Compression of computed tomography (CT) projection data reduces CT scanner bandwidth and storage costs. Since
fixed-rate compression guarantees predictable bandwidth, fixed-rate compression is preferable to lossless compression,
but fixed-rate compression can introduce image artifacts. This research demonstrates clinically acceptable image quality
at 3:1 compression as judged by a radiologist and as estimated by an image quality metric called local structural
similarity (SSIM). We examine other common, quantitative image quality metrics from image processing, including
peak signal-to-noise (PSNR), contrast-to-noise ratio (CNR), and difference image statistics to quantify the magnitude
and location of image artifacts caused by fixed-rate compression of CT projection data. Masking effects caused by local
contrast, air and bone pixels, and image reconstruction effects at the image's periphery and iso-center explain why
artifacts introduced by compression are not noticed by radiologists. SSIM metrics in this study nearly always exceeds
0.98 (even at 4:1 compression ratios), which is considered visually indistinguishable. The excellent correlation of local
SSIM and subjective image quality assessment confirms that fixed-rate 3:1 projection data compression on CT images
does not affect clinical diagnosis and is rarely noticed. Local SSIM metrics can be used to significantly reduce the
number of viewed images in medical image quality studies.
With the advancement of Computed Tomography technology, improving image quality while reducing patient dose has
been a big technical challenge. The recent CT750 HD system from GE Healthcare provides significantly improved
spatial resolution and the capability to reduce dose during routine clinical imaging. This paper evaluates the image
quality of this system. Spatial resolution, dose reduction, noise, and low contrast detectability have been quantitatively
characterized. Results show a quantifiable and visually discernable higher spatial resolution for both body and cardiac
scanning modes without compromise of image noise. Further, equivalent image quality performance with up to 50%
lower dose has been achieved.
Compression of computed tomography (CT) projection samples reduces slip ring and disk drive costs. A lowcomplexity,
CT-optimized compression algorithm called Prism CTTM achieves at least 1.59:1 and up to 2.75:1 lossless
compression on twenty-six CT projection data sets. We compare the lossless compression performance of Prism CT to
alternative lossless coders, including Lempel-Ziv, Golomb-Rice, and Huffman coders using representative CT data sets.
Prism CT provides the best mean lossless compression ratio of 1.95:1 on the representative data set. Prism CT
compression can be integrated into existing slip rings using a single FPGA. Prism CT decompression operates at 100
Msamp/sec using one core of a dual-core Xeon CPU. We describe a methodology to evaluate the effects of lossy
compression on image quality to achieve even higher compression ratios. We conclude that lossless compression of raw
CT signals provides significant cost savings and performance improvements for slip rings and disk drive subsystems in
all CT machines. Lossy compression should be considered in future CT data acquisition subsystems because it provides
even more system benefits above lossless compression while achieving transparent diagnostic image quality. This result
is demonstrated on a limited dataset using appropriately selected compression ratios and an experienced radiologist.
Future generations of CT systems would need a mean to cover an entire organ in a single rotation. A way to accomplish
this is to physically increase detector size to provide, e.g., 120~160mm z (head-foot) coverage at iso. The x-ray cone
angle of such a system is usually 3~4 times of that of a 64-slice (40mm) system, which leads to more severe cone beam
artifacts in cardiac scans. In addition, the extreme x-ray take-off angles for such a system cause severe heel effect,
which would require an increase in anode target angle to compensate for it. One shortcoming of larger target angle is
that tube output likely decreases because of shorter thermal length. This would result in an increase of image noise. Our
goal is to understand from a physics and math point of view, what is the clinical acceptable level of artifacts, resolution,
and noise impact. The image artifacts are assessed through computer simulation of a helical body phantom and visual
comparison of reconstructed images between a 140mm system and a 64-slice system. The IQ impact from target angle
increase is studied analytically and experimentally by first finding the proper range of target angles that give the
acceptable heel effect, then estimating the impact on peak power (flux) and z resolution using an empirical model of
heel effect for given target angle and analytical models of z resolution and tube current loading factor for given target
thermal length. The results show that, for a 140mm system, 24.5% of imaging volume exhibits more severe cone beam
artifacts than a 64-slice system, which also brings up a patient dose concern. In addition, this system may suffer from a
36% peak power (flux) loss, which is equivalent to about 20% image noise increase. Therefore, a wide coverage CT
system using a single x-ray source is likely to face some severe challenges in IQ and clinical accuracy.
Third-generation CT architectures are approaching fundamental limits. Spatial resolution is limited by the focal spot size and the detector cell size. Temporal resolution is limited by mechanical constraints on gantry rotation speed, and alternative geometries such as electron-beam CT and two-tube-two-detector CT come with severe tradeoffs in terms of image quality, dose-efficiency and complexity. Image noise is fundamentally linked to patient dose, and dose-efficiency is limited by finite detector efficiency and by limited spatio-temporal control over the X-ray flux. Finally, volumetric coverage is limited by detector size, scattered radiation, conebeam artifacts, Heel effect, and helical over-scan. We propose a new concept, multi-source inverse geometry CT, which allows CT to break through several of the above limitations. The proposed architecture has several advantages compared to third-generation CT: the detector is small and can have a high detection efficiency, the optical spot size is more consistent throughout the field-of-view, scatter is minimized even when eliminating the anti-scatter grid, the X-ray flux from each source can be modulated independently to achieve an optimal noise-dose tradeoff, and the geometry offers unlimited coverage without cone-beam artifacts. In this work we demonstrate the advantages of multi-source inverse geometry CT using computer simulations.
The technology for x-ray computed tomography (CT) has experienced tremendous growth in recent years. Since the introduction of 4-slice helical scanners in 1998, rapid improvement has been made on CT scanners in terms of the volume coverage, spatial resolution, scan speed, and the number of slices. These advancements not only significantly impact clinical applications, but also bring huge challenges to the CT system design. Because of the complexity of the volumetric CT (VCT) system, various strategies have to be utilized in the design process. These methodologies include theoretical analysis, computer simulation for system performance prediction, bench-top experiments for analysis confirmation, automated image analysis tools for automatically evaluating image performance, and double-blind tests with human observers for parameter optimization. In this paper, we present some of the system design considerations and optimization processes for a 64-slice scanner. These design processes ensure the optimal performance of the cone beam CT scanner. Initial clinical feedback has demonstrated the effectiveness of our approach.