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Laser Doppler flowmetry (LDF) is now a well-established, non-invasive technique for measuring microvascular blood perfusion. However, there are a number of factors which can seriously affect the interpretation of the laser Doppler signal which are often not considered during routine use. These include: consideration of signal processing limitations, choice of processing bandwidth, problems with motion artefact and instrument calibration, the effect of probe pressure on the skin, and the type of laser used. This paper reviews many of the problems and limitations frequently encountered using the laser Doppler technique.

The limited spatial resolution of clinical CT systems causes difficulties in the measurement of the density and thickness of thin structures such as the vertebral cortical shell. We simulated the imaging process by convolving experimentally determined point spread functions with rectangular and Gaussian profiles, for various fields of view or pixel sizes and reconstruction kernels. The simulations successfully explained the reported overestimation of thickness and underestimation of density when imaging thin structures. Both effects are larger for Gaussian profiles. For the rectangular profiles, experimental estimates of thickness and density will only be accurate when the true thickness is greater than about 1.5 times (for the bone reconstruction kernel) or 2.0 times (for the standard kernel) the full width at half maximum of the point spread function (PSF) of the imaging system. For Gaussian profiles imaged by a system with a Gaussian PSF, there are straightforward analytical expressions for the overestimation of thickness and underestimation of density: and these are useful approximations to the simulations of Gaussian profiles with experimental (pseudo-Gaussian) PSFs. We have demonstrated that thresholding of the vertebral image cannot provide accurate estimates of cortical thickness and density because the appropriate threshold level requires foreknowledge of the cortical thickness. To circumvent such difficulties we suggest that the average value of the peak CT numbers measured along the medial axis of the cortical shell be adopted as an index of cortical shell strength, since its value depends on both the density and the thickness of the shell.

The purpose of this study is to accurately and effectively automate the calculation of the water‐equivalent diameter (normalDnormalW) from 3D CT images for estimating the size‐specific dose. normalDnormalW is the metric that characterizes the patient size and attenuation. In this study, normalDnormalW was calculated for standard CTDI phantoms and patient images. Two types of phantom were used, one representing the head with a diameter of 16 cm and the other representing the body with a diameter of 32 cm. Images of 63 patients were also taken, 32 who had undergone a CT head examination and 31 who had undergone a CT thorax examination. There are three main parts to our algorithm for automated normalDnormalW calculation. The first part is to read 3D images and convert the CT data into Hounsfield units (HU). The second part is to find the contour of the phantoms or patients automatically. And the third part is to automate the calculation of normalDnormalW based on the automated contouring for every slice (normalDW,all). The results of this study show that the automated calculation of normalDnormalW and the manual calculation are in good agreement for phantoms and patients. The differences between the automated calculation of normalDnormalW and the manual calculation are less than 0.5%. The results of this study also show that the estimating of normalDW,all using normalDW,n=1 (central slice along longitudinal axis) produces percentage differences of −0.92%±3.37% and 6.75%±1.92%, and estimating normalDW,all using normalDW,n=9 produces percentage differences of 0.23%±0.16% and 0.87%±0.36%, for thorax and head examinations, respectively. From this study, the percentage differences between normalized size‐specific dose estimate for every slice (nSSDEall) and nSSDEnormaln=1 are 0.74%±2.82% and −4.35%±1.18% for thorax and head examinations, respectively; between nSSDEall and nSSDEnormaln=9 are 0.00%±0.46% and −0.60%±0.24% for thorax and head examinations, respectively.PACS number(s): 87.57.Q‐, 87.57.uq‐