Quantification Errors Research Articles

Simultaneous radionuclide determination using PSresin: 2in2 and 2in1 tandem configuration.

Analyzing mixtures of radionuclides is a complex task. Two situations are the mixtures of 89Sr with 90Sr and 90Sr with plutonium isotopes. The challenge arises in emergency scenarios resulting from accidents where the activity of 89Sr is over 20 times higher than that of 90Sr, complicating its quantification and requiring delayed measurements. In scenarios involving accidents, both 90Sr and plutonium isotopes, may be present within various matrices. Given their distinct elemental properties, separation is typically achieved using different methods, followed by a measurement post-elution through alpha spectrometry and scintillation techniques. Therefore, methods which allow the fast and reliable determination of those mixtures are still required in routine laboratories. A novel method for the simultaneous determination of radionuclide mixtures using PSresin is introduced. This innovative approach utilizes PSresins in tandem and has been applied to the analysis of 89Sr and 90Sr, as well as mixtures of 90Sr and plutonium isotopes. Two strategies were investigated: using two PSresin in two cartridges and two measurements (2in2); and using two PSresin in a single cartridge and one measurement employing α/β discrimination (2in1). The methods were validated with standard solutions and applied to river water samples, achieving quantification errors below 10% for both strategies within 5 and 8h, respectively, from sample reception. This represents a great improvement for 89Sr and 90Sr quantification which is achieved in less than 5h since sample reception reducing the 30 days required by conventional methods. Additionally, the simultaneous measurement of 90Sr and plutonium isotopes reduce the number of required analysis to half, reducing the overall time and cost of the analysis.

MIRD Pamphlet No. 32: A MIRD Recovery Coefficient Model for Resolution Characterization and Shape-Specific Partial-Volume Correction.

Accurate quantification in emission tomography is essential for internal radiopharmaceutical therapy dosimetry. Mean activity concentration measurements in objects with diameters less than 10 times the full width at half maximum of the imaging system's spatial resolution are significantly affected (>10%) by the partial-volume effect. This study develops a framework for PET and SPECT spatial resolution characterization and proposes 2 MIRD recovery coefficient models-a geometric mean approximation (RECOVER-GM) and an empirical model (RECOVER-EM)-that provide shape-specific partial-volume correction (PVC). The models were validated using simulations and phantom experiments, with a comparative PVC test on ellipsoidal phantoms demonstrating that the RECOVER models significantly reduced error in activity quantification by factors of approximately 1.3-5.7 compared with conventional sphere-based corrections. The proposed recovery coefficient models and PVC methodology provide a robust framework for improved region-based PVC, including corrections for nonspherical tumor volumes. This work is part of the ongoing MIRDsoft.org project that aims to enhance accessibility to advanced dosimetry tools for improved disease characterization, treatment planning, and radiopharmaceutical therapy dosimetry.

Binocular Video-Based Automatic Pixel-Level Crack Detection and Quantification Using Deep Convolutional Neural Networks for Concrete Structures

Crack detection and quantification play crucial roles in assessing the condition of concrete structures. Herein, a novel real-time crack detection and quantification method that leverages binocular vision and a lightweight deep learning model is proposed. In this methodology, the proposed method based on the following four modules is adopted: a lightweight classification algorithm, a high-precision segmentation algorithm, a semi-global block matching algorithm (SGBM), and a crack quantification technique. Based on the crack segmentation results, a framework is developed for quantitative analysis of the major geometric parameters, including crack length, crack width, and crack angle of orientation at the pixel level. Results indicate that, by incorporating channel attention and spatial attention mechanisms in the MBConv module, the detection accuracy of the improved EfficientNetV2 increased by 1.6% compared with the original EfficientNetV2. Results indicate that using the proposed quantification method can achieve low quantification errors of 2%, 4.5%, and 4% for the crack length, width, and angle of orientation, respectively. The proposed method can contribute to crack detection and quantification in practical use by being deployed on smart devices.

Importance of neural network complexity for the automatic segmentation of individual thigh muscles in MRI images from patients with neuromuscular diseases.

Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles. U-Net architectures with different complexities have been compared for the quantification of the fat fraction in each muscle group selected in the central part of the thigh region. The corresponding performance has been assessed in terms of Dice score (DSC) and FF quantification error. The database contained 1450 thigh images from 59 patients and 14 healthy subjects (age: 47 ± 17years, sex: 36F, 37M). Ten individual muscles were segmented in each image. The performance of each model was compared to nnU-Net, a complex architecture with 4.35 107 parameters, 12.8 Gigabytes of peak memory usage and 167h of training time. As expected, nnU-Net achieved the highest DSC (94.77 ± 0.13%). A simpler U-Net (5.81 105 parameters, 2.37 Gigabytes, 14h of training time) achieved a lower DSC but still above 90%. Surprisingly, both models achieved a comparable FF estimation. The poor correlation between observed DSC and FF indicates that less complex architectures, reducing GPU memory utilization and training time, can still accurately quantify FF.

ORACLE: An analytical approach for T1, T2, proton density, and off-resonance mapping with phase-cycled balanced steady-state free precession.

To develop and validate a novel analytical approach simplifying , , proton density (PD), and off-resonance quantifications from phase-cycled balanced steady-state free precession (bSSFP) data. Additionally, to introduce a method to correct aliasing effects in undersampled bSSFP profiles. Off-resonant-encoded analytical parameter quantification using complex linearized equations (ORACLE) provides analytical solutions for bSSFP profiles. which instantaneously quantify , , proton density (PD), and . An aliasing correction formalism was derived to allow undersampling of bSSFP profiles. ORACLE was used to quantify , , PD, / , and based on fully sampled ( ) bSSFP profiles from numerical simulations and 3T MRI experiments in phantom and 10 healthy subjects' brains. Obtained values were compared with reference scans in the same scan session. Aliasing correction was validated in subsampled ( ) bSSFP profiles in numerical simulations and human brains. ORACLE quantifications agreed well with input values from simulations and phantom reference values (R2 = 0.99). In human brains, and quantifications when compared with reference methods showed coefficients of variation below 2.9% and 3.9%, biases of 182 and 16.6 ms, and mean white-matter values of 642 and 51 ms using ORACLE. The quantification differed less than 3 Hz between both methods. PD and maps had comparable histograms. The maps effectively identified cerebrospinal fluid. Aliasing correction removed aliasing-related quantification errors in undersampled bSSFP profiles, significantly reducing scan time. ORACLE enables simplified and rapid quantification of , , PD, and from phase-cycled bSSFP profiles, reducing acquisition time and eliminating biomarker maps' coregistration issues.

Digital instrument simulator to optimize the development of hyperspectral systems: application for intraoperative functional brain mapping.

Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. These areas can be assessed by monitoring cerebral hemodynamics and metabolism. Robust quantification of these biomarkers is complicated to perform during neurosurgery due to the critical context of the operating room. In actual devices, the inhomogeneities of the optical properties of the exposed brain cortex are poorly taken into consideration, which introduce quantification errors of biomarkers of brain functionality. Moreover, the best choice of spectral configuration is still based on an empirical approach. We propose a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This simulator can provide realistic modeling of the cerebral cortex and the identification of the optimal wavelengths to monitor cerebral hemodynamics (oxygenated and deoxygenated hemoglobin Hb) and metabolism (oxidized state of cytochromes and and cytochrome-c-oxidase oxCytb, oxCytc, and oxCCO). The digital instrument simulator is computed with white Monte Carlo simulations of a volume created from a real image of exposed cortex. We developed an optimization procedure based on a genetic algorithm to identify the best wavelength combinations in the visible and near-infrared range to quantify concentration changes in , Hb, oxCCO, and the oxidized state of cytochrome and (oxCytb and oxCytc). The digital instrument allows the modeling of intensity maps collected by a camera sensor as well as images of path length to take into account the inhomogeneities of the optical properties. The optimization procedure helps to identify the best wavelength combination of 18 wavelengths that reduces the quantification errors in , Hb, and oxCCO by 47%, 57%, and 57%, respectively, compared with the gold standard of 121 wavelengths between 780 and 900nm. The optimization procedure does not help to resolve changes in cytochrome and in a significant way but helps to better resolve oxCCO changes. We proposed a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This digital instrument simulator and this optimization framework could be used to optimize the design of hyperspectral imaging devices.

Scalable graphene nanogrid FET biosensors: Fabrication & characterization for sub-femtomolar detection of viral proteins

The development of highly sensitive biosensors has been significantly advanced by graphene nanostructures, particularly nanogrids, due to their remarkable surface area-to-volume ratio, which enhances biomolecule detection at ultra-low concentrations. This study introduces a scalable fabrication technique for graphene nanogrids integrated into field-effect transistor (FET) biosensors, specifically designed for sub-femtomolar detection of viral proteins. Through the optimization of pore size and distribution, the graphene nanogrid FET biosensors demonstrated high signal-to-noise ratios (SNR) and overcame challenges associated with non-specific antigen interference. The surface antigen was detected at 0.25 fM in serum, even in the presence of Hepatitis C. In addition to these advancements, structural analysis techniques, including XPS and TEM, were employed to observe the effects of lattice temperature and core structures on the electronic band gap, which directly influenced electron mobility. Lattice temperature effects were analyzed for their impact on sensitivity, revealing that optimized temperature control significantly improved detection performance, while precise adjustments in pore morphology and material composition reduced device non-linearity and minimized quantification errors. By operating in heterodyne mode (80 kHz–2 MHz) and utilizing a probabilistic neural network (PNN), the sensor mitigated Debye screening effects, achieving a 70 % increase in SNR, detecting 0.20 fM of Hepatitis B viral proteins with 60 % sensitivity and 6 % standard deviation, demonstrating potential for ultra-sensitive biomedical detection with enhanced electron mobility and reduced non-linearity.

Using CT images to assist the segmentation of MR images via generalization: Segmentation of the renal parenchyma of renal carcinoma patients.

Developing deep learning models for segmenting medical images in multiple modalities with less data and annotation is an attractive and challenging task, which was previously discussed as being accomplished by complex external frameworks for bridging the gap between different modalities. Exploring the generalization ability of networks in medical images in different modalities could provide more simple and accessible methods, yet comprehensive testing could still be needed. To explore the feasibility and robustness of using computed tomography (CT) images to assist the segmentation of magnetic resonance (MR) images via the generalization, in the segmentation of renal parenchyma of renal cell carcinoma (RCC) patients. Nephrographic CT images and fat-suppressed T2-weighted (fs-T2W) images were retrospectively collected. The pure CT dataset included 116 CT images. Additionally, 240 MR images were randomly divided into subsets A and B. From subset A, three training datasets were constructed, each containing 40, 80, and 120 images, respectively. Similarly, three datasets were constructed from subset B. Subsequently, datasets with mixed modality were created by combining these pure MR datasets with the 116 CT images. The 3D-UNET models for segmenting the renal parenchyma in two steps were trained using these 13 datasets: segmenting kidneys and then the renal parenchyma. These models were evaluated in internal MR (n=120), CT (n=65) validation datasets, and an external validation dataset of CT (n=79), using the mean of the dice similarity coefficient (DSC). To demonstrate the robustness of generalization ability over different proportions of modalities, we compared the models trained with mixed modality in three different proportions and pure MR, using repeated measures analysis of variance (RM-ANOVA). We developed a renal parenchyma volume quantification tool by the trained models. The mean differences and Pearson correlation coefficients between the model segmentation volume and the ground truth segmentation volume were calculated for its evaluation. The mean DSCs of models trained with 116 data in CT in the validation of MR were 0.826, 0.842, and 0.953, respectively, for the predictions of kidney segmentation model on whole image, renal parenchymal segmentation model on kidneys with RCC and without RCC. For all models trained with mixed modality, the means of DSC were above 0.9, in all validations of CT and MR. According to the results of the comparison between models trained with mixed modality and pure MR, the means of DSC of the former were significantly greater or equal to the latter, at all three different proportions of modalities. The differences of volumes were all significantly lower than one-third of the volumetric quantification error of a previous method, and the Pearson correlation coefficients of volumes were all above 0.96 on kidneys with and without RCC of three validations. CT images could be used to assist the segmentation of MR images via the generalization, with or without the supervision of MR data. This ability showed acceptable robustness. A tool for accurately measuring renal parenchymal volume on CT and MR images was established.

A multi-scale deep residual network-based guided wave imaging evaluation method for fatigue crack quantification

Abstract As a promising structural health monitoring technology, guided wave (GW) imaging is gaining increasing attention for crack monitoring of aircraft structures. However, actual fatigue crack propagation is a complex dynamically evolving process affected by various variabilities. It is still challenging to accurately track and quantify the dynamic fatigue crack propagation with GW imaging methods. Therefore, in order to achieve more accurate fatigue crack quantification, this paper proposes a multi-scale deep residual network-based GW imaging evaluation method. A convolutional neural network (CNN) is utilized to evaluate the entire pixel distribution of GW imaging maps to fuse damage-related information from multiple GW monitoring paths. By designing multi-scale convolutional kernels and deep residual learning, a robust quantitative image feature extraction is ensured with the dynamic evolution process of fatigue crack growth and the performance degradation is avoided as the CNN goes deeper, thereby improving the quantification accuracy. The method is validated on a fatigue test of landing gear beams, which are important load-carrying aircraft structural components. The results demonstrate that the proposed method can extract multi-scale crack length-related features and accurately track fatigue crack propagations. For batch specimens, the maximum quantification error is reduced from the original 6.1 mm to 1.6 mm, marking a significant improvement.

A Novel Defect Quantification Method Utilizing Multi-Sensor Magnetic Flux Leakage Signal Fusion

In the assessment of pipeline integrity using magnetic flux leakage (MFL) detection, it is crucial to quantify defects accurately and efficiently using MFL signals. However, in complex detection environments, traditional defect inversion methods exhibit low quantification accuracy and efficiency due to the complexity of their algorithms or excessive reliance on a priori knowledge and expert experience. To address these issues, this study presents a novel defect quantification method based on multi-sensor signal fusion (MSSF). The method employs a multi-sensor probe to fuse the MFL signals under multiple lift-off values, enhancing the diversity of defect information. This enables defect-opening profile recognition using the characteristic approximation approach (CAA). Subsequently, the MSSF method is based on a 3D magnetic dipole model and integrates the structural features of multi-sensor probes to develop an algorithm. This algorithm iteratively determines the defect depth at multiple data acquisition points within the defect region to obtain the maximum defect depth. The feasibility of the MSSF quantification method is validated through finite element simulation and physical experiments. The results demonstrate that the proposed method achieves accurate defect quantification while enhancing efficiency, with the number of iterations for each defect depth calculation point consistently requiring fewer than 15 iterations. For rectangular metal loss, perforation, and conical defects, quantification errors are less than 10%, meeting practical inspection requirements.

An improved infrared image post-processing method for metals and composites

Post-processing of the infrared thermography image is crucial for nondestructive testing, which will reduce noise and disturbance considerably. In this work, an improved image post-processing method based on the temperature ratio is proposed, namely the Thermal Image Division (TID) method. To validate this method, we carried out experiments on different materials such as composite CFRP (carbon fiber reinforced polymer) plates, 304 stainless steel plates and aluminum alloy samples. The results show the proposed method is pretty useful, significantly improves the signal-to-noise ratio of the image by comparing with other post-processing methods. It’s not only beneficial for identifying the edge of the defect, but also facilitates the detection of smaller defects with deeper depth. The signal-to-noise ratio is improved by nearly 117.5 % from practice. The TID method was applied to the defect size detection of pulse thermography and ultrasonic thermography. The results indicate TID method has good robustness, significantly reduces the error of defect size quantification.

Optimization of reconstruction in quantitative brain PET images: Benefits from PSF modeling and correction of edge artifacts.

Modern PET reconstruction algorithms incorporate point-spread-function (PSF) correction to mitigate partial volume effect. However, PSF correction can introduce edge artifacts that lead to quantification errors. Consequently, current international guidelines advise against using PSF correction in brain PET reconstruction. We aimed to investigate PSF-induced quantification errors in recent digital PET systems and identify conditions that mitigate them. This study utilized brain PET imaging with alginate-based realistic phantoms, simulating lesion-to-background activity ratios of 10:1 and 2:1, with eleven reconstruction parameter sets. Phantoms were prepared using a commercial anthropomorphic head phantom and two homemade inserts. Each insert contained a homogeneous 18F-FDG alginate background with hot spheres of varying diameter (3, 4, 6, 8, 10, 12, and 15mm). PET imaging was conducted on a digital PET-CT system Biograph Vision 600 (Siemens), with a 10 min scan duration. Imaging was performed with and without PSF correction, with 2, 4, 6, 12, 18, or 24 iterations in reconstruction, and with or without additional Gaussian postfiltering. We assessed the recovery coefficient (RC), contrast recovery coefficient (CRC), variability, and CRC-to-variability ratios for each sphere size and reconstruction parameter set. PSF-corrected images of the 10:1 spheres exhibited a nonmonotonic CRC-to-sphere diameter relationship due to edge artifacts overshoot in the 10mm-diameter sphere. In contrast, PSF images of the 2:1 spheres showed a monotonically increasing relationship. Non-PSF images of both phantoms showed an expected monotonically increasing CRC-to-sphere diameter relationship but with lower CRC values compared to PSF images. The nonmonotonic relationship observed with 10:1 spheres was mitigated by applying a 3-mm FWHM Gaussian postfiltering. For both phantoms, reconstructions with 6 iterations, PSF correction, and additional 3-mm FWHM Gaussian postfiltering demonstrated the highest CRC-to-variability ratios. Our findings indicate that Gaussian postfiltering suppresses PSF artifacts. This parameter set corrected the nonmonotonic CRC-to-sphere diameter relationship and improved the CRC-to-variability ratio compared to non-PSF reconstructions. Therefore, to enhance lesion detectability without compromising quantification accuracy, PSF correction coupled with Gaussian postfiltering should be used in brain PET.

Ultra-fast, selective and pseudo-quantitative analysis of 99Tc in nuclear waste for screening purposes

Controlling radioactivity is essential in various fields, such as the decommissioning of nuclear power plants and nuclear medicine. In some cases, a full characterization of samples is not required; instead, a screening analysis that provides an overall indication of the activity present can be sufficient to determine if a sample is radioactive. This article introduces a new system called PSkits designed specifically for ultra-fast and selective screening detection of 99Tc. PSkits consist of a plastic scintillation layer attached to the bottom of a scintillation vial, coated with aliquat·336® as a selective extractant. In this study, the preparation of PSkits was optimized by adjusting the proportions of crosslinker, porogen, and the type of vial used. The analysis method was developed, and the selectivity against common interferences was tested by optimizing the rinsing media. Finally, PSkits were validated by analysing simulated nuclear waste samples and urine spiked samples, achieving satisfactory results with quantification errors below 50 %, demonstrating their effectiveness for the intended purpose

Protocol for Quantifying All Electrolyte Compositions in Aged Lithium‐ion Batteries

AbstractThe aging of lithium‐ion batteries (LIBs) typically accompanies the degradation of electrolyte, but the relationship between them remains unclear. Therefore, quantifying residual electrolyte in batteries at different states of health (SOH) is a crucial issue. Here, we have developed a comprehensive characterization method to quantitatively analyze the electrolyte salts, solvents, and additives in commercial pouch cell, achieving quantification of all electrolyte compositions with high accuracy. Compared to the reported external standard method used in gas chromatography‐mass spectrometry (GC‐MS), we developed an internal standard method, which offers higher accuracy and reliability, with the maximum error decreased from 9.54 % to 3.48 %. Moreover, the quantitative accuracy of the calibration curves remains unchanged after 2 months. Multi‐instruments analysis is also utilized for the extraction and quantitative analysis of electrolyte in practical battery systems, achieving less than 5 % quantification error for all compositions. With our proposed method, it becomes possible to determine the absolute amounts of all electrolyte compositions, rather than obtaining limited information such as concentration or relative content. It is believed that this protocol of quantifying electrolyte compositions in commercial cells could serve as a baseline for further studies to reveal the relationship between electrolyte degradation and battery aging.

A New Evaluation Metric for Quantitative Accuracy of LC-MS/MS-Based Proteomics with Data-Independent Acquisition.

Data-independent acquisition (DIA) has improved the identification and quantitation coverage of peptides and proteins in liquid chromatography-tandem mass spectrometry-based proteomics. However, different DIA data-processing tools can produce very different identification and quantitation results for the same data set. Currently, benchmarking studies of DIA tools are predominantly focused on comparing the identification results, while the quantitative accuracy of DIA measurements is acknowledged to be important but insufficiently investigated, and the absence of suitable metrics for comparing quantitative accuracy is one of the reasons. A new metric is proposed for the evaluation of quantitative accuracy to avoid the influence of differences in false discovery rate control stringency. The part of the quantitation results with high reliability was acquired from each DIA tool first, and the quantitative accuracy was evaluated by comparing quantification error rates at the same number of accurate ratios. From the results of four benchmark data sets, the proposed metric was shown to be more sensitive to discriminating the quantitative performance of DIA tools. Moreover, the DIA tools with advantages in quantitative accuracy were consistently revealed by this metric. The proposed metric can also help researchers in optimizing algorithms of the same DIA tool and sample preprocessing methods to enhance quantitative accuracy.

Optimal Spectral Performance on Pediatric Photon-Counting CT: Investigating Phantom-Based Size-Dependent kV Selection for Spectral Body Imaging.

The comprehensive evaluation of kV selection on photon-counting computed tomography (PCCT) has yet to be performed. The aim of the study is to evaluate and determine the optimal kV options for variable pediatric body sizes on the PCCT unit. In this study, 4 phantoms of variable sizes were utilized to represent abdomens of newborn, 5-year-old, 10-year-old, and adult-sized pediatric patients. One solid water and 4 solid iodine inserts with known concentrations (2, 5, 10, and 15 mg I/mL) were inserted into phantoms. Each phantom setting was scanned on a PCCT system (Siemens Alpha) with 4 kV options (70 and 90 kV under Quantum Mode, 120 and 140 kV under QuantumPlus Mode) and clinical dual-source (3.0 pitch) protocol. For each phantom setting, radiation dose (CTDIvol) was determined by clinical dose settings and matched for all kV acquisitions. Sixty percent clinical dose images were also acquired. Reconstruction was matched across all acquisitions using Qr40 kernel and QIR level 3. Virtual monoenergetic images (VMIs) between 40 and 80 keV with 10 keV interval were generated on the scanner. Low-energy and high-energy images were reconstructed from each scan and subsequently used to generate an iodine map (IM) using an image-based 2-material decomposition method. Image noise of VMIs from each kV acquisition was calculated and compared between kV options. Absolute percent error (APE) of iodine CT number accuracy in VMIs was calculated and compared. Root mean square error (RMSE) and bias of iodine quantification from IMs were compared across kV options. At the newborn size and 50 keV VMI, noise is lower at low kV acquisitions (70 kV: 10.5 HU, 90 kV: 10.4 HU), compared with high kV acquisitions (120 kV: 13.8 HU, 140 kV: 13.9 HU). At the newborn size and 70 keV VMI, the image noise from different kV options is comparable (9.4 HU for 70 kV, 8.9 HU for 90 kV, 9.7 HU for 120 kV, 10.2 HU for 140 kV). For APE of VMI, high kV (120 or 140 kV) performed overall better than low kV (70 or 90 kV). At the 5-year-old size, APE of 90 kV (median: 3.6%) is significantly higher (P < 0.001, Kruskal-Wallis rank sum test with Bonferroni correction) than 140 kV (median: 1.6%). At adult size, APE of 70 kV (median: 18.0%) is significantly higher (P < 0.0001, Kruskal-Wallis rank sum test with Bonferroni correction) than 120 kV (median: 1.4%) or 140 kV (median: 0.8%). The high kV also demonstrated lower RMSE and bias than the low kV across all controlled conditions. At 10-year-old size, RMSE and bias of 120 kV are 1.4 and 0.2 mg I/mL, whereas those from 70 kV are 1.9 and 0.8 mg I/mL. The high kV options (120 or 140 kV) on the PCCT unit demonstrated overall better performance than the low kV options (70 or 90 kV), in terms of image quality of VMIs and IMs. Our results recommend the use of high kV for general body imaging on the PCCT.

A generalized signal model for dual-module velocity-selective arterial spin labeling.

To develop a generalized signal model for dual-module velocity-selective arterial spin labeling (dm-VSASL) that can integrate arbitrary saturation and inversion profiles. A recently developed mathematical framework for single-module VSASL is extended to address the increased complexity of dm-VSASL and to model the use of realistic velocity-selective profiles in the label-control and vascular crushing modules. Expressions for magnetization difference, arterial delivery functions, labeling efficiency, and cerebral blood flow (CBF) estimation error are presented. Sources of error are examined and timing requirements to minimize quantification errors are derived. For ideal velocity-selective profiles, the predicted signals match those of prior work. With realistic profiles, a CBF-dependent estimation error can occur when velocity-selective inversion (VSI) is used for the labeling modules and velocity-selective saturation (VSS) is used for the vascular crushing module. The error reflects a mismatch between the leading and trailing edges of the delivery function for the second bolus and can be minimized by choosing a nominal labeling cutoff velocity that is lower than the nominal saturation cutoff velocity. In the presence of and inhomogeneities, the labeling efficiency of dual-module VSI is more attenuated than that of dual-module VSS. The proposed signal model will enable researchers to more accurately assess and compare the performance of realistic dm-VSASL implementations and improve the quantification of dm-VSASL CBF measures.

Impact of hydrolysis on the quantification of sulfonated acrylamide copolymers by size exclusion chromatography with UV detection

AbstractSize exclusion chromatography with UV detection (SEC‐UV) is a precise method for quantifying polyacrylamides in water. However, its accuracy can be compromised in complex matrices due to chemical degradation of the polymer. This study focuses on the impact of hydrolysis on quantifying sulfonated polyacrylamide copolymers (PAM‐AMPS) via SEC‐UV, analyzing key influencing factors. Using controlled hydrolysis, hydrolyzed copolymer (HPAM‐AMPS) standards with varying hydrolysis degree (HD) (HD = 7%–33%) were prepared without significant changes in polymer chain size. SEC‐UV analysis revealed that quantification accuracy is highly dependent on hydrolysis extent. Errors are minimal for HD up to 10%, but significantly higher for HDs of 22%–33%. Furthermore, this effect is modulated by polymer concentration. Our findings indicate that there are two compensating factors for the decrease in molar absorptivity due to amide‐to‐carboxylate substitution: the sulfonate group acting as an additional chromophore, and a redshift in absorption spectra due to hydrogen bonding at higher concentrations. This interplay between HD and concentration in this type of sulfonated polyacrylamide can help reduce quantification errors in complex aqueous samples prone to chemical degradation, like those found in environmental and subsurface applications.

Image analysis for bright-field HER2 in situ hybridization: validation for clinical use.

The aim of the present study was to develop and validate a quantitative image analysis (IA) algorithm to aid pathologists in assessing bright-field HER2 in situ hybridization (ISH) tests in solid cancers. A cohort of 80 sequential cases (40 HER2-negative and 40 HER2-positive) were evaluated for HER2 gene amplification with bright-field ISH. We developed an IA algorithm using the ISH Module from HALO software to automatically quantify HER2 and CEP17 copy numbers per cell as well as the HER2/CEP17 ratio. We observed a high correlation of HER2/CEP17 ratio, an average of HER2 and CEP17 copy number per cell between visual and IA quantification (Pearson's correlation coefficient of 0.842, 0.916, and 0.765, respectively). IA was able to count from 124 cells to 47,044 cells (median of 5565 cells). The margin of error for the visual quantification of the HER2/CEP17 ratio and of the average of HER2 copy number per cell decreased from a median of 0.23 to 0.02 and from a median of 0.49 to 0.04, respectively, in IA. Curve estimation regression models showed that a minimum of 469 or 953 invasive cancer cells per case is needed to reach an average margin of error below 0.1 for the HER2/CEP17 ratio or for the average of HER2 copy number per cell, respectively. Lastly, on average, a case took 212.1s to execute the IA, which means that it evaluates about 130 cells/s and requires 6.7s/mm2. The concordance of the IA software with the visual scoring was 95%, with a sensitivity of 90% and a specificity of 100%. All four discordant cases were able to achieve concordant results after the region of interest adjustment. In conclusion, this validation study underscores the usefulness of IA in HER2 ISH testing, displaying excellent concordance with visual scoring and significantly reducing margins of error.

Improving Quantitative Analysis with Cross Instrument-Sparse Bayesian Learning (CI-SBL) Raman Spectroscopy Analysis Algorithm.

Qualitative and quantitative analysis of Raman spectroscopy is a widely used nondestructive analytical technique in many fields. It utilizes the Raman scattering effect of lasers to obtain molecular vibration information on samples. By comparison with the Raman spectra of standard substances, qualitative and quantitative analyses can be achieved on unknown samples. However, current Raman spectroscopy analysis algorithms still have many drawbacks. They struggled to handle quantitative analysis between different instruments. Their prediction accuracy for concentration is generally low, with poor robustness. Therefore, this study addresses these deficiencies by designing the cross instrument-sparse Bayesian learning (CI-SBL) Raman spectroscopy analysis algorithm. CI-SBL can facilitate spectroscopic analysis between different instruments through the cross instrument module. CI-SBL converts data from portable instruments into data from scientific instruments, with high similarity between the converted spectrum and the spectrum from the scientific instruments reaching 98.6%. The similarity between the raw portable instrument spectrum and the scientific instrument spectrum is often lower than 90%. The cross instrument effect of the CI-SBL is remarkable. Moreover, CI-SBL employs sparse Bayesian learning (SBL) as the core module for analysis. Through multiple iterations, the SBL algorithm effectively identified various components within mixtures. In experiments, CI-SBL can achieve a qualitative accuracy of 100% for the majority of binary and multicomponent mixtures. On the other hand, the previous Raman spectroscopy analysis algorithms predominantly yield a qualitative accuracy below 80% for the same data. Additionally, CI-SBL incorporates a quantitative module to calculate the concentration of each component within the mixed samples. In the experiment, the quantification error for all substances was below 3%, with the majority of the substances exhibiting an error of approximately 1%. These experimental results illustrate that CI-SBL significantly enhances the accuracy of qualitative judgment of mixture spectra and the prediction of mixture concentrations compared with previous Raman spectroscopy analysis algorithms. Furthermore, the cross instrument module of CI-SBL allows for a flexible handling of data acquired from different instruments.