&lt;title&gt;Comparison of fan- and cone-beam imaging capabilities on a portable x-ray imaging system&lt;/title&gt;

White, Timothy A.; Roney, T. J.; Pink, Robert J.; Noo, Frédéric; Clackdoyle, Rolf; Smith, M. H.; Jones, Warren F.

doi:10.1117/12.363715

Cited by 4 publications

(5 citation statements)

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“…Uncertainties in the estimated scanner parameters can be obtained as explained in section 3.5. To test the reliability of the parameters, we also carry out successive evaluations using different sets of projections, as shown in table 2, and perform resolution tests on the reconstructions of point and rods objects, as illustrated in White et al (1999).…”

Section: Discussionmentioning

confidence: 99%

“…In CB tomography, it is well known that using inaccurate parameters can produce severe artefacts (see Li et al 1994a, b, Rizo et al 1994, Wang et al 1998. Even errors as low as 0.3 • in one geometric parameter (the orientation angle of pixels in the detector plane) can have visibly detrimental effects on the reconstructed image (see White et al (1999) for illustrations). The purpose of this work is to describe a new method for calibration of a CB scanner.…”

Section: Introductionmentioning

confidence: 99%

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Analytic method based on identification of ellipse parameters for scanner calibration in cone-beam tomography

et al. 2000

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This paper is about calibration of cone-beam (CB) scanners for both x-ray computed tomography and single-photon emission computed tomography. Scanner calibration refers here to the estimation of a set of parameters which fully describe the geometry of data acquisition. Such parameters are needed for the tomographic reconstruction step. The discussion is limited to the usual case where the cone vertex and planar detector move along a circular path relative to the object. It is also assumed that the detector does not have spatial distortions. We propose a new method which requires a small set of measurements of a simple calibration object consisting of two spherical objects, that can be considered as 'point' objects. This object traces two ellipses on the detector and from the parametric description of these ellipses, the calibration geometry can be determined analytically using explicit formulae. The method is robust and easy to implement. However, it is not fully general as it is assumed that the detector is parallel to the rotation axis of the scanner. Implementation details are given for an experimental x-ray CB scanner.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analytic method based on identification of ellipse parameters for scanner calibration in cone-beam tomography

et al. 2000

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“…In addition to localization and scale, alignment errors may also affect the quality of reconstructed images, as investigated in previous paper. 11,13,14,25 In alignment tests with the physical phantom and robotic SPECT system, angles θ and were in all cases estimated to within 1 • of the values given by the CT scan and its registration to the robot base and tool coordinate frames. For angle , radius of rotation y det , and detector translations x det and z det , errors were within 2 • , 2 mm, and 3 mm, respectively.…”

Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

A line‐source method for aligning on‐board and other pinhole SPECT systems

2013

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Purpose: In order to achieve functional and molecular imaging as patients are in position for radiation therapy, a robotic multipinhole SPECT system is being developed. Alignment of the SPECT system-to the linear accelerator (LINAC) coordinate frame and to the coordinate frames of other on-board imaging systems such as cone-beam CT (CBCT)-is essential for target localization and image reconstruction. An alignment method that utilizes line sources and one pinhole projection is proposed and investigated to achieve this goal. Potentially, this method could also be applied to the calibration of the other pinhole SPECT systems. Methods: An alignment model consisting of multiple alignment parameters was developed which maps line sources in three-dimensional (3D) space to their two-dimensional (2D) projections on the SPECT detector. In a computer-simulation study, 3D coordinates of line-sources were defined in a reference room coordinate frame, such as the LINAC coordinate frame. Corresponding 2D line-source projections were generated by computer simulation that included SPECT blurring and noise effects. The Radon transform was utilized to detect angles (α) and offsets (ρ) of the line-source projections. Alignment parameters were then estimated by a nonlinear least squares method, based on the α and ρ values and the alignment model. Alignment performance was evaluated as a function of number of line sources, Radon transform accuracy, finite line-source width, intrinsic camera resolution, Poisson noise, and acquisition geometry. Experimental evaluations were performed using a physical linesource phantom and a pinhole-collimated gamma camera attached to a robot. Results: In computer-simulation studies, when there was no error in determining angles (α) and offsets (ρ) of the measured projections, six alignment parameters (three translational and three rotational) were estimated perfectly using three line sources. When angles (α) and offsets (ρ) were provided by the Radon transform, estimation accuracy was reduced. The estimation error was associated with rounding errors of Radon transform, finite line-source width, Poisson noise, number of line sources, intrinsic camera resolution, and detector acquisition geometry. Statistically, the estimation accuracy was significantly improved by using four line sources rather than three and by thinner linesource projections (obtained by better intrinsic detector resolution). With five line sources, median errors were 0.2 mm for the detector translations, 0.7 mm for the detector radius of rotation, and less than 0.5• for detector rotation, tilt, and twist. In experimental evaluations, average errors relative to a different, independent registration technique were about 1.8 mm for detector translations, 1.1 mm for the detector radius of rotation (ROR), 0.5• and 0.4• for detector rotation and tilt, respectively, and 1.2• for detector twist. Conclusions: Alignment parameters can be estimated using one pinhole projection of line sources. Alignment errors are largely associated with limite...

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“…1 and 2͒, but recently its importance in x-ray CT scanners has been recognized. [3][4][5] It has been shown that residual misalignment in medical scanners can be responsible for stair-step artifacts even in single-row spiral CT scanners. 5 Instead of trying to perfectly align the scanner, which is often too complex, too costly, or sometimes not possible at all, one can acquire imperfect data, measure misalignment, and correct for misalignment during reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a cone‐beam reconstruction algorithm for the single‐circle source orbit with embedded misalignment correction using homogeneous coordinates

et al. 2001

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We present an efficient implementation of an approximate cone-beam image reconstruction algorithm for application in tomography, which accounts for scanner mechanical misalignment. The implementation is based on the algorithm proposed by Feldkamp et al. and is directed at circular scan paths. The algorithm has been developed for the purpose of reconstructing volume data from projections acquired in an experimental x-ray micro-tomography (microCT) scanner. To mathematically model misalignment we use matrix notation with homogeneous coordinates to describe the scanner geometry, its misalignment, and the acquisition process. For convenience analysis is carried out for x-ray CT scanners, but it is applicable to any tomographic modality, where two-dimensional projection acquisition in cone beam geometry takes place, e.g., single photon emission computerized tomography. We derive an algorithm assuming misalignment errors to be small enough to weight and filter original projections and to embed compensation for misalignment in the backprojection. We verify the algorithm on simulations of virtual phantoms and scans of a physical multidisk (Defrise) phantom.

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<title>Comparison of fan- and cone-beam imaging capabilities on a portable x-ray imaging system</title>

Cited by 4 publications

References 0 publications

Analytic method based on identification of ellipse parameters for scanner calibration in cone-beam tomography

Analytic method based on identification of ellipse parameters for scanner calibration in cone-beam tomography

A line‐source method for aligning on‐board and other pinhole SPECT systems

Implementation of a cone‐beam reconstruction algorithm for the single‐circle source orbit with embedded misalignment correction using homogeneous coordinates

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