Image reconstruction with laterally truncated projections in helical cone-beam CT: Linear prediction based projection completion techniques

Anoop, K P; Rajgopal, K.

doi:10.1016/j.compmedimag.2009.01.007

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Several strategies have been developed to suppress the artefacts in RoI tomography. Truncated tomography comprises various techniques to extrapolate the truncated projections (Lewitt & Bates, 1978a,b; Ogawa et al , 1984; Sahiner & Yagle, 1995; Ohnesorge et al , 2000; Gea et al , 2005; Anoop & Rajgopal, 2009) as well as methods based on wavelets (Olson & DeStefano, 1994; Bonnet et al , 2000; Hsung & Lun, 2000). Lambda tomography (Smith & Keinert, 1985; Spyra et al , 1990; Faridani et al, 1992, 1997) uses the second derivative of the projections, whereas the Hilbert transform‐based methods (Noo et al , 2004; Clackdoyle et al , 2004; Defrise et al , 2006; Ye et al , 2007a,b) are based on the first derivative.…”

Section: Introductionmentioning

confidence: 99%

Region-of-interest tomography using filtered backprojection: assessing the practical limits

Kyrieleis

Titarenko

Ibison

et al. 2010

Journal of Microscopy

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SummaryThere are many cases where it is desirable to reconstruct at high resolution a small volume from a larger sample. Here we describe the outcomes of a reconstruction trial based on real samples aimed at delineating the practical limits to which a small region of interest can be viewed from a large sample. Our approach has been to artificially truncate the sinograms of whole sample scans to simulate region of interest tomography. A simple filtered back projection algorithm has been applied, with the sinograms extended laterally in a simple manner to make up for the truncated portions. The impact of the degree of truncation (from 0% down to 99%), the number of projections used, as well as the position of the region of interest, on the faithfulness of the reconstruction is evaluated for a range of sample types. We have assessed the nature of, and extent to which, artefacts are introduced and the degree to which simple strategies can minimize them to an acceptable level without the need for complex reconstruction algorithms, projection stitching strategies or very large detectors. It is found that for a wide range of objects the effect of truncation on feature detection is negligible and that excellent images can be reconstructed if the number of projections is calculated not on the basis of the number of pixels on the camera, but on the number of pixels that would be required to scan the whole sample at the chosen pixel resolution. This paper demonstrates that in many cases more sophisticated reconstruction strategies are not necessary.

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Section: Introductionmentioning

confidence: 99%

Region-of-interest tomography using filtered backprojection: assessing the practical limits

Kyrieleis

Titarenko

Ibison

et al. 2010

Journal of Microscopy

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“…Since the standard Feldkamp-Davis-Kress (FDK) reconstruction algorithm (Feldkamp et al 1984) leads to truncation artifacts under this circumstance, great efforts have been made over the years to improve image quality in this so-called interior tomography problem. Examples include interchanging the orders of back-projection and differential operators (Noo et al 2004, Tang et al 2006, Cho et al 2008, Dennerlein and Maier 2013, extrapolating truncated projection data (Rajgopal and Anoop 2006), borrowing prior information to ensure the uniqueness of the solution (Ye et al 2007, Yu et al 2008 or taking advantages of compressed sensing approaches (Yu and Wang 2009). Nonetheless, reconstructing the image inside ROI alone cannot meet the requirements of radiotherapy, particularly in ART.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing cone-beam CT with spatially varying qualities for adaptive radiotherapy: a proof-of-principle study

Yan

et al. 2014

Phys. Med. Biol.

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With the aim of maximally reducing imaging dose while meeting requirements for adaptive radiation therapy (ART), we propose in this paper a new cone beam CT (CBCT) acquisition and reconstruction method that delivers images with a low noise level inside a region of interest (ROI) and a relatively high noise level outside the ROI. The acquired projection images include two groups: densely sampled projections at a low exposure with a large field of view (FOV) and sparsely sampled projections at a high exposure with a small FOV corresponding to the ROI. A new algorithm combining the conventional filtered back-projection algorithm and the tight-frame iterative reconstruction algorithm is also designed to reconstruct the CBCT based on these projection data. We have validated our method on a simulated head-and-neck (HN) patient case, a semi-real experiment conducted on a HN cancer patient under a full-fan scan mode, as well as a Catphan phantom under a half-fan scan mode. Relative root-mean-square errors (RRMSE) of less than 3% for the entire image and ~1% within the ROI compared to the ground truth have been observed. These numbers demonstrate the ability of our proposed method to reconstruct high-quality images inside the ROI. As for the part outside ROI, although the images are relatively noisy, it can still provide sufficient information for radiation dose calculations in ART. Dose distributions calculated on our CBCT image and on a standard CBCT image are in agreement, with a mean relative difference of 0.082% inside the ROI and 0.038% outside the ROI. Compared with the standard clinical CBCT scheme, an imaging dose reduction of approximately 3–6 times inside the ROI was achieved, as well as an 8 times outside the ROI. Regarding computational efficiency, it takes 1–3 min to reconstruct a CBCT image depending on the number of projections used. These results indicate that the proposed method has the potential for application in ART.

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Image reconstruction with laterally truncated projections in helical cone-beam CT: Linear prediction based projection completion techniques

Cited by 2 publications

References 21 publications

Region-of-interest tomography using filtered backprojection: assessing the practical limits

Region-of-interest tomography using filtered backprojection: assessing the practical limits

Reconstructing cone-beam CT with spatially varying qualities for adaptive radiotherapy: a proof-of-principle study

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