PURPOSE: To investigate the predictive value of the ischemic lesion size, as depicted in the acute stroke phase on diffusion-weighted magnetic resonance (MR) images and time-to-peak (TTP) maps of tissue perfusion imaging, for infarct size, as derived from T2-weighted imaging in the postacute phase. MATERIALS AND METHODS: Fifty patients who underwent diffusion-weighted and perfusion imaging within 1–24 hours after stroke onset and a follow-up T2-weighted investigation after about 8 days were included. Lesion volumes were evaluated by using a semiautomatic thresholding technique. Volumetric results of acute diffusion-weighted and perfusion imaging were analyzed in comparison with follow-up T2-weighted images and in terms of the time difference between symptom onset and initial MR imaging. RESULTS: At diffusion-weighted imaging, the acute lesion defined by a signal intensity increase of more than 20%, compared with the contralateral side, showed the best correlation with the infarct size after 1 week. At per...

The signal of diffusion‐weighted imaging of the human kidney differs from the signal in brain examinations due to the different microscopic structure of the tissue. In the kidney, the deviation of the signal behavior of monoexponential characteristics is pronounced. The aim of the study was to analyze whether a mono‐ or biexponential or a distribution function model fits best to describe diffusion characteristics in the kidney. To determine the best regression, different statistical parameters were utilized: correlation coefficient (R²), Akaike's information criterion, Schwarz criterion, and F‐test (F_ratio). Additionally, simulations were performed to analyze the relation between the different models and their dependency on signal noise. Statistical tests showed that the biexponential model describes the signal of diffusion‐weighted imaging in the kidney better than the distribution function model. The monoexponential model fits the diffusion‐weighted imaging data the least but is the most robust against signal noise. From a statistical point of view, diffusion‐weighted imaging of the kidney should be modeled biexponentially under the precondition of sufficient signal to noise. Magn Reson Med, 2010.

Purpose: To evaluate the influence of pulsatile blood flow on apparent diffusion coefficients (ADC) and the fraction of pseudodiffusion (F_P) in the human kidney. Materials and Methods: The kidneys of 6 healthy volunteers were examined by a 3-T magnetic resonance scanner. Electrocardiogram (ECG)-gated and respiratory-triggered diffusion-weighted imaging (DWI) and phase-contrast flow measurements were performed. Flow imaging of renal arteries was carried out to quantify the dependence of renal blood flow on the cardiac cycle. ECG-triggered DWI was acquired in the coronal plane with 16 b values in the range of 0 s/mm² and 750 s/mm² at the time of minimum (MIN) (20 milliseconds after R wave) and maximum renal blood flow (MAX) (197 ± 24 milliseconds after R wave). The diffusion coefficients were calculated using the monoexponential approach as well as the biexponential intravoxel incoherent motion model and correlated to phase-contrast flow measurements. Results: Flow imaging showed pulsatile renal blood flow depending on the cardiac cycle. The mean flow velocity at MIN was 45 cm/s as compared with 61 cm/s at MAX. F_p at MIN (0.29) was significantly lower than at MAX (0.40) (P = 0.001). Similarly, ADC_mono, derived from the monoexponential model, also showed a significant difference (P < 0.001) between MIN (ADC_mono = 2.14 ± 0.08 × 10⁻³ mm²/s) and MAX (ADC_mono = 2.37 ± 0.04 × 10⁻³ mm²/s). The correlation between renal blood flow and F_p (r = 0.85) as well as ADC_mono (r = 0.67) was statistically significant. Conclusion: Temporally resolved ECG-gated DWI enables for the determination of the diffusion coefficients at different time points of the cardiac cycle. ADC_mono and F_P vary significantly among acquisitions at minimum (diastole) and maximum (systole) renal blood flow. Temporally resolved ECG-gated DWI might therefore serve as a novel technique for the assessment of pulsatility in the human kidney.

Background: Human emotional expressions serve an important communicatory role allowing the rapid transmission of valence information among individuals. We aimed at exploring the neural networks mediating the recognition of and empathy with human facial expressions of emotion. Methods: A principal component analysis was applied to event-related functional magnetic imaging (fMRI) data of 14 right-handed healthy volunteers (29 +/- 6 years). During scanning, subjects viewed happy, sad and neutral face expressions in the following conditions: emotion recognition, empathizing with emotion, and a control condition of simple object detection. Functionally relevant principal components (PCs) were identified by planned comparisons at an alpha level of p < 0.001. Results: Four PCs revealed significant differences in variance patterns of the conditions, thereby revealing distinct neural networks: mediating facial identification (PC 1), identification of an expressed emotion (PC 2), attention to an expressed emotion (PC 12), and sense of an emotional state (PC 27). Conclusion: Our findings further the notion that the appraisal of human facial expressions involves multiple neural circuits that process highly differentiated cognitive aspects of emotion.