Biomedical subjects

Pierre Gravel

Publications and source records attributed to Pierre Gravel.

4 recordsLinked to original sources

PubMed2006

Resolution enhancement in digital x-ray imaging.

We have developed a restoration method for radiographs that enhances image sharpness and reveals bone microstructures that were initially hidden in the soft-tissue glare. The method is two fold: the image is first deconvolved using the Richardson-Lucy algorithm and is then divided with a signal modelling the soft-tissue distribution to increase the overall contrast. Each step has its own merits but the power of the restoration method lies in their combination. The originality of the method is its reliance on a priori information at each step in the processing. We have measured and modelled analytically the point-spread function of a low-dose gas microstrip x-ray detector at several beam energies. We measured the relationship between the local image intensity and the noise variance for these images. The soft-tissue signal was also modelled using a minimum-curvature filtering technique. These results were then combined into an image deconvolution procedure that uses wavelet filtering to reduce restoration noise while keeping the enhanced small-scale features. The method was applied successfully to images of a human-torso phantom and improved the contrast of small details on the bones and in the soft tissues. We measured a mean 54% increase in signal to noise ratio and a mean 105% increase in contrast to noise ratio in the 70 and 140 kVp images we analysed. The method was designed to facilitate the analysis of radiographs by relying on two levels of visual inspection. The contrast of the full image is first enhanced by division with the signal modelling the soft-tissue distribution. Based on the result, a radiologist might decide to zoom in on a given image section. The full restoration method is then applied to that region of interest. Indeed, full image deconvolution is often unnecessary since enhanced small-scale details are not visible at large scale; only the section of interest is processed which is more efficient.

Algorithms↗

PubMed2005

Physical characteristics of a low-dose gas microstrip detector for orthopedic x-ray imaging.

A new scanning slit gas detector dedicated to orthopedic x-ray imaging is presented and evaluated in terms of its fundamental imaging characteristics. The system is based on the micromesh gaseous structure detector and achieves primary signal amplification through electronic avalanche in the gas. This feature, together with high quantum detection efficiency and fan-beam geometry, allows for imaging at low radiation levels. The system is composed of 1764 channels spanning a width of 44.8 cm and is capable of imaging an entire patient at speeds of up to 15 cm/s. The resolution was found to be anisotropic and significantly affected by the beam quality in the horizontal direction, but otherwise sufficient for orthopedic studies. As a consequence of line-by-line acquisition, the images contain some ripple components due to mechanical vibrations combined with variations in the x-ray tube output power. The reported detective quantum efficiency (DQE) values are relatively low (0.14 to 0.20 at 0.5 mm(-1)) as a consequence of a suboptimal collimation geometry. The DQE values were found to be unaffected by the exposure down to 7 microGy, suggesting that the system is quantum limited even for low radiation levels. A system composed of two orthogonal detectors is already in use and can produce dual-view full body scans at low doses. This device could contribute to reduce the risk of radiation induced cancer in sensitive clientele undergoing intensive x-ray procedures, like young scoliotic women.

Calibration↗

PubMed2004

A method for modeling noise in medical images.

We have developed a method to study the statistical properties of the noise found in various medical images. The method is specifically designed for types of noise with uncorrelated fluctuations. Such signal fluctuations generally originate in the physical processes of imaging rather than in the tissue textures. Various types of noise (e.g., photon, electronics, and quantization) often contribute to degrade medical images; the overall noise is generally assumed to be additive with a zero-mean, constant-variance Gaussian distribution. However, statistical analysis suggests that the noise variance could be better modeled by a nonlinear function of the image intensity depending on external parameters related to the image acquisition protocol. We present a method to extract the relationship between an image intensity and the noise variance and to evaluate the corresponding parameters. The method was applied successfully to magnetic resonance images with different acquisition sequences and to several types of X-ray images.

Algorithms↗

PubMed2003

Computer-aided method for quantification of cartilage thickness and volume changes using MRI: validation study using a synthetic model.

The primary objective of this study was to develop a computer-aided method for the quantification of three-dimensional (3-D) cartilage changes over time in knees with osteoarthritis (OA). We introduced a local coordinate system (LCS) for the femoral and tibial cartilage boundaries that provides a standardized representation of cartilage geometry, thickness, and volume. The LCS can be registered in different data sets from the same patient so that results can be directly compared. Cartilage boundaries are segmented from 3-D magnetic resonance (MR) slices with a semi-automated method and transformed into offset-maps, defined by the LCS. Volumes and thickness are computed from these offset-maps. Further anatomical labeling allows focal volumes to be evaluated in predefined subregions. The accuracy of the automated behavior of the method was assessed, without any human intervention, using realistic, synthetic 3-D MR images of a human knee. The error in thickness evaluation is lower than 0.12 mm for the tibia and femur. Cartilage volumes in anatomical subregions show a coefficient of variation ranging from 0.11% to 0.32%. This method improves noninvasive 3-D analysis of cartilage thickness and volume and is well suited for in vivo follow-up clinical studies of OA knees.

Algorithms↗