Assessment of tumor blood flow distribution by dynamic contrast-enhanced CT.

A distinct feature of the tumor vasculature is its tortuosity and irregular branching of vessels, which can translate to a wider dispersion and higher variability of blood flow in the tumor. To enable tumor blood flow variability to be assessed in vivo by imaging, a tracer kinetic model that accounts for flow dispersion is developed for use with dynamic contrast-enhanced (DCE) CT. The proposed model adopts a multiple-pathway approach and allows for the quantification of relative dispersion in the blood flow distribution, which reflects flow variability in the tumor vasculature. Monte Carlo simulation experiments were performed to study the possibility of reducing the number of model parameters based on the Akaike information criterion approach and to explore possible noise and tissue conditions in which the model might be applicable. The model was used for region-of-interest analysis and to generate perfusion parameter maps for three patient DCE CT cases with cerebral tumors, to illustrate clinical applicability.

Issues of discontinuity in the impulse residue function for deconvolution analysis of dynamic contrast-enhanced MRI data.

Recent dynamic contrast-enhanced MRI studies using the adiabatic tissue homogeneity model have highlighted potential issues of difficulty in convergence during data-fitting and reduced parameter precision, due to discontinuities in the adiabatic tissue homogeneity model. This study presents two solutions (an analytic approach and a discrete correction method) to such convergence problems and show that these problems can be attributed to an inaccurate approximation of the convolution integral based on the standard trapezoidal quadrature. It is further explained that such issues of discontinuity in the impulse residue function do not pertain only to the adiabatic tissue homogeneity model, but are generic to all tracer kinetic models, if the difference in bolus arrival time between the arterial input and tissue voxels were to be accounted for simultaneously during model-fitting.

Impact of severe respiratory syndrome on anxiety levels of front-line health care workers.

OBJECTIVE: To identify anxiety levels among front-line health care workers during the 2003 severe acute respiratory syndrome outbreak. DESIGN: Questionnaire survey. SETTING: Regional hospital, Hong Kong. PARTICIPANTS: All hospital staff were given a questionnaire; administrative staff who had not had any patient contact served as controls. MAIN OUTCOME MEASURES: Levels of contact with patients who had severe acute respiratory syndrome were measured and correlated with anxiety levels as determined by the State-Trait Anxiety Inventory. RESULTS: Of 4252 questionnaires distributed between May and June 2003, 2040 (48.0%) were returned and 1926 (45.3%) were valid for analysis. Overall, 534 (27.7%) respondents had had contact with patients with severe acute respiratory syndrome. Anxiety scores ranged from 20 to 80, and mean (standard deviation) scores were higher among staff who had had contact with patients with severe acute respiratory syndrome than among those who had not (52.6 [10.5] versus 49.8 [10.1], respectively; P<0.01). Mean anxiety levels were higher among workmen, health care assistants, and nurses than among administrative staff controls or doctors (P<0.01). Anxiety scores were correlated with burnout scores (Pearson's correlation coefficient, 0.52-0.59) and with discomfort from wearing protective gear (0.21-0.32). CONCLUSION: Severe acute respiratory syndrome has likely stressed the public health care system. Prediction and early identification of adverse factors in a crisis situation would allow early implementation of interventions to reduce and counteract the impact of this stress.