Laser Doppler flowmetry (LDF) is now a well-established, noninvasive 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.

We propose a novel index for quantitating arterial tortuosity, based on second differences of the coordinates of the vessel midline. The index is independent of the magnification of the image and of sampling frequency. We have demonstrated its validity as an indicator of changes in morphology using simulated shapes. It is superior to two other putative indices based on vessel elongation and the variance of differences in the vessel midline coordinates, presented previously in the literature. The proposed index is easily generalised to three dimensions, allowing a true three-dimensional tortuosity index to be calculated from biplane angiograms or serial tomographic slices. It has a number of clinical applications. Preliminary data, using 82 aortograms from radiographic archives, showed a significant correlation between the tortuosity of the abdominal aorta and subject age (r=0.684, P<0.001). For this group, increased tortuosity was especially evident for subjects above 40 yr of age.

The purpose of this study is to accurately and effectively automate the calculation of the water‐equivalent diameter () from 3D CT images for estimating the size‐specific dose. is the metric that characterizes the patient size and attenuation. In this study, 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 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 based on the automated contouring for every slice (). The results of this study show that the automated calculation of and the manual calculation are in good agreement for phantoms and patients. The differences between the automated calculation of and the manual calculation are less than 0.5%. The results of this study also show that the estimating of using (central slice along longitudinal axis) produces percentage differences of and , and estimating using produces percentage differences of and , for thorax and head examinations, respectively. From this study, the percentage differences between normalized size‐specific dose estimate for every slice () and are and for thorax and head examinations, respectively; between and are and for thorax and head examinations, respectively.PACS number(s): 87.57.Q‐, 87.57.uq‐

Fractal analysis is a method of characterizing complex shapes such as the trabecular structure of bone. Numerous algorithms for estimating fractal dimension have been described, but the Fourier power spectrum method is particularly applicable to self-affine fractals, and facilitates corrections for the effects of noise and blurring in an image. We found that it provided accurate estimates of fractal dimension for synthesized fractal images. For natural texture images fractality is limited to a range of scales, and the fractal dimension as a function of spatial frequency presents as a fractal signature. We found that the fractal signature was more successful at discriminating between these textures than either the global fractal dimension or other metrics such as the mean width and root-mean-square width of the spectral density plots. Different natural textures were also readily distinguishable using lacunarity plots, which explicitly characterize the average size and spatial organization of structural sub-units within an image. The fractal signatures of small regions of interest (32x32 pixels), computed in the frequency domain after corrections for imaging system noise and MTF, were able to characterize the texture of vertebral trabecular bone in CT images. Even small differences in texture due to acquisition slice thickness resulted in measurably different fractal signatures. These differences were also readily apparent in lacunarity plots, which indicated that a slice thickness of 1 mm or less is necessary if essential architectural information is not to be lost. Since lacunarity measures gap size and is not predicated on fractality, it may be particularly useful for characterizing the texture of trabecular bone.