Enhancing soil texture classification with multivariate scattering correction and residual neural networks using visible near-infrared spectra.

Soil texture is one of the most important indicators of soil physical properties, which has traditionally been measured through laborious procedures. Approaches utilizing visible near-infrared spectroscopy, with their advantages in efficiency, eco-friendliness and non-destruction, are emerging as potent alternatives. Nevertheless, these approaches often suffer from limitations in classification accuracy, and the substantial impact of spectral preprocessing, model integration, and sample matrix effect is commonly disregarded. Here a novel 11-class soil texture classification strategy that address this challenge by combining Multiplicative Scatter Correction (MSC) with Residual Network (ResNet) models was presented, resulting in exceptional classification accuracy. Utilizing the LUCAS dataset, collected by the Land Use and Cover Area frame Statistical Survey project, we thoroughly evaluated eight spectral preprocessing methods. Our findings underscored the superior performance of MSC in reducing spatial complexity within spectral data, showcasing its crucial role in enhancing model precision. Through comparisons of three 1D CNN models and two ResNet models integrated with MSC, we established the superior performance of the MSC-incorporated ResNet model, achieving an overall accuracy of 98.97 % and five soil textures even reached 100.00 %. The ResNet model demonstrated a marked superiority in classifying datasets with similar features, as observed by the confusion matrix analysis. Moreover, we investigated the potential benefit of pre-categorization based on land cover type of the soil samples in enhancing the accuracy of soil texture classification models, achieving overall classification accuracies exceeding 99.39 % for woodland, grassland, and farmland with the 2-layer ResNet model. The proposed work provides a pioneering and efficient strategy for rapid and precise soil texture identification via visible near-infrared spectroscopy, demonstrating unparalleled accuracy compared to existing methods, thus significantly enhancing the practical application prospects in soil, agricultural and environmental science.

Image processing setting adaptions according to image dose and radiologist preference can improve image quality in computed radiography of the equine distal limb: A cadaveric study.

Image processing (IP) in digital radiography has been steadily refined to improve image quality. Adaptable settings enable users to adjust systems to their specific requirements. This prospective, analytical study aimed to investigate the influence of different IP settings and dose reductions on image quality. Included were 20 cadaveric equine limb specimens distal to the metacarpophalangeal and metatarsophalangeal joints. Images were processed with the Dynamic Visualization II system (Fujifilm) using five different IP settings including multiobjective frequency processing, flexible noise control (FNC), and virtual grid processing (VGP). Seven criteria were assessed by three veterinary radiology Diplomates and one veterinary radiology resident in a blinded study using a scoring system. Algorithm comparison was performed using an absolute visual grading analysis. The rating of bone structures was improved by VGP at full dose (P < .05; AUCVGC  = 0.45). Überschwinger artifact perception was enhanced by VGP (P < .001; AUCVGC  = 0.66), whereas image noise perception was suppressed by FNC (P < .001; AUCVGC  = 0.29). The ratings of bone structures were improved by FNC at 50% dose (P < .05; AUCVGC  = 0.44), and 25% dose (P < .001; AUCVGC  = 0.32), and clinically acceptable image quality was maintained at 50% dose (mean rating 2.16; 95.8% ratings sufficient or better). The favored IP setting varied among observers, with higher agreement at lower dose levels. These findings supported using individualized IP settings based on the radiologist's preferences and situational image requirements, rather than using default settings.

No impact of attenuation and scatter correction on the interpretation of dopamine transporter SPECT in patients with clinically uncertain parkinsonian syndrome.

PURPOSE: The benefit from attenuation and scatter correction (ASC) of dopamine transporter (DAT)-SPECT for the detection of nigrostriatal degeneration in clinical routine is still a matter of debate. The current study evaluated the impact of ASC on visual interpretation and semi-quantitative analysis of DAT-SPECT in a large patient sample. METHODS: One thousand seven hundred forty consecutive DAT-SPECT with 123I-FP-CIT from clinical routine were included retrospectively. SPECT images were reconstructed iteratively without and with ASC. Attenuation correction was based on uniform attenuation maps, scatter correction on simulation. All SPECT images were categorized with respect to the presence versus the absence of Parkinson-typical reduction of striatal 123I-FP-CIT uptake by three independent readers. Image reading was performed twice to assess intra-reader variability. The specific 123I-FP-CIT binding ratio (SBR) was used for automatic categorization, separately with and without ASC. RESULTS: The mean proportion of cases with discrepant categorization by the same reader between the two reading sessions was practically the same without and with ASC, about 2.2%. The proportion of DAT-SPECT with discrepant categorization without versus with ASC by the same reader was 1.66% ± 0.50% (1.09-1.95%), not exceeding the benchmark of 2.2% from intra-reader variability. This also applied to automatic categorization of the DAT-SPECT images based on the putamen SBR (1.78% discrepant cases between without versus with ASC). CONCLUSION: Given the large sample size, the current findings provide strong evidence against a relevant impact of ASC with uniform attenuation and simulation-based scatter correction on the clinical utility of DAT-SPECT to detect nigrostriatal degeneration in patients with clinically uncertain parkinsonian syndrome.

An Excel fitting routine for correcting protein absorption spectra for scatter.

Protein concentrations are often determined in a non-destructive manner by measuring the absorbance at 280 nm. However, light scattering in protein samples can complicate such assessment. We here describe a simple Excel Solver-based fitting routine to correct full protein UV absorption spectra for both Rayleigh and Mie scattering. Using samples displaying various degrees of natural and artificially induced scattering, we show that our multi-wavelength fitting method is not only capable of aiding in the determination of protein concentrations but can also be employed in the spectral analysis of protein structural changes that are accompanied by alterations in scatter intensity.

Enhanced visualization of mobile chest X-ray images in the intensive care setting using software scatter correction.

BACKGROUND: Mobile chest X-ray (CXR) scans are performed within intensive treatment units (ITU) without anti-scatter grids for confirming tube and line hardware placement. Assessment is therefore challenging due to degraded subject contrast resulting from scatter. PURPOSE: To evaluate the efficacy of a software scatter correction method (commercially named Trueview) for enhanced hardware visualization and diagnostic quality in the ITU setting. MATERIAL AND METHODS: A total of 30 CXR scans were processed using Trueview and compared with standard original equipment manufacturer (OEM) images via observer scoring study involving two radiology and four ITU doctors to compare visualization of tubes and lines. Results were analyzed to determine observer preference and likelihood of diagnostic quality. RESULTS: Reviewers were more likely to score Trueview higher than OEM for mediastinal structures, bones, retrocardiac region, tube visibility, and tube safety (P < 0.01). Visual grading characteristic analysis suggested a clinical preference for Trueview compared with OEM for mediastinal structures (area under the visual grading characteristic curve [AUCVGC] = 0.60, 95% confidence interval [CI] = 0.55-0.65), bones (AUCVGC = 0.61, 95% CI = 0.55-0.66), retrocardiac region (AUCVGC = 0.64, 95% CI = 0.59-0.69), tube visibility (AUCVGC = 0.65, 95% CI = 0.60-0.70), and tube safety (AUCVGC = 0.68, 95% CI = 0.64-0.73). Reviewers were indifferent to visualization of the lung fields (AUCVGC = 0.49, 95% CI = 0.44-0.55). Registrars (3/6 reviewers) were indifferent to the mediastinal structure regions (AUCVGC = 0.54, 95% CI = 0.47-0.62). CONCLUSION: Reviewers were more confident in identifying the placement and safety of tubes and lines when reviewing Trueview images than they were when reviewing OEM.

Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography.

PURPOSE: This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (µ) of the annihilation photons in PET. METHODS: One of the approaches uses a CNN to generate µ-maps from the non-attenuation-corrected (NAC) PET images (µ-CNNNAC). In the other method, CNN is used to improve the accuracy of µ-maps generated using maximum likelihood estimation of activity and attenuation (MLAA) reconstruction (µ-CNNMLAA). We investigated the improvement in the CNN performance by combining the two methods (µ-CNNMLAA+NAC) and the suitability of µ-CNNNAC for providing the scatter distribution required for MLAA reconstruction. Image data from 18F-FDG (n = 100) or 68 Ga-DOTATOC (n = 50) PET/CT scans were used for neural network training and testing. RESULTS: The error of the attenuation correction factors estimated using µ-CT and µ-CNNNAC was over 7%, but that of scatter estimates was only 2.5%, indicating the validity of the scatter estimation from µ-CNNNAC. However, CNNNAC provided less accurate bone structures in the µ-maps, while the best results in recovering the fine bone structures were obtained by applying CNNMLAA+NAC. Additionally, the µ-values in the lungs were overestimated by CNNNAC. Activity images (λ) corrected for attenuation using µ-CNNMLAA and µ-CNNMLAA+NAC were superior to those corrected using µ-CNNNAC, in terms of their similarity to λ-CT. However, the improvement in the similarity with λ-CT by combining the CNNNAC and CNNMLAA approaches was insignificant (percent error for lung cancer lesions, λ-CNNNAC = 5.45% ± 7.88%; λ-CNNMLAA = 1.21% ± 5.74%; λ-CNNMLAA+NAC = 1.91% ± 4.78%; percent error for bone cancer lesions, λ-CNNNAC = 1.37% ± 5.16%; λ-CNNMLAA = 0.23% ± 3.81%; λ-CNNMLAA+NAC = 0.05% ± 3.49%). CONCLUSION: The use of CNNNAC was feasible for scatter estimation to address the chicken-egg dilemma in MLAA reconstruction, but CNNMLAA outperformed CNNNAC.

Simplification of dosimetry in 90Y-radioembolization therapy by dual planar images.

AIM: The purpose was to provide a practical and effective method for performing reliable 90Y dosimetry based on 99mTc-MAA and SPEC/CT. The impact of scatter correction (SC) and attenuation correction (AC) on the injected 90Y activity, lung shunt fraction (LSF) and the delivered dose to lung and liver compartments was investigated within the scope of the study. MATERIAL AND METHODS: Eighteen eligible patients (F: 3, M: 15) were subjected to 90Y therapy. 99mTc-MAA (111-222 MBq) was injected into the targeted liver, followed by whole-body scan (WBS) with peak-window at 140 keV (15% width) and one down-scatter window. SPECT/CT scan was subsequently acquired encompassing lung and liver regions. The LSFs were fashioned from standard WBS LSFwb (St), scatter corrected WBS LSFwb (Sc), only scatter corrected SPECT LSFspect (NoAC-SC) and SPECT/CT with attenuation and scatter correction LSFspect (AC-SC). The absorbed doses that would be delivered to tumor and injected healthy liver were estimated using different calculation modes involving AC-SC (SPECT/CT), NoAC-SC (SPECT), NoAC-NoSC+LSFwb (SC), AC-SC + LSFwb (St), and NoAC-NoSC+LSFwb (St). RESULTS: The average deviations (range) in LSF values between standard LSFwb (St) and those from SPECT/CT (AC-SC), SPECT (NoAC-SC), and LSFwb (SC) were - 50% (- 29/- 71), - 32% (- 8/- 67), and - 45% (- 13/80), respectively. The suggested 90Y activity (GBq/Gy) was decreased within a range of 2-11%, 1-9%, and 2-7% by using LSFspect (AC-SC), LSFspect (NoAC-SC), and LSFwb (SC), respectively. Overall, two-sample t-test yielded no statistically significant difference (p < 0.05) in the absorbed doses to tumor and injected healthy liver between AC-SC (SPECT) and the rest of approaches with/and without AC and SC. However, a statistically significant difference (p < 0.05) was demonstrated in the lung shunt fractions and lung doses due to AC and SC. The LSFs from scatter corrected planar images LSFwb (SC) exhibited well agreement (R2 = 0.92) with SPECT/CT (AC-SC) and there was no statistically significant difference (Pvalue > 0.05) between both methods. CONCLUSION: It was deduced that SPECT/CT with attenuation and scatter correction plays a crucial role in the measurements of lung shunt fraction and dose as well as the total number of 90Y treatments. However, the absorbed dose to tumors and injected healthy liver was minimally affected by AC and SC. Besides, a good agreement was observed between LSF datasets from SPECT/CT versus scatter corrected WBS that can be alternatively and effectively used in 90Y dosimetry.

Efficacy of the scatter correction algorithm in portable chest radiography.

PURPOSE: Portable chest radiographs (CXRs) continue to be a vital diagnostic tool for emergency and critical care medicine. The scatter correction algorithm (SCA) is a post-processing algorithm aiming to reduce scatter within portable images. This study aimed to assess whether the SCA improved image quality (IQ) in portable CXRs. METHODS: Objective and subjective IQ assessments were undertaken on both phantom and clinical images, respectively. For objective analysis, attenuators were placed on the anterior surface of the patient's thorax to simulate pathologies present within uniform regions of the phantom's lung and heart. Phantom CXRs were acquired with three different tube-current-times (mAs). Phantom images were processed with different SCA strengths. Contrast to noise ratios (CNR) within the attenuator were determined for each algorithm strength and compared to non-SCA images. For subjective analysis, two independent radiologists graded 30 clinical images with and without the SCA activated. The images were graded for IQ in different anatomical structures and overall diagnostic confidence. RESULTS: Objectively, most strengths of the SCA improved the CNR in both regions. However, a detrimental effect was recorded for some algorithm strengths in regions of high contrast. Subjectively, both observers recorded the SCA significantly improved IQ in clinical CXRs in all anatomical regions. Observers indicated the greatest improvement in the lung and hilar regions, and least improvement in the chest wall and bone. All images with and without the SCA were deemed diagnostic. CONCLUSION: This study shows the potential radiation dose neutral IQ improvement when using an SCA in clinical patient CXRs.

Correction of Temperature Variation with Independent Water Samples to Predict Soluble Solids Content of Kiwifruit Juice Using NIR Spectroscopy.

Using the framework of aquaphotomics, we have sought to understand the changes within the water structure of kiwifruit juice occurring with changes in temperature. The study focuses on the first (1300-1600 nm) and second (870-1100 nm) overtone regions of the OH stretch of water and examines temperature differences between 20, 25, and 30 °C. Spectral data were collected using a Fourier transform-near-infrared spectrometer with 1 mm and 10 mm transmission cells for measurements in the first and second overtone region, respectively. Water wavelengths affected by temperature variation were identified. Aquagrams (water spectral patterns) highlight slightly different responses in the first and second overtone regions. The influence of increasing temperature on the peak absorbance of the juice was largely a lateral wavelength shift in the first overtone region and a vertical amplitude shift in the second overtone region of water. With the same data set, we investigated the use of external parameter orthogonalisation (EPO) and extended multiple scatter correction (EMSC) pre-processing to assist in building temperature-independent partial least square regression models for predicting soluble solids concentration (SSC) of kiwifruit juice. The interference component selected for correction was the first principal component loading measured using pure water samples taken at the same three temperatures (20, 25, and 30 °C). The results show that the EMSC method reduced SSC prediction bias from 0.77 to 0.1 °Brix in the first overtone region of water. Using the EPO method significantly reduced the prediction bias from 0.51 to 0.04 °Brix, when applying a model made at one temperature (30 °C) to measurements made at another temperature (20 °C) in the second overtone region of water.

Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network.

OBJECTIVE: We demonstrate the feasibility of direct generation of attenuation and scatter-corrected images from uncorrected images (PET-nonASC) using deep residual networks in whole-body 18F-FDG PET imaging. METHODS: Two- and three-dimensional deep residual networks using 2D successive slices (DL-2DS), 3D slices (DL-3DS) and 3D patches (DL-3DP) as input were constructed to perform joint attenuation and scatter correction on uncorrected whole-body images in an end-to-end fashion. We included 1150 clinical whole-body 18F-FDG PET/CT studies, among which 900, 100 and 150 patients were randomly partitioned into training, validation and independent validation sets, respectively. The images generated by the proposed approach were assessed using various evaluation metrics, including the root-mean-squared-error (RMSE) and absolute relative error (ARE %) using CT-based attenuation and scatter-corrected (CTAC) PET images as reference. PET image quantification variability was also assessed through voxel-wise standardized uptake value (SUV) bias calculation in different regions of the body (head, neck, chest, liver-lung, abdomen and pelvis). RESULTS: Our proposed attenuation and scatter correction (Deep-JASC) algorithm provided good image quality, comparable with those produced by CTAC. Across the 150 patients of the independent external validation set, the voxel-wise REs (%) were - 1.72 ± 4.22%, 3.75 ± 6.91% and - 3.08 ± 5.64 for DL-2DS, DL-3DS and DL-3DP, respectively. Overall, the DL-2DS approach led to superior performance compared with the other two 3D approaches. The brain and neck regions had the highest and lowest RMSE values between Deep-JASC and CTAC images, respectively. However, the largest ARE was observed in the chest (15.16 ± 3.96%) and liver/lung (11.18 ± 3.23%) regions for DL-2DS. DL-3DS and DL-3DP performed slightly better in the chest region, leading to AREs of 11.16 ± 3.42% and 11.69 ± 2.71%, respectively (p value < 0.05). The joint histogram analysis resulted in correlation coefficients of 0.985, 0.980 and 0.981 for DL-2DS, DL-3DS and DL-3DP approaches, respectively. CONCLUSION: This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body 18F-FDG PET images using emission-only data via a deep residual network. The proposed approach achieved accurate attenuation and scatter correction without the need for anatomical images, such as CT and MRI. The technique is applicable in a clinical setting on standalone PET or PET/MRI systems. Nevertheless, Deep-JASC showing promising quantitative accuracy, vulnerability to noise was observed, leading to pseudo hot/cold spots and/or poor organ boundary definition in the resulting PET images.

A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions.

PURPOSE: A major challenge for accurate quantitative SPECT imaging of some radionuclides is the inadequacy of simple energy window-based scatter estimation methods, widely available on clinic systems. A deep learning approach for SPECT/CT scatter estimation is investigated as an alternative to computationally expensive Monte Carlo (MC) methods for challenging SPECT radionuclides, such as 90Y. METHODS: A deep convolutional neural network (DCNN) was trained to separately estimate each scatter projection from the measured 90Y bremsstrahlung SPECT emission projection and CT attenuation projection that form the network inputs. The 13-layer deep architecture consisted of separate paths for the emission and attenuation projection that are concatenated before the final convolution steps. The training label consisted of MC-generated "true" scatter projections in phantoms (MC is needed only for training) with the mean square difference relative to the model output serving as the loss function. The test data set included a simulated sphere phantom with a lung insert, measurements of a liver phantom, and patients after 90Y radioembolization. OS-EM SPECT reconstruction without scatter correction (NO-SC), with the true scatter (TRUE-SC) (available for simulated data only), with the DCNN estimated scatter (DCNN-SC), and with a previously developed MC scatter model (MC-SC) were compared, including with 90Y PET when available. RESULTS: The contrast recovery (CR) vs. noise and lung insert residual error vs. noise curves for images reconstructed with DCNN-SC and MC-SC estimates were similar. At the same noise level of 10% (across multiple realizations), the average sphere CR was 24%, 52%, 55%, and 67% for NO-SC, MC-SC, DCNN-SC, and TRUE-SC, respectively. For the liver phantom, the average CR for liver inserts were 32%, 73%, and 65% for NO-SC, MC-SC, and DCNN-SC, respectively while the corresponding values for average contrast-to-noise ratio (visibility index) in low-concentration extra-hepatic inserts were 2, 19, and 61, respectively. In patients, there was high concordance between lesion-to-liver uptake ratios for SPECT reconstruction with DCNN-SC (median 4.8, range 0.02-13.8) compared with MC-SC (median 4.0, range 0.13-12.1; CCC = 0.98) and with 90Y PET (median 4.9, range 0.02-11.2; CCC = 0.96) while the concordance with NO-SC was poor (median 2.8, range 0.3-7.2; CCC = 0.59). The trained DCNN took ~ 40 s (using a single i5 processor on a desktop computer) to generate the scatter estimates for all 128 views in a patient scan, compared to ~ 80 min for the MC scatter model using 12 processors. CONCLUSIONS: For diverse 90Y test data that included patient studies, we demonstrated comparable performance between images reconstructed with deep learning and MC-based scatter estimates using metrics relevant for dosimetry and for safety. This approach that can be generalized to other radionuclides by changing the training data is well suited for real-time clinical use because of the high speed, orders of magnitude faster than MC, while maintaining high accuracy.

Machine Learning in PET: from Photon Detection to Quantitative Image Reconstruction.

Machine learning has found unique applications in nuclear medicine from photon detection to quantitative image reconstruction. While there have been impressive strides in detector development for time-of-flight positron emission tomography, most detectors still make use of simple signal processing methods to extract the time and position information from the detector signals. Now with the availability of fast waveform digitizers, machine learning techniques have been applied to estimate the position and arrival time of high-energy photons. In quantitative image reconstruction, machine learning has been used to estimate various corrections factors, including scattered events and attenuation images, as well as to reduce statistical noise in reconstructed images. Here machine learning either provides a faster alternative to an existing time-consuming computation, such as in the case of scatter estimation, or creates a data-driven approach to map an implicitly defined function, such as in the case of estimating the attenuation map for PET/MR scans. In this article, we will review the abovementioned applications of machine learning in nuclear medicine.

Contextual loss based artifact removal method on CBCT image.

PURPOSE: Cone beam computed tomography (CBCT) offers advantages such as high ray utilization rate, the same spatial resolution within and between slices, and high precision. It is one of the most actively studied topics in international computed tomography (CT) research. However, its application is hindered owing to scatter artifacts. This paper proposes a novel scatter artifact removal algorithm that is based on a convolutional neural network (CNN), where contextual loss is employed as the loss function. METHODS: In the proposed method, contextual loss is added to a simple CNN network to correct the CBCT artifacts in the pelvic region. The algorithm aims to learn the mapping from CBCT images to planning CT images. The 627 CBCT-CT pairs of 11 patients were used to train the network, and the proposed algorithm was evaluated in terms of the mean absolute error (MAE), average peak signal-to-noise ratio (PSNR) and so on. The proposed method was compared with other methods to illustrate its effectiveness. RESULTS: The proposed method can remove artifacts (including streaking, shadowing, and cupping) in the CBCT image. Furthermore, key details such as the internal contours and texture information of the pelvic region are well preserved. Analysis of the average CT number, average MAE, and average PSNR indicated that the proposed method improved the image quality. The test results obtained with the chest data also indicated that the proposed method could be applied to other anatomies. CONCLUSIONS: Although the CBCT-CT image pairs are not completely matched at the pixel level, the method proposed in this paper can effectively correct the artifacts in the CBCT slices and improve the image quality. The average CT number of the regions of interest (including bones, skin) also exhibited a significant improvement. Furthermore, the proposed method can be applied to enhance the performance on such applications as dose estimation and segmentation.

Adaptive Structured Light with Scatter Correction for High-Precision Underwater 3D Measurements.

High-precision underwater 3D cameras are required to automate many of the traditional subsea inspection, maintenance and repair (IMR) operations. In this paper we introduce a novel multi-frequency phase stepping (structured light) method for high-precision 3D estimation even in turbid water. We introduce an adaptive phase-unwrapping procedure which uses the phase-uncertainty to determine the highest frequency that can be reliably unwrapped. Light scattering adversely affects the phase estimate. We propose to remove the effect of forward scatter with an unsharp filter and a model-based method to remove the backscatter effect. Tests in varying turbidity show that the scatter correction removes the adverse effect of scatter on the phase estimates. The adaptive frequency unwrapping with scatter correction results in images with higher accuracy and precision and less phase unwrap errors than the Gray-Code Phase Stepping (GCPS) approach.

NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface.

In this study, a hyperspectral imaging system of 866.4-1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface. In order to obtain the optimal model combination, three preprocessing methods (standard normal variate (SNV), Savitzky-Golay first derivative (SG1), and multivariate scatter correction (MSC)), three feature extraction algorithms (successive projections algorithm (SPA), random frog (RF), and neighborhood component analysis (NCA)), and three classifier models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were applied to make a comparison. Firstly, based on the full wavelengths modeling analysis, it was found that the spectral data after MSC processing performed best in the three classifier models. Secondly, three feature extraction algorithms were used to extract the feature wavelength of MSC processed data and based on feature wavelengths modeling analysis. As a result, the MSC-NCA-SVM model performed best and was selected as the best model. Finally, in order to verify the reliability of the selected model, the hyperspectral image was substituted into the MSC-NCA-SVM model and the object-wise method was used to visualize the image classification. The overall classification accuracy of the four types of wheat kernels reached 98.75%, which indicates that the selected model is reliable.

[Usefulness of Post-processing Scatter Correction in Portable Abdominal Radiography Using a Low Ratio Anti-scatter Grid].

OBJECTIVES: The aim of this study was to evaluate an influence of post-processing scatter correction in portable abdominal radiography using a low ratio anti-scatter grid (grid). METHODS: To assess tube voltage on portable abdominal radiography, a burger phantom was used to measure for inverse of image quality figure (IQFinv). For evaluation of the influence on using or not the grid, IQFinv were measured. Abdominal phantom radiographies were assessed subjectively, in random order, by six radiologic technologists. The radiographies were performed without scatter correction [IG (-)] and with scatter correction at equivalent for grid ratio 6 [IG (6)] and 8 [IG (8)]. RESULTS: There was no significant decrease in IQFinv with 75 and 80 kV in comparison of 70 kV. Even processing scatter correction, IQFinv with using the grid was significantly higher than that without using the grid. The ability to detect nasogastric tube and stomach gas were significantly better in the scatter correction. Deviation index for IG (6) and IG (8) were significantly lower than that of IG (-). DISCUSSION: Portable abdominal radiographies will be improved image quality by utilizing scatter correction, although, it is necessary to consider the scatter correction processing as this may significant decrease deviation index in the practical situation. CONCLUSION: The post-processing scatter correction should be useful for detection nasogastric tube and stomach gas in portable abdominal radiography.

Adapting the chemical unfolding assay for high-throughput protein screening using experimental and spectroscopic corrections.

The chemical unfolding (denaturation) assay can be used to calculate the change in the Gibbs free energy of unfolding, ΔG, and inflection point of unfolding, to collectively inform on molecule stability. Here, we evaluated methods for calculating the ΔG across 23 monoclonal antibody sequence variants. These methods are based on how the measured output (intrinsic fluorescence intensity) is treated, including utilizing (a) a single wavelength, (b) a ratio of two wavelengths, (c) a ratio of a single wavelength to an area, and (d) a scatter correction plus a ratio of a single wavelength to an area. When applied to the variants, the three ratio methods showed comparable results, with a similar pooled standard deviation for the ΔG calculation, while the single-wavelength method is shown as inadequate for the data in this study. However, when light scattering is introduced to simulated data, only the scatter-correction area normalization method proves robust. Using this method, common plate-based spectrophotometers found in many laboratories can be used for high-throughput screening of mAb variants and formulation stability studies.

Technical aspects of cardiac PET/MRI.

PET/MRI is a novel modality that enables to combine PET and MR images, and has significant potential to evaluate various cardiac diseases through the combination of PET molecular imaging and MRI functional imaging. Precise management of technical issues, however, is necessary for cardiac PET/MRI. This article describes several technical points, including patient preparation, MR attenuation correction, parallel acquisition of PET with MRI, clinical aspects, and image quality control.

Clinical Evaluation of 68Ga-PSMA-II and 68Ga-RM2 PET Images Reconstructed With an Improved Scatter Correction Algorithm.

OBJECTIVE: Gallium-68-labeled radiopharmaceuticals pose a challenge for scatter estimation because their targeted nature can produce high contrast in these regions of the kidneys and bladder. Even small errors in the scatter estimate can result in washout artifacts. Administration of diuretics can reduce these artifacts, but they may result in adverse events. Here, we investigated the ability of algorithmic modifications to mitigate washout artifacts and eliminate the need for diuretics or other interventions. MATERIALS AND METHODS: The model-based scatter algorithm was modified to account for PET/MRI scanner geometry and challenges of non-FDG tracers. Fifty-three clinical 68Ga-RM2 and 68Ga-PSMA-11 whole-body images were reconstructed using the baseline scatter algorithm. For comparison, reconstruction was also processed with modified sampling in the single-scatter estimation and with an offset in the scatter tail-scaling process. None of the patients received furosemide to attempt to decrease the accumulation of radiopharmaceuticals in the bladder. The images were scored independently by three blinded reviewers using the 5-point Likert scale. RESULTS: The scatter algorithm improvements significantly decreased or completely eliminated the washout artifacts. When comparing the baseline and most improved algorithm, the image quality increased and image artifacts were reduced for both 68Ga-RM2 and for 68Ga-PSMA-11 in the kidneys and bladder regions. CONCLUSION: Image reconstruction with the improved scatter correction algorithm mitigated washout artifacts and recovered diagnostic image quality in 68Ga PET, indicating that the use of diuretics may be avoided.

Impact on dose and image quality of a software-based scatter correction in mammography.

Background In 2014, Siemens developed a new software-based scatter correction (Progressive Reconstruction Intelligently Minimizing Exposure [PRIME]), enabling grid-less digital mammography. Purpose To compare doses and image quality between PRIME (grid-less) and standard (with anti-scatter grid) modes. Material and Methods Contrast-to-noise ratio (CNR) was measured for various polymethylmethacrylate (PMMA) thicknesses and dose values provided by the mammograph were recorded. CDMAM phantom images were acquired for various PMMA thicknesses and inverse Image Quality Figure (IQFinv) was calculated. Values of incident entrance surface air kerma (ESAK) and average glandular dose (AGD) were obtained from the DICOM header for a total of 1088 pairs of clinical cases. Two experienced radiologists compared subjectively the image quality of a total of 149 pairs of clinical cases. Results CNR values were higher and doses were lower in PRIME mode for all thicknesses. IQFinv values in PRIME mode were lower for all thicknesses except for 40 mm of PMMA equivalent, in which IQFinv was slightly greater in PRIME mode. A mean reduction of 10% in ESAK and 12% in AGD in PRIME mode with respect to standard mode was obtained. The clinical image quality in PRIME and standard acquisitions resulted to be similar in most of the cases (84% for the first radiologist and 67% for the second one). Conclusion The use of PRIME software reduces, in average, the dose of radiation to the breast without affecting image quality. This reduction is greater for thinner and denser breasts.