Computational tools and methods for objective assessment of image quality in x-ray CT and SPECT

Computational tools of use in the objective assessment of image quality for tomography systems were developed for computer processing units (CPU) and graphics processing units (GPU) in the image quality lab at the University of Arizona. Fast analytic x-ray projection code called IQCT was created to compute the mean projection image for cone beam multi-slice helical computed tomography (CT) scanners. IQCT was optimized to take advantage of the massively parallel architecture of GPUs. CPU code for computing single photon emission computed tomography (SPECT) projection images was written calling upon previous research in the image quality lab.;IQCT and the SPECT modeling code were used to simulate data for multi-modality SPECT/CT observer studies. The purpose of these observer studies was to assess the benefit in image quality of using attenuation information from a CT measurement in myocardial SPECT imaging. The observer chosen for these studies was the scanning linear observer. The tasks for the observer were localization of a signal and estimation of the signal radius. For the localization study, area under the localization receiver operating characteristic curve (A LROC) was computed as AMeasLROC = 0.89332 ± 0.00474 and ANoLROC = 0.89408 ± 0.00475, where "Meas" implies the use of attenuation information from the CT measurement, and "No" indicates the absence of attenuation information. For the estimation study, area under the estimation receiver operating characteristic curve (AEROC) was quantified as AMeasEROC = 0.55926 ± 0.00731 and ANoEROC = 0.56167 ± 0.00731. Based on these results, it was concluded that the use of CT information did not improve the scanning linear observer's ability to perform the stated myocardial SPECT tasks. The risk to the patient of the CT measurement was quantified in terms of excess effective dose as 2.37 mSv for males and 3.38 mSv for females.;Another image quality tool generated within this body of work was a singular value decomposition (SVD) algorithm to reduce the dimension of the eigenvalue problem for tomography systems with rotational symmetry. Agreement in the results of this reduced dimension SVD algorithm and those of a standard SVD algorithm are shown for a toy problem. The use of SVD toward image quality metrics such as the measurement and null space are also presented.