A Mathematical Model for Adaptive Computed Tomography Sensing

Barkan, Oren; Weill, Jonathan; Dekel, Shai; Averbuch, Amir

doi:10.1109/tci.2017.2736788

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…Lowdose imaging and reconstruction (with dense projection view sampling) has been more widely studied than the few-views imaging. This is probably because the former does not involve a strategy for selection of the set of view angles, which in itself is an active field of research [6,7,8]. For long, almost all of the commercial CT machines used FBP 1 as the standard reconstruction technique [9].…”

Section: Previous Workmentioning

confidence: 99%

Low radiation tomographic reconstruction with and without template information

Gopal¹,

Chandran²,

Svalbe³

et al. 2019

Preprint

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Low-dose tomography is highly preferred in medical procedures for its reduced radiation risk when compared to standard-dose Computed Tomography (CT). However, the lower the intensity of X-rays, the higher the acquisition noise and hence the reconstructions suffer from artefacts. A large body of work has focussed on improving the algorithms to minimize these artefacts. In this work, we propose two new techniques, rescaled non-linear least squares and Poisson-Gaussian convolution, that reconstruct the underlying image making use of an accurate or near-accurate statistical model of the noise in the projections. We also propose a reconstruction method when prior knowledge of the underlying object is available in the form of templates. This is applicable to longitudinal studies wherein the same object is scanned multiple times to observe the changes that evolve in it over time. Our results on 3D data show that prior information can be used to compensate for the low-dose artefacts, and we demonstrate that it is possible to simultaneously prevent the prior from adversely biasing the reconstructions of new changes in the test object, via a method called "re-irradiation". Additionally, we also present two techniques for automated tuning of the regularization parameters for tomographic inversion.

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Section: Previous Workmentioning

confidence: 99%

Low radiation tomographic reconstruction with and without template information

Gopal¹,

Chandran²,

Svalbe³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…In this regard, there are two lines of pursuit. One is to intelligently choose those sets of projection views that capture most information [1,2,3], and the other is to improve the reconstruction algorithms to get the most accurate recovery of the underlying slice, given the measurements from any limited set of views [4,5,6]. This paper deals with the latter scenario.…”

Section: Introductionmentioning

confidence: 99%

Tomographic reconstruction to detect evolving structures

Gopal,

Chandran,

Svalbe

et al. 2019

Preprint

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The need for tomographic reconstruction from sparse measurements arises when the measurement process is potentially harmful, needs to be rapid, or is uneconomical. In such cases, information from previous longitudinal scans of the same object ('templates' forming the 'prior'), helps to reconstruct the current object ('test') while requiring significantly fewer updating measurements. In this work, we improve the state of the art by proposing the context under which priors can be effectively used based on the final goal of the application at hand.Our work is based on longitudinal data acquisition scenarios where we wish to study new changes that evolve within an object over time, such as in repeated scanning for disease monitoring, or in tomography-guided surgical procedures.While this is easily feasible when measurements are acquired from a large number of projection angles (referred to as 'views' henceforth), it is challenging when the number of views is limited. If the goal is to track the changes while simultaneously reducing sub-sampling artefacts, we propose (1) acquiring measurements from a small number of views and using a global unweighted prior-based reconstruction. If the goal is to observe details of new changes, we propose (2) acquiring measurements from a moderate number of views and using a more involved reconstruction routine. We show that in the latter case, a weighted technique is necessary in order to prevent the prior from adversely affecting the reconstruction of new structures that are absent in any of the earlier scans. The

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Practical Part-Specific Trajectory Optimization for Robot-Guided Inspection via Computed Tomography

et al. 2022

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Robot-guided computed tomography enables the inspection of parts that are too large for conventional systems and allows, for instance, the non-destructive and volumetric evaluation of mechanical joining components within already assembled cars in the automotive industry. However, the typical scan time required by such setups is still significant and represents a major barrier for its industrial large-scale application. As an approach to mitigate the necessary time demand, we propose a part-specific adjustment of the acquisition trajectory. Common circular standard trajectories are inherently inefficient, since they are applied independently of the considered inspection task, while the use of acquisition orbits tailored particularly to the investigated object effectively allows a reduction of the required number of projections, which in turn has the potential to directly decrease the scan time significantly. In contrast to former simulation-guided approaches, this work is considered to be the first successful task-specific trajectory optimization being performed on a robot-based industrial CT platform and aims towards providing a first proof of concept that such methods can be practically applied in a shop floor environment. Based on representative results, a reduction of the number of required projections by approx. 55 % or an image quality improvement according to the root-mean squared error by approx. 40 % compared to the conventionally applied planar acquisition trajectory was achieved.

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A Mathematical Model for Adaptive Computed Tomography Sensing

Cited by 6 publications

References 26 publications

Low radiation tomographic reconstruction with and without template information

Low radiation tomographic reconstruction with and without template information

Tomographic reconstruction to detect evolving structures

Practical Part-Specific Trajectory Optimization for Robot-Guided Inspection via Computed Tomography

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