Reduce Background Noise Research Articles

Deep-learning image-reconstruction algorithm for dual-energy CT angiography with reduced iodine dose: preliminary results

To evaluate the computed tomography (CT) attenuation values, background noise, arterial depiction, and image quality in whole-body dual-energy CT angiography (DECTA) at 40 keV with a reduced iodine dose using deep-learning image reconstruction (DLIR) and compare them with hybrid iterative reconstruction (IR). Whole-body DECTA with a reduced iodine dose (200 mg iodine/kg) was performed in 22 patients, and DECTA data at 1.25-mm section thickness with 50% overlap were reconstructed at 40 keV using 40% adaptive statistical iterative reconstruction with Veo (hybrid-IR group), and DLIR at medium and high levels (DLIR-M and DLIR-H groups). The CT attenuation values of the thoracic and abdominal aortas and iliac artery and background noise were measured. Arterial depiction and image quality on axial, multiplanar reformatted (MPR), and volume-rendered (VR) images were assessed by two readers. Quantitative and qualitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H groups. The vascular CT attenuation values were almost comparable between the three groups (p=0.013-0.97), but the background noise was significantly lower in the DLIR-H group than in the hybrid-IR and DLIR-M groups (p<0.001). The arterial depictions on axial and MPR images and in almost all arteries on VR images were comparable (p=0.14-1). The image quality of axial, MPR, and VR images was significantly better in the DLIR-H group (p<0.001-0.015). DLIR significantly reduced background noise and improved image quality in DECTA at 40 keV compared with hybrid-IR, while maintaining the arterial depiction in almost all arteries.

Impact of image averaging on vessel detection using optical coherence tomography angiography in eyes with macular oedema and in healthy eyes.

To assess the repeatability of multiple automatic vessel density (VD) measurements and the effect of image averaging on vessel detection by optical coherence tomography angiography (OCTA). An observational study was conducted in a series of healthy volunteers and patients with macular oedema. Five sequential OCTA images were acquired for each eye using the OptoVue HD device. The effect of the averaging of the 5 acquisitions on vessel detection was analysed quantitatively using a pixel-by-pixel automated analysis. In addition, two independent retina experts qualitatively assessed the change in vessel detection in averaged images segmented in 9 boxes and compared to the first non-averaged image. The automatic VD measurement in OCTA images showed a good repeatability with an overall mean intra-class correlation coefficient (ICC) of 0.924. The mean ICC was higher in healthy eyes compared to eyes with macular oedema (0.877 versus 0.960; p < 0.001) and in the superficial vascular plexus versus the deep vascular complex (0.967 versus 0.888; p = 0.001). The quantitative analysis of the effect of the averaging showed that averaged images had a mean gain of 790.4 pixels/box, located around or completing interruptions in the vessel walls, and a mean loss of 727.2 pixels/box. The qualitative analysis of the averaged images showed that 99.6% of boxes in the averaged images had a gain in vessel detection (i.e., vessels detected in the averaged image but not in the non-averaged image). The loss of pixels was due to a reduction in background noise and motion artifacts in all cases and no case of loss of vessel detection was observed. The automatic VD measurement using the OptoVue HD device showed a good repeatability in 5 acquisitions in a row setting. Averaging images increased vessel detection, and in about a third of boxes, decreased the background noise, both in healthy eyes and, in a greater proportion, in eyes with macular oedema.

Design and Implementation of Harmonic RFID Based on Conventional UHF System

Abstract There has been growing interest in the use of passive harmonic RFIDs for diverse range of applications. Conventional RFIDs are prone to self-jamming and multipath interference, and these challenges can be mitigated using the harmonic RFID design. Recently several harmonic RFID designs have been demonstrated. However, there are many designs related, packaging and intellectual property challenges associated with new tag designs. It has been well known that conventional RFIDs produce harmonic content, which is typically suppressed to reduce background noise. Previous experiments have demonstrated that the harmonics generated by conventional RFIDs can be utilized to enhance their performance. In this paper, an RFID chip is characterized for the generation of harmonic frequencies. This is carried out by designing a high frequency board that contains calibration structures along with structures to characterize the RFID chip using a one port network. An equivalent model is then developed, which in turn is used to design a dual band antenna that works at the fundamental and harmonic frequencies. In addition, the conventional RFID interrogator is modified to accommodate the measurement of harmonics generated by the RFIDs. A complete harmonic tag system is designed and implemented, and an example application of harmonic RFID is demonstrated. Here, the harmonic RFID tag is used in an industrial setting where there is large clutter (large reflections from metal structures).

Effect of Sampling Rate and Data Pretreatment for Targeted and Nontargeted Analysis by Means of Liquid Chromatography Coupled to Drift Time Ion Mobility Quadruple Time-of-Flight Mass Spectrometry.

Ion mobility as an additional separation dimension can help to resolve and annotate metabolite and lipid biomarkers and provides important information about the components in a sample. Identifying relevant information in the resulting data is challenging because of the complexity of the data and data evaluation strategies for both targeted or nontargeted workflows. Frequently, feature analysis is used as a first step to search for differences between samples in discovery workflows. However, follow-up experimentation often leads to more targeted data extraction methods. In both cases, optimizing data sets for data extraction can make an important contribution to the overall results. In this work, we evaluate the effect of experimental conditions including acquisition sampling rate and data pretreatment on lipid standards and lipid extracts as examples of complex biological samples analyzed by liquid chromatography coupled to drift time ion mobility quadrupole time-of-flight mass spectrometry. The results show that a reduction of both peak variation and background noise can be achieved by optimizing the sampling rate. The use of data pretreatment including data smoothing, intensity thresholding, and spike removal also play an important role in improving detection and annotation of analytes from complex biological samples, whereas nonoptimal data sampling rates and preprocessing can lead to adverse effects including the loss or alternation of small, or closely eluting, low-abundant peaks.

SWS-DAN: Subtler WS-DAN for fine-grained image classification

Currently, weakly supervised data augmentation network (WS-DAN) has been proved to be one of the state-of-the-art methods for fine-grained image classification due to its effectiveness on attention-guided data augmentation and bilinear attention pooling. Taking WS-DAN as the backbone, in this paper, we further propose a subtler WS-DAN recognition network, namely, SWS-DAN. Specifically, we first construct a novel “salience-guided data augmentation” scheme composed of cutblock, part-aware cropping, and SCutMix operations, which can more effectively expand the number of training dataset and improve the weakness addressed in the data augmentation procedure of WS-DAN. Meanwhile, the novel data-augmentation manner reduces background noise and mines more discriminative regions simultaneously, thereby avoiding the overfitting. In caring about the key issue in fine-grained image classification task is how to distinguish the extremely similar subclasses (e.g., Artic Tern, Elegant Tern, and Forsters Tern), we then design a “Top-k” loss function that mainly focuses on the similar classes so as to find their extraordinary subtle differences. Extensive experiments carried out on common fine-grained image datasets demonstrate that SWS-DAN can surpass WS-DAN with a significant margin in the classification performance.

De-Noising Process in Room Impulse Response with Generalized Spectral Subtraction

The generalized spectral subtraction algorithm (GBSS), which has extraordinary ability in background noise reduction, is historically one of the first approaches used for speech enhancement and dereverberation. However, the algorithm has not been applied to de-noise the room impulse response (RIR) to extend the reverberation decay range. The application of the GBSS algorithm in this study is stated as an optimization problem, that is, subtracting the noise level from the RIR while maintaining the signal quality. The optimization process conducted in the measurements of the RIRs with artificial noise and natural ambient noise aims to determine the optimal sets of factors to achieve the best noise reduction results regarding the largest dynamic range improvement. The optimal factors are set variables determined by the estimated SNRs of the RIRs filtered in the octave band. The acoustic parameters, the reverberation time (RT), and early decay time (EDT), and the dynamic range improvement of the energy decay curve were used as control measures and evaluation criteria to ensure the reliability of the algorithm. The de-noising results were compared with noise compensation methods. With the achieved optimal factors, the GBSS contributes to a significant effect in terms of dynamic range improvement and decreases the estimation errors in the RTs caused by noise levels.

The Opportunities and Challenges of Molecular Tagging Next-Generation Sequencing in Liquid Biopsy.

Liquid biopsy (LB) is a promising tool that is rapidly evolving as a standard of care in early and advanced stages of cancer settings. Next-generation sequencing (NGS) methods have become essential in molecular diagnostics and clinical laboratories dealing with LB analytes, i.e., cell-free DNA and RNA. The sensitivity and high-throughput capacity of NGS enable us to overcome technical issues that are mainly attributable to low-abundance (below 1% mutated allelic frequency) tumour genetic material circulating within biological fluids. In this context, the introduction of unique molecular identifiers (UMIs), also known as molecular barcodes, applied to various NGS platforms greatly improved the characterization of rare genetic alterations, as they resulted in a drastic reduction in background noise while maintaining high levels of positive predictive value and sensitivity. Different UMI strategies have been developed, such as single (e.g., safe-sequencing system, Safe-SeqS) or double (duplex-sequencing system, Duplex-Seq) strand-based labelling, and, currently, considerable results corroborate their potential implementation in a routine laboratory. Recently, the US Food and Drug Administration approved the clinical use of two comprehensive UMI-based NGS assays (FoundationOne Liquid CDx and Guardant360 CDx) in cfDNA mutational assessment. However, to definitively translate LB into clinical practice, UMI-based NGS protocols should meet certain feasibility requirements in terms of cost-effectiveness, wet laboratory performance and easy access to web-source and bioinformatic tools for downstream molecular data.

Feasibility of measuring noise sources on a detailed nose landing gear at low cost development

This work aimed to develop an aeroacoustics analysis of a nose landing gear through rational and low-cost wind tunnel tests to develop potential noise mitigation technologies of aircraft components. This approach led to using a nose landing gear model of the Boeing 777 aircraft, whose more complex parts were 3D-printed. The aeroacoustics and aerodynamics tests were performed in a hard-wall wind tunnel with the capability to perform aeroacoustics tests after corner-vanes and fan section treatment to reduce background noise. Improved beamforming techniques were applied for noise source localization through microphone array measurements. Also, near field noise analysis was performed by microphones installed inside model cavities as part of the correlation procedures for noise investigation and creation of reference data for further CAA simulations. Additionally, using wake-flow mapping and lateral aeroacoustics measurements allowed the characterization of the flow field and noise source positions at low subsonic speeds in the approach condition. The flexibility of the model construction allowed a systematic assembly of the main parts and structures, and therefore, a noise build-up analysis showed the contribution of each component to the overall noise of the nose landing gear. Near-field measurements provided better insights into the noise generation mechanisms of each component. Despite some limitations identified by following this approach, the results were consistent and revealed that the noise sources are concentrated in the bay cavity, at the upper structure, and between the wheels. The feasibility of measuring noise sources on a detailed nose landing gear has been achieved with relatively low-cost development, providing useful data for further CAA computations as well as noise mitigation development.

Abstract 512: Tetramer and pentamer assays for antigen-specific lymphocytes: Implementation in clinical trials

Abstract The success of inhibiting PD1 on T cells for cancer immunotherapy initiated strong interest in studying therapeutic antibodies to alter the adaptive immune system. As the knowledge of neoantigens, oncogenes, and autoantigens is rapidly accelerating, the search for antigen-specific lymphocytes for better treatment outcomes is increasing. A tetramer or pentamer assay is an essential tool to immunophenotype and enumerate antigen-specific lymphocytes. These assays offer a powerful technique able to monitor immune status, using peripheral whole blood as a matrix, in contrast to invasive tissue biopsies. However, the assays have a number of obstacles to overcome in clinical research. Over several years, we have developed and validated tetramer and pentamer assays with both successes and failures. Rare events and precision: Clinical studies require robust reproducibility and high precision. As a standard in judging the acceptability of a flow cytometric biomarker test, the majority of tests are set up to have 20-30% CV between results. However, if a biomarker is expressed in less than 1% of a target population, the criteria can fail. The tetramer assay requires precision analyses that are even more stringent. These cells are rare events among millions of hematopoietic cells. The precision requirements may be at one hundredth percent or lower. A successful sign for the assay is that over a one-year period during the course of the clinical trial, some biomarkers remain at a stable level. Increasing signals and reduction of background noise: Unlike single marker based antibodies, however, tetramers and pentamers are highly sensitive. It is critical, therefore, to increase signals and reduce background noise. A combination of 7AAD (to remove dead cells) and incorporation of a dump channel (to remove non-target populations) significantly improves the precision of the assays. Data analyses and management: With new and powerful flow cytometers, a single sample can be labelled with multiple surface markers along with fluorescent tetramer or pentamers. The file size can reach megabytes when two to five million cells are tagged. This creates a bottleneck for data analysis and analytical software, especially in an assay with a panel of multiple tubes. The solution is to cut non-essential cell numbers including those with high side-scatter (SSC) numbers in half and to re-export data from cytometers. Controls setup: The most challenging part of these assays is to have strict controls, both for reagents and cells. Customized reagents and a source for characterized specific peripheral blood cells (PBMC) with HLA-A markers are necessary for developing and validating these complex assays. In conclusion, tetramer and pentamer assays targeting antigen-specific lymphocytes are deliverables for cancer immunotherapy research, cancer vaccine development and for use in studying other autoimmune diseases, such as type 1 diabetes. Citation Format: Chengsen Xue, Weiguo Zhong, Yeji Cho, Joanne Cuomo, Christina D. Swenson, Thomas W. Mc Closkey. Tetramer and pentamer assays for antigen-specific lymphocytes: Implementation in clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 512.

Plasma LRG1 Protein Are Overexpressed in Acute Myeloid Leukemia with High Sensitivity and High Specificity in Differentiating Leukemic Plasma from Normal Plasma — a Potential Biomarker for Minimal Residual Disease Detection

Background:Acute myeloid leukemia (AML) is a heterogenous disease with variable outcome depending on cytogenetic as well as molecular risk at presentation. 70-80% of AML patients would achieve complete remission after induction chemotherapy. However, more than half would suffer a relapse. Currently there is no specific test available in clinical practice to detect early relapse. Relapse is usually suspected when there is a change in peripheral blood count or rising LDH. This often proven to be a frank relapse disease by bone marrow examination. A sensitive and specific biomarker in plasma to detect early disease relapse is not available. In this study, we aimed to identify a new biomarker in AML plasma that could potentially be used in clinical practice for detection of early disease relapse.Method:Plasma samples were obtained from 8 newly diagnosed AML patients. Patients with fever, renal impairment, heart failure, and abnormal liver function test were excluded to reduce background noise in proteomic analysis. These 8 samples were than matched with 8 normal blood donors plasma that were age and gender matched. Plasma samples would be processed for iTRAQ (isobaric tagging for relative and absolute quantification) and analysed. 1 mL of plasma sample would be delipidated and 7 of the most abundant proteins removed using the MARS Hu-7 affinity column (Agilent Technologies, USA) to prevent the masking of low abundance proteins. Samples comprising 100mg of proteins were then reduced, alkylated, digested and labeled with iTRAQ reagents that differ by 1 atomic mass unit. Protein identification and relative quantification with statistical analysis are performed using in-house QSTAR Elite Q-TOF LC/MS/MS mass spectrometer coupled with ProteinPilotTM software (version 2.1). Proteins that were overexpressed in AML patients, which has potential relation with leukemogenesis would be shortlisted and further quantified in 42 other AML patients and 40 normal blood donor plasma for validation. Correlation with clinical data were carried out.Results:13 proteins were found to be overexpressed in all 8 AML samples with an average expression ratio ranging from 1.75 to 4.50 (see figure 1). Two proteins were shortlisted for its relation with haematopoietic cell - Brain acid soluble protein 1 (BASP1) and Leucine-rich alpha-2-glycoprotein (LRG1). Further validation in normal plasma and AML plasma revealed that BASP1 protein did not have sufficiently good sensitivity and specificity. BASP1 mean concentration in normal plasma was 8.05 ng/mL (SD: 6,29 ng/mL), and in AML samples was 4.41 ng/mL (SD:1.26 ng/mL). In contrast, LRG1 protein showed a significant difference in concentration. LRG1 mean concentration in normal plasma was 2.03 μg/mL (SD: 0.49 μg/mL), compared to AML samples - 13.29 μg/mL (SD: 7.80 μg/mL), p<0.001 (see figure 2). ROC analysis of LRG1 protein for differentiating AML samples from normal plasma sample revealed an AUC of 0.998 (see figure 3), p<0.001. Cut-off level of 3.17 μg/mL would give a sensitivity level of 97.6% (95%CI: 87.4-99.9%), and specificity level of 97.5% (95%CI: 88.8-99.9%). Correlation with clinical data revealed significant correlation with total white cell count at diagnosis, Pearson correlation value of 0.41, p=0.009. In this small cohort of 42 patients, we were unable to demonstrate any correlation of LRG1 with clinical outcome.Conclusion:Plasma LRG1 protein showed a high sensitivity and high specificity in differentiating AML plasma from normal plasma. It also showed a strong correlation with total white cell count in AML patients. Further evaluation of LRG1 protein plasma level in AML patients in longitudinal manner is required to validate its utility in monitoring for minimal residual disease and early detection of leukemic relapse. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Nanoscale Focus Pinspot for High-Purity Quantum Emitters via Focused-Ion-Beam-Induced Luminescence Quenching.

Epitaxially grown quantum dots (QDs), especially embedded in photonic structures, play an essential role in various quantum photonic systems as on-demand single-photon sources. However, these QDs often suffer from adjacent unwanted emitters, which contribute to the background noise of the QD emission and fundamentally limit the single-photon purity. In this paper, a nanoscale focus pinspot (NFP) technique using focused-ion-beam-induced luminescence quenching enables us to improve single-photon purity from site-controlled QD as a proof-of-concept experiment. The optical quality of the QD emission is not degraded while the signal-to-noise ratio of the QD is improved. Moreover, the QD after the NFP technique reveals the single-photon nature at further elevated temperatures owing to the reduced background noise. As the NFP technique is nondestructive, it retains the apparent physical structures and photonic functions, thereby indicating its promising potential for applying diverse high-purity quantum emitters, particularly integrated in photonic devices and circuits.

Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration.

To evaluate the image quality and iodine concentration (IC) measurements in pancreatic protocol dual-energy computed tomography (DECT) reconstructed using deep learning image reconstruction (DLIR) and compare them with those of images reconstructed using hybrid iterative reconstruction (IR). The local institutional review board approved this prospective study. Written informed consent was obtained from all participants. Thirty consecutive participants with pancreatic cancer (PC) underwent pancreatic protocol DECT for initial evaluation. DECT data were reconstructed at 70 keV using 40% adaptive statistical iterative reconstruction-Veo (hybrid-IR) and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The diagnostic acceptability and conspicuity of PC were qualitatively assessed using a 5-point scale. IC values of the abdominal aorta, pancreas, PC, liver, and portal vein; standard deviation (SD); and coefficient of variation (CV) were calculated. Qualitative and quantitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H groups. The diagnostic acceptability and conspicuity of PC were significantly better in the DLIR-M group compared with those in the other groups (p < .001-.001). The IC values of the anatomical structures were almost comparable between the three groups (p = .001-.9). The SD of IC values was significantly lower in the DLIR-H group (p < .001) and resulted in the lowest CV (p < .001-.002) compared with those in the hybrid-IR and DLIR-M groups. DLIR could significantly improve image quality and reduce the variability of IC values than could hybrid-IR. Image quality and conspicuity of pancreatic cancer were the best in DLIR-M. DLIR significantly reduced background noise and improved SNR and CNR. The variability of iodine concentration was reduced in DLIR.

Afterglow Amplification for Fast and Sensitive Detection of Porphyria in Whole Blood.

Porphyria is a group of genetic photodermatoses that cause too much porphyrin to accumulate in the blood, skin, and liver, resulting in skin photosensitivity and damage, liver disease, or potential liver failure. Conventional detection methods include high-performance liquid chromatography and fluorescence spectrometry. However, these methods usually require complicated pretreatment and time-consuming processes. Therefore, efficient and fast detection of porphyria is urgently needed. Herein, we develop a molecular afterglow reporter-based sensing scheme for the detection of porphyrins in whole blood. The afterglow reporter can respond to the production of singlet oxygen (1O2) of porphyrins after light excitation, and the detection signals can be amplified through adjusting the amount of singlet oxygen and afterglow reporter molecules. Moreover, without the use of a real-time excitation source, afterglow signals can avoid the scattering and autofluorescence interference in biological samples, thereby reducing background noise. More importantly, we prove the applicability of the afterglow reporter in the quantitative detection of porphyrins in whole blood and demonstrate its great clinical potential.

NoisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise.

High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimizing technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches.

(Invited) Non-Blinking Long Lifetime Quantum Dots and Passive Cell Delivery Strategies

Over the past ~20 years there has been considerable effort towards the optimization of the photophysical properties of emissive nanomaterials to engender efficacy for biological imaging and sensing. The development of core-shell CdSe/ZnS and CdSe/CdS quantum dots (QDs) was instrumental due to those materials’ resistance to photobleaching and high quantum yields. However, these particles experience fluorescence intermittency (“blinking”), which is detrimental for single particle tracking studies. Furthermore, ~10 nanosecond lifetimes are not compatible with time-gating visualization, a technique that reduces background noise. Last, cytosolic delivery of QDs is a well-known problem due to the propensity for QDs to become sequestered in the endosomes of live cells.Presented are developments that address these issues. Long lifetimes, >100 ns, and non-blinking behavior have been realized in “giant” quantum dots with a highly alloyed core/shell interface. This results in type-II behavior that separates the electron and hole, which slows emissive recombination. Also presented is a novel cell-penetrating peptide that can be conjugated to surfactants that are commonly employed for QD water solubilization. These materials can be passively delivered to the cytosol of live T-cell lymphocytes. Single particle tracking studies reveal fast diffusion of single dots that are not consistent with endosomal capture, which is observed in control nanomaterials. These developments create new inroads for chemical and biological sensing studies inside of live cells for basic research and diagnostic purposes. Figure 1

Damage Identification on Impact and Lightning Damage of Flax Composite Laminates (Linum usitatissimum) Using Long-Pulse Thermography of a Low-Resolution Infrared Camera

The demand for composite fiber material is significantly high due to its excellent mechanical properties and its use in various industries. Recently, with the increasing awareness of environmental issues, researchers are now focusing more on eco-friendly and green materials. A biocomposite offers a good balance of strength and stiffness ratio, bending and membrane mechanical properties, balanced thermal distortion stability, reduced weight and cost, improved fatigue resistance, reduced notch sensitivity, and, comparatively, better performance than synthetic composites. Yet, due to the complex anisotropy of the composite material, the inspection and detection of inner defects become a challenge. Long-pulse thermography is one of the non-destructive evaluations (NDEs) used to detect defects in composite materials. However, very limited research has been carried out on the usage of a low-resolution infrared camera to perform defect or damage inspection on flax composite laminates. In this paper, an experimental setup of a long pulse thermography system using low-resolution infrared camera was performed on flax bio-composite to identify impact and lightning damage. The result highlights that with control parameters, a low-resolution infrared camera has the capability to capture the lightning and impact defects of flax biocomposites using the long-pulse thermography system. An image processing method is then applied to the defect to improve the quality of defect detection and reduce background noise.

PET image reconstruction with deep progressive learning

Convolutional neural networks (CNNs) have recently achieved state-of-the-art results for positron emission tomography (PET) imaging problems. However direct learning from input image to target image is challenging if the gap is large between two images. Previous studies have shown that CNN can reduce image noise, but it can also degrade contrast recovery for small lesions. In this work, a deep progressive learning (DPL) method for PET image reconstruction is proposed to reduce background noise and improve image contrast. DPL bridges the gap between low quality image and high quality image through two learning steps. In the iterative reconstruction process, two pre-trained neural networks are introduced to control the image noise and contrast in turn. The feedback structure is adopted in the network design, which greatly reduces the parameters. The training data come from uEXPLORER, the world’s first total-body PET scanner, in which the PET images show high contrast and very low image noise. We conducted extensive phantom and patient studies to test the algorithm for PET image quality improvement. The experimental results show that DPL is promising for reducing noise and improving contrast of PET images. Moreover, the proposed method has sufficient versatility to solve various imaging and image processing problems.

Improving CERES-Wheat Yield Forecasts by Assimilating Dynamic Landsat-Based Leaf Area Index: A Case Study in Iran

In this study, we tried to address the applicability of using dynamic remotely sensed data into a static crop model to capture the yield spatiotemporal variability at the field scale. Taking the example of the crop environment resource synthesis for wheat (CERES-wheat), the model was calibrated, improved, and validated using three years of winter wheat field measurement data (growing seasons of 2017–2019). We assimilated the Landsat-based leaf area index (LAI) into the model using the particle filter approach. Four vegetation indices, including NDVI, SAVI, EVI, and EVI-2, were evaluated to identify winter wheat LAI’s best estimator. A linear regression of Landsat-EVI-2 was found to be the most accurate representation of LAI (LAI = 10.08 × EVI-2 − 0.53) with R2 = 0.87, and mean bias error = − 2.04. The higher LAI accuracy from EVI-2 was attributed to the soil and canopy background noise reduction and accounting for certain atmospheric conditions. Assimilating the LAI based on Landsat-EVI-2 into the CERES model improved the model’s overall performance, particularly for grain yield and biomass simulations. The default model predicted LAImax, grain yield, and biomass at 5.1 cm2 cm−2, 8.3 Mg ha−1, and 14.9 Mg ha−1 with RMSE of 1.44, 0.91 Mg ha−1, and 1.2 Mg ha−1, respectively, while the modified model (using the Landsat-EVI-2 data) predicated these values at 6.6 cm2 cm−2, 9.9 Mg ha−1, and 16.6 Mg ha−1 with RMSE of 0.81, 0.54 Mg ha−1, and 0.62 Mg ha−1, respectively.

Effect of coronavirus lockdowns on the ambient seismic noise levels in Gujarat, northwest India

The Covid-19 pandemic created havoc and forced lockdowns in almost all the countries worldwide, to inhibit social spreading. In India as well, as a precautionary measure, complete and partial lockdowns were announced in phases during March 25 to May 31, 2020. The restricted human activities led to a drastic reduction in seismic background noise in the high frequency range of 1–20 Hz, representative of cultural noise. In this study, we analyse the effect of anthropogenic activity on the Earth vibrations, utilizing ambient noise recorded at twelve broadband seismographs installed in different environmental and geological conditions in Gujarat. We find that the lockdowns caused 1–19 dB decrease in seismic noise levels. The impact of restricted anthropogenic activities is predominantly visible during the daytime in urban areas, in the vicinity of industries and/or highways. A 27–79% reduction in seismic noise ground displacement (drms) is observed in daytime during the lockdown, in populated areas. However, data from station MOR reveals a drastic decrease in drms amplitude both during the day (79%) and night times (87%) since factories in this area operate round the clock. The noise at stations located in remote areas and that due to microseisms, shows negligible variation.

Audiometric Tests without Booths.

Audiometric booths are used to reduce background noise levels at testing locations to below values specified in the standards. As such, they are considered inherent parts of the audiometric testing equipment. This paper presents the results from a literature search of solutions that could ensure that background noise levels are acceptable outside booths. The technology used is especially valuable for survey tests and for locations where booths are unavailable or cannot be used for different reasons. However, its use is recommendable for only screening hearing tests but not for clinical or research applications.