Detection Index Research Articles

MSH-5: malignant knee tumors timely detection index

MSH-5: malignant knee tumors timely detection index

Fault identification, classification, and localization in microgrids using superimposed components and Wigner distribution function

The integration of Distributed Energy Resources (DERs) into distribution grids has become increasingly feasible and sustainable due to the development of microgrids. However, the development of an effective protection strategy remains a challenge in the implementation of microgrids. To address this challenge, this paper presents a simple and novel microgrid protection method based on superimposed components, Wigner distribution function (WDF) and alienation index-based. The proposed method develops a new fault detection index (FDI) by applying the alienation coefficient and WDF on a superimposed current signal to detect faulty events in the microgrid. The scheme is inherently phase segregated because the FDI is obtained for each phase individually. In addition, the proposed strategy introduces a new fault zone identification method based on the superimposed positive sequence reactive power (SPSQ). After obtaining the complete fault information, a relevant trip signal is generated to isolate the faulty section from the rest of the grid. The proposed methodology is evaluated through simulations using MATLAB/SIMULINK software. Various fault types, with varying parameters are simulated to validate the proposed approach. The results indicate that the proposed methodology is capable of recognizing, classifying, and locating all fault types in both grid-connected and islanded modes of operation.

Electrocortical correlates of attention differentiate individual capacity in associative learning

Associative learning abilities vary considerably among individuals, with attentional processes suggested to play a role in these variations. However, the relationship between attentional processes and individual differences in associative learning remains unclear, and whether these variations reflect in event-related potentials (ERPs) is unknown. This study aimed to investigate the relationship between attentional processes and associative learning by recording electrocortical activity of 38 young adults (18–32 years) during an associative learning task. Learning performance was assessed using the signal detection index d’. EEG topographic analyses and source localizations were applied to examine the neural correlates of attention and associative learning. Results revealed that better learning scores are associated with (1) topographic differences during early (126–148 ms) processing of the stimulus, coinciding with a P1 ERP component, which corresponded to a participation of the precuneus (BA 7), (2) topographic differences at 573–638 ms, overlapping with an increase of global field power at 530–600 ms, coinciding with a P3b ERP component and localized within the superior frontal gyrus (BA11) and (3) an increase of global field power at 322–507 ms, underlay by a stronger participation of the middle occipital gyrus (BA 19). These insights into the neural mechanisms underlying individual differences in associative learning suggest that better learners engage attentional processes more efficiently than weaker learners, making more resources available and displaying increased functional activity in areas involved in early attentional processes (BA7) and decision-making processes (BA11) during an associative learning task. This highlights the crucial role of attentional mechanisms in individual learning variability.

Unveiling dynamics changes: Singular spectrum analysis-based method for detecting concept drift in industrial data streams

Industrial data streams frequently experience concept drifts. Current drift detection methods, focusing on prediction performance or data distribution, often neglect temporal dependencies and require prior distribution assumptions. These limitations have catalyzed the development of dynamic theory-based approaches that identify drifts by discerning variations in data dynamics. However, the majority of these exhibit high complexity in dynamics characterization and still underperform in industrial drift detection. To overcome these challenges, we propose a singular spectrum analysis-based drift detection method for industry data streams, comprising three modules: common dynamics extraction, noise-robust drift detection, and threshold adaptation. Our approach uses singular spectrum analysis (SSA) as the dynamics representation method instead of traditional high-dimensional phase space embedding and Fourier transform. SSA allows for the efficient description of principal dynamics by intelligently mixing the Fourier basis functions according to given data. The first two modules are specifically engineered to address the common dynamics and stochastic noise inherent in industrial data streams through a well-designed online common dynamics extraction algorithm and a noise-robust detection index respectively. Threshold adaptation module provides a more reasonable way of updating the drift threshold based on the extreme value theory, ensuring global consistency for parameter estimation of extreme value distribution. Our proposed method was tested on simulated examples and a real-world sintering process, exhibiting enhanced drift detection performance.

Early Detection of Rice Leaf Blast Disease Using Unmanned Aerial Vehicle Remote Sensing: A Novel Approach Integrating a New Spectral Vegetation Index and Machine Learning

Leaf blast is recognized as one of the most devastating diseases affecting rice production in the world, seriously threatening rice yield. Therefore, early detection of leaf blast is extremely important to limit the spread and propagation of the disease. In this study, a leaf blast-specific spectral vegetation index RBVI = 9.78R816−R724 − 2.08(ρ736/R724) was designed to qualitatively detect the level of leaf blast disease in the canopy of a field and to improve the accuracy of early detection of leaf blast by remote sensing by unmanned aerial vehicle. Stacking integrated learning, AdaBoost, and SVM were used to compare and analyze the performance of the RBVI and traditional vegetation index for early detection of leaf blast. The results showed that the stacking model constructed based on the RBVI spectral index had the highest detection accuracy (OA: 95.9%, Kappa: 93.8%). Compared to stacking, the detection accuracy of the SVM and AdaBoost models constructed based on the RBVI is slightly degraded. Compared with conventional SVIs, the RBVI had higher accuracy in its ability to qualitatively detect leaf blast in the field. The leaf blast-specific spectral index RBVI proposed in this study can more effectively improve the accuracy of UAV remote sensing for early detection of rice leaf blast in the field and make up for the shortcomings of UAV hyperspectral detection, which is susceptible to interference by environmental factors. The results of this study can provide a simple and effective method for field management and timely control of the disease.

Industrial defect detection and location based on greedy membrane clustering algorithm

This paper introduces a related model of membrane calculation in the defect detection and positioning of industrial components. It has the characteristics of distributed and parallel computing, and can efficiently search for better solutions in a given feature space. Inspired by the membrane clustering algorithm, this paper proposes a greedy membrane clustering algorithm and names it GMCA. GMCA is applied after the extraction of local features of normal samples. It uses a greedy strategy to construct a sub-feature set that describes the local characteristics of normal samples. During training, GMCA can learn the membrane cluster center of normal image blocks and each sub-feature within the cluster. At test time, the anomaly map is obtained by calculating the distance from the test sample block to the corresponding cluster center and the maximum distance from the cluster center to the nearest neighbor in the training sample. This solves the limitation of traditional algorithms requiring dataset alignment. In the unsupervised dataset MvTec AD, samples can be divided into object categories and texture categories according to the background of images. The pixel-level anomaly location index (AUROC) of this method on object category data reaches 98.3%. The image-level anomaly detection index (AUROC) on texture category data reaches 99.1%.

Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks

High-voltage direct current (HVDC) transmission is preferred over high-voltage alternating current (HVAC) for long power lines for asynchronous power grid interconnection and high-level renewable energy integration. The control and protection functions associated with HVDC systems help with fast and secure clearance of faults. The control and protection challenges in the embedded HVDC network are of great concern for the stable and secure operation of an HVDC network. The DC fault current may reach an extremely high level in a rather short period because of the low impedance in a DC system, which is dangerous for converters, and disturbances in the AC network directly influence the performance of the HVDC system. Sometimes, faults on the AC side may lead to disconnection or failure of the DC link, causing reliability problems as well as huge economic losses. AC and DC protection solutions are being developed for HVDC systems to enhance their sustainability and reliability. As such, AC and DC faults should be detected and cleared at a faster rate. Therefore, in this article, the feasibility of the synchro-squeezed transform (SST) is analyzed for detection purposes. For more accurate and faster detection, the signal is first decomposed using the empirical mode decomposition (EMD) technique, and then the SST is applied. A discrete Teager energy (DTE) spectrum is obtained with the processed signal, which works as the detection index. The algorithm shows low sampling frequency requirements, with higher efficiency and reliability for the purpose. PSCAD/EMTDC version 4.6 software and MATLAB 2022a software is used for the modeling and simulation.

Egg Yolk Selenopeptides: Preparation, Characterization, and Immunomodulatory Activity.

In this study, egg yolk selenium peptides (Se-EYP) were prepared using double-enzyme hydrolysis combined with a shearing pretreatment. The properties of the selenopeptides formed were then characterized, including their yield, composition, molecular weight distribution, antioxidant activity, in vitro digestion, and immunomodulatory activity. The peptide yield obtained after enzymatic hydrolysis using a combination of alkaline protease and neutral protease was 74.5%, of which 82.6% had a molecular weight <1000 Da. The selenium content of the lyophilized solid product was 4.01 μg/g. Chromatography-mass spectrometry analysis showed that 88.6% of selenium in Se-EYP was in the organic form, of which SeMet accounted for 60.3%, SeCys2 for 21.8%, and MeSeCys for 17.9%. After being exposed to in vitro simulated digestion, Se-EYP still had 65.1% of oligopeptides present, and the in vitro antioxidant activity was enhanced. Moreover, Se-EYP exhibited superior immune detection indices, including immune organ index, level of immune factors in the serum, histopathological changes in the spleen, and selenium content in the liver. Our results suggest that Se-EYP may be used as selenium-enriched ingredients in functional food products.

Automatic Fault Detection Method of Rotating Machinery Bearing Based on Complexity Analysis

In order to improve the working condition stability of rotating machinery bearings, an automatic fault detection method of rotating machinery bearings based on complexity analysis is proposed. The parameters of rotating machinery bearings are detected by using discrete sensor detection method, and the fault characteristics of rotating machinery bearings are extracted by fault type parameter extraction method. The complexity of rotating machinery bearings is detected by wavelet transform and fuzzy logic analysis. Considering the load mutation, noise disturbance, control strategy and other factors of rotating machinery bearings, the fault diagnosis and feature analysis model of rotating machinery bearings is established. The clustering and feature detection of rotating machinery bearings’ fault types are realized by the method of discrete complexity fusion clustering. Based on adaptive feedback compensation and parameter estimation, the output diagnostic error is taken as the fault detection index parameter, and the feature estimation in normal state and fault state is realized by harmonic level detection, so as to realize automatic fault detection of rotating machinery bearings. The simulation results show that this method has high accuracy and small deviation in fault detection of rotating machinery bearings, which improves the ability of automatic fault diagnosis.

ROC curve measurements using euro-rack modules (and some DIY)

Acoustic signal processing projects encourage DIY electronics as well as hardware designed to expose principles found either in nature or from man-made sources. Searching within the Eurorack format of the modular synthesis community, functional equivalents to more common DIY units are identified. Some signal generators and measurements are still underrepresented commercially and require DIY Eurorack modules to be built. One goal is to use existing Eurorack components where DIY modules are designed as necessary in this format. A Receiver Operating Characteristic curve (ROC) is calculated for three cases using analog energy detection and probability density function (pdf) circuitry. The hypothesis for noise (N) is a fixed Gaussian noise distribution. The three hypotheses for the signal plus noise (S+N) have signals characterized as; a constant sinusoid with random phase, a broadband unknown Gaussian signal, and an unknown narrow band Gaussian signal utilizing a narrow band carrier frequency. Our classroom demonstration involves two loudspeakers, one for noise, the other for signal. The detected microphone’s voltage is bandpass filtered, amplified, squared, integrated and then fed into the pdf circuitry where pulses are counted for 4 seconds in each voltage window. Increasing the signal amplitude will increase the detection index.

A clustering effectiveness measurement model based on merging similar clusters.

This article presents a clustering effectiveness measurement model based on merging similar clusters to address the problems experienced by the affinity propagation (AP) algorithm in the clustering process, such as excessive local clustering, low accuracy, and invalid clustering evaluation results that occur due to the lack of variety in some internal evaluation indices when the proportion of clusters is very high. First, depending upon the "rough clustering" process of the AP clustering algorithm, similar clusters are merged according to the relationship between the similarity between any two clusters and the average inter-cluster similarity in the entire sample set to decrease the maximum number of clusters Kmax. Then, a new scheme is proposed to calculate intra-cluster compactness, inter-cluster relative density, and inter-cluster overlap coefficient. On the basis of this new method, several internal evaluation indices based on intra-cluster cohesion and inter-cluster dispersion are designed. Results of experiments show that the proposed model can perform clustering and classification correctly and provide accurate ranges for clustering using public UCI and NSL-KDD datasets, and it is significantly superior to the three improved clustering algorithms compared with it in terms of intrusion detection indices such as detection rate and false positive rate (FPR).

A CT deep learning reconstruction algorithm: Image quality evaluation for brain protocol at decreasing dose indexes in comparison with FBP and statistical iterative reconstruction algorithms

PurposeTo characterise the impact of Precise Image (PI) deep learning reconstruction algorithm on image quality, compared to filtered back-projection (FBP) and iDose4 iterative reconstruction for brain computed tomography (CT) phantom images. MethodsCatphan-600 phantom was acquired with an Incisive CT scanner using a dedicated brain protocol, at six different dose levels (volume computed tomography dose index (CTDIvol): 7/14/29/49/56/67 mGy). Images were reconstructed using FBP, levels 2/5 of iDose4, and PI algorithm (Sharper/Sharp/Standard/Smooth/Smoother). Image quality was assessed by evaluating CT numbers, image histograms, noise, image non-uniformity (NU), noise power spectrum, target transfer function, and detectability index. ResultsThe five PI levels did not significantly affect the mean CT number. For a given CTDIvol using Sharper-to-Smoother levels, the spatial resolution for all the investigated materials and the detectability index increased while the noise magnitude decreased, slightly affecting noise texture. For a fixed PI level increasing the CTDIvol the detectability index increased, the noise magnitude decreased. From 29 mGy, NU values converged within 1 Hounsfield Unit from each other without a substantial improvement at higher CTDIvol values. ConclusionsThe improved performances of intermediate PI levels in brain protocols compared to conventional algorithms seem to suggest a potential reduction of CTDIvol.

Misfire Detection Index Distinguishing the Difference of the Engine Angular Acceleration Between Two Specified Teeth of the Sensor Wheel

Misfire Detection Index Distinguishing the Difference of the Engine Angular Acceleration Between Two Specified Teeth of the Sensor Wheel

An electrochemical impedance spectroscopy study on ion-fouling of forward osmosis membranes

Forward osmosis (FO) has been rapidly developed and employed in the industrial and environmental wastewater treatment, whose performance is affected profoundly by membrane fouling. Electrochemical impedance spectroscopy (EIS), as a nondestructive and low-cost evaluation technique, has been increasingly used for the characterization of membrane structures and membrane fouling. However, monitoring ion fouling of FO membranes is difficult with EIS due to the weak signal. In this work, the ion-fouling of various FO membranes was successfully characterized by EIS and the inductance variation was firstly utilized to interpret the degree of FO membrane fouling. Multi-walled carbon nanotubes (MWCNTs) and polydopamine (PDA) were modified onto the thin film nanofibrous composite membrane (#FO-0) as anti-fouling modification (#FO-MWCNTs and #FO-PDA), respectively. The structural measurements of #FO-0, #FO-MWCNTs, and #FO-PDA were achieved by a two-electrode EIS system. Then the ion fouling of FO membranes was monitored by a four-electrode EIS system. Through the equivalent circuit fitting, the correlation between the inductance variation and the membrane fouling was established. The FO experiments further validated the effectiveness of the proposed EIS method. The results indicate that inductance as a detection index is expected to be a convenient tool for studying the ion-fouling phenomenon in the FO process.

Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study.

To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms. An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions. NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy. DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'.

Thin slice photon-counting CT coronary angiography compared to conventional CT: Objective image quality and clinical radiation dose assessment.

Photon-counting CT (PCCT) is the next-generation CT scanner that enables improved spatial resolution and spectral imaging. For full spectral processing, higher tube voltages compared to conventional CT are necessary to achieve the required spectral separation. This generated interest in the potential influence of thin slice high tube voltage PCCT on overall image quality and consequently on radiation dose. This study first evaluated tube voltages and radiation doses applied in patients who underwent coronary CT angiography with PCCT and energy-integrating detector CT (EID-CT). Next, image quality of PCCT and EID-CT was objectively evaluated in a phantom study simulating different patient sizes at these tube voltages and radiation doses. We conducted a retrospective analysis of clinical doses of patients scanned on a conventional and PCCT system. Average patient water equivalent diameters for different tube voltages were extracted from the dose reports for both EID-CT and PCCT. A conical phantom made of polyethylene with multiple diameters (26/31/36cm) representing different patient sizes and containing an iodine insert was scanned with a EID-CT scanner using tube voltages and phantom diameters that match the patient scans and characteristics. Next, phantom scans were made with PCCT at a fixed tube voltage of 120kV and with CTDIVOL values and phantom diameters identical to the EID-CT scans. Clinical image reconstructions at 0.6mm slice thickness for conventional CT were compared to PCCT images with 0.4mm slice thickness. Image quality was quantified using the detectability index (d'), which estimated the visibility of a 3mm diameter contrast-enhanced coronary artery by considering noise, contrast, resolution, and human visual perception. Alongside d', noise, contrast and resolution were also individually assessed. In addition, the influence of various kernels (Bv40/Bv44/Bv48/Bv56), quantum iterative reconstruction strengths (QIR, 3/4) and monoenergetic levels (40/45/50/55keV) for PCCT on d' was investigated. In this study, 143 patients were included: 47 were scanned on PCCT (120kV) and the remaining on EID-CT (74 small-sized at 70kV, 18 medium-sized at 80kV and four large-sized at 90kV). EID-CT showed 7%-17% higher d' than PCCT with Bv40 kernel and strength four for small/medium patients. Lower monoenergetic images (40keV) helped mitigate the difference to 1%-6%. For large patients, PCCT's detectability was up to 31% higher than EID-CT. PCCT has thinner slices but similar noise levels for similar reconstruction parameters. The noise increased with lower keV levels in PCCT (≈30% increase), but higher QIR strengths reduced noise. PCCT's iodine contrast was stable across patient sizes, while EID-CT had 33% less contrast in large patients than in small-sized patients. At 120kV, thin slice PCCT enables CCTA in phantom scans representing large patients without raising radiation dose or affecting vessel detectability. However, higher doses are needed for small and medium-sized patients to obtain a similar image quality as in EID-CT. The alternative of using lower mono-energetic levels requires further evaluation in clinical practice.

Exploration of PCL-5 symptom validity indices for detection of exaggerated and feigned PTSD

ABSTRACT Introduction There are very few symptom validity indices directly examining overreported posttraumatic stress disorder (PTSD) symptomatology, and, until recently, there were no symptom validity indices embedded within the PTSD Checklist for the DSM-5 (PCL-5), which is one of the most commonly used PTSD measures. Given this, the current study sought to develop and cross-validate symptom validity indices for the PCL-5. Method Multiple criterion groups comprised of Veteran patients were utilized (N = 210). Patients were determined to be valid or invalid responders based on Personality Asessment Inventory symptom validity indices. Three PCL-5 symptom validity indices were then examined: the PCL-5 Symptom Severity scale (PSS), the PCL-5 Extreme Symptom scale (PES), and the PCL-5 Rare Items scale (PRI). Results Area under the curve statistics ranged from .78 to .85. The PSS and PES both met classification accuracy statistic goals, with the PES achieving the highest sensitivity rate (.39) when maintaining specificity at .90 or above across all criterion groups. When an ad hoc analysis was performed, which included only patients with exceptionally strong evidence of invalidity, sensitivity rates increased to .60 for the PES while maintaining specificity at .90. Conclusions These findings provide preliminary support for new PTSD symptom validity indices embedded within one of the most frequently used PTSD measures.

Automatic image quality evaluation in digital radiography using for-processing and for-presentation images.

To investigate the impact of digital image post-processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions. A custom-made phantom constructed according to the instructions given in the IAEA Human Health Series No.39 publication was used, along with the respective software that automatically calculates various IQ metrics. Images with various exposure parameters were acquired with a digital radiography unit, which for each acquisition produces two images: one for-processing (raw) and one for-presentation (clinical). Various examination protocols were used, which incorporate diverse post-processing algorithms. The IQ metrics' values (IQ-scores) obtained were analyzed to investigate the effects of increasing incident air kerma (IAK) on the image receptor, tube potential (kVp), additional filtration, and examination protocol on image quality, and the differences between image type (raw or clinical). The IQ-scores were consistent for repeated identical exposures for both raw and clinical images. The effect that changes in exposure parameters and examination protocol had on IQ-scores were different depending on the IQ metric and image type. The expected positive effect that increasing IAK and decreasing tube potential should have on IQ was clearly exhibited in two IQ metrics only, the signal difference-to-noise-ratio (SDNR) and the detectability index (d'), for both image types. No effect of additional filtration on any of the IQ metrics was detected on images of either type. An interesting finding of the study was that for all different image acquisition selections the d' scores were larger in raw images, whereas the other IQ metrics were larger in clinical images for most of the cases. Since IQ-scores of raw and their respective clinical images may be largely different, the same type of image should be consistently used for monitoring IQ constancy and when results from different X-ray systems are compared.

Channelized hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom: Part II. Repeatability study.

Objective and quantitative evaluation for low-contrast detectability that correlates with human observer performance is lacking for routine CT quality control testing. Channelized Hotelling observer (CHO) is considered a strong candidate to fill the need but has long been deemed impractical to implement due to its requirement of a large number of repeated scans in order to provide accurate and precise estimates of index of detectability (d'). In our previous work, we optimized a CHO model observer on the American College of Radiology (ACR) CT accreditation phantom and achieved accurate measurement of d' with only 1-3 repeat scans. In this work, we aim to validate the repeatability of the proposed CHO-based low-contrast evaluation on four scanner models using the ACR CT accreditation phantom. The repeatability test was performed on four different scanners from two major CT manufacturers: Siemens Force and Alpha; Canon Prism and Prime SP. An ACR CT phantom was scanned 10 times, each time after repositioning of the phantom. For each repositioning, 3 repeated scans were acquired at 24, 12, and 6 mGy on all four scanner models. CHO was applied at the measured dose levels for different low-contrast object sizes (4-6mm). The CHO was also applied to images created using deep learning-based reconstructions on Canon Prism and to four different scan/reconstruction modes on the Siemens Alpha, a photon-counting-detector (PCD)-CT. The repeatability was evaluated by the probability that a measurement would fall within the±15% tolerance (P<15% ). With the CHO setting optimized for the ACR phantom and the use of 3 repeated scans and 9 non-overlapping slices per scan, the CHO measurement could provide high repeatability with P<15% of 98.8%-99.9% at 12 mGy with IR reconstruction on all four scanners. On scanner A, P<15% were 91.5%-99.9% at the three dose levels and for all three object sizes while the numbers were 93.6%-99.998% on scanner B. P<15% were 96.5%-97.2% for the two deep learning reconstructions and 97.0%-99.97% for the four scan/reconstruction modes on the PCD-CT. The CHO provided highly repeatable measurements with over 95% probability that a CHO measurement would lie within the±15% tolerance for most of the dose levels and object sizes on the ACR phantom. The repeatability was maintained when the CHO was applied to images created with a commercial deep learning-based reconstruction and various scan/reconstruction modes on a PCD-CT. This study demonstrates that practical implementation of CHO for routine quality control and performance evaluation is feasible.

Grating-based x-ray dark-field CT for lung cancer diagnosis in mice

BackgroundThe low absorption of x-rays in lung tissue and the poor resolution of conventional computed tomography (CT) limits its use to detect lung disease. However, x-ray dark-field imaging can sense the scattered x-rays deflected by the structures being imaged. This technique can facilitate the detection of small alveolar lesions that would be difficult to detect with conventional CT. Therefore, it may provide an alternative imaging modality to diagnose lung disease at an early stage.MethodsEight mice were inoculated with lung cancers simultaneously. Each time two mice were scanned using a grating-based dark-field CT on days 4, 8, 12, and 16 after the introduction of the cancer cells. The detectability index was calculated between nodules and healthy parenchyma for both attenuation and dark-field modalities. High-resolution micro-CT and pathological examinations were used to crosscheck and validate our results. Paired t-test was used for comparing the ability of dark-field and attenuation modalities in pulmonary nodule detection.ResultsThe nodules were shown as a signal decrease in the dark-field modality and a signal increase in the attenuation modality. The number of nodules increased from day 8 to day 16, indicating disease progression. The detectability indices of dark-field modality were higher than those of attenuation modality (p = 0.025).ConclusionsCompared with the standard attenuation CT, the dark-field CT improved the detection of lung nodules.Relevance statementDark-field CT has a higher detectability index than conventional attenuation CT in lung nodule detection. This technique could improve the early diagnosis of lung cancer.Key points• Lung cancer progression was observed using x-ray dark-field CT.• Dark-field modality complements with attenuation modality in lung nodule detection.• Dark-field modality showed a detectability index higher than that attenuation in nodule detection.Graphical