Background Suppression Research Articles

Design and Application of a Hemispherical XRF Secondary Target

ABSTRACTIn x‐ray fluorescence analysis (XRFA), the use of secondary targets to suppress measurement background is a common practice aimed at reducing detection limits. However, conventional polarized secondary targets often exhibit low conversion efficiency of primary x‐rays. In this paper, we propose a novel hemispherical secondary target designed to exploit a focusing‐like phenomenon for primary x‐rays, thereby enhancing the conversion efficiency and achieving improved background suppression. The optimal dimensions of the hemispherical secondary target are determined through theoretical calculations, and further validated through Monte Carlo simulations and experimental analysis. Our findings reveal a substantial enhancement in the peak‐to‐background ratio when compared to traditional polarized secondary targets. Specifically, for a 1000‐ppm Ce sample, the peak‐to‐background ratio increased from 8.45 to 9.78 with the implementation of the hemispherical secondary target. Similar improvements were observed when measuring a 1000‐ppm Ce sample, relative to direct excitation. Furthermore, the relative error between the system and the ICP‐MS measurement results of the rare earth sample remains within 5%, underscoring the effectiveness of the hemispherical secondary target in XRFA applications.

Lightweight Neural Network for Centroid Detection of Weak, Small Infrared Targets via Background Matching in Complex Scenes

In applications such as aerial object interception and ballistic estimation, it is crucial to precisely detect the centroid position of the target rather than to merely identify the position of the target bounding box or segment all pixels belonging to the target. Due to the typically long distances between targets and imaging devices in such scenarios, targets often exhibit a low contrast and appear as dim, obscure shapes in infrared images, which represents a challenge for human observation. To rapidly and accurately detect small targets, this paper proposes a lightweight, end-to-end detection network for small infrared targets. Unlike existing methods, the input of this network is five consecutive images after background matching. This design significantly improves the network’s ability to extract target motion features and effectively reduces the interference of static backgrounds. The network mainly consists of a local feature aggregation module (LFAM), which uses multiple-sized convolution kernels to capture multi-scale features in parallel and integrates multiple spatial attention mechanisms to achieve accurate feature fusion and effective background suppression, thereby enhancing the ability to detect small targets. To improve the accuracy of predicted target centroids, a centroid correction algorithm is designed. In summary, this paper presents a lightweight centroid detection network based on background matching for weak, small infrared targets. The experimental results show that, compared to directly inputting a sequence of images into the neural network, inputting a sequence of images processed by background matching can increase the detection rate by 9.88%. Using the centroid correction algorithm proposed in this paper can therefore improve the centroid localization accuracy by 0.0134.

Advanced Object Tracking through Conditional Online Updates and Noise Suppression

Object tracking under complex environmental conditions, such as background clutter, occlusion, and rapid motion, presents significant challenges. This paper addresses these issues by proposing a tracking algorithm integrating background suppression, target region enhancement, and an adaptive online template update mechanism to improve tracking accuracy. The proposed method uses the initial bounding box of the target object as a reference template and selectively updates specific regions online to suppress noise and retain critical features. We evaluated the proposed method using the OTB dataset to validate it. The baseline model without the proposed method showed a success rate of 0.417 and a precision of 0.586, while the algorithm with the proposed method achieved improved values of 0.524 and 0.728, respectively. Qualitative evaluations further confirmed the robustness of the proposed method, demonstrating high performance in scenarios with occlusion and complex backgrounds. Rather than updating all regions indiscriminately, the proposed method selectively updates the template using representative values from the target object's information. This selective update mechanism ensures the incorporation of the most relevant and accurate features, enabling the algorithm to adapt to changes in the target's appearance while minimizing noise integration. Emphasizing the feature regions and suppressing noise are also critical for maintaining a clear and precise representation of the target object, reducing the likelihood of confusion by irrelevant background information. Future research will focus on developing balanced update strategies that integrate new information while maintaining stable and reliable target characteristics.

Enhancing defect detection in active infrared thermography using adaptive background suppression techniques

Enhancing defect detection in active infrared thermography using adaptive background suppression techniques

A Systematic Review of EEG and MRI Features for Predicting Long-Term Neurological Outcomes in Cooled Neonates With Hypoxic-Ischemic Encephalopathy (HIE).

Neonatal hypoxic-ischemic encephalopathy (HIE) represents a significant global disease burden, but more importantly, it leaves a lasting impact of disability on individual children and their families.HIE outcome prognostication is important for guiding clinical interventions and counseling families. The objective of this study was to systematically review early electroencephalogram (EEG) and magnetic resonance imaging (MRI) features associated with long-term neurological outcomes in infants after perinatal HIE. Articles were extracted from PubMed, CINAHL, and Scopus. Twenty studies were included that assessed EEG and/or MRI patterns in neonates who underwent therapeutic hypothermia and were followed to determine long-term outcomes. Articles that did not meet the inclusion criteria were excluded. Covidence review manager (Melbourne, Australia: Covidence) was used to extract, evaluate, and synthesize review results. Of the articles included, eight focused on EEG features, eight on MRI features, and four on assessments using both EEG and MRI. Abnormal EEG background and burst suppression severity were associated with poor outcomes. Higher MRI injury scores in the basal ganglia and thalamus were also correlated with poor outcomes. Finally, studies also revealed restricted diffusion and greater lesion size in the subcortical gray matter correlated with poor outcomes. We also identified limitations in the included studies which primarily involved sample size, potential for MRI pseudonormalization, and the potential tradeoff between retention of infants able to receive long-term follow-up and attrition of those lost to follow-up. We conclude that EEG background patterns, MRI scoring, subcortical lesion burden, and MRI diffusivity are sensitive metrics for predicting outcomes. Both early EEG and MRI features may serve as high-fidelity biomarkers for secondary energy failure and for counseling families of neonates at high risk for devastating neurologic outcomes. Additionally, there is a paucity of information on the impact of HIE on brain areas outside of the standard clinical basal-ganglia and watershed patterns, especially in locations like the corpus callosum. Finally, MRI pseudonormalization may underestimate the extent of injury in these studies.

Phase imaging through a single multimode fiber.

Phase imaging techniques are pivotal for achieving high-contrast visualization of unstained biological specimens in vitro, which is typically not applicable in narrow spaces. Recently, multimode fiber (MMF) has shown promise in enabling high-resolution in vivo endoscopy in biological research. Herein, we introduce a novel, to the best of our knowledge, phase imaging microscopy technique employing a single multimode fiber, showcasing remarkable capabilities in high-contrast imaging and quantitative shape reconstruction through frequency-domain modulation. Our method, validated through comparisons with reflection and phase-contrast results, demonstrates exceptional ability in imaging diverse samples, including microspheres, semiconductor chips, and oral epithelial cells. Notably, the quantitative reconstruction of surface shape achieves a sensitivity of less than 100 nm, enabling the extraction of three-dimensional information from single focal plane images. Moreover, our technique excels in contrast enhancement and defocused background suppression, presenting a promising avenue for minimally invasive, high-contrast, label-free in vivo phase imaging.

Role of Diffusion Weighted Imaging with Background Body Signal Suppression (DWIBS) in Evaluation of Breast Masses and Correlation with Histopathological Findings

Abstract Background Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is an important tool in breast imaging. Yet, it has two major limitations which are represented by its specificity and the use of contrast material. Diffusion weighted imaging (DWI) provides important functional information without the need for contrast material. A newly introduced Diffusion weighted imaging with back-ground suppression (DWIBS) sequence is an accurate and rapid tool for the identification and characterization of breast lesions, with its short examination time, high lesion-to-background contrast, and lack of need for intravenous contrast agents. Objective To assess the role of DWIBS sequence for evaluating breast masses and to compare DWIBS results with DCE-MRI in correlation with histopathological findings for intermediate, suspicious and malignant imaging results. Methods The study was conducted during the period from December 2020 t& January 2023. Thirty five patients were included in the study, referred from Ain Shams university hospitals sonomammography clinic to Ain Shams university hospitals MRI unit for further MRI assessment of breast masses (BIRADS 3, BIRADS 4& BIRADS 5), probably benign, suspicious and malignant- lesions on sonomammography imaging results. MRI breast protocol which included DCE-MRI and DWIBS sequences were obtained for characterization and were verified by core needle biopsy or excisional biopsy. The results were statistically analyzed. Sensitivity, specificity, diagnostic accuracy (DA), positive predictive value (PPV) and negative predictive value (NPV) were calculated for DCE-MRI and DWIBS. Apparent diffusion co-efficient (ADC) values were calculated with ADC <1.2 x 10-3 mm2/s were considered suspicious for malignancy. The results were then compared with the histological findings. Results 35 female patients had 39 breast masses included in our study. By DCE-MRI, 8 (20.5%) of masses were categorized as benign and 31(79.5%) of masses were categorized as malignant. By DWIBS sequence, 7 (17.9%) of masses were categorized as benign and 32 (82.1%) of masses were categorized as malignant. By histopathology, 14 (35.9%) of masses were benign and 25 (64.1%) were malignant. DCE-MRI obtained accuracy, sensitivity, specificity, PPV and NPV values of 84.6, 100, 57.1, 80.6, 100 %, respectively. DWIBS sequences obtained accuracy, sensitivity, specificity, PPV and NPV values of 82.1, 100, 50, 78.1, 100 %, respectively. Conclusions DWIBS can be added to DCE breast MRI, to improve DCE-MRI sensitivity and specificity. It can as well be used instead of DCE -MRI sequences in certain circumstances as in cases of renal impairment, but with some limitations as DWIBS cannot assess the extent of the malignant process.

A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalanced number between the target and the background spatially and the high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation-learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and spatial–spectral features to improve its representation ability. Firstly, a Graph Convolutional Network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing the detection and background suppression capabilities. The performance of the proposed approach is evaluated using the average scores of AUCD,F, AUCF,τ, AUCBS, and AUCSNPR. The corresponding values are 0.99660, 0.00078, 0.99587, and 333.629, respectively. These results demonstrate the accuracy of the model in different evaluation metrics, with AUCD,F achieving the highest score, indicating strong detection performance across varying thresholds. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.

A gamma-ray events discrimination method based on convolutional neural network in a HPGe spectrometer

Discriminating between full- and partial-energy deposition events in a high-purity germanium detection system can achieve the subtraction of background events and reduce the system’s minimum detectable activity (MDA). Traditional anticoincidence techniques and pulse shape discrimination techniques can both achieve event type discrimination, but the former has a complex equipment structure and is difficult to maintain, and the latter still has room for improvement in recognition accuracy. This paper proposes a method using convolutional neural networks (CNNs) to learn and recognize pulse waveform features for event type discrimination. We studied the impact of space-filling curve encoding methods and convolution kernel size on the training effect of the model. The results show that the zigzag curve encoding method results in a higher discrimination accuracy of the model; when the convolution kernel size is 9 × 9, the model has the best convergence speed and training effect. Finally, we compared the performance of fully connected neural network (FC network), 1D-CNN, and 2D-CNN in event type discrimination using MDA as the indicator. The results show that compared to FC network, CNNs perform better in reducing the MDA across the entire energy range, especially 2D-CNN, which performs better in the medium and low energy range compared to 1D-CNN. After using 2D-CNN for event discrimination and background suppression, the MDA is reduced by 21.23% (122 keV), 16.32% (244 keV), 8.43% (344 keV), 13.76% (662 keV), 5.54 % (1173 keV), and 26.35% (1332 keV).

Synthetic minority oversampling and iterative fluorescence-suppression integrated algorithm for Raman spectrum pesticide detection system

Raman spectrum preprocessing method for automatic denoising and suppression of the fluorescent background. In this method, noise is reduced using wavelet transform, and a modified polynomial curve fitting method is implemented such that an algorithm can independently identify the optimal curve parameters for fluorescent background suppression. To address the problem of imbalanced datasets, the present study employed a synthetic minority oversampling technique to increase the volume of data in minority classes. This technique enables the prediction of pesticides that are otherwise difficult to detect, and the prediction accuracy is comparable to that of detection with large data volumes. The proposed convolutional neural network model was verified to accurately identify the type of single pesticides and composition of mixed pesticides. The prediction accuracy for mixed pesticides reached 99.1%.

Real-time fMRI neurofeedback of the anterior insula using arterial spin labelling.

Arterial spin labelling (ASL) is the only non-invasive technique that allows absolute quantification of perfusion and is increasingly used in brain activation studies. Contrary to the blood oxygen level-dependent (BOLD) effect ASL measures the cerebral blood flow (CBF) directly. However, the ASL signal has a lower signal-to-noise ratio (SNR), than the BOLD signal, which constrains its utilization in neurofeedback studies. If successful, ASL neurofeedback can be used to aid in the rehabilitation of health conditions with impaired blood flow, for example, stroke. We provide the first ASL-based neurofeedback study incorporating a double-blind, sham-controlled design. A pseudo-continuous ASL (pCASL) approach with background suppression and 3D GRASE readout was combined with a real-time post-processing pipeline. The real-time pipeline allows to monitor the ASL signal and provides real-time feedback on the neural activity to the subject. In total 41 healthy adults (19-56 years) divided into three groups underwent a neurofeedback-based emotion imagery training of the left anterior insula. Two groups differing only in the explicitness level of instruction received real training and a third group received sham feedback. Only those participants receiving real feedback with explicit instruction showed significantly higher absolute CBF values in the trained region during neurofeedback than participants receiving sham feedback. However, responder analyses of percent signal change values show no differences in activation between the three groups. Persisting limitations, such as the lower SNR, confounding effects of arterial transit time and partial volume effects still impact negatively the implementation of ASL neurofeedback.

Advancing 7T perfusion imaging by pulsed arterial spin labeling: Using a parallel transmit coil for enhanced labeling robustness and temporal SNR.

Non-invasive perfusion imaging by Arterial spin labeling (ASL) can be advantageous at Ultra-high field (UHF) MRI, since the image SNR and the T1 relaxation time both increase with the static field. However, ASL implementation, especially at 7T, is not trivial. Especially for ASL, UHF MRI comes with many challenges, mainly due to B1+ inhomogeneities. This study aimed to investigate the effects of different transmit coil configurations on perfusion-weighted imaging at 7T using a flow-sensitive alternating inversion recovery (FAIR) technique with time-resolved frequency offset corrected inversion (TR-FOCI) pulses for labeling and background suppression. We conducted a performance comparison between a parallel transmit (pTx) system equipped with 32 receive (Rx) and 8 transmit (Tx) channels and a standard setup with 32Rx and 2Tx channels. Our findings demonstrate that the pTx system, characterized by a more homogeneous B1 transmit field, resulted in a significantly higher contrast-to-noise ratio, temporal signal-to-noise ratio, and lower coefficient of variance (CoV) than the standard 2Tx setup. Additionally, both setups demonstrated comparable capabilities for functional mapping of the hand region in the motor cortex, achieving reliable results within a short acquisition time of approximately 5 minutes.

Integration of an autoencoder and background suppression for hyperspectral anomaly detection

ABSTRACT The key to hyperspectral image (HSI) anomaly detection is effectively distinguishing anomalies from the background. Various detectors, such as low-rank sparse representation and background estimation, have been proposed. However, they usually exhibit poor performance due to complex backgrounds and similar anomalies and backgrounds. Considering that most of the pixels in HSI are background, the autoencoder can emphatically and automatically extract and reconstruct background features. And background suppression can further enhance the spectral differentiation of the anomalies and backgrounds. Therefore, we propose a new anomaly detector by integrating autoencoder and background suppression. First, the anomaly coefficient of HSI data is extracted via the local characteristics. The HSI data is fed into the 2Dimensional (2D) convolution autoencoder to reconstruct the background. Since the reconstruction background contains incorrect anomalies, the anomaly coefficient isused to suppress the reconstruction image. Then, the detection map is obtained with the Mahalanobis distance. The experiment is performed in different HSI datasets. The results illustrate that the proposed method is effective and outperforms other advanced methods in terms of detection performance.

Ion-optical simulations for the NEXT solenoid separator

In this paper, we present ion-optical simulation for the design studies of a new solenoid separator as part of the NEXT experiment. Our aim is to separate Neutron-rich, EXotic, heavy nuclei produced in multinucleon Transfer reactions (NEXT) from the primary beam and un-wanted by-products within the magnetic field. The goal of the simulation is to find the optimum arrangement of the components within the NEXT solenoid separator to achieve the highest transmission efficiencies of transfer products and a good suppression of background. In our simulations, we focused on two complementary reactions to produce transfermium isotopes and to produce neutron-rich nuclei around the neutron number N = 126. Transmission yields above 50% for the transfermium isotopes of interest were reached in the simulations. The simulated yields for N = 126 lie between 5% and 20%.

Modulation of the internal wave regime over a tropical seamount ecosystem by basin-scale oceanographic processes

Shallow seamounts are becoming increasingly recognised as key habitats for conservation due to their role as biological refuges, particularly throughout oligotrophic oceans. Traditionally, Taylor caps have been invoked as the mechanism driving biomass aggregation over seamounts but emerging evidence based on higher resolution measurements highlights the importance of internal waves (IW) to the local ecosystem. These waves can flush the benthic habitat with cool water from depth and impact on nutrient supply over short time scales through turbulent mixing that may also influence fish behaviour. They are dependent on the regional stratification, however, and thus influenced by planetary-scale variability in oceanographic conditions. We present here detailed observations of the internal wave regime over a shallow seamount, called Sandes, in the central Indian Ocean throughout different phases of the Indian Ocean Dipole (IOD) that modulated the regional stratification. A deep thermocline, caused by the 2019 IOD event precluded internal wave activity over the summit, whereas a thermocline collocated with the summit during 2020 when the IOD reversed polarity resulted in a 30 m amplitude internal tide signal (t ∼ 12.5 h). A shallow thermocline, observed during 2022, resulted in propagation of IWs over the summit with less visible internal tide. Harmonic analysis shows the presence of high frequency waves (t ∼ 15 min) on both flanks of the seamount during 2020 & 2022, which are likely a result of local shear instability, whereas 2019 shows an asymmetric response, potentially due to the strong background current and suppression of the thermocline beneath the depth of the summit. The potential importance of the waves over the summit to the local ecosystem may be attributed to the elevated turbulence measured at the thermocline during internal wave propagation, with ε > 10-5 W kg-1 routinely observed. Our results highlight the ability of thermocline depth to act as a gating condition for internal wave evolution over the summit. These results show that, whilst the water column exhibits variability at short spatiotemporal scales compared to the frequently cited Taylor cap dynamics, it is also regulated by the wider basin scale processes. Thus, a more integrated approach is needed when assessing these dynamic and environmentally critical habitats to include the effects of physical oceanographic controls across multiple spatiotemporal scales.

Background suppression for volcano muography with machine learning

Abstract A machine learning algorithm (deep neural network) is presented to suppress background in muography applications mainly targeting volcanoes. Additionally it could be applied for large scale geological structures, such as ophiolites. The detector system investigated in this article is designed to suppress the low energy background by applying up to 5 lead absorber layers arranged among 8 detectors. This complicated system was simulated with a Monte-Carlo based particle simulation to provide teaching sample for the machine learning algorithm. It is shown that the developed deep neural network is capable of suppressing the low energy background considerably better than the classical tracking algorithm, therefore this additional suppression with machine learning yields in a significant improvement. The target areas of volcanoes lie beneath approximately a kilometer of rock that only fraction of a percent of muons have enough energy to penetrate. The machine learning algorithm takes advantage of the directional changes in the absorbers, as well as the correlation between the muons energy and the deposited energy in the detectors. Identifying very high energy muons is also a challenge: the classical algorithm discards considerable fraction of 1 TeV muons which create multiple hits due to brehmstrahlung, while the machine learning algorithm easily adapts to accept such patterns.

Particle identification using plastic scintillators in the COMET Phase-I experiment

The COMET (COherent Muon to Electron Transition) Phase-I experiment aims to search for muon to electron conversion with a single event sensitivity of O (10−15). In COMET, a Cylindrical Trigger Hodoscope (CTH), consisting of segmented plastic scintillators, provides a primary trigger signal and timing measurement while suppressing backgrounds under the high rate environment. This paper studies the particle identification capability of CTH to suppress one of the serious backgrounds induced by cosmic-rays. We demonstrated that a background suppression factor greater than 10 is achievable with a signal efficiency higher than 90%.

Utility of Echo-planar Imaging with Compressed Sensitivity Encoding (EPICS) in the Evaluation of Small Breast Cancers Using Diffusion-weighted Imaging with Background Suppression (DWIBS).

High b-value acquisition and diffusion-weighted imaging with background suppression (DWIBS) are desirable in high-specificity breast cancer diagnosis on non-contrast-enhanced magnetic resonance imaging; however, this inherently results in a lower signal-to-noise ratio (SNR). Compressed sensitivity encoding (C-SENSE), which combines SENSE with compressed sensing, improves the SNR by reducing noise. Recent technological improvements allow us to incorporate this acceleration technique into echo-planar imaging, called echo-planar imaging with C-SENSE (EPICS). This study aimed to compare image quality and reliability of the apparent diffusion coefficient (ADC) between DWIBS obtained using SENSE and EPICS in patients with small breast cancers. Thirty-seven patients with pathologically confirmed breast cancer underwent DWIBS, and images were reconstructed using both conventional SENSE (SENSE-DWIBS) and EPICS (EPICS-DWIBS). Two board-certified radiologists independently evaluated lesion conspicuity (LC) and noise using a 5-point grading scale. The same 2 radiologists independently measured SNR, contrast-to-noise ratio (CNR), and the mean cancer ADC. The Pearson coefficient and Bland-Altman plot were applied to assess the accuracy of ADCs. LC scores were higher with EPICS than with SENSE, reaching significance for one reviewer but not the other reviewer. Noise ratings on visual evaluation were significantly lower with EPICS than with SENSE (P < 0.001 for both reviewers). SNR was significantly higher with EPICS than with SENSE (P < 0.005 for both reviewers). CNR was significantly higher with EPICS than with SENSE (P < 0.001 for both reviewers). Bland-Altman plots of cancer ADCs using EPICS-DWIBS and SENSE-DWIBS showed excellent concordance, with a bias of 0.026 × 10-3 mm2/s and limits of agreement ranging 0.054 × 10-3 mm2/s; the Pearson's correlation coefficient was 0.997 (P < 0.0001). EPICS enhances breast DWIBS image quality, with improved SNR and CNR and reduced noise levels. The ADCs of breast cancers obtained using EPICS were almost perfectly correlated with those obtained using conventional SENSE.

Lung counting with a highly segmented HPGe detector

A highly segmented High-Purity Germanium (HPGe) detector was used to measure 241Am activity located inside the lungs of an anthropomorphic phantom with various active and passive shield configurations. It was found that the background suppression shield does not play a significant role in reducing the Minimum Detectable Activity (MDA) after veto, based on the segmentation in the depth direction of the HPGe, when measuring low-energy gamma rays. A reduction of up to 57% in the MDA was achieved. The MDA could be further improved by a thinner lateral segmentation and an optimized anti-Compton shield coupled with an active or passive backplate. The new detector application would be particularly useful in mobile whole-body counting units, where the natural background radiation poses a challenge when measuring low-energy gamma rays.

The pros and cons of nucleic acid-amplified immunoassays—a comparative study on the quantitation of prostate-specific antigen with and without rolling circle amplification

Integrating isothermal nucleic acid amplification strategies into immunoassays can significantly decrease analytical limits of detection (LODs). On the other hand, an amplification step adds time, complication, reagents, and costs to the assay format. To evaluate the pros and cons in the context of heterogeneous multistep immunoassays, we quantified prostate-specific antigen (PSA) with and without rolling circle amplification (RCA). In addition, we compared time-gated (TG) with continuous-wave (CW) photoluminescence (PL) detection using a terbium complex and a fluorescein dye, respectively. For both direct (non-amplified) and amplified assays, TG PL detection provided circa four- to eightfold lower LODs, illustrating the importance of autofluorescence background suppression even for multi-wash assay formats. Amplified assays required an approximately 2.4 h longer assay time but led to almost 100-fold lower LODs down to 1.3 pg/mL of PSA. Implementation of TG-FRET (using a Tb-Cy5.5 donor–acceptor pair) into the RCA immunoassay resulted in a slightly higher LOD (3.0 pg/mL), but the ratiometric detection format provided important benefits, such as higher reproducibility, lower standard deviations, and multiplexing capability. Overall, our direct comparison demonstrated the importance of biological background suppression even in heterogeneous assays and the potential of using isothermal RCA for strongly decreasing analytical LODs, making such assays viable alternatives to conventional enzyme-linked immunosorbent assays (ELISAs).Graphical