Biomedical subjects

G Collewet

Publications and source records attributed to G Collewet.

4 recordsLinked to original sources

PubMed2005

The SIMRI project: a versatile and interactive MRI simulator.

This paper gives an overview of SIMRI, a new 3D MRI simulator based on the Bloch equation. This simulator proposes an efficient management of the T2* effect, and in a unique simulator integrates most of the simulation features that are offered in different simulators. It takes into account the main static field value and enables realistic simulations of the chemical shift artifact, including off-resonance phenomena. It also simulates the artifacts linked to the static field inhomogeneity like those induced by susceptibility variation within an object. It is implemented in the C language and the MRI sequence programming is done using high level C functions with a simple programming interface. To manage large simulations, the magnetization kernel is implemented in a parallelized way that enables simulation on PC grid architecture. Furthermore, this simulator includes a 1D interactive interface for pedagogic purpose illustrating the magnetization vector motion as well as the MRI contrasts.

Algorithms↗

PubMed2004

Influence of MRI acquisition protocols and image intensity normalization methods on texture classification.

Texture analysis methods quantify the spatial variations in gray level values within an image and thus can provide useful information on the structures observed. However, they are sensitive to acquisition conditions due to the use of different protocols and to intra- and interscanner variations in the case of MRI. The influence was studied of two protocols and four different conditions of normalization of gray levels on the discrimination power of texture analysis methods applied to soft cheeses. Thirty-two samples of soft cheese were chosen at two different ripening periods (16 young and 16 old samples) in order to obtain two different microscopic structures of the protein gel. Proton density and T(2)-weighted MR images were acquired using a spin echo sequence on a 0.2 T scanner. Gray levels were normalized according to four methods: original gray levels, same maximum for all images, same mean for all images, and dynamics limited to micro +/- 3sigma. Regions of interest were automatically defined, and texture descriptors were then computed for the co-occurrence matrix, run length matrix, gradient matrix, autoregressive model, and wavelet transform. The features with the lowest probability of error and average correlation coefficient were selected and used for classification with 1-nearest neighbor (1-NN) classifier. The best results were obtained when using the limitation of dynamics to micro +/- 3sigma, which enhanced the differences between the two classes. The results demonstrated the influence of the normalization method and of the acquisition protocol on the effectiveness of the classification and also on the parameters selected for classification. These results indicate the need to evaluate sensitivity to MR acquisition protocols and to gray level normalization methods when texture analysis is required.

Cheese↗

PubMed2003

Assessment by MRI of local porosity in dough during proving. theoretical considerations and experimental validation using a spin-echo sequence.

Proving is a key stage in the development of the final structure of bread, as invasive measurements may provoke dough collapse. Therefore, better understanding and better control of the nucleation and the growth of bubbles require the development of non-invasive methods of measurement. In the present work, a non-invasive method is presented for the measurement of local dough porosity from MR image analysis. For this, a direct relation between the gray level of a voxel and its gas fraction was established in the absence of heat and mass transfer. At whole dough scale for a one-dimensional expansion, the porosity estimated from the gray level was compared with the porosity estimated from total dough volume measurements in a range of [0.10, 0.74 m(3) of gas/m(3) of dough]. For short proving times (<30 min), MR image analysis underestimated porosity by a maximum of 0.03 m(3) of gas/m(3) of dough, but otherwise the difference between the two means of measurement was within the standard error of total dough measurements (+/-0.01 m(3) of gas/m(3) of dough). Maps of local porosity in dough during proving are also presented and discussed.

Algorithms↗

PubMed2002

Correction of intensity nonuniformity in spin-echo T(1)-weighted images.

This paper presents a method to correct intensity nonuniformity in spin-echo T(1)-weighted images and particularly the inhomogeneities due to RF transmission imperfections which have tissue-dependent effects through the T(1) relaxation times. This method is based on the use of a uniform phantom, first for classic normalization by division by the phantom images, and second for T(1)-correction using the RF transmitted cartography. We present experimental results from a bi-phasic (oil/water) phantom and from a salmon with a 0.2 T imager. The results demonstrate the efficiency of the method in the two cases and its ability to cope with partial volume effect.

Animals↗