Multi-source inverse-geometry CT: From system concept to research prototype

Man, Bruno De; Caiafa, Antonio; Cao, Yang; Frutschy, Kristopher; Harrison, Daniel; Inzinna, Lou; Longtin, Randy; Neculaes, Bogdan; Reynolds, Joseph; Roy, Jaydeep; Short, Jonathan; Uribe, J.; Waters, William E.; Yin, Zhye; Zhang, Xi; Zou, Y.; Senzig, Bob; Baek, Jongduk; Pelc, Norbert J.

doi:10.1109/nssmic.2009.5401808

Cited by 12 publications

(14 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each module consists of 64 by 16 cells of about 1.1 mm × 1.0 mm each. 12 modules are tiled in z producing a total sensitive area of about 70 mm in the x direction and 192 mm in the z direction [4,5]. Figure 3 shows a picture of the integrated MS-IGCT gantry.…”

Section: System Overviewmentioning

confidence: 99%

“…A second series of sources are aligned at a longitudinal offset relative to the first series, and increase the volumetric coverage and provide additional sampling of the object, which reduces cone-beam artifacts. Using a small 2D detector array can reduce the detector cost and the amount of scatter, and by modulating the x-ray flux of each source independently, a concept called virtual bowtie, the effective dose to the patient can be minimized [1][2][3][4][5]. During a scan, X-ray source intensity fluctuations often occur and these must be accounted for in order to avoid artifacts in the reconstructed image.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Raw data normalization for a multi source inverse geometry CT system

et al. 2015

View full text Add to dashboard Cite

Abstract:A multi-source inverse-geometry CT (MS-IGCT) system consists of a small 2D detector array and multiple x-ray sources. During data acquisition, each source is activated sequentially, and may have random source intensity fluctuations relative to their respective nominal intensity. While a conventional 3 rd generation CT system uses a reference channel to monitor the source intensity fluctuation, the MS-IGCT system source illuminates a small portion of the entire field-of-view (FOV). Therefore, it is difficult for all sources to illuminate the reference channel and the projection data computed by standard normalization using flat field data of each source contains error and can cause significant artifacts. In this work, we present a raw data normalization algorithm to reduce the image artifacts caused by source intensity fluctuation. The proposed method was tested using computer simulations with a uniform water phantom and a Shepp-Logan phantom, and experimental data of an ice-filled PMMA phantom and a rabbit. The effect on image resolution and robustness of the noise were tested using MTF and standard deviation of the reconstructed noise image. With the intensity fluctuation and no correction, reconstructed images from simulation and experimental data show high frequency artifacts and ring artifacts which are removed effectively using the proposed method. It is also observed that the proposed method does not degrade the image resolution and is very robust to the presence of noise.

show abstract

Section: System Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Raw data normalization for a multi source inverse geometry CT system

et al. 2015

View full text Add to dashboard Cite

show abstract

“…3 A gantry-based prototype IGCT system was developed capable of 1-second gantry rotation (GE Global Research, Niskayuna, NY). 7,8 Unlike the scanning beam technology used by previous IGCT prototypes, the gantry-based system uses multiple x-ray tubes that are sequentially switched on and off during acquisition, as illustrated in Figure 2d. The prototype system contains 2 rows of sources separated by 10 cm in the axial direction.…”

Section: Inverted Geometries and Hardwarementioning

confidence: 99%

“…Phantom and small animal images were successfully acquired on the prototype system. 7,8 IGCT systems illustrated in Figure 2 represent specific examples from a continuum of inverted geometries, as explained and illustrated by De Man et al 2 Starting with the fully inverted geometry (Fig. 2a), the source size can be reduced while adding detector arrays to maintain the FOV Figure 1 Conventional CT geometry with a cone-beam of x-rays emitted from a small source spot and irradiating a largearea detector.…”

Section: Inverted Geometries and Hardwarementioning

confidence: 99%

What is inverse-geometry CT?

Schmidt

2011

Journal of Cardiovascular Computed Tomography

View full text Add to dashboard Cite

“…To overcome several limitations in MDCT, a new CT concept, multi-source inverse-geometry CT(MS-IGCT), was proposed [4][5][6][7]. While conventional MDCT scanners use a point x-ray source and a large detector array, the MS-IGCT system has a distributed source array and a relatively small detector array (shown in Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Initial results with a multisource inverse-geometry CT system

et al. 2012

View full text Add to dashboard Cite

The multi-source inverse-geometry CT(MS-IGCT) system is composed of multiple sources and a small 2D detector array. An experimental MS-IGCT system was built and we report initial results with 2×4 x-ray sources, a 75 mm inplane field-of-view (FOV) and 160 mm z-coverage in a single gantry rotation. To evaluate the system performance, experimental data were acquired from several phantoms and a post-mortem rat. Before image reconstruction, geometric calibration, data normalization, beam hardening correction and detector spectral calibration were performed. For reconstruction, the projection data were rebinned into two full cone beam data sets, and the FDK algorithm was used. The reconstructed volumes from the upper and lower source rows shared an overlap volume which was combined in image space. The reconstructed images of the uniform cylinder phantom showed good uniformity of the reconstructed values without any artifacts. The rat data were also reconstructed reliably. The initial experimental results from this rotating-gantry MS-IGCT system demonstrated its ability to image a complex anatomical object without any significant image artifacts and to ultimately achieve large volumetric coverage in a single gantry rotation.

show abstract

Multi-source inverse-geometry CT: From system concept to research prototype

Cited by 12 publications

References 1 publication

Raw data normalization for a multi source inverse geometry CT system

Raw data normalization for a multi source inverse geometry CT system

What is inverse-geometry CT?

Initial results with a multisource inverse-geometry CT system

Contact Info

Product

Resources

About