Diffusion Coefficient Measurements Research Articles

Diffusion Mri in Intracranial Hypertension: Quantitative Assesment.

Intracranial hypertension (IH) is a neurological disease that is characterized by increased intracranial pressure. Idiopathic intracranial hypertension (IIH) is characterized by an increase in intracranial pressure without an underlying neuroradiological cause (1-3). The IH that is associated with a reason such as a mass, hydrocephalus, or drug use, is referred to as secondary intracranial hypertension (SIH). We aimed to detect determine whether the increased intracranial pressure causes a change in the diffusion values of brain in the diffusion MRI images. The study includes 24 consecutive patients that were diagnosed with IIH and 18 consecutive patients that were diagnosed with secondary intracranial hypertension (SIH). The control group included 24 patients. Measurement of apparent diffusion coefficient (ADC) was performed using DWI sections obtained from subcortical white matter and the cortex of the frontal lobe in the basal ganglia plane, caudate nucleus head, thalamus, the posterior leg of the internal capsule, corpus callosum splenium; in the centrum semiovale plane, from the central white matter region. with 1.5T MRI using b=500s/mm2 and b=1000s/mm2 values both in patients and control groups. Mean ADC values were compared between IIH, SIH patients and control groups. The ADC values from the head of the caudate nucleus and the cortex were significantly higher in the IIH group, compared to the control group. When the ADC values of the SIH and control groups were compared, it was found that some of the ADC measurements (subcortical white matter, cortex and semioval center) were significantly different. The comparison of the IIH and the SIH groups revealed that the ADC measurements central white matter in the centrum semiovale, the subcortical white matter and the posterior leg of the internal capsule were significantly different. Conclusıons: We have found increased diffusion IIH and SIH patients, which supports the development of brain edema. Even though the mechanism of the brain edema in IIH are not entirely clear, it is thought that the mechanism is different from the brain edema caused by a mass or a sinus thrombosis.

A double-pass optical beam deflection instrument for the measurement of diffusion, thermodiffusion and Soret coefficients in liquid mixtures and its application to polymer analysis

Abstract We have developed a new double-pass optical beam deflection instrument for the measurement of diffusion, thermodiffusion and Soret coefficients in liquid mixtures. The increased sensitivity of the instrument results from a second passage of the readout laser beam through the Soret cell containing the sample. An elegant description of the total beam deflection is achieved by means of a transfer matrix formalism. The higher sensitivity allows for a reduction of the length of the detection arm and a compact and stiff design of the instrument. The performance of the new apparatus is demonstrated by its application to polymer analysis for the determination of the molar mass distribution of the polymer from the distribution of diffusion rates by means of the CONTIN algorithm.

Validation of echo planar imaging based diffusion-weighted magnetic resonance imaging on a 0.35 T MR-Linac

Background and PurposeThe feasibility of acquiring diffusion-weighted imaging (DWI) images on an MR-Linac for quantitative response assessment during radiotherapy was explored. DWI data obtained with a Spin Echo Echo Planar Imaging sequence adapted for a 0.35 T MR-Linac were examined and compared with DWI data from a conventional 3 T scanner. Materials and MethodsApparent diffusion coefficient (ADC) measurements and a distortion correction technique were investigated using DWI-calibrated phantoms and in the brains of seven volunteers. All DWI utilized two phase-encoding directions for distortion correction and off-resonance field estimation. ADC maps in the brain were analyzed for automatically segmented normal tissues. ResultsPhantom ADC measurements on the MR-Linac were within a 3 % margin of those recorded by the 3 T scanner. The maximum distortion observed in the phantom was 2.0 mm prior to correction and 1.1 mm post-correction on the MR-Linac, compared to 6.0 mm before correction and 3.6 mm after correction at 3 T. In vivo, the average ADC values for gray and white matter exhibited variations of 14 % and 4 %, respectively, for different selections of b-values on the MR-Linac. Distortions in brain images before correction, estimated through the off-resonance field, reached 2.7 mm on the MR-Linac and 12 mm at 3 T. ConclusionAccurate ADC measurements are achievable on a 0.35 T MR-Linac, both in phantom and in vivo. The selection of b-values significantly influences ADC values in vivo. DWI on the MR-Linac demonstrated lower distortion levels, with a maximum distortion reduced to 1.1 mm after correction.

Machine learning-assisted measurement of lithium transport using operando optical microscopy

Diffusion coefficients of electrode materials are often determined using galvanostatic (GITT) or potentiostatic intermittent titration technique (PITT), electrochemical impedance spectroscopy (EIS) or cyclic voltammetry (CV). However, these methods require special care as for each, their formal derivations use quite restrictive assumptions. As an alternative, a machine learning model is developed to extend a previously proposed optical method of studying lithium transport by operando microscopy. The herein reported model enables the measurement of concentration-dependent diffusion coefficients. For this purpose, a python code is developed that determines concentration profiles from RGB images using support vector regression (SVR), a flexible machine learning tool. To evaluate the diffusion coefficient, an inverse Boltzmann-Matano concept is applied. Representing the diffusion coefficient with generalized Redlich-Kister polynomials, concentration profiles are predicted and fit to the measured data. The method is demonstrated here on the example of delithiation of LiMn2O4, but it can, in-principle, be extended to any other battery material showing significant optical response on lithiation, which most of them do.

Pre-excitation gradients for eddy current nulled convex optimized diffusion encoding (Pre-ENCODE).

To evaluate the use of pre-excitation gradients for eddy current-nulled convex optimized diffusion encoding (Pre-ENCODE) to mitigate eddy current-induced image distortions in diffusion-weighted MRI (DWI). DWI sequences using monopolar (MONO), ENCODE, and Pre-ENCODE were evaluated in terms of the minimum achievable echo time (TE ) and eddy current-induced image distortions using simulations, phantom experiments, and in vivo DWI in volunteers ( ). Pre-ENCODE provided a shorter TE than MONO (71.0 17.7ms vs. 77.6 22.9ms) and ENCODE (71.0 17.7ms vs. 86.2 14.2ms) in 100 of the simulated cases for a commercial 3T MRI system with b-values ranging from 500 to 3000 s/mm and in-plane spatial resolutions ranging from 1.0 to 3.0mm . Image distortion was estimated by intravoxel signal variance between diffusion encoding directions near the phantom edges and was significantly lower with Pre-ENCODE than with MONO (10.1 vs. 22.7 , ) and comparable to ENCODE (10.1 vs. 10.4 , ). In vivo measurements of apparent diffusion coefficients were similar in global brain pixels (0.37 [0.28,1.45] mm /s vs. 0.38 [0.28,1.45] mm /s, ) and increased in edge brain pixels (0.80 [0.17,1.49] mm /s vs. 0.70 [0.18,1.48] mm /s, ) for MONO compared to Pre-ENCODE. Pre-ENCODE mitigated eddy current-induced image distortions for diffusion imaging with a shorter TE than MONO and ENCODE.

Evaluating Strategies to Enhance Li Transference in Salt-in-Ionic Liquid Electrolytes: Mixed Anions, Coordinating Cations, and High Salt Concentration.

The increased safety of salt-in-ionic liquid electrolytes compared with established carbonate-based systems has promoted intense research in this field, but low conductivities, slow lithium transport, and unfavorable lithium anion correlations still prevent a mass market application. In particular, strong Li-anion correlations lead to dominant vehicular Li transport with the same drift direction for anions and lithium in the electric field. Here, three different strategies and their mutual interplay are evaluated, which could reduce Li-anion coordination, i.e., high salt concentration, a mixed-anion composition, as well as an ether functionalization of the organic cation. To this end, two series of highly concentrated IL-based electrolytes, based on either ethylmethylimidazolium (EMIM) or the ether-functionalized 1-methoxyethyl-1-methylpyrrolidinium (Pyr12O1) organic cation, and employing mixed bis(fluorosulfonyl)imide/bis(trifluoromethylsulfonyl)imide (FSI/TFSI) anions are investigated. Measurements of conductivities, diffusion coefficients, and electrophoretic mobilities reveal no beneficial effect due to the increased heterogeneity of the FSI/TFSI-based electrolyte matrix, generally showing improved transport properties with increasing FSI share. However, a combination of both the ether-functionalized cation and high FSI content is proven successful, as lithium mobilities are positive, and vehicular transport is overcome by structural Li transport. Our study demonstrates the decisive role of synergy of the different approaches: While the single effect of a high salt concentration, weakly lithium-coordinating anions, or organic cations with lithium-affine functional groups is too weak to prevent vehicular transport, their joint effect can overcome vehicular Li transport, leading to improved Li conduction in ionic liquids.

Intrinsic Water Transport in Moisture-Capturing Hydrogels.

Moisture-capturing hydrogels have emerged as attractive sorbent materials capable of converting ambient humidity into liquid water. Recent works have demonstrated exceptional water capture capabilities of hydrogels while simultaneously exploring different strategies to accelerate water capture and release. However, on the material level, an understanding of the intrinsic transport properties of moisture-capturing hydrogels is currently missing, which hinders their rational design. In this work, we combine absorption and desorption experiments of macroscopic hydrogel samples in pure vapor with models of water diffusion in the hydrogels to demonstrate the first measurements of the intrinsic water diffusion coefficient in hydrogel-salt composites. Based on these insights, we pattern hydrogels with micropores to significantly decrease the required absorption and desorption times by 19% and 72%, respectively, while reducing the total water capacity of the hydrogel by only 4%. Thereby, we provide an effective strategy toward hydrogel material optimization, with a particular significance in pure-vapor environments.

Quantification of diffusion coefficients of commonly used high-intensity sweeteners through mucin

Low- and no-calorie sweeteners reduce the amount of carbohydrates in foods and beverages. However, concerns about taste perception surrounding the role of non-nutritive sweeteners in the oral cavity remain unanswered. One of the parameters that influences taste perception is the diffusion coefficient of the sweetener molecules inside the mucin layer lining the mouth. This study investigated the impact of diffusion coefficients of common high-intensity sweeteners on taste perception focusing on the sweeteners’ diffusion through mucin. Transwell Permeable Support well plates were used to measure diffusion coefficients of samples that were collected at specific intervals to estimate the coefficients based on concentration measurements. The diffusion coefficients of acesulfame-K, aspartame, rebaudioside M, sucralose, and sucrose with and without NaCl were compared. We found that different sweeteners show different diffusion behavior through mucin and that the presence of salt enhances the diffusion. These findings contribute insights into the diffusion of high-intensity sweeteners, offer a way to evaluate diffusion coefficients in real-time, and inform the development of products with improved taste profiles.

Quantitative Assessment of Breast Tumor: Comparison of Four Methods of Positioning Region of Interest for Synthetic Relaxometry and Diffusion Measurement

ObjectivesTo compare the differences in apparent diffusion coefficient (ADC) and synthetic magnetic resonance (MR) measurements of four region of interest (ROI) placement methods for breast tumor and to investigate their diagnostic performance. Methods110 (70 malignant, 40 benign) newly diagnosed breast tumors were evaluated. The patients underwent 3.0T MR examinations including diffusion-weighted imaging and synthetic MR. Two radiologists independently measured ADCs, T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) using four ROI methods: round, square, freehand, and whole-tumor volume (WTV). The interclass correlation coefficient (ICC) was used to assess their measurement reliability. Diagnostic performance was evaluated using multivariate logistic regression analysis and the receiver operating characteristic (ROC) curves. ResultsThe mean values of all ROI methods showed good or excellent interobserver reproducibility (0.79~0.99) and showed the best diagnostic performance compared to the minimum and maximum values. The square ROI exhibited superior performance in differentiating between benign from malignant breast lesions, followed by the freehand ROI. T2, PD, and ADC values were significantly lower in malignant breast lesions compared to benign ones for all ROI methods (p < 0.05). Multiparameters of T2 + ADC demonstrated the highest AUC values (0.82~0.95), surpassing the diagnostic efficacy of ADC or T2 alone (p < 0.05). ConclusionsROI placement significantly influences ADC and synthetic MR values measured in breast tumors. Square ROI and mean values showed superior performance in differentiating benign and malignant breast lesions. The multiparameters of T2 + ADC surpassed the diagnostic efficacy of a single parameter.

Use of Hydrogel Electrolyte in Zn-MnO2 Rechargeable Batteries: Characterization of Safety, Performance, and Cu2+ Ion Diffusion.

Achieving commercially acceptable Zn-MnO2 rechargeable batteries depends on the reversibility of active zinc and manganese materials, and avoiding side reactions during the second electron reaction of MnO2. Typically, liquid electrolytes such as potassium hydroxide (KOH) are used for Zn-MnO2 rechargeable batteries. However, it is known that using liquid electrolytes causes the formation of electrochemically inactive materials, such as precipitation Mn3O4 or ZnMn2O4 resulting from the uncontrollable reaction of Mn3+ dissolved species with zincate ions. In this paper, hydrogel electrolytes are tested for MnO2 electrodes undergoing two-electron cycling. Improved cell safety is achieved because the hydrogel electrolyte is non-spillable, according to standards from the US Department of Transportation (DOT). The cycling of "half cells" with advanced-formulation MnO2 cathodes paired with commercial NiOOH electrodes is tested with hydrogel and a normal electrolyte, to detect changes to the zincate crossover and reaction from anode to cathode. These half cells achieved ≥700 cycles with 99% coulombic efficiency and 63% energy efficiency at C/3 rates based on the second electron capacity of MnO2. Other cycling tests with "full cells" of Zn anodes with the same MnO2 cathodes achieved ~300 cycles until reaching 50% capacity fade, a comparable performance to cells using liquid electrolyte. Electrodes dissected after cycling showed that the liquid electrolyte allowed Cu ions to migrate more than the hydrogel electrolyte. However, measurements of the Cu diffusion coefficient showed no difference between liquid and gel electrolytes; thus, it was hypothesized that the gel electrolytes reduced the occurrence of Cu short circuits by either (a) reducing electrode physical contact to the separator or (b) reducing electro-convective electrolyte transport that may be as important as diffusive transport.

Predicting the Consistency of Pituitary Macroadenomas: The Utility of Diffusion-Weighted Imaging and Apparent Diffusion Coefficient Measurements for Surgical Planning.

Understanding the consistency of pituitary macroadenomas is crucial for neurosurgeons planning surgery. This retrospective study aimed to evaluate the utility of diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) as non-invasive imaging modalities for predicting the consistency of pituitary macroadenomas. This could contribute to appropriate surgical planning and therefore reduce the likelihood of incomplete resections. The study included 45 patients with pathologically confirmed pituitary macroadenomas. Conventional MRI sequences, DWIs, ADC maps, and pre- and post-contrast MRIs were performed. Two neuroradiologists assessed all of the images. Neurosurgeons assessed the consistency of the tumor macroscopically, and histopathologists examined it microscopically. The MRI findings were compared with postoperative data. According to the operative data, macroadenomas were divided into the two following categories based on their consistency: aspirable (n = 27) and non-aspirable tumors (n = 18). A statistically significant difference in DWI findings was found when comparing macroadenomas of different consistencies (p < 0.001). Most aspirable macroadenomas (66.7%) were hyperintense according to DWI and hypointense on ADC maps, whereas most non-aspirable macroadenomas (83.3%) were hypointense for DWI and hyperintense on ADC maps. At a cut-off value of 0.63 × 10-3 mm2/s, the ADC showed a sensitivity of 85.7% and a specificity of 75% for the detection of non-aspirable macroadenomas (AUC, 0.946). The study concluded that DWI should be routinely performed in conjunction with ADC measurements in the preoperative evaluation of pituitary macroadenomas. This approach may aid in surgical planning, ensure that appropriate techniques are utilized, and reduce the risk of incomplete resection.

Measurement of Diffusion Coefficients in Binary Mixtures and Solutions by the Taylor Dispersion Method

The theory and application of the Taylor Dispersion technique for measuring diffusion coefficients in binary systems is reviewed. The theory discussed in this paper includes both the ideal Taylor–Aris model and the estimation of corrections required to account for small deviations from this ideal associated with a practical apparatus. Based on the theoretical treatment, recommendations are given for the design of practical instruments together with suggestions for calibration, data acquisition and reduction, and the rigorous estimation of uncertainties. The analysis indicates that relative uncertainties on the order of 1% are achievable in practice.

The value of varying diffusion curvature MRI for assessing the microvascular invasion of hepatocellular carcinoma.

Varying diffusion curvature (VDC) MRI is an emerging diffusion-weighted imaging (DWI) technique that can capture non-Gaussian diffusion behavior and reflect tissue heterogeneity. However, its clinical utility has hardly been evaluated. We aimed to investigate the value of the VDC technique in noninvasively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC). 74 patients with HCCs, including 39 MVI-positive and 35 MVI-negative HCCs were included into this prospective study. Quantitative metrics between subgroups, clinical risk factors, as well as diagnostic performance were evaluated. The power analysis was also carried out to determine the statistical power. MVI-positive HCCs exhibited significantly higher VDC-derived structural heterogeneity measure, D1 (0.680 ± 0.100 × 10-3 vs 0.572 ± 0.148 × 10-3mm2/s, p = 0.001) and lower apparent diffusion coefficient (ADC) (1.350 ± 0.166 × 10-3 vs 1.471 ± 0.322 × 10-3mm2/s, p = 0.0495) compared to MVI-negative HCCs. No statistical significance was observed for VDC-derived diffusion coefficient, D0 between the subgroups (p = 0.562). Tumor size (odds ratio (OR) = 1.242) and alpha-fetoprotein (AFP) (OR = 2.527) were identified as risk factors for MVI. A predictive nomogram was constructed based on D1, ADC, tumor size, and AFP, which exhibited the highest diagnostic accuracy (AUC = 0.817), followed by D1 (AUC = 0.753) and ADC (AUC = 0.647). The diagnostic performance of the nomogram-based model was also validated by the calibration curve and decision curve. VDC can aid in the noninvasive and preoperative diagnosis of HCC with MVI, which may result in the clinical benefit in terms of prognostic prediction and clinical decision-making.

Fluoride Ion in Alcohols: Isopropanol vs Hexafluoroisopropanol.

The utility of alcohol as a hydrogen bonding donor is considered a providential avenue for moderating the high basicity and reactivity of the fluoride ion, typically used with large cations. However, the practicality of alcohol-fluoride systems in reactions is hampered by the limited understanding of the pertinent interactions between the OH group and F-. Therefore, this study comparatively investigates the thermal, structural, and physical properties of the CsF-2-propanol and CsF-1,1,1,3,3,3-hexafluoro-2-propanol systems to explicate the effects of the fluoroalkyl group on the interaction of alcohols and F-. The two systems exhibit vastly different phase diagrams despite the similar saturated concentrations. A combination of spectroscopic analyses, alcohol activity coefficient measurements, and theoretical calculations reveal the fluorinated alcohol system harbors the stronger OH···F- interactions between the two systems. The diffusion coefficient and ionic conductivity measurements attribute the present results to disparate states of ion association in the two systems.

Refractive Laser Beam Measuring Diffusion Coefficient of Concentrated Battery Electrolytes

A thorough understanding of electrolyte transport properties is crucial in the development of alternative battery technology. As a key parameter, the diffusion coefficient offers important insights into the behavior of electrolytes, especially for fast charge of high-energy batteries. Existing methods of measurement are often limited by redox species or offer questionable accuracy due to side reactions and/or disruption of the diffusion profile. This work provides a novel optical method for measuring diffusion coefficients of liquid-phase concentrated battery electrolytes without electrochemical reactions. The method relies on the deflection of a refractive laser beam passing through an electrolyte of a minor concentration gradient in a triangular diffusion column. The diffusion coefficient, D, for a range of zinc sulfate electrolytes was successfully extracted by correlating the position of the laser beam to its concentration. Several other physicochemical properties of the same electrolytes are studied to correlate to the concentration-dependent diffusion coefficients, including viscosity, conductivity, and microstructure analysis based on vibrational spectroscopy (Infrared and Raman). Also included is the future application of the triangular column for in situ electrochemical measurements.

Measurement of binary diffusion at elevated Knudsen numbers using laser absorption spectroscopy

Mass diffusion coefficients of gas mixtures have been measured for more than 100 years. However, the experimental data for the mass diffusion coefficient of gas mixtures in the rarefied gas regimes at Knudsen numbers (Kn) above 0.01 are few and remain uncertain due to the inherent precision limitations of the available state-of-the-art measurement techniques. The increased frequency of gas-wall collision, wall-friction, and surface-diffusion over the wall surface at Kn &amp;gt; 0.01 increases the uncertainty of the diffusive mass transport processes for internal gas flow in microcapillaries. Due to the growing interest in microfluidic applications at rarefied gas conditions, accurate diffusion coefficient measurements are needed to inform theoretical predictions and empirical relations in rarefied gas regimes. Thus, this article introduces a new experiment methodology consisting of a two-bulb (TB) diffusion configuration accompanied by a tunable diode laser absorption spectroscopy (TDLAS) detection technique that uses the measured time history of path-integrated absorbance to provide a non-intrusive, species-specific, in situ measurement of mass diffusion for a He–CO2 binary gas mixture at Kn &amp;gt; 0.01. To demonstrate the TB-TDLAS method's capability, the effective diffusion coefficient for a He–CO2 binary gas mixture was measured in the transition gas regime at Knudsen numbers relative to the tube radius in the range 0.1 &amp;lt; Kn &amp;lt; 5.4, and the results are compared against the Bosanquet empirical relation.

Apparent diffusion coefficient values of the white matter in magnetic resonance imaging of the neonatal brain may help predict outcome in congenital cytomegalovirus infection

BackgroundWhite matter change is a well-known abnormality in congenital cytomegalovirus (cCMV) infection, but grading remains challenging and clinical relevance unclear.ObjectiveTo investigate if quantitative measurement of white matter apparent diffusion coefficient (ADC) values in magnetic resonance imaging (MRI) of the neonatal brain can predict outcome in cCMV.Materials and methodsA retrospective, single-center observational study, including patients with cCMV who had a neonatal brain MRI with diffusion-weighted imaging, was performed between 2007 and 2020. Regions of interest were systematically placed in the white matter on the ADC maps. Two pediatric radiologists independently scored additional brain abnormalities. Outcome measures were neonatal hearing and cognitive and motor development. Statistical analysis included simple and penalized elastic net regression.ResultsNeonatal brain MRI was evaluated in 255 patients (median age 21 days, 25–75 percentiles: 14–28 days, 121 male). Gyral abnormalities were noted in nine patients (3.5%), ventriculomegaly in 24 (9.4%), and subependymal cysts in 58 (22.7%). General white matter ADC was significantly higher in patients with neonatal hearing loss and cognitive and motor impairment (P< 0.05). For neonatal hearing loss, simple logistic regression using only general white matter was the best prediction model, with a receiver operating characteristic area under the curve (AUC)=0.76. For cognitive impairment, interacting elastic net regression, including other brain abnormalities and frontoparietal white matter ADC, performed best, with AUC=0.89. For motor impairment, interacting elastic net regression, including other brain abnormalities and deep anterior frontal white matter performed best, with AUC=0.73.ConclusionNeonatal white matter ADC was significantly higher in patients with clinical impairments. Quantitative ADC measurement may be a useful tool for predicting clinical outcome in cCMV.Graphical

Diffusion weighted imaging with reverse encoding distortion correction: Improvement of image quality and distortion for accurate ADC evaluation in in vitro and in vivo studies

PurposeThe purpose of this in vivo study was to determine the effect of reverse encoding direction (RDC) on apparent diffusion coefficient (ADC) measurements and its efficacy for improving image quality and diagnostic performance for differentiating malignant from benign tumors on head and neck diffusion-weighted imaging (DWI). MethodsForty-eight patients with head and neck tumors underwent DWI with and without RDC and pathological examinations. Their tumors were then divided into two groups: malignant (n = 21) and benign (n = 27). To determine the utility of RDC for DWI, the difference in the deformation ratio (DR) between DWI and T2-weighted images of each tumor was determined for each tumor area. To compare ADC measurement accuracy of DWIs with and without RDC for each patient, ADC values for tumors and spinal cord were determined by using ROI measurements. To compare DR and ADC between two methods, Student’s t-tests were performed. Then, ADC values were compared between malignant and benign tumors by Student’s t-test on each DWI. Finally, sensitivity, specificity and accuracy were compared by means of McNemar’s test. ResultsDR of DWI with RDC was significantly smaller than that without RDC (p < 0.0001). There were significant differences in ADC between malignant and benign lesions on each DWI (p < 0.05). However, there were no significant difference of diagnostic accuracy between the two DWIs (p > 0.05). ConclusionRDC can improve image quality and distortion of DWI and may have potential for more accurate ADC evaluation and differentiation of malignant from benign head and neck tumors.

Water Effective Diffusion Coefficient in Dairy Powder Calculated by Digital Image Processing and through Machine Learning Algorithms of CLSM Micrographs.

Rehydration of dairy powders is a complex and essential process. A relatively new quantitative mechanism for monitoring powders' rehydration process uses the effective diffusion coefficient. This research focused on modifying a previously used labor-intensive method that will be able to automatically measure the real-time water diffusion coefficient in dairy powders based on confocal microscopy techniques. Furthermore, morphological characteristics and local hydration of individual particles were identified using an imaging analysis procedure written in Matlab©-R2023b and image analysis through machine learning algorithms written in Python™-3.11. The first model includes segmentation into binary images and labeling particles during water diffusion. The second model includes the expansion of data set selection, neural network training and particle markup. For both models, the effective diffusion follows Fick's second law for spherical geometry. The effective diffusion coefficient on each particle was computed from the dye intensity during the rehydration process. The results showed that effective diffusion coefficients for water increased linearly with increasing powder particle size and are in agreement with previously used methods. In summary, the models provide two independent machine measurements of effective diffusion coefficient based on the same set of micrographs and may be useful in a wide variety of high-protein powders.

Deep particle diffusometry: convolutional neural networks for particle diffusometry in the presence of flow and thermal gradients

Diffusion coefficient measurement is a helpful tool in revealing various properties of a fluid such as viscosity and temperature. However, determining the diffusion coefficient often requires specialized equipment. Particle-based techniques allow the use of conventional cameras to determine flow properties without any specialized measurement devices. However, the performance of existing methods such as single-particle and correlation-based measurements degrade drastically in the presence of real-world scenarios such as flow and thermal gradients. This work introduces a new method of estimating diffusion coefficient in the presence of flow and thermal gradients named deep particle diffusometry (DPD). The technique uses temporally averaged particle images as inputs and uses convolutional neural networks to predict the underlying diffusion coefficient. The results show that a high fit coefficient R 2 value of 0.99 was achieved with no or known fluid flow conditions and an R 2 value of 0.95 was achieved if the fluid had an arbitrary flow. Next, the generalization ability of the network was shown by training the DPD models on no gradient datasets and testing on datasets with a diffusion coefficient gradient. The networks maintained comparably high R 2 values of 0.96. Next, the DPD models were tested against three conventional methods on various simulated datasets, showing their superior performance in situations where an arbitrary flow was present along with diffusion. Finally, the networks were tested on experimental data and the predictions were compared with conventional methods which resulted in R2 values of 0.97 under the no-flow condition. The results show that the proposed method provides performance similar to existing methods on datasets with no flow or with a known flow and can surpass their performance on datasets that have an arbitrary flow.