3 resultados para Image processing technique
em DigitalCommons@The Texas Medical Center
Resumo:
Extensive experience with the analysis of human prophase chromosomes and studies into the complexity of prophase GTG-banding patterns have suggested that at least some prophase chromosomal segments can be accurately identified and characterized independently of the morphology of the chromosome as a whole. In this dissertation the feasibility of identifying and analyzing specified prophase chromosome segments was thus investigated as an alternative approach to prophase chromosome analysis based on whole chromosome recognition. Through the use of prophase idiograms at the 850-band-stage (FRANCKE, 1981) and a comparison system based on the calculation of cross-correlation coefficients between idiogram profiles, we have demonstrated that it is possible to divide the 24 human prophase idiograms into a set of 94 unique band sequences. Each unique band sequence has a banding pattern that is recognizable and distinct from any other non-homologous chromosome portion.^ Using chromosomes 11p and 16 thru 22 to demonstrate unique band sequence integrity at the chromosome level, we found that prophase chromosome banding pattern variation can be compensated for and that a set of unique band sequences very similar to those at the idiogram level can be identified on actual chromosomes.^ The use of a unique band sequence approach in prophase chromosome analysis is expected to increase efficiency and sensitivity through more effective use of available banding information. The use of a unique band sequence approach to prophase chromosome analysis is discussed both at the routine level by cytogeneticists and at an image processing level with a semi-automated approach to prophase chromosome analysis. ^
Resumo:
Purpose: Respiratory motion causes substantial uncertainty in radiotherapy treatment planning. Four-dimensional computed tomography (4D-CT) is a useful tool to image tumor motion during normal respiration. Treatment margins can be reduced by targeting the motion path of the tumor. The expense and complexity of 4D-CT, however, may be cost-prohibitive at some facilities. We developed an image processing technique to produce images from cine CT that contain significant motion information without 4D-CT. The purpose of this work was to compare cine CT and 4D-CT for the purposes of target delineation and dose calculation, and to explore the role of PET in target delineation of lung cancer. Methods: To determine whether cine CT could substitute 4D-CT for small mobile lung tumors, we compared target volumes delineated by a physician on cine CT and 4D-CT for 27 tumors with intrafractional motion greater than 1 cm. We assessed dose calculation by comparing dose distributions calculated on respiratory-averaged cine CT and respiratory-averaged 4D-CT using the gamma index. A threshold-based PET segmentation model of size, motion, and source-to-background was developed from phantom scans and validated with 24 lung tumors. Finally, feasibility of integrating cine CT and PET for contouring was assessed on a small group of larger tumors. Results: Cine CT to 4D-CT target volume ratios were (1.05±0.14) and (0.97±0.13) for high-contrast and low-contrast tumors respectively which was within intraobserver variation. Dose distributions on cine CT produced good agreement (< 2%/1 mm) with 4D-CT for 71 of 73 patients. The segmentation model fit the phantom data with R2 = 0.96 and produced PET target volumes that matched CT better than 6 published methods (-5.15%). Application of the model to more complex tumors produced mixed results and further research is necessary to adequately integrate PET and cine CT for delineation. Conclusions: Cine CT can be used for target delineation of small mobile lesions with minimal differences to 4D-CT. PET, utilizing the segmentation model, can provide additional contrast. Additional research is required to assess the efficacy of complex tumor delineation with cine CT and PET. Respiratory-averaged cine CT can substitute respiratory-averaged 4D-CT for dose calculation with negligible differences.
Resumo:
Arterial spin labeling (ASL) is a technique for noninvasively measuring cerebral perfusion using magnetic resonance imaging. Clinical applications of ASL include functional activation studies, evaluation of the effect of pharmaceuticals on perfusion, and assessment of cerebrovascular disease, stroke, and brain tumor. The use of ASL in the clinic has been limited by poor image quality when large anatomic coverage is required and the time required for data acquisition and processing. This research sought to address these difficulties by optimizing the ASL acquisition and processing schemes. To improve data acquisition, optimal acquisition parameters were determined through simulations, phantom studies and in vivo measurements. The scan time for ASL data acquisition was limited to fifteen minutes to reduce potential subject motion. A processing scheme was implemented that rapidly produced regional cerebral blood flow (rCBF) maps with minimal user input. To provide a measure of the precision of the rCBF values produced by ASL, bootstrap analysis was performed on a representative data set. The bootstrap analysis of single gray and white matter voxels yielded a coefficient of variation of 6.7% and 29% respectively, implying that the calculated rCBF value is far more precise for gray matter than white matter. Additionally, bootstrap analysis was performed to investigate the sensitivity of the rCBF data to the input parameters and provide a quantitative comparison of several existing perfusion models. This study guided the selection of the optimum perfusion quantification model for further experiments. The optimized ASL acquisition and processing schemes were evaluated with two ASL acquisitions on each of five normal subjects. The gray-to-white matter rCBF ratios for nine of the ten acquisitions were within ±10% of 2.6 and none were statistically different from 2.6, the typical ratio produced by a variety of quantitative perfusion techniques. Overall, this work produced an ASL data acquisition and processing technique for quantitative perfusion and functional activation studies, while revealing the limitations of the technique through bootstrap analysis. ^