Raw data normalization for a multi source inverse geometry CT system

Baek, Jongduk; Man, Bruno De; Harrison, Daniel; Pelc, Norbert J.

doi:10.1364/oe.23.007514

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…Conventional CT scanners monitor intensity fluctuations using reference channels at the edges of the detector (outside the projection of the FOV), but in an IGCT geometry, most sources do not irradiate the reference detectors without going through the scanned object. We developed a data normalization technique to correct for the IGCT intensity fluctuations, 24 essentially making sure that adjacent patches of projection data are consistent by slightly rescaling their intensities. The projection data of adjacent focal spots in the IGCT system share an overlap region in Radon space.…”

Section: D System Calibrationmentioning

confidence: 99%

“…We used the projection data of the source irradiating the outermost FOV as a reference for data normalization. 24 Other types of calibration and correction are similar to conventional CT systems. Beam hardening and spectral nonlinearity of the detector were corrected based on uniform cylindrical phantoms, using a second order polynomial fit between the analytic projection data and the real scan data.…”

Section: D System Calibrationmentioning

confidence: 99%

“…We previously presented the development of multisource reconstruction methods [12][13][14][15][16][17][18][19][20] and calibration methods. [21][22][23][24] Several progress updates on the system development and initial results were published in Refs. [25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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Multisource inverse-geometry CT. Part I. System concept and development

et al. 2016

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Purpose: This paper presents an overview of multisource inverse-geometry computed tomography (IGCT) as well as the development of a gantry-based research prototype system. The development of the distributed x-ray source is covered in a companion paper [V. B. Neculaes et al., "Multisource inverse-geometry CT. Part II. X-ray source design and prototype," Med. Phys. 43, 4617-4627 (2016)]. While progress updates of this development have been presented at conferences and in journal papers, this paper is the first comprehensive overview of the multisource inverse-geometry CT concept and prototype. The authors also provide a review of all previous IGCT related publications. Methods: The authors designed and implemented a gantry-based 32-source IGCT scanner with 22 cm field-of-view, 16 cm z-coverage, 1 s rotation time, 1.09 × 1.024 mm detector cell size, as low as 0.4 × 0.8 mm focal spot size and 80-140 kVp x-ray source voltage. The system is built using commercially available CT components and a custom made distributed x-ray source. The authors developed dedicated controls, calibrations, and reconstruction algorithms and evaluated the system performance using phantoms and small animals. Results: The authors performed IGCT system experiments and demonstrated tube current up to 125 mA with up to 32 focal spots. The authors measured a spatial resolution of 13 lp/cm at 5% cutoff. The scatter-to-primary ratio is estimated 62% for a 32 cm water phantom at 140 kVp. The authors scanned several phantoms and small animals. The initial images have relatively high noise due to the low x-ray flux levels but minimal artifacts. Conclusions: IGCT has unique benefits in terms of dose-efficiency and cone-beam artifacts, but comes with challenges in terms of scattered radiation and x-ray flux limits. To the authors' knowledge, their prototype is the first gantry-based IGCT scanner. The authors summarized the design and implementation of the scanner and the authors presented results with phantoms and small animals. C 2016 American Association of Physicists in Medicine.[http://dx

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Section: D System Calibrationmentioning

confidence: 99%

Section: D System Calibrationmentioning

confidence: 99%

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Multisource inverse-geometry CT. Part I. System concept and development

et al. 2016

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“…A good understanding of the origin of RAs is believed to offer strategies that should outperform heuristic post-processing techniques. In particular, a detailed study of the spatial and temporal inhomogeneity of the incident beam/detected signal (flat-field) appears promising (Titarenko et al, 2010a,b;Baek et al, 2015). However, when flat-field images are shot at different instants, they are observed to vary with time (Davis & Elliott, 2006).…”

Section: Introductionmentioning

confidence: 99%

On the use of flat-fields for tomographic reconstruction

et al. 2017

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Seeking for quantitative tomographic images, it is of utmost importance to limit reconstruction artifacts. Detector imperfections, inhomogeneity of the incident beam, as classically observed in synchrotron beamlines, and their variations in time are a major cause of reconstruction bias such as `ring artifacts'. The present study aims at proposing a faithful estimate of the incident beam local intensity for each acquired projection during a scan, without revisiting the process of data acquisition itself. Actual flat-fields (acquired without specimen in the beam) and sinogram borders (when the specimen is present), which are not masked during the scan, are exploited to construct a suited instantaneous detector-wide flat-field. The proposed treatment is fast and simple. Its performance is assessed on a real scan acquired at ESRF ID19 beamline. Different criteria are used including residuals, i.e. difference between projections of reconstruction and actual projections. All confirm the benefit of the proposed procedure.

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“…This, in turn, hampers quantitative analysis and complicates post processing such as noise reduction or segmentation [5]. If the pattern noise is truly stationary (i.e., exactly the same in each acquired projection image), substantial reduction of fixed pattern noise is easy.…”

Section: Introductionmentioning

confidence: 99%

Dynamic intensity normalization using eigen flat fields in X-ray imaging

Nieuwenhove¹,

Beenhouwer²,

Carlo³

et al. 2015

Opt. Express

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In X-ray imaging, it is common practice to normalize the acquired projection data with averaged flat fields taken prior to the scan. Unfortunately, due to source instabilities, vibrating beamline components such as the monochromator, time varying detector properties, or other confounding factors, flat fields are often far from stationary, resulting in significant systematic errors in intensity normalization. In this work, a simple and efficient method is proposed to account for dynamically varying flat fields. Through principal component analysis of a set of flat fields, eigen flat fields are computed. A linear combination of the most important eigen flat fields is then used to individually normalize each Xray projection. Experiments show that the proposed dynamic flat field correction leads to a substantial reduction of systematic errors in projection intensity normalization compared to conventional flat field correction. DOI Abstract:In X-ray imaging, it is common practice to normalize the acquired projection data with averaged flat fields taken prior to the scan. Unfortunately, due to source instabilities, vibrating beamline components such as the monochromator, time varying detector properties, or other confounding factors, flat fields are often far from stationary, resulting in significant systematic errors in intensity normalization. In this work, a simple and efficient method is proposed to account for dynamically varying flat fields. Through principal component analysis of a set of flat fields, eigen flat fields are computed. A linear combination of the most important eigen flat fields is then used to individually normalize each X-ray projection. Experiments show that the proposed dynamic flat field correction leads to a substantial reduction of systematic errors in projection intensity normalization compared to conventional flat field correction.

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Raw data normalization for a multi source inverse geometry CT system

Cited by 10 publications

References 15 publications

Multisource inverse-geometry CT. Part I. System concept and development

Multisource inverse-geometry CT. Part I. System concept and development

On the use of flat-fields for tomographic reconstruction

Dynamic intensity normalization using eigen flat fields in X-ray imaging

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