Biexponential Model Research Articles

The Value of Whole-Tumor Histogram and Texture Analysis Using Intravoxel Incoherent Motion in Differentiating Pathologic Subtypes of Locally Advanced Gastric Cancer.

PurposeTo determine if whole-tumor histogram and texture analyses using intravoxel incoherent motion (IVIM) parameters values could differentiate the pathologic characteristics of locally advanced gastric cancer.MethodsEighty patients with histologically confirmed locally advanced gastric cancer who received surgery in our institution were retrospectively enrolled into our study between April 2017 and December 2018. Patients were excluded if they had lesions with the smallest diameter < 5 mm and severe image artifacts. MR scanning included IVIM sequences (9 b values, 0, 20, 40, 60, 100, 150,200, 500, and 800 s/mm2) used in all patients before treatment. Whole tumors were segmented by manually drawing the lesion contours on each slice of the diffusion-weighted imaging (DWI) images (with b=800). Histogram and texture metrics for IVIM parameters values and apparent diffusion coefficient (ADC) values were measured based on whole-tumor volume analyses. Then, all 24 extracted metrics were compared between well, moderately, and poorly differentiated tumors, and between different Lauren classifications, signet-ring cell carcinomas, and other poorly cohesive carcinomas using univariate analyses. Multivariate logistic analyses and multicollinear tests were used to identify independent influencing factors from the significant variables of the univariate analyses to distinguish tumor differentiation and Lauren classifications. ROC curve analyses were performed to evaluate the diagnostic performance of these independent influencing factors for determining tumor differentiation and Lauren classifications and identifying signet-ring cell carcinomas. The interobserver agreement was also conducted between the two observers for image quality evaluations and parameter metric measurements.ResultsFor diagnosing tumor differentiation, the ADCmedian, pure diffusion coefficient median (Dslowmedian), and pure diffusion coefficient entropy (Dslowentropy) showed the greatest AUCs: 0.937, 0.948, and 0.850, respectively, and no differences were found between the three metrics, P>0.05). The 95th percentile perfusion factor (FP P95th) was the best metric to distinguish diffuse-type GCs vs. intestinal/mixed (AUC=0.896). The ROC curve to distinguish signet-ring cell carcinomas from other poorly cohesive carcinomas showed that the Dslowmedian had AUC of 0.738. For interobserver reliability, image quality evaluations showed excellent agreement (interclass correlation coefficient [ICC]=0.85); metrics measurements of all parameters indicated good to excellent agreement (ICC=0.65-0.89), except for the Dfast metric, which showed moderate agreement (ICC=0.41-0.60).ConclusionsThe whole-tumor histogram and texture analyses of the IVIM parameters based on the biexponential model provided a non-invasive method to discriminate pathologic tumor subtypes preoperatively in patients with locally advanced gastric cancer. The metric FP P95th derived from IVIM performed better in determining Lauren classifications than the mono-exponential model.

Oxidation of 2,4-Dichlorophenoxyacetic Acid by Persulfate or Peroxymonosulfate with Iron(II) as an Activator

Several approaches can be used to activate persulfate anion (S2O82−) and peroxymonosulfate anion (HSO5−) to generate sulfate free radical (SO4−∙), a powerful oxidizing agent. In this study, we investigate ferrous ion (Fe2+) as an activator to generate SO4−∙ for the oxidation of 2,4-dichlorophenoxyacetic acid (2,4-D), a common herbicide. Experiments were conducted using different molar ratios of 2,4-D, oxidants, and Fe2+. The 2,4-D concentrations used in the experiments were fixed at 0.045 mM. Results showed that 2,4-D was rapidly oxidized within minutes but plateaued to an asymptotic concentration. A possible reason for the slowdown in oxidation was the rapid conversion of Fe2+ to Fe3+, which in turn slowed the activation process. Experiments with sequential addition of Fe2+ at different times resulted in further 2,4-D oxidation, which indicated that Fe2+ was limiting in the activation process. The optimal molar ratio of persulfate:Fe2+ or peroxymonosulfate:Fe2+ for the oxidation of 2,4-D was found to be 1:1. Of the two kinetic models used, the bi-exponential first-order model gave the best fit of the experimental data with R2 values greater than 0.9 as compared with the availability-adjusted first-order model. Results of the experiments showed that a low cost material such as Fe2+ can be used to activate persulfate or peroxymonosulfate for the removal of 2,4-D and other organic compounds in contaminated groundwater or drinking water.

Bi-exponential modelling of W^{^{prime}} reconstitution kinetics in trained cyclists

PurposeThe aim of this study was to investigate the individual W^{^{prime}} reconstitution kinetics of trained cyclists following repeated bouts of incremental ramp exercise, and to determine an optimal mathematical model to describe W^{^{prime}} reconstitution.MethodsTen trained cyclists (age 41 ± 10 years; mass 73.4 ± 9.9 kg; dot{V}{text{O}}_{2max } 58.6 ± 7.1 mL kg min−1) completed three incremental ramps (20 W min−1) to the limit of tolerance with varying recovery durations (15–360 s) on 5–9 occasions. W^{^{prime}} reconstitution was measured following the first and second recovery periods against which mono-exponential and bi-exponential models were compared with adjusted R2 and bias-corrected Akaike information criterion (AICc).ResultsA bi-exponential model outperformed the mono-exponential model of W^{^{prime}} reconstitution (AICc 30.2 versus 72.2), fitting group mean data well (adjR2 = 0.999) for the first recovery when optimised with parameters of fast component (FC) amplitude = 50.67%; slow component (SC) amplitude = 49.33%; time constant (τ)FC = 21.5 s; τSC = 388 s. Following the second recovery, W′ reconstitution reduced by 9.1 ± 7.3%, at 180 s and 8.2 ± 9.8% at 240 s resulting in an increase in the modelled τSC to 716 s with τFC unchanged. Individual bi-exponential models also fit well (adjR2 = 0.978 ± 0.017) with large individual parameter variations (FC amplitude 47.7 ± 17.8%; first recovery: (τ)FC = 22.0 ± 11.8 s; (τ)SC = 377 ± 100 s; second recovery: (τ)FC = 16.3.0 ± 6.6 s; (τ)SC = 549 ± 226 s).ConclusionsW′ reconstitution kinetics were best described by a bi-exponential model consisting of distinct fast and slow phases. The amplitudes of the FC and SC remained unchanged with repeated bouts, with a slowing of W′ reconstitution confined to an increase in the time constant of the slow component.

Bifurcated Topological Optimization for IVIM.

In this work, we shed light on the issue of estimating Intravoxel Incoherent Motion (IVIM) for diffusion and perfusion estimation by characterizing the objective function using simplicial homology tools. We provide a robust solution via topological optimization of this model so that the estimates are more reliable and accurate. Estimating the tissue microstructure from diffusion MRI is in itself an ill-posed and a non-linear inverse problem. Using variable projection functional (VarPro) to fit the standard bi-exponential IVIM model we perform the optimization using simplicial homology based global optimization to better understand the topology of objective function surface. We theoretically show how the proposed methodology can recover the model parameters more accurately and consistently by casting it in a reduced subspace given by VarPro. Additionally we demonstrate that the IVIM model parameters cannot be accurately reconstructed using conventional numerical optimization methods due to the presence of infinite solutions in subspaces. The proposed method helps uncover multiple global minima by analyzing the local geometry of the model enabling the generation of reliable estimates of model parameters.

Characterization of the diffusion signal of breast tissues using multi-exponential models.

Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues. The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging. A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D1,3 = 0, D2,3 = 1.5 × 10-3 , and D3,3 = 10.8 × 10-3 mm2 /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent. Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.

Quantification of Sodium Relaxation Times and Concentrations as Surrogates of Proteoglycan Content of Patellar CARTILAGE at 3T MRI.

Sodium MRI has the potential to depict cartilage health accurately, but synovial fluid can influence the estimation of sodium parameters of cartilage. Therefore, this study aimed to reduce the impact of synovial fluid to render the quantitative compositional analyses of cartilage tissue technically more robust. Two dedicated protocols were applied for determining sodium and relaxation times. For each protocol, data were acquired from 10 healthy volunteers and one patient with patellar cartilage damage. Data recorded with multiple repetition times for measurement and multi-echo data acquired with an additional inversion recovery pulse for measurement were analysed using biexponential models to differentiate longitudinal relaxation components of cartilage () and synovial fluid (), and short () from long () transversal relaxation components. Sodium relaxation times and concentration estimates in patellar cartilage were successfully determined: = 14.5 ± 0.7 ms; = 37.9 ± 2.9 ms; c(-protocol) = 200 ± 48 mmol/L; = 0.4 ± 0.1 ms; = 12.6 ± 0.7 ms; c(-protocol) = 215 ± 44 mmol/L for healthy volunteers. In conclusion, a robust determination of sodium relaxation times is possible at a clinical field strength of 3T to quantify sodium concentrations, which might be a valuable tool to determine cartilage health.

PERFORMANCE EVALUATION OF THE POPULAR SWRC MODELS AT DIFFERENT SOIL BULK DENSITY RANGES

Soil bulk density is an important soil property which influences the hydraulic properties of soil. Soil bulk density has never been a fixed soil property, but it varies spatially due to soil changes and temporally due to human activities, soil management and effects of climate change. This changes in soil bulk density affects the characterization of hydraulic properties of soil especially when mathematical soil retention models are used. Since the measurement of the soil water retention characteristics in the field is expensive, labor intensive and time consuming, mathematical models have been developed to predict these properties. Therefore, it is important to establish the impact of the soil bulk density on the performance of these mathematical models especially their accuracy to fit measured data using measured data. The aim of this study was performance evaluation of the water retention models at different soil bulk density ranges. The study used the dataset downloaded from the global database (ISRIC). The study used Residual Standard Error (RSE) and R2 to measure the accuracy of the model in predicting or quantify the goodness of fit to the experimental data. The low values of RSE shows that experimental data are close to the model predictions. This paper discussed the popular Soil Water Retention Characteristic (SWRC) models then it assessed their performance on varying soil bulk density ranges. The results from the study showed that the performance of the models improved with increase in values of field bulk density. It was established that Gardner and Bi-exponential models were better suited to describe SWRC irrespective of the variation of soil bulk density because they displayed consistent best performance (R2 greater than 0.98) at low and high soil bulk density. From the study, the soil scientist can choose these models to apply especially when the soil bulk density varies greatily.

Dynamics and turnover of memory CD8 T cell responses following yellow fever vaccination.

Understanding how immunological memory lasts a lifetime requires quantifying changes in the number of memory cells as well as how their division and death rates change over time. We address these questions by using a statistically powerful mixed-effects differential equations framework to analyze data from two human studies that follow CD8 T cell responses to the yellow fever vaccine (YFV-17D). Models were first fit to the frequency of YFV-specific memory CD8 T cells and deuterium enrichment in those cells 42 days to 1 year post-vaccination. A different dataset, on the loss of YFV-specific CD8 T cells over three decades, was used to assess out of sample predictions of our models. The commonly used exponential and bi-exponential decline models performed relatively poorly. Models with the cell loss following a power law (exactly or approximately) were most predictive. Notably, using only the first year of data, these models accurately predicted T cell frequencies up to 30 years post-vaccination. Our analyses suggest that division rates of these cells drop and plateau at a low level (0.1% per day, ∼ double the estimated values for naive T cells) within one year following vaccination, whereas death rates continue to decline for much longer. Our results show that power laws can be predictive for T cell memory, a finding that may be useful for vaccine evaluation and epidemiological modeling. Moreover, since power laws asymptotically decline more slowly than any exponential decline, our results help explain the longevity of immune memory phenomenologically.

Study of Diffusion Weighted Imaging Derived Diffusion Parameters as Biomarkers for the Microenvironment in Gliomas

ObjectivesTo explore the efficacy of diffusion weighted imaging (DWI)-derived metrics under different models as surrogate indicators for molecular biomarkers and tumor microenvironment in gliomas.MethodsA retrospective study was performed for 41 patients with gliomas. The standard apparent diffusion coefficient (ADCst) and ADC under ultra-high b values (ADCuh) (b values: 2500 to 5000 s/mm2) were calculated based on monoexponential model. The fraction of fast diffusion (f), pseudo ADC (ADCfast) and true ADC (ADCslow) were calculated by bi-exponential model (b values: 0 to 2000 s/mm2). The apparent diffusional kurtosis (Kapp) was derived from the simplified diffusion kurtosis imaging (DKI) model (b values: 200 to 3000 s/mm2). Potential correlations between DWI parameters and immunohistological indices (i.e. Aquaporin (AQP)1, AQP4, AQP9 and Ki-67) were investigated and DWI parameters were compared between high- and low-grade gliomas, and between tumor center and peritumor. Receiver operator characteristic (ROC) curve and area under the curve (AUC) were calculated to determine the performance of independent or combined DWI parameters in grading gliomas.ResultsThe ADCslow and ADCuh at tumor center showed a stronger correlation with Ki-67 than other DWI metrics. The ADCst, ADCslow and ADCuh at tumor center presented correlations with AQP1 and AQP4 while AQP9 did not correlate with any DWI metric. Kapp showed a correlation with Ki-67 while no significant correlation with AQPs. ADCst (p < 0.001) and ADCslow (p = 0.001) were significantly lower while the ADCuh (p = 0.006) and Kapp (p = 0.005) were significantly higher in the high-grade than in the low-grade gliomas. ADCst, f, ADCfast, ADCslow, ADCuh, Kapp at the tumor center had significant differences with those in peritumor when the gliomas grade became high (p < 0.05). Involving ADCuh and Kapp simultaneously into an independent ADCst model (AUC = 0.833) could further improve the grading performance (ADCst+ADCuh+Kapp: AUC = 0.923).ConclusionDifferent DWI metrics fitted within different b-value ranges (low to ultra-high b values) have different efficacies as a surrogate indicator for molecular expression or microstructural complexity in gliomas. Further studies are needed to better explain the biological meanings of these DWI parameters in gliomas.

Assessment of Impaired Cerebrovascular Reactivity in Chronic Cerebral Ischemia using Intravoxel Incoherent Motion Magnetic Resonance Imaging

BackgroundThe severity of chronic cerebral ischemia can be assessed using cerebrovascular reactivity (CVR) to acetazolamide (ACZ) challenge, which is measured by single-photon emission computed tomography (SPECT); however, this is an invasive method. We investigated whether intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) can assess impaired CVR in preoperative patients with chronic cerebral ischemia and compared it to SPECT-CVR. MethodsForty-seven patients with unilateral cervical carotid artery stenosis underwent diffusion-weighted MRI with 11 b-values in the range of 0–800 s/mm2 and cerebral perfusion SPECT with the ACZ challenge. The perfusion fraction (f) and diffusion coefficient (D) of the IVIM parameters were calculated using a bi-exponential model. The f and D values and these ratios of the ipsilateral middle cerebral artery territory against the contralateral side were compared with the CVR values of the affected side calculated from the SPECT data. ResultsThe IVIM-f and D values in the affected side were significantly higher than those in the unaffected side (median: 7.74% vs. 7.45%, p = 0.027; 0.816 vs. 0.801 10−3mm2/s, p < 0.001; respectively). However, there were no significant correlations between the f or D values and SPECT-CVR values in the affected side. In contrast, the f ratio showed a moderate negative correlation with the SPECT-CVR values (r = -0.40, p = 0.006) and detected impaired CVR (< 18.4%) with a sensitivity/specificity of 0.71/0.90. ConclusionThe IVIM perfusion parameter, f, can noninvasively assess impaired CVR with high sensitivity and specificity in patients with unilateral cervical carotid artery stenosis.

W' Recovery Kinetics after Exhaustion: A Two-Phase Exponential Process Influenced by Aerobic Fitness.

The aims of this study were 1) to model the temporal profile of W' recovery after exhaustion, 2) to estimate the contribution of changing V˙O2 kinetics to this recovery, and 3) to examine associations with aerobic fitness and muscle fiber type (MFT) distribution. Twenty-one men (age = 25 ± 2 yr, V˙O2peak = 54.4 ± 5.3 mL·min-1·kg-1) performed several constant load tests to determine critical power and W' followed by eight trials to quantify W' recovery. Each test consisted of two identical exhaustive work bouts (WB1 and WB2), separated by a variable recovery interval of 30, 60, 120, 180, 240, 300, 600, or 900 s. Gas exchange was measured and muscle biopsies were collected to determine MFT distribution. W' recovery was quantified as observed W' recovery (W'OBS), model-predicted W' recovery (W'BAL), and W' recovery corrected for changing V˙O2 kinetics (W'ADJ). W'OBS and W'ADJ were modeled using mono- and biexponential fitting. Root-mean-square error (RMSE) and Akaike information criterion (∆AICC) were used to evaluate the models' accuracy. The W'BAL model (τ = 524 ± 41 s) was associated with an RMSE of 18.6% in fitting W'OBS and underestimated W' recovery for all durations below 5 min (P < 0.002). Monoexponential modeling of W'OBS resulted in τ = 104 s with RMSE = 6.4%. Biexponential modeling of W'OBS resulted in τ1 = 11 s and τ2 = 256 s with RMSE = 1.7%. W'ADJ was 11% ± 1.5% lower than W'OBS (P < 0.001). ∆AICC scores favored the biexponential model for W'OBS, but not for W'ADJ. V˙O2peak (P = 0.009) but not MFT distribution (P = 0.303) was associated with W'OBS. We showed that W' recovery from exhaustion follows a two-phase exponential time course that is dependent on aerobic fitness. The appearance of a fast initial recovery phase was attributed to an enhanced aerobic energy provision resulting from changes in V˙O2 kinetics.

Experimental study on shear strength characteristics of mud construction waste lightweight soil

Mud construction waste lightweight soil (MCWLS) is made by dredged mud, lightweight construction waste (LCW), cement. Consolidated-drained (CD) shear tests were conducted to investigate the shear characteristics of MCWLS. The stress-strain relationships were divided into three types: strain-hardening, strain-softening and ideal elastic-plastic. A straight line was presented as the failure envelope. The cohesion and internal friction angle were first increased then decreased with the increase of LCW content, the peak value corresponding to LCW content of 50%. Failure modes showed the drum type and contraction type. A new bi-exponential expression model was proposed to describe the three stress-strain curves simultaneously.

Analytical and Numerical Connections between Fractional Fickian and Intravoxel Incoherent Motion Models of Diffusion MRI

Impaired tissue perfusion underlies many chronic disease states and aging. Diffusion-weighted imaging (DWI) is a noninvasive MRI technique that has been widely used to characterize tissue perfusion. Parametric models based on DWI measurements can characterize microvascular perfusion modulated by functional and microstructural alterations in the skeletal muscle. The intravoxel incoherent motion (IVIM) model uses a biexponential form to quantify the incoherent motion of water molecules in the microvasculature at low b-values of DWI measurements. The fractional Fickian diffusion (FFD) model is a parsimonious representation of anomalous superdiffusion that uses the stretched exponential form and can be used to quantify the microvascular volume of skeletal muscle. Both models are established measures of perfusion based on DWI, and the prognostic value of model parameters for identifying pathophysiological processes has been studied. Although the mathematical properties of individual models have been previously reported, quantitative connections between IVIM and FFD models have not been examined. This work provides a mathematical framework for obtaining a direct, one-way transformation of the parameters of the stretched exponential model to those of the biexponential model. Numerical simulations are implemented, and the results corroborate analytical results. Additionally, analysis of in vivo DWI measurements in skeletal muscle using both biexponential and stretched exponential models is shown and compared with analytical and numerical models. These results demonstrate the difficulty of model selection based on goodness of fit to experimental data. This analysis provides a framework for better interpreting and harmonizing perfusion parameters from experimental results using these two different models.

Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition-overall survival modeling framework.

The objectives of the study were to use tumor size data from 10 phase II/III atezolizumab studies across five solid tumor types to estimate tumor growth inhibition (TGI) metrics and assess the impact of TGI metrics and baseline prognostic factors on overall survival (OS) for each tumor type. TGI metrics were estimated from biexponential models and posttreatment longitudinal data of 6699 patients. TGI‐OS full models were built using parametric survival regression by including all significant baseline covariates from the Cox univariate analysis followed by a backward elimination step. The model performance was evaluated for each trial by 1000 simulations of the OS distributions and hazard ratios (HR) of the atezolizumab‐containing arms versus the respective controls. The tumor growth rate estimate was the most significant predictor of OS across all tumor types. Several baseline prognostic factors, such as inflammatory status (C‐reactive protein, albumin, and/or neutrophil‐to‐lymphocyte ratio), tumor burden (sum of longest diameters, number of metastatic sites, and/or presence of liver metastases), Eastern Cooperative Oncology Group performance status, and lactate dehydrogenase were also highly significant across multiple studies in the final multivariate models. TGI‐OS models adequately described the OS distribution. The model‐predicted HRs indicated good model performance across the 10 studies, with observed HRs within the 95% prediction intervals for all study arms versus controls. Multivariate TGI‐OS models developed for different solid tumor types were able to predict treatment effect with various atezolizumab monotherapy or combination regimens and could be used to support design and analysis of future studies.

Pilot study quantifying muscle glycosaminoglycan using bi-exponential T1\u03c1 mapping in patients with muscle stiffness after stroke

Post stroke muscle stiffness is a common problem, which left untreated can lead to disabling muscle contractures. The purpose of this pilot study was to evaluate the feasibility of bi-exponential T1ρ mapping in patients with arm muscle stiffness after stroke and its ability to measure treatment related changes in muscle glycosaminoglycans (GAGs). Five patients with muscle stiffness after stroke and 5 healthy controls were recruited for imaging of the upper arm with 3D-T1ρ mapping. Patients were scanned before and after treatment with hyaluronidase injections, whereas the controls were scanned once. Wilcoxon Mann–Whitney tests compared patients vs. controls and patients pre-treatment vs. post-treatment. With bi-exponential modeling, the long component, T1ρl was significantly longer in the patients (biceps P = 0.01; triceps P = 0.004) compared to controls. There was also a significant difference in the signal fractions of the long and short components (biceps P = 0.03, triceps P = 0.04). The results suggest that muscle stiffness is characterized by increased muscle free water and GAG content. Post-treatment, the T1ρ parameters shifted toward control values. This pilot study demonstrates the application of bi-exponential T1ρ mapping as a marker for GAG content in muscle and as a potential treatment monitoring tool for patients with muscle stiffness after stroke.

Comparing critical speed modelling approaches and exploring relationships with match-play variables in elite male youth soccer players.

A novel bi-exponential method has emerged to estimate critical speed (CS) and D-prime (D') from a 3-min all-out test (3MT). To compare CS analysis methods to determine whether parameter estimations were interchangeable. Reference values and relationships with key soccer match-play variables were explored. Thirteen elite male youth (14-15 years old) players completed a 30 m shuttle run 3MT to estimate CS, D', rate of speed decline time constant, maximal speed (S max), time to S max (t max), and fatigue index (FI), using the traditional method and bi-exponential model on average (Bi-ExpAverage) and max speed settings (Bi-ExpMax-Speed). High-speed running (HSR) and sprinting distances and counts, and the number of accelerations were collected from two matches. Magnitude-based inferences (p < 0.05) with smallest worthwhile change of 0.2 effect sizes were used to analyse differences. Pearson's and Spearman's correlation coefficients were used to measure associations between CS model variables and match-play parameters. There were significant differences between the traditional method and both bi-exponential models for CS and D', as well as between the bi-exponential models for all variables except t max. Using the Bi-ExpAverage model, strong correlations (r = 0.70-0.73; p < 0.05) were observed for D' and FI with the number of standardised and individualised HSRs, respectively. With the Bi-ExpMax-Speed model, there were strong correlations (r/ρ = 0.64-0.68; p < 0.05) between D' and the number of standardised HSRs and sprints, and the number of individualised sprints. There is a lack of interchangeability between analysis methods. It appears that D' and FI from the bi-exponential models could be associated with high-intensity actions in soccer match-play.

Time to Lung Volume Stability After Pressure Change During High-Frequency Oscillatory Ventilation.

Prospective observational study. Regional quaternary teaching hospital neonatal ICU. Thirteen term or near-term infants receiving high-frequency oscillatory ventilation and muscle relaxants. One to two cm H2O mean airway pressure changes every 10 minutes as part of an open lung strategy based on oxygen response. Continuous lung volume measurements (respiratory inductive plethysmography) were made during the mean airway pressure changes. Volume signals were analyzed with a biexponential model to calculate the time to stable lung volume if the model R 2 was greater than 0.6. If volume stability did not occur within 10 minutes, the model was extrapolated to maximum 3,600 s. One-hundred ninety-six mean airway pressure changes were made, with no volume change in 33 occurrences (17%). One-hundred twenty-five volume signals met modeling criteria for inclusion; median (interquartile range) R 2, 0.96 (0.91-0.98). The time to stable lung volume was 1,131 seconds (718-1,959 s) (mean airway pressure increases) and 647 seconds (439-1,309 s) (mean airway pressure decreases), with only 17 (14%) occurring within 10 minutes and time to stability being longer when the lung was atelectatic. During high-frequency oscillatory ventilation, the time to stable lung volume after a mean airway pressure change is variable, often requires more than 10 minutes, and is dependent on the preceding volume state.

Comparison of mono-exponential, bi-exponential, and stretched exponential diffusion-weighted MR imaging models in differentiating hepatic hemangiomas from liver metastases

PurposeThis study aims to compare the diagnostic values of mono-exponential, bi-exponential, and stretched exponential diffusion-weighted imaging (DWI) in differentiating hepatic hemangiomas and liver metastases. MethodThis prospective study was approved by our institutional review board, and written informed consent was obtained from all patients. In this study, 244 patients with known or suspected liver disease underwent magnetic resonance imaging. Among them, 37 patients who had focal hepatic lesions with a maximum diameter of ≥10 mm were evaluated. Using home-built software, two radiologists measured the DWI parameters of hepatic lesions for the three models: the apparent diffusion coefficient (ADC) from a mono-exponential model; the true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from a bi-exponential model; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) from a stretched exponential model. The parameters were compared between hepatic hemangiomas and liver metastases. ResultsIn total, 64 focal hepatic lesions were evaluated, of which 22 were identified to be hepatic hemangiomas and 42 were liver metastases. ADC, D, f, and DDC values were significantly lower in liver metastases than in hepatic hemangiomas (P < 0.0001, < 0.0001, 0.015, and < 0.0001, respectively); whereas, the α value was significantly higher in liver metastases than in hepatic hemangiomas (P = 0.028). The areas under the ROC curve (AUCs) for differentiating hepatic hemangiomas and liver metastases in ADC, D, D*, f, DDC, and α were 0.940, 0.908, 0.608, 0.686, 0.952, and 0.667, respectively. The AUC values of ADC and DDC were significantly greater than those of D* (P < 0.0001), f (P = 0.0001), and α values (P = 0.0001). ConclusionADC and DDC values from the mono-exponential and stretched exponential models could be considered as quantitative imaging biomarkers for differentiating hepatic hemangiomas and liver metastases.

Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models

BackgroundTo investigate the performance of diffusion-weighted (DW) MRI with mono-, bi- and stretched-exponential models in predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) for breast cancer, and further outline a predictive model of pCR combining DW MRI parameters, contrast-enhanced (CE) MRI findings, and/or clinical-pathologic variables.MethodsIn this retrospective study, 144 women who underwent NACT and subsequently received surgery for invasive breast cancer were included. Breast MRI including multi-b-value DW imaging was performed before (pre-treatment), after two cycles (mid-treatment), and after all four cycles (post-treatment) of NACT. Quantitative DW imaging parameters were computed according to the mono-exponential (apparent diffusion coefficient [ADC]), bi-exponential (pseudodiffusion coefficient and perfusion fraction), and stretched-exponential (distributed diffusion coefficient and intravoxel heterogeneity index) models. Tumor size and relative enhancement ratio of the tumor were measured on contrast-enhanced MRI at each time point. Pre-treatment parameters and changes in parameters at mid- and post-treatment relative to baseline were compared between pCR and non-pCR groups. Receiver operating characteristic analysis and multivariate regression analysis were performed.ResultsOf the 144 patients, 54 (37.5%) achieved pCR after NACT. Overall, among all DW and CE MRI measures, flow-insensitive ADC change (ΔADC200,1000) at mid-treatment showed the highest diagnostic performance for predicting pCR, with an area under the receiver operating characteristic curve (AUC) of 0.831 (95% confidence interval [CI]: 0.747, 0.915; P < 0.001). The model combining pre-treatment estrogen receptor and human epidermal growth factor receptor 2 statuses and mid-treatment ΔADC200,1000 improved the AUC to 0.905 (95% CI: 0.843, 0.966; P < 0.001).ConclusionMono-exponential flow-insensitive ADC change at mid-treatment was a predictor of pCR after NACT in breast cancer.

Exploring the diagnostic potential of adding T2 dependence in diffusion-weighted MR imaging of the prostate.

Magnetic resonance imaging (MRI) is essential in the detection and staging of prostate cancer. However, improved tools to distinguish between low-risk and high-risk cancer are needed in order to select the appropriate treatment. To investigate the diagnostic potential of signal fractions estimated from a two-component model using combined T2- and diffusion-weighted imaging (T2-DWI). 62 patients with prostate cancer and 14 patients with benign prostatic hyperplasia (BPH) underwent combined T2-DWI (TE = 55 and 73 ms, b-values = 50 and 700 s/mm2) following clinical suspicion of cancer, providing a set of 4 measurements per voxel. Cancer was confirmed in post-MRI biopsy, and regions of interest (ROIs) were delineated based on radiology reporting. Signal fractions of the slow component (SFslow) of the proposed two-component model were calculated from a model fit with 2 free parameters, and compared to conventional bi- and mono-exponential apparent diffusion coefficient (ADC) models. All three models showed a significant difference (p<0.0001) between peripheral zone (PZ) tumor and normal tissue ROIs, but not between non-PZ tumor and BPH ROIs. The area under the receiver operating characteristics curve distinguishing tumor from prostate voxels was 0.956, 0.949 and 0.949 for the two-component, bi-exponential and mono-exponential models, respectively. The corresponding Spearman correlation coefficients between tumor values and Gleason Grade Group were fair (0.370, 0.499 and -0.490), but not significant. Signal fraction estimates from a two-component model based on combined T2-DWI can differentiate between tumor and normal prostate tissue and show potential for prostate cancer diagnosis. The model performed similarly to conventional diffusion models.