Index of content:
Volume 31, Issue 12, December 2004
- PH. D. THESES ABSTRACTS
31(2004); http://dx.doi.org/10.1118/1.1812891View Description Hide Description
Helical tomotherapy is a new intensity-modulated radiotherapy(IMRT) delivery process developed at the University of Wisconsin and TomoTherapy Inc. Tomotherapy may be of advantage in lungcancertreatment due to its rotational delivery mode. As with conventional IMRT delivery, however, intrafraction respiratory motion during a tomotherapy treatment causes unnecessary radiation to the healthy tissue. Possible solutions to these problems associated with intrafraction motion have been studied in this thesis. A spirometer is useful for monitoring breathing because of its direct correlation with lung volume changes. However, its inherent drift prevents its application in long-term breathing monitoring. With calibration and stabilization algorithms, a spirometer is able to provide accurate, long-term lung volume change measurements. Such a spirometer system is most suited for deep inspiration breath-hold (DIBH) treatments. An improved laser-spirometer combined system has also been developed for target tracking in 4-D treatment. Spirometer signals are used to calibrate the displacement measurements into lung volume changes, thereby eliminating scaling errors from daily setup variations. The laser displacement signals may also be used to correct spirometer drifts during operation. A new 4-D treatment technique has been developed to account for intrafraction motion in treatment planning. The patient’s breathing and the beam delivery are synchronized, and the target motion/deformation is incorporated into treatment plan optimization. Results show that this new 4D treatment technique significantly reduces motion effects and provides improved patient tolerance.
31(2004); http://dx.doi.org/10.1118/1.1820014View Description Hide Description
SPECTbrainimaging of the dopaminergic system using and labeled agents, especially the simultaneous imaging of both pre- and postsynaptic neurons, promises to provide accurate diagnosis and differentiation of Parkinsonism. However, there are many degrading factors that affect the quality and quantitative accuracy of the SPECTimages. These degrading factors limit the potential clinical applications of brainSPECTimaging. In this work, we studied these degrading factors by developing and validating a Monte Carlo(MC) method that provides accurate SPECT simulation with detailed modeling of the photon interactions inside the collimatordetector system. To compensate for the partial volume effect (PVE) in the SPECTimages caused by finite spatial resolution, we developed a new PVE compensation method that takes into account the effects of nonlinearity in iterative reconstruction-based compensation for image degrading factors, including attenuation, scatter, and collimatordetector response. Compensation using the new method greatly improved the quantitative accuracy of brainSPECTimages. We have also developed model-based method that can accurately estimate the downscatter and crosstalk contamination in the imaging and the simultaneous dual-isotope imaging. Based on the model-based method, two different approaches to model-based downscatter and crosstalk contamination compensation were proposed. Both methods are based on iterative reconstruction and include compensation for other imaging degrading factors. The model-based downscatter and crosstalk compensation method provided greatly improved accuracy of activity estimates with little effect on the precision. Finally, optimization of energy windows for simultaneous acquisition was performed to find the energy windows with the best trade-off between minimizing the crosstalk and maximizing the detection efficiency for simultaneous acquisitions. In summary, comprehensive methods were developed and evaluated to compensate for image degrading factors in simultaneous dual-isotope brainSPECTimaging. Application of these methods in the imaging of the dopaminergic system has the potential to provide improved accuracy for diagnosis of Parkinsonism.