Scatter Correction Research Articles

Discordance between 90Y-PET/CT(MR)-estimated activity and dose calibrator measured administered activity: an international study in SIRT patients treated with resin and glass microspheres

PurposeTherapeutic administration of 90Y-loaded microspheres is routinely used for primary and secondary liver tumours. For activity-based therapeutic prescription the activity must be within 10% of the intended activity. Previous studies reported significant discrepancies between manufacturer-declared vial activities and both experimental and Monte-Carlo assessments, greater than 10%, for resin/glass 90Y-microspheres. The objective of this work was to investigate whether these discrepancies were also seen in patients.MethodsWe analysed patient 90Y-PET reconstructions (99 glass and 15 resin microspheres) from 4 different institutions and 4 different systems. We considered tail-fitting background scaling (TFBS) and absolute scaling (ABS), for scatter correction. Residuals after therapeutic injection were measured. Eighty-one patients were imaged with PET/CT and 33 with PET/MR. The PET measured activity (APET) was assessed in the whole liver. The ratio APET/Acalibrator was calculated for each patient, where Acalibrator was the injected activity measured by the dose calibrator corrected for residual and lung shunt.ResultsQuantification ratio between calibrators and PET was significantly different from 1, regardless of the scatter correction used. In glass microspheres, the mean APET/CT/Acalibrator was 0.84 ± 0.06 for TFBS and 0.90 ± 0.06 for ABS (0.66 ± 0.09 and 0.76 ± 0.07 for (APET/MR/Acalibrator)). The mean APET/CT/Acalibrator ratio for resin microspheres was 1.16 ± 0.09 for TFBS and 1.30 ± 0.12 for ABS.ConclusionsWe observed in patients similar activity discrepancies as reported for vials, with a relative difference of 44 ± 16% between glass and resin 90Y-loaded microspheres. In 90Y hepatic radioembolization, the 10% accuracy prerequisite on knowing the administered therapeutic activity is then unlikely to be met.

Machine learning driven benchtop Vis/NIR spectroscopy for online detection of hybrid citrus quality.

The aim of this study was to explore application of visible and near-infrared (Vis/NIR) spectroscopy combined with machine learning models for SSC and TA prediction of hybrid citrus. The Vis/NIR spectra of samples including navel-region, equator-region and multi-region combination spectra in navel-region and equator-region were collected using a benchtop equipment. The performance of SSC and TA prediction models with different region spectra, including partial least squares (PLS), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM) and multilayer feedforward neural network (MFNN), was assessed. The accuracy of SSC and TA prediction models with multi-region combination (raw) spectra was better compared to navel-region and equator-region, suggesting that multi-region combination spectra collection method was more suitable. Subsequently, the spectral pre-processing, including Savitzky-Golay smoothing (SGS), maximum normalization (MN), multiplicative scatter correction (MSC), linear baseline correction (LBC) and first derivative (1stD), were performed. The performance of SSC and TA prediction models with different pre-processing spectra was further compared. The PLS with SGS spectra (SGS-PLS) and MFNN with raw spectra (Raw-MFNN) exhibited superior validation effects for SSC and TA prediction, respectively. In a subsequent prediction in new samples, SGS-PLS achieved an RP2 of 0.875, an RMSEP of 0.572% and a MAEP of 0.469% for SSC prediction, and Raw-MFNN achieved an RP2 of 0.800, an RMSEP of 0.0322% and a MAEP of 0.0249% for TA prediction, indicating excellent generalization ability. These results indicate the great potential of benchtop Vis/NIR spectroscopy for online detection of hybrid citrus quality at mass-scale level.

Refining thyroid function evaluation: a comparative study of preprocessing methods in diffuse reflectance spectroscopy

Thyroid dysfunction, comprising conditions such as hyperthyroidism and hypothyroidism, represents a substantial global health challenge, necessitating timely and precise diagnosis for effective therapeutic intervention and patient welfare. Conventional diagnostic modalities often involve invasive procedures, that could cause discomfort and inconvenience for individuals. The non-invasive techniques like diffuse reflectance spectroscopy (DRS) can offer a promising alternative. This study underscores the critical role of preprocessing methods in enhancing the accuracy of thyroid hormone functionality through a non-invasive approach. In the proposed study the spectral data acquired from the DRS setup are subjected to different preprocessing techniques to improve the efficacy of the prediction model. Thirty individuals with thyroid dysfunction were included in the study, and preprocessing methods such as baseline correction, multiplicative scatter correction (MSC), and standard normal variate (SNV), were systematically evaluated. The study highlights that SNV preprocessing outperformed other methods with a root mean square error (RMSE) of 0.005 and an R² of 0.99. In contrast, MSC resulted in an RMSE of 0.87 and an R² of 0.86, while baseline correction showed a RMSE of 0.84 and an unusual R² of 1.09, indicating potential issues. SNV proved to be the most effective technique.

A dual-domain network with division residual connection and feature fusion for CBCT scatter correction.

This study aims to propose a dual-domain network that not only reduces scatter artifacts but also retains structure details in CBCT. The proposed network comprises a projection-domain sub-network and an image-domain sub-network. The projection-domain sub-network utilizes a division residual network to amplify the difference between scatter signals and imaging signals, facilitating the learning of scatter signals. The image-domain sub-network contains dual encoders and a single decoder. The dual encoders extract features from two inputs parallelly, and the decoder fuses the extracted features from the two encoders and maps the fused features back to the final high-quality image. Of the two input images to the image-domain sub-network, one is the scatter-contaminated image analytically reconstructed from the scatter-contaminated projections, and the other is the pre-processed image reconstructed from the pre-processed projections produced by the projection-domain sub-network. Experimental results on both synthetic and real data demonstrate that our method can effectively reduce scatter artifacts and restore image details. Quantitative analysis using synthetic data shows the mean absolute error (MAE) was reduced by 74% and peak signal-to-noise ratio (PSNR) increased by 57% compared to the scatter-contaminated ones. Testing on real data found a 38% increase in contrast-to-noise ratio (CNR) with our method compared to the scatter-contaminated image. Additionally, our method consistently outperforms comparative methods such as U-Net, DSE-Net, RDCNN and the collimator-based method. A dual-domain network that leverages projection-domain division residual connection and image-domain feature fusion has been proposed for CBCT scatter correction. It has potential applications for reducing scatter artifacts and preserving image details in CBCT.

Feasibility study on fingerprinting organic and conventional mango fruits, chips, and juice using portable near-infrared spectroscopy.

This research examined the distinction between organic and conventional mango fruits, chips, and juice using portable near-infrared (NIR) spectroscopy. A comprehensive analysis was conducted on a sample of 100 mangoes (comprising 50 organic and 50 conventional) utilising a portable NIR spectrometer that spans a wavelength range from 900 to 1700 nm. The mangoes were assessed in their entirety and their juice and chip forms. The spectral data underwent pre-processing through methodologies such as multiplicative scatter correction (MSC), standard normal variate (SNV), and derivatives to enhance the precision of the models. Principal component analysis (PCA) and various multivariate classification algorithms, including linear discriminant analysis (LDA), random forest (RF), k-nearest neighbors (kNN), and partial least squares discriminant analysis (PLSDA), were utilised to categorise the samples effectively. The findings indicated that the random forest method and specific pre-processing techniques achieved the highest classification accuracy for distinguishing organic and conventional mango products. For mango fruit and chips, it achieved 88.76% and 77.98% accuracy, respectively, when pre-processed using the second derivative, while for juice, it achieved 87.53% accuracy without pre-processing. This investigation demonstrates the efficacy of portable NIR spectroscopy as a dependable and non-invasive method for verifying organic mango products, thereby enhancing the integrity of food labelling and fostering consumer confidence.

A critique of multiplicative scatter correction pre-treatment of NIR spectra

Multiplicative scatter correction (MSC) pre-treatment of NIR spectra adds spectral trend deviations from its average value to the spectral set average (inversely interpolated smooth estimate). This means any scatter components from the original spectrum are carried over into the calculated new MSC spectrum. Use of the ordinary least squares regression method can lead to slope attenuation.

Classification of grassland conditions using a hyperspectral camera and deep learning

ABSTRACT Grassland is the most widespread vegetation type in terrestrial ecosystem, but it has been threatened by degradation in recent years. Developing an operational species detection model is necessary for achieving grassland monitoring and making management plans. Therefore, this study aims to quickly and accurately classify grassland species based on hyperspectral imaging (HSI) technology and deep learning algorithms. In the present study, 16,200 hyperspectral data are collected from grassland samples over a period of 3 years using a hyperspectral imager, with a wavelength range of 400–1000 nm. Second, the hyperspectral data are preprocessed by the multiple scatter correction and mean normalization, improving the quality of input data and thereby enhancing modelling capabilities. Finally, four models are established, including temporal convolutional neural network (TempCNN), recurrent neural network with long short-term memory (LSTM-RNN), Transformer, and support vector machines (SVM). The results show that the preprocessed data have stronger modelling ability than the original data, and the classification performance of the model is ranked in descending order as Transformer, LSTM-RNN, TempCNN, and SVM. Among them, the classification performance of Medicago sativa L. in the TempCNN is superior to other combinations. The LSTM-RNN achieved accuracy of 1 for Agropyron cristatum var. pectinatum and Leymus chinensis (Trin.) Tzvel. The accuracy of both the Transformer and SVM for Leymus chinensis (Trin.) Tzvel. and Lotus corniculatus L. is 1. The results indicate the effectiveness and robustness of the proposed hyperspectral imaging technology combined with deep learning model, which has well classification performance for specific forage varieties.

Development of an image quality evaluation system for bedside chest X-ray images using scatter correction processing.

In plain radiography, scattered X-ray correction processing (Virtual Grid: VG) is used to estimate and correct scattered rays in images. We developed an objective evaluation system for bedside chest X-ray images using VG and investigated its usefulness. First, we trained the blind/referenceless image spatial quality evaluator (BRISQUE) on 200 images obtained by portable chest radiography. We then evaluated optimal chest phantom VG images as well as those that deviated from the VG setting conditions using BRISQUE. Furthermore, we conducted a subjective evaluation using the mean opinion score (MOS) and established an objective evaluation system for VG images. Finally, the degree of agreement between the MOS subjectively evaluated by 14 radiological technologists and that determined by the objective evaluation system for 100 clinical images obtained by portable chest radiography was calculated using Cohen's kappa coefficient. The correlation coefficient between the BRISQUE score and MOS for chest phantom images was - 0.96 (p < 0.05). The two scores showed a very high linear correlation, indicating the potential of the BRISQUE score as an alternative to MOS. The Cohen's kappa coefficient for the objective evaluation system using the optimal conversion table was 0.42. Conversely, there was a very high detection rate of 82.86% for poor-quality images. An objective evaluation system for bedside chest X-ray images using VG that uses no-reference image quality evaluation helps provide proper image quality. Furthermore, such a system can be constructed with a small amount of training data, which increases the possibility of introducing it to a variety of facilities.

Advanced analytical methods for multi-spectral transmission imaging optimization: enhancing breast tissue heterogeneity detection and tumor screening with hybrid image processing and deep learning.

Light sources exhibit significant absorption and scattering effects during the transmission through biological tissues, posing challenges in identifying heterogeneities in multi-spectral images. This paper introduces a fusion of techniques encompassing the spatial pyramid matching model (SPM), modulation and demodulation (M_D), and frame accumulation (FA). These techniques not only elevate image quality but also augment the precision of heterogeneous classification in multi-spectral transmission images (MTI) within deep learning network models (DLNM). Initially, experiments are designed to capture MTI of phantoms. Subsequently, the images are preprocessed separately through a combination of different techniques such as SPM, M_D and FA. Ultimately, multi-spectral fusion pseudo-color images derived from U-Net semantic segmentation are fed into VGG16/19 and ResNet50/101 networks for heterogeneous classification. Among them, different combinations of SPM, M_D and FA significantly enhance the quality of images, facilitating the extraction of heterogeneous feature information from multi-spectral images. In comparison to the classification accuracy achieved in the original image VGG and ResNet network models, all images after preprocessing effectively improved the classification accuracy of heterogeneities. Following scatter correction, images processed with 3.5 Hz modulation-demodulation combined with frame accumulation (M_D-FA) attain the highest classification accuracy for heterogeneities in the VGG19 and ResNet101 models, achieving accuracies of 95.47% and 98.47%, respectively. In conclusion, this paper utilizes different combinations of SPM, M_D and FA techniques to not only enhance the quality of images but also further improve the accuracy of DLNM in heterogeneous classification, which will promote the clinical application of MTI technique in breast tumor screening.

Effects of Simulated Degradation Conditions on the Authentication of Fresh Quail Eggs Using a Miniaturized NIR Spectrometer and DD-SIMCA

This study developed a reliable and non-destructive analytical method for quality control of quail eggs under simulated degradation conditions, including temperature variations and UV radiation exposure, using a miniaturized near-infrared (NIR) spectrometer combined with Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA). A total of 336 quail egg samples were evaluated, with 90 samples stored under optimal conditions for up to 14 days, while the remaining 246 samples underwent controlled storage at temperatures of 30 and 40 °C for 24, 48, and 72 h, and were exposed to UV irradiation for 2, 2.5, 3, 3.5, and 4 h. DD-SIMCA models utilizing NIR spectra pre-processed with multiplicative scatter correction (MSC) and Savitzky-Golay first derivative (SGD) achieved remarkable sensitivity and specificity of 100% in the test set, demonstrating an effective and low-cost technology for ensuring the authenticity of fresh quail eggs.

Artificial intelligence with wrapper multioutput regression to identify chemical contents of fresh and dried manure samples using NIR spectroscopy.

Analyzing manure nutrients such as total ammonium nitrogen (NH+4), dry matter (DM), calcium oxide (CaO), total nitrogen (-N), phosphorus pentoxide (P2O5), magnesium oxide (MgO), and potassium oxide (K2O) helps in fulfilling crop nutritional needs while improving the profitability and a lower risk of pollutants. This study used two Near Infra Red (NIR) spectral datasets of fresh and dried manure. The freshly prepared NH4Cl, CaO, Ca(OH)2, P2O5, MgO, and K2O samples were used for spectral signature peak identification and calibration. The pre-processing methods, Savitzky-Golay (SG), Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV), were applied to correct the spectra and PCA to reduce the dimension of these pre-processed spectra. Several regressors and classifiers with wrapper multioutput regression algorithms (WMRA) and stacking generalization models were applied to identify the chemical contents. In the regression analysis, the WMRA achieved the highest coefficient of determination (R2) of 0.983 and RPD of 7.68 for fresh manure, while R2 of 0.949 and RPD of 4.42 for dried manure. In classification analysis, k-nearest neighbors (KNN) achieved 100.0% highest accuracy, while 98.1% 5-fold average accuracy. The overall findings of this study indicate that NIR, in conjunction with WMRA and KNN, has a high potential for quick detection of nutrients in manure.

Transfer Learning Estimation and Transferability of LNC and LMA Across Different Datasets

Leaf mass per area (LMA) and leaf nitrogen concentration (LNC) are both essential parameters in plant ecology, which can reflect the growth status of plants. The features of LMA and LNC can be captured using spectral reflectance in a remote sensing approach. While the relationships between spectra and leaf trait variance across different species with estimation performance are unclear, the development of assessment and transferable models to predicate LMA and LNC are prevented. Hence, we analyzed the variance of raw spectra and spectral data difference with four pretreated approaches (SG—Savitzky–Golay filter, SNV—Standard Normalized Variate, MSC—Multiplicative Scatter Correction analysis, and normalize), LMA, and LNC over six remote sensing datasets by a transfer component analysis (TCA) approach. Spectra combined with the Successive Projections Algorithm (SPA) were also presented to extract wavelengths with higher important coefficients to minimize the redundancy of datasets. The variance of normalized spectra between different datasets showed a minor degree of variance, and LNC spectra variance was decreased by the SPA. The results also showed that a smaller LMA and LNC variance is presented over different datasets when the trait values with higher distribution probabilities are close to each other. The LNC and LMA estimation performance in transfer models established by partial least squares regression (PLS), support vector regression (SVR), extreme gradient boosting (XGB), and random forest regression (RFR) algorithms across different datasets were employed, in which the RFR transfer models performed good prediction results. The relationships between spectra and leaf trait variance and estimation performance in RFR transfer models over different datasets were evaluated. LMA distance has a significant influence on estimation performance in the transfer model, and the variance of spectra with all pretreated approaches showed a very significant effect on LNC accession performance. Furthermore, we proposed a weight coefficient of spectral data updating combined with the TCA and RFR approach (WDT-RFR) transfer model to improve transferability between datasets and promote estimation performance in the transfer model. Compared to the RFR transfer model using spectra without updating, the root mean square error (RMSE) of the WDT-RFR transfer model with 5% samples transferred to estimate LMA and LNC increased by 7.9% and 4.8% on average, respectively. The estimation results showed that our transfer model showed a superior estimation performance.

Combination ATR-FTIR with Multiple Classification Algorithms for Authentication of the Four Medicinal Plants from Curcuma L. in Rhizomes and Tuberous Roots

Curcumae Longae Rhizoma (CLRh), Curcumae Radix (CRa), and Curcumae Rhizoma (CRh), derived from the different medicinal parts of the Curcuma species, are blood-activating analgesics commonly used for promoting blood circulation and relieving pain. Due to their certain similarities in chemical composition and pharmacological effects, these three herbs exhibit a high risk associated with mixing and indiscriminate use. The diverse methods used for distinguishing the medicinal origins are complex, time-consuming, and limited to intraspecific differentiation, which are not suitable for rapid and systematic identification. We developed a rapid analysis method for identification of affinis and different medicinal materials using attenuated total reflection-Fourier-transform infrared spectroscopy (ATR-FTIR) combined with machine learning algorithms. The original spectroscopic data were pretreated using derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and smoothing (S) methods. Among them, 1D + MSC + 13S emerged as the best pretreatment method. Then, t-distributed stochastic neighbor embedding (t-SNE) was applied to visualize the results, and seven kinds of classification models were constructed. The results showed that support vector machine (SVM) modeling was superior to other models and the accuracy of validation and prediction was preferable, with a modeling time of 127.76 s. The established method could be employed to rapidly and effectively distinguish the different origins and parts of Curcuma species and thus provides a technique for rapid quality evaluation of affinis species.

Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy.

Rapid and accurate detection of protein content is essential for ensuring the quality of maize. Near-infrared spectroscopy (NIR) technology faces limitations due to surface effects and sample homogeneity issues when measuring the protein content of whole maize grains. Focusing on maize grain powder can significantly improve the quality of data and the accuracy of model predictions. This study aims to explore a rapid detection method for protein content in maize grain powder based on near-infrared spectroscopy. A method for determining protein content in maize grain powder was established using near-infrared (NIR) reflectance spectra in the 940-1660 nm range. Various preprocessing techniques, including Savitzky-Golay (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), and the first derivative (1D), were employed to preprocess the raw spectral data. Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. Feature wavelengths were selected to enhance model accuracy further using the Successive Projections Algorithm (SPA) and Uninformative Variable Elimination (UVE). Experimental results indicated that the PLSR model, preprocessed with 1D + MSC, yielded the best performance, achieving a root mean square error of prediction (RMSEP) of 0.3 g/kg, a correlation coefficient (Rp) of 0.93, and a residual predictive deviation (RPD) of 3. The associated methods and theoretical foundation provide a scientific basis for the quality control and processing of maize.

基于高光谱成像的大豆籽粒含水率检测研究

Using hyperspectral imaging technology for rapid, non-destructive detection of soybean grain moisture content provides technical support for high-quality soybean harvesting. A total of 90 samples of soybean grains from different varieties were collected, with hyperspectral images acquired in the wavelength range of 900–1700 nm. The moisture content of each soybean grain sample was determined using the direct drying method as specified in GB 5009.3-2016. The samples were divided into a calibration set and a prediction set based on a 4:1 ratio using the sample partitioning method of Joint X-Y Distance. Eight preprocessing methods were applied to the raw spectral data, including baseline correction, moving average, Savitzky-Golay filtering, normalization, standard normal variate transformation, multiple scatter correction, first derivative, and deconvolution. Feature wavelengths were then extracted using the successive projections algorithm and the competitive adaptive reweighted sampling algorithm. Finally, a partial least squares regression model for predicting the moisture content of soybean grains was developed based on these feature wavelengths. The results show that the correlation coefficient and the root mean square error of the optimal model for the prediction set were 0.92 and 0.2371, respectively. The moisture spectrum inversion model can precisely and rapidly predict the moisture content of soybean grains non-destructively, thereby determining the timing of mechanical soybean harvesting and enhancing the quality of soybean harvesting, storage, and processing.

Model-based CBCT scatter correction with dual-layer flat-panel detector.

Recently, the popularity of dual-layer flat-panel detector (DL-FPD) based dual-energy cone-beam CT (CBCT) imaging has been increasing. However, the image quality of dual-energy CBCT remains constrained by the Compton scattered x-rayphotons. The objective of this study is to develop a novel scatter correction method, named e-Grid, for DL-FPD based CBCTimaging. In DL-FPD, a certain portion of the x-ray photons (mainly low-energy [LE] primary and scattered photons) passing through the object are captured by the top detector layer, while the remaining x-ray photons (mainly high-energy [HE] primary and scattered photons) are collected by the bottom detector layer. A linear signal model was approximated between the HE primary and scatter signals and the LE primary and scatter signals. Physical calibration experiments were performed on cone beam and fan beam to validate the aforementioned signal model via linear fittings. Monte Carlo (MC) simulations of a 10 cm diameter water phantom were conducted on GATE at first to verify this newly developed scatter estimation method. In addition, physical validation experiments of water phantom, head phantom, and abdominal phantom were carried out on a DL-FPD based benchtop CBCT imaging system. The image non-uniformity (NU), which represents the relative difference between the center and the edges of CT images, was measured to quantify the reduction of image shading artifacts. Finally, multi-material decomposition wasconducted. The MC results, CBCT images and line profiles, showed that the newly proposed e-Grid approach was able to accurately predict the scatter distributions in both shape and intensity. As a result, uniform CBCT images that are close to the scatter artifact-free reference images can be obtained. Moreover, the physical experiments demonstrated that the e-Grid method can greatly reduce the shading artifacts in both LE and HE CBCT images acquired from DL-FPD. Results also demonstrated that the e-Grid method is effective for varied objects that having different diameters (from 10 to 28 cm). Quantitatively, the NU value was reduced by over 77% in the LE CBCT image and by over 66% in the HE CBCT image on average. As a consequence, the accuracy of the decomposed multi-material bases, iodine and gadolinium, was substantially improved. The Compton scattered x-ray signals could be significantly reduced using the proposed e-Grid method for DL-FPD based dual-energy CBCT imagingsystems.

Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging

In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean. In the experiments, hyperspectral imaging equipment was used to collect hyperspectral images of leaves, and the regions of interest were extracted within the spectral range of 400 to 1000 nm. These regions included one or more infected areas on the leaves to obtain hyperspectral data. This approach aimed to enhance the accurate discrimination of different types of diseases, providing more effective technical support for the detection and control of crop diseases. The preprocessing of hyperspectral data involved four methods: Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and 1st Derivative (1st Der). The 1st Der was found to be the optimal preprocessing method for hyperspectral data of maize and soybean diseases. Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) were employed for feature extraction on the optimal preprocessed data. The Support Vector Machines (SVM), Bidirectional Long Short-Term Memory Network (BiLSTM) and Dung Beetle Optimization-Bidirectional Long Short-Term Memory Network (DBO-BiLSTM) were established for the discrimination of maize and soybean diseases. Comparative analysis indicated that, in the classification of maize and soybean diseases, the DBO-BiLSTM model based on the CARS extraction method (1st Der-CARS-DBO-BiLSTM) demonstrated the highest classification rate, reaching 98.7% on the test set. The research findings suggest that integrating hyperspectral imaging with both traditional and deep learning methods is a viable and effective approach for classifying diseases in the intercropping model of maize and soybean. These results offer a novel method and a theoretical foundation for the non-invasive, precise, and efficient identification of diseases in the intercropping model of maize and soybean, carrying positive implications for agricultural production.

Scatter correction for contrast-enhanced digital breast tomosynthesis with a dual-layer detector.

Contrast-enhanced digital breast tomosynthesis (CEDBT) highlights breast tumors with neo-angiogenesis. A recently proposed CEDBT system with a dual-layer (DL) flat-panel detector enables simultaneous acquisition of high-energy (HE) and low-energy (LE) projection images with a single exposure, which reduces acquisition time and eliminates motion artifacts. However, x-ray scatter degrades image quality and lesion detectability. We propose a practical method for accurate and robust scatter correction (SC) for DL-CEDBT. The proposed hybrid SC method combines the advantages of a two-kernel iterative convolution method and an empirical interpolation strategy, which accounts for the reduced scatter from the peripheral breast region due to thickness roll-off and the scatter contribution from the region outside the breast. Scatter point spread functions were generated using Monte Carlo simulations with different breast glandular fractions, compressed thicknesses, and projection angles. Projection images and ground truth scatter maps of anthropomorphic digital breast phantoms were simulated to evaluate the performance of the proposed SC method and three other kernel- and interpolation-based methods. The mean absolute relative error (MARE) between scatter estimates and ground truth was used as the metric for SC accuracy. DL-CEDBT shows scatter characteristics different from dual-shot, primarily due to the two energy peaks of the incident spectrum and the structure of the DL detector. Compared with the other methods investigated, the proposed hybrid SC method showed superior accuracy and robustness, with MARE of for all LE and HE projection images of different phantoms in both cranial-caudal and mediolateral-oblique views. After SC, cupping artifacts in the dual-energy image were removed, and the signal difference-to-noise ratio was improved by 82.0% for 8mm iodine objects. A practical SC method was developed, which provided accurate and robust scatter estimates to improve image quality and lesion detectability for DL-CEDBT.

Accurate Discrimination of Mold-Damaged Citri Reticulatae Pericarpium Using Partial Least-Squares Discriminant Analysis and Selected Wavelengths.

Unscrupulous merchants sell the mold-damaged Citri Reticulatae Pericarpium (CRP) after removing the mold. In this study, an accurate and non-destructive strategy was developed for the discrimination of mold-damaged CRPs using portable near-infrared (NIR) spectroscopy and chemometrics. The outer surface and inner surface spectra were obtained without destroying CRPs. The discrimination models were established using partial least squares-discriminant analysis (PLS-DA) and wavelength selection strategy was used to further improve the discrimination ability. The predictive ability of models was assessed using the test set and an independent test set obtained one month later. The results demonstrate that the models of the outer surface outperform those of the inner surface. With multiplicative scatter correction (MSC)-PLS-DA, 100% accuracies were obtained in test and independent test sets. Furthermore, the wavelength selection strategy simplified the models with 100% discrimination accuracy. In addition, the randomization test (RT)-PLS-DA model developed in this study combines both the benefits of high accuracy and robustness, which can be applied for the accurate discrimination of mold-damaged CRPs.

Rapid and non-destructive classification of salinity levels in brined kimchi cabbage using hyperspectral imaging

In this study, we demonstrate the potential of a non-destructive hyperspectral imaging processing method in the near-infrared (NIR) region (874–1734 nm) for classifying the quality of brined kimchi cabbage. The salinity level of brined kimchi cabbage is an important indicator of consumer preference and the quality of kimchi. Hence, we compared the water content and salinity of brined kimchi cabbage via hyperspectral data. We extracted the optimal wavelengths from the hyperspectral image dataset to classify the salinity level of the predicted brined kimchi cabbage, and thus, established a novel approach for classifying kimchi samples into quality-unacceptable and quality-acceptable groups. Standard normal variate and multiplicative scatter correction (MSC) were used for pathlength correction. The Savitzky–Golay first and second derivatives were used for the deconvolution of the raw spectral data. The experimental results confirmed that the decision tree model combined with MSC pathlength correction and Savitzky–Golay first derivative preprocessing was the best classification model. The proposed hyperspectral image–NIR system can be applied to the detection of salinity during industrial kimchi manufacturing.