPBV scan. The image of lung parenchyma that showed the greatest mean attenuation in dynamic series was defined as the peak dynamic LPBV image. The differences and correlations in the mean attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), histogram skewness and histogram kurtosis were evaluated according to the type of disease in static and dynamic LPBV images. ResultsStatic LPBV images showed significantly larger mean attenuation (Rt:24.2, Lt: 24.2), SNR (Rt:2.31, Lt:2.30), and CNR (Rt:2.40, Lt:2.39), and smaller kurtosis values (Rt:1.06, Lt:0.61) values in comparison to dynamic LPBV images (p < 0.001); however, with the exception of kurtosis of the left lung (r = 0.17), these values were well-corrected with that of the dynamic LPBV images in these values (r = 0.4–0.77, p ≤ 0.001) without kurtosis of left lung (r = 0.17) in all patients. The histogram kurtosis of static LPBV image showed a good correlation with that of dynamic LPBV (r = 0.41–0.77, p < 0.05), especially in patients with PTE. ConclusionIn patients with PTE, the static LPBV image valueswere well correlated with the peak dynamic LPBV images which demonstrated pulmonary artery-dominant flow.

Activity Level and Function 2 Years After Anterior Talofibular Ligament Repair: A Comparison Between Arthroscopic Repair and Open Repair Procedures

Background: The open modified Broström anatomic repair technique is widely accepted as the reference standard for lateral ankle stabilization. Despite recent increases in publications regarding arthroscopic repair of the anterior talofibular ligament (ATFL) for treatment of chronic ankle instability, research is lacking that compares the functional outcomes between arthroscopic repair and open repair procedures for chronic ankle instability. Purpose: To compare function and activity level after arthroscopic repair versus open repair of the ATFL in patients with lateral ankle instability. Study Design: Cohort study; Level of evidence, 3. Methods: All patients who underwent arthroscopic or open surgical Broström repair of the ATFL between January 2012 and August 2014 were invited to participate in this study. All of the patients had consented for arthroscopic repair if feasible. In cases in which arthroscopic repair was impossible, the open modified Broström procedure was performed after arthroscopy. Patients accepted a systematic rehabilitation program postoperatively. American Orthopaedic Foot and Ankle Society (AOFAS) score, Karlsson Ankle Functional Score (KAFS), and Tegner activity score were used to evaluate ankle function preoperatively and at a minimum follow-up of 2 years. Magnetic resonance imaging (MRI) was performed to evaluate the signal to noise ratio (SNR) of the repaired ATFL. Results: A total of 60 patients were included in this study. They were assigned to 1 of 2 groups according to their surgical procedure: 23 patients underwent arthroscopic repair (arthroscopic group) and 37 patients underwent open repair (open group). No patient in either group had ankle instability at follow-up postoperatively. After surgery, the AOFAS score (P < .001), KAFS (P < .001), and Tegner activity score (P < .001) increased significantly in both groups. However, no significant difference was found in AOFAS score (93.3 ± 8.9 vs 92.4 ± 8.6; P = .7), KAFS (90.3 ± 12.5 vs 89.4 ± 10.6; P = .75), and Tegner score (5 ± 2 vs 5 ± 2; P = .61) between the arthroscopic group and the open group, respectively. As well, no significant difference was found in the mean SNR value of ATFL between the arthroscopic group and the open group (9.1 ± 2.7 vs 8.8 ± 2.3; P = .39, respectively). Conclusion: When compared with open lateral ankle repair, arthroscopic repair of lateral ankle ligament when feasible produced similarly favorable outcomes. Arthroscopic ATFL repair, as a minimally invasive technique, provided favorable outcomes.

Comparison and Optimization of 3.0 T Breast Images Quality of Diffusion-Weighted Imaging with Multiple B-Values

Breast 3.0 T magnetic resonance diffusion-weighted imaging (MR-DWI) of benign and malignant lesions were obtained to measure and calculate the signal-to-noise ratio (SNR), signal intensity ratio (SIR), and contrast-to-noise ratio (CNR) of lesions at different b-values. The variation patterns of SNR and SIR were analyzed with different b-values and the images of DWI were compared at four different b-values with higher image quality. The effect of SIR on the differential diagnostic efficiency of benign and malignant lesions was compared using receiver operating characteristic curves to provide a reference for selecting the optimal b-value. A total of 96 qualified patients with 112 lesions and 14 patients with their contralateral 14 normal breasts were included in this study. The single-shot echo planar imaging sequence was used to perform the DWI and a total of 13 b-values were used: 0, 50, 100, 200, 400, 600, 800, 1000, 1200, 1500, 1800, 2000, and 2500 s/mm2. On DWI, the suitable regions of interest were selected. The SNRs of normal breasts (SNRnormal), SNRlesions, SIR, and CNR of benign and malignant lesions were measured on DWI with different b-values and calculated. The variation patterns of SNR, SIR, and CNR values on DWI for normal breasts, benign lesions, and malignant lesions with different b-values were analyzed by using Pearson correlation analysis. The SNR and SIR of benign and malignant lesions with the same b-values were compared using t-tests. The diagnostic efficiencies of SIR with different b-values for benign and malignant lesions were evaluated using receiver operating characteristic curves. Breast DWI had higher CNR for b-values ranging from 600 to 1200 s/mm2. It had the best CNR at b = 1000 s/mm2 for the benign lesions and at b = 1200 s/mm2 for the malignant lesions. The signal intensity and SNR values of normal breasts decreased with increasing b-values, with a negative correlation (r = -0.945, P < 0.01). The mean SNR values of benign and malignant lesions were negatively correlated (r = -0.982 and -0.947, respectively, and P < 0.01), gradually decreasing with increasing b-values. The mean SIR value of benign lesions gradually decreased with increasing b-values, a negative correlation (r = -0.991, P < 0.01). The mean SIR values of malignant lesions gradually increased with increasing b-values between 0 and 1200 s/mm2, and gradually decreased with increasing b-values ≥ 1500 s/mm2. For b-values of 600, 800, 1000, and 1200 s/mm2, the sensitivity and specificity of SIR in identifying benign and malignant lesions gradually increased with increasing b-values, peaking at 1200 s/mm2. Breast DWI had higher image quality for b-values ranging from 600 to 1200 s/mm2, and was best for b-values ranging from 1000 to 1200 s/mm2. The SIR had the highest diagnostic efficiency in differentiating benign and malignant lesions for a b-value of 1200 s/mm2.

Modeling signal-to-noise ratio of otoacoustic emissions in workers exposed to different industrial noise levels.

Introduction:Noise is considered as the most common cause of harmful physical effects in the workplace. A sound that is generated from within the inner ear is known as an otoacoustic emission (OAE). Distortion-product otoacoustic emissions (DPOAEs) assess evoked emission and hearing capacity. The aim of this study was to assess the signal-to-noise ratio in different frequencies and at different times of the shift work in workers exposed to various levels of noise. It was also aimed to provide a statistical model for signal-to-noise ratio (SNR) of OAEs in different frequencies based on the two variables of sound pressure level (SPL) and exposure time.Materials and Methods:This case–control study was conducted on 45 workers during autumn 2014. The workers were divided into three groups based on the level of noise exposure. The SNR was measured in frequencies of 1000, 2000, 3000, 4000, and 6000 Hz in both ears, and in three different time intervals during the shift work. According to the inclusion criterion, SNR of 6 dB or greater was included in the study. The analysis was performed using repeated measurements of analysis of variance, spearman correlation coefficient, and paired samples t-test.Results:The results showed that there was no statistically significant difference between the three exposed groups in terms of the mean values of SNR (P > 0.05). Only in signal pressure levels of 88 dBA with an interval time of 10:30–11:00 AM, there was a statistically significant difference between the right and left ears with the mean SNR values of 3000 frequency (P = 0.038). The SPL had a significant effect on the SNR in both the right and left ears (P = 0.023, P = 0.041). The effect of the duration of measurement on the SNR was statistically significant in both the right and left ears (P = 0.027, P < 0.001).Conclusion:The findings of this study demonstrated that after noise exposure during the shift, SNR of OAEs reduced from the beginning to the end of the shift.

Audio Compression using Multiple Transformation Techniques

paper presents a comparative study of audio compression using multiple transformation techniques. Audio compression with different transform techniques like Discrete Cosine Transform, Wavelet Transform, Wavelet Packet Transform (W.P.T) & Cosine Packet Transform is analyzed and compression ratio for each of the transformation techniques is obtained. Mean Compression ratio is calculated for all of the techniques and compared. Performance measures like signal to noise ratio (SNR), normalized root mean square error (NRMSE), retained signal energy (RSE) are also calculated and compared for each transform technique. Transform based compressed signals are encoded with encoding techniques like Run-length Encoding (R.L.E) and Mu-Law Encoding to reduce the redundancies. From the comparison it is clear that Discrete wavelet transform gives better compression ratio of about 27.8593 compared with the other three transforms. Mean SNR value is minimum for DCT 29.2830, and comparatively higher mean SNR value 43.4037 for CPT.