The accurate determination of x-ray signal properties is important to several computed tomography (CT) research and development areas, notably for statistical reconstruction algorithms and dose-reduction simulation. The most commonly used model of CT signal formation, assuming monoenergetic x-ray sources with quantum counting detectors obeying simple Poisson statistics, does not reflect the actual physics of CT acquisition. This paper describes a more accurate model, taking into account the energy-integrating detection process, nonuniform flux profiles, and data-conditioning processes. Methods are developed to experimentally measure and theoretically calculate statistical distributions, as well as techniques to analyze CT signal properties. Results indicate the limitations of current models and suggest improvements for the description of CT signal properties.

The objective of this research was to develop and validate a custom computed tomography dose-reduction simulation technique for producing images that have an appearance consistent with the same scan performed at a lower mAs (with fixed kVp, rotation time, and collimation). Synthetic noise is added to projection (sinogram) data, incorporating a stochastic noise model that includes energy-integrating detectors, tube-current modulation, bowtie beam filtering, and electronic system noise. Experimental methods were developed to determine the parameters required for each component of the noise model. As a validation, the outputs of the simulations were compared to measurements with cadavers in the image domain and with phantoms in both the sinogram and image domain, using an unbiased root-mean-square relative error metric to quantify agreement in noise processes. Four-alternative forced-choice (4AFC) observer studies were conducted to confirm the realistic appearance of simulated noise, and the effects of various system model components on visual noise were studied. The "just noticeable difference (JND)" in noise levels was analyzed to determine the sensitivity of observers to changes in noise level. Individual detector measurements were shown to be normally distributed (p > 0.54), justifying the use of a Gaussian random noise generator for simulations. Phantom tests showed the ability to match original and simulated noise variance in the sinogram domain to within 5.6% +/- 1.6% (standard deviation), which was then propagated into the image domain with errors less than 4.1% +/- 1.6%. Cadaver measurements indicated that image noise was matched to within 2.6% +/- 2.0%. More importantly, the 4AFC observer studies indicated that the simulated images were realistic, i.e., no detectable difference between simulated and original images (p = 0.86) was observed. JND studies indicated that observers' sensitivity to change in noise levels corresponded to a 25% difference in dose, which is far larger than the noise accuracy achieved by simulation. In summary, the dose-reduction simulation tool demonstrated excellent accuracy in providing realistic images. The methodology promises to be a useful tool for researchers and radiologists to explore dose reduction protocols in an effort to produce diagnostic images with radiation dose "as low as reasonably achievable".

ObjectiveTo determine the ordering of changes in Alzheimer disease (AD) biomarkers among cognitively normal individuals.MethodsCross-sectional data, including cerebrospinal fluid (CSF) analytes, molecular imaging of cerebral fibrillar β-amyloid with positron emission tomography (PET) using the [11C] benzothiazole tracer, Pittsburgh Compound-B (PiB), magnetic resonance imaging (MRI)-based brain structures, and clinical/cognitive outcomes harmonized from 8 studies, collectively involving 3,284 cognitively normal individuals of 18–101 years, were analyzed. The age at which each marker exhibited an accelerated change (called the change-point) was estimated, and compared across the markers.ResultsAccelerated changes in CSF Aβ1-42 (Aβ42) occurred at 48.28 years of age and Aβ42/Aβ40 ratio at 46.02 years, followed by PiB mean cortical standardized uptake value ratio (SUVR) with a change-point at 54.47 years. CSF total tau (Tau) and tau phosphorylated at threonine 181 (Ptau) had a change-point at about 60 years, similar to those for MRI hippocampal volume and cortical thickness. The change-point for a cognitive composite occurred at 62.41 years. The change-points for CSF Aβ42 and Aβ42/Aβ40 ratio, albeit not significantly different from that for PiB SUVR, occurred significantly earlier than that for CSF Tau, Ptau, MRI markers and the cognitive composite. Adjusted analyses confirmed that accelerated changes in CSF Tau, Ptau, MRI markers, and the cognitive composite occurred at ages not significantly different from each other.ConclusionsOur findings support the hypothesized early changes of amyloid in preclinical AD, and suggest that changes in neuronal injury and neurodegeneration markers occur close in time to cognitive decline.