In many clinical practices, radiologists rely on the value of measured CT numbers to differentiate healthy tissue from disease pathology. Although this practice is not recommended by most CT manufacturers (unless the difference between the healthy and diseased tissues is large), it underlines the importance of producing accurate CT numbers. Two aspects factor into CT number accuracy: CT number consistency and uniformity. CT number consistency dictates that if the same phantom is scanned with different slice thicknesses, at different times, or in the presence of other objects, the CT numbers of the reconstructed phantom should not be affected. CT number uniformity dictates that for a uniform phantom, the CT number measurement should not change with the location of the selected ROI or with the phantom position relative to the iso-center of the scanner. For illustration, Fig. 5.30 shows a reconstructed 20-cm water phantom. The average CT numbers in two ROI locations should be identical. Because of the effects of beam hardening, scatter, CT system stability, and many other factors, both CT number consistency and uniformity can be maintained within only a reasonable range. As long as radiologists understand the system limitations and the factors that influence the performance, the pitfalls of using absolute CT numbers for diagnosis can be avoided.It is important to point out that the CT number may change significantly with different reconstruction algorithms. Most kernels used for reconstruction

Multislice helical computed tomography scanning offers the advantages of faster acquisition and wide organ coverage for routine clinical diagnostic purposes. However, image reconstruction is faced with the challenges of three-dimensional cone-beam geometry, data completeness issues, and low dosage. Of all available reconstruction methods, statistical iterative reconstruction (IR) techniques appear particularly promising since they provide the flexibility of accurate physical noise modeling and geometric system description. In this paper, we present the application of Bayesian iterative algorithms to real 3D multislice helical data to demonstrate significant image quality improvement over conventional techniques. We also introduce a novel prior distribution designed to provide flexibility in its parameters to fine-tune image quality. Specifically, enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies. Clinical results also illustrate the capabilities of the algorithm on real patient data. Although computational load remains a significant challenge for practical development, superior image quality combined with advancements in computing technology make IR techniques a legitimate candidate for future clinical applications.

PGT coronary CT angiography offers improved image quality and substantially reduced effective radiation dose compared with traditional RGH coronary CT angiography.

The quality of a computed tomography (CT) image is often degraded by streaking artifacts resulting from excessive x-ray quantum noise. Often, a patient has to be rescanned at a higher technique or at a larger slice thickness in order to obtain an acceptable image for diagnosis. This results in a higher dose to the patient, a degraded cross plane resolution, or a reduced patient throughput. In this paper, we propose an adaptive filtering approach in Radon space based on the local statistical properties of the CT projections. We first model the noise characteristics of a projection sample undergoing important preprocessing steps. A filter is then designed such that its parameters are dynamically adjusted to adapt to the local noise characteristics. Because of the adaptive nature of the filter, a proper balance between streak artifact suppression and spatial resolution preservation is achieved. Phantom and clinical studies have been conducted to evaluate the robustness of our approach. Results demonstrate that the adaptive filtering approach is effective in reducing or eliminating quantum noise induced artifacts in CT. At the same time, the impact on the spatial resolution is kept at a low level.

ASIR lowers noise and improves diagnostic confidence in and conspicuity of subtle abdominal lesions at 8.4 mGy when images are reconstructed with 30% ASIR blending and at 4.2 mGy in patients weighing 90 kg or less when images are reconstructed with 50% or 70% ASIR blending.