Histogram analysis in prostate cancer: a comparison of diffusion kurtosis imaging model versus monoexponential model

Background There is still little research about histogram analysis of diffusion kurtosis imaging (DKI) using in prostate cancer at present. Purpose To verify the utility of histogram analysis of DKI model in detection and assessment of aggressiveness of prostate cancer, compared with monoexponential model (MEM). Material and Methods Twenty-three patients were enrolled in this study. For DKI model and MEM, the Dapp, Kapp, and apparent diffusion coefficient (ADC) were obtained by using single-shot echo-planar imaging sequence. The pathologies were confirmed by in-bore magnetic resonance (MR)-guided biopsy. Regions of interest (ROI) were drawn manually in the position where biopsy needle was put. The mean values and histogram parameters in cancer and noncancerous foci were compared using independent-samples T test. Receiver operating characteristic curves were used to investigate the diagnostic efficiency. Spearman’s test was used to evaluate the correlation of parameters and Gleason scores. Results The mean, 10th, 25th, 50th, 75th, and 90th percentiles of ADC and Dapp were significantly lower in prostate cancer than non-cancerous foci ( P < 0.001). The mean, 50th, 75th, and 90th percentiles of Kapp were significantly higher in prostate cancer ( P < 0.05). There was no significant difference between the AUCs of two models (0.971 vs. 0.963, P > 0.05). With the increasing Gleason scores, the 10th ADC decreased ( ρ = −0.583, P = 0.018), but the 90th Kapp increased ( ρ = 0.642, P = 0.007). Conclusion Histogram analysis of DKI model is feasible for diagnosing and grading prostate cancer, but it has no significant advantage over MEM.

Aging is associated with altered vasodilator kinetics in dynamically contracting muscle: role of nitric oxide

We tested the hypothesis that aging would be associated with slowed vasodilator kinetics in contracting muscle in part due to a reduced nitric oxide (NO) bioavailability. Young ( n = 10; 24 ± 2 yr) and older ( n = 10; 67 ± 2 yr) adults performed rhythmic forearm exercise (4 min each) at 10, 20, and 30% of max during saline infusion (control) and NO synthase (NOS) inhibition. Brachial artery diameter and velocities were measured using Doppler ultrasound. Forearm vascular conductance (FVC) was calculated for each duty cycle (1 s contraction/2 s relaxation) from forearm blood flow (FBF; ml/min) and blood pressure (mmHg) and fit with a monoexponential model. The main parameters derived from the model were the amplitude of the FBF and FVC response and the number of duty cycles for FBF and FVC to change 63% of the steady-state amplitude (τFBF and τFVC). Under control conditions 1) the amplitude of the FVC response at 30% maximal voluntary contraction (MVC) was lower in older compared with young adults (319 ± 33 vs. 462 ± 52 ml·min−1·100 mmHg−1; P < 0.05) and 2) τFVC was slower in older (10 ± 1, 13 ± 1, and 15 ± 1 duty cycles) compared with young (6 ± 1, 9 ± 1, and 11 ± 1 duty cycles) adults at all intensities ( P < 0.05). In young adults, NOS inhibition blunted the amplitude of the FVC response at 30% MVC and prolonged the τFVC at all intensities (10 ± 2, 12 ± 1, and 16 ± 2 duty cycles; P < 0.05), whereas it did not change in older adults. Our data indicate that the blood flow and vasodilator kinetics in exercising muscle are altered with aging possibly due to blunted NO signaling.

Accuracies and Contrasts of Models of the Diffusion-weighted-dependent Attenuation of the Mri Signal at Intermediate B-values

The diffusion-weighted-dependent attenuation of the MRI signal E( b) is extremely sensitive to microstructural features. The aim of this study was to determine which mathematical model of the E( b) signal most accurately describes it in the brain. The models compared were the monoexponential model, the stretched exponential model, the truncated cumulant expansion (TCE) model, the biexponential model, and the triexponential model. Acquisition was performed with nine b-values up to 2500 s/mm2 in 12 healthy volunteers. The goodness-of-fit was studied with F-tests and with the Akaike information criterion. Tissue contrasts were differentiated with a multiple comparison corrected nonparametric analysis of variance. F-test showed that the TCE model was better than the biexponential model in gray and white matter. Corrected Akaike information criterion showed that the TCE model has the best accuracy and produced the most reliable contrasts in white matter among all models studied. In conclusion, the TCE model was found to be the best model to infer the microstructural properties of brain tissue.

Optimal Experiment Design for Monoexponential Model Fitting: Application to Apparent Diffusion Coefficient Imaging

The monoexponential model is widely used in quantitative biomedical imaging. Notable applications include apparent diffusion coefficient (ADC) imaging and pharmacokinetics. The application of ADC imaging to the detection of malignant tissue has in turn prompted several studies concerning optimal experiment design for monoexponential model fitting. In this paper, we propose a new experiment design method that is based on minimizing the determinant of the covariance matrix of the estimated parameters (D-optimal design). In contrast to previous methods, D-optimal design is independent of the imaged quantities. Applying this method to ADC imaging, we demonstrate its steady performance for the whole range of input variables (imaged parameters, number of measurements, and range ofb-values). Using Monte Carlo simulations we show that the D-optimal design outperforms existing experiment design methods in terms of accuracy and precision of the estimated parameters.