Radiation therapy (RT) is a standard treatment after lumpectomy for breast cancer, involving a typical course of
approximately 6-7 weeks of daily treatment. Many women find this cumbersome and costly, and therefore many are
left with the option of mastectomy. Many groups are now investigating novel ways to deliver RT, by using different
techniques and shortening the course of treatment. However, the efficacy and side effects of these strategies are not
known. In this project, we wish to develop noninvasive imaging tools that would allow us to measure radiation dose
effects in women with breast cancer. We hope this will lead to new ways to identify individuals who may not need
radiation therapy, who may safely be treated with new accelerated techniques, or who should be treated with the
standard radiation therapy approach. We propose to study the effect of radiation therapy using a combination of two
imaging modalities: 1) magnetic resonance imaging (MRI) which will provide detailed information on breast
structures and blood vessels and 2) near infra-red diffuse optical spectroscopy (DOS), which measures local biologic
properties of breast tissue. Our hypothesis is that by using a combination of modalities we will be able to better
characterize radiation effects in breast tissue, by measuring differences between the radiated and non-irradiated
breast. The development of novel non-invasive tools providing information about how individuals respond to
radiation therapy can lead to important improvement of radiation treatment, and ultimately help guide individualized
treatment programs in the future.
The objective of this study was to develop a segmentation technique to quantify breast tissue and total breast volume from MRI data. The goal of our research is to quantify breast density using MRI to help better assess breast cancer risk for certain high-risk populations for whom mammography is of limited usefulness due to their high breast density. A semi-automatic segmentation technique was implemented based on a fuzzy inference system to segment 3D breast tissue from fat, and quantify the total volume of the breast in order to obtain an index of MR breast density on 10 healthy volunteers. The algorithm was based on two non-contrast 3D MR sequences. A fuzzy c-means algorithm was used to provide a first estimate of the segmentation of breast tissue from fat on specific slices. Based on the means and standard deviations of the segmented groups (breast tissue and fat) Sugeno-type fuzzy inference systems were built and then used as the main segmentation tools to segment surrounding slices. Results of volumetric measurements and breast density index obtained with the semi-automated method were compared with quantitative results obtained using classical global thresholding segmentation technique.
The general objective of our study is the development of a clinically robust three-dimensional segmentation and quantification technique of Magnetic Resonance (MR) data, for the objective and quantitative evaluation of the osteonecrosis (ON) of the femoral head. This method will help evaluate the effects of joint preserving treatments for femoral head osteonecrosis from MR data. The disease is characterized by tissue changes (death of bone and marrow cells) within the weight-bearing portion of the femoral head. Due to the fuzzy appearance of lesion tissues and their different intensity patterns in various MR sequences, we proposed a semi-automatic multispectral segmentation of MR data introducing data constraints (anatomical and geometrical) and using a classical K-means unsupervised clustering algorithm. The method was applied on ON patient data. Results of volumetric measurements and configuration of various tissues obtained with the semi- automatic method were compared with quantitative results delineated by a trained radiologist.
In this study we compared trabecular bone mineral density (BMD) with textural parameters (cooccurence matrices features) extracted from trabecular bone structures in radiographic images of the hand. Our data consists of 12 cadaver hands radiographed and digitized. After application of a specific preprocessing step on all images, the textural parameters were calculated within 4 regions of interest defined within the metacarpal and proximal phalanges on trabecular bone. The results show that using a combination of textural parameters calculated at different directions within the ROI could increase significantly the correlation with BMD. Some further research will validate this finding on a larger set of data. This work is intended to be applicable in the study of bone fractures associated with osteoporosis, and could be of great benefit to a large segment of the population at risk.
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