SparseCT: interrupted-beam acquisition and sparse reconstruction for radiation dose reduction

Koesters, Thomas; Knoll, Florian; Sodickson, Aaron D; Sodickson, Daniel K.; Otazo, Ricardo

doi:10.1117/12.2255522

Cited by 15 publications

(13 citation statements)

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“…Such sparse projection data are well-suited to compressed sensing or other advanced regularization schemes 6 . 7 Spatial-spectral filters permit flexibility in the number of spectra that can be incorporated, and can potentially be combined with other spectral methods for improved decomposition performance.…”

Section: Introductionmentioning

confidence: 99%

Optimized spatial-spectral CT for multi-material decomposition

Tivnan

Wang

Tilley

et al. 2019

15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine

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Spectral CT is an emerging modality that uses a data acquisition scheme with varied spectral responses to provide enhanced material discrimination in addition to the structural information of conventional CT. Existing clinical and preclinical designs with this capability include kV-switching, split-filtration, and dual-layer detector systems to provide two spectral channels of projection data. In this work, we examine an alternate design based on a spatialspectral filter. This source-side filter is made up a linear array of materials that divide the incident x-ray beam into spectrally varied beamlets. This design allows for any number of spectral channels; however, each individual channel is sparse in the projection domain. Model-based iterative reconstruction methods can accommodate such sparse spatialspectral sampling patterns and allow for the incorporation of advanced regularization. With the goal of an optimized physical design, we characterize the effects of design parameters including filter tile order and filter tile width and their impact on material decomposition performance. We present results of numerical simulations that characterize the impact of each design parameter using a realistic CT geometry and noise model to demonstrate feasibility. Results for filter tile order show little change indicating that filter order is a low-priority design consideration. We observe improved performance for narrower filter widths; however, the performance drop-off is relatively flat indicating that wider filter widths are also feasible designs.

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Section: Introductionmentioning

confidence: 99%

Optimized spatial-spectral CT for multi-material decomposition

Tivnan

Wang

Tilley

et al. 2019

15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine

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“…The drawback of this scheme is that current clinical CT systems cannot pulse the x‐ray source on the needed millisecond timeframe, since the thermal inertia of the cathode cannot be overcome quickly enough. For this reason, several previous works have proposed a different undersampling scheme that partially blocks the beam between the x‐ray tube and the patient to undersample along the detector row direction within each view . SparseCT is one such undersampling approach.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, several previous works have proposed a different undersampling scheme that partially blocks the beam between the x-ray tube and the patient to undersample along the detector row direction within each view. [17][18][19][20][21][22][23][24] SparseCT is one such undersampling approach. It interrupts the continuous beam with a multislit collimator (MSC) to acquire projection data that are undersampled along the detector row direction.…”

Section: Introductionmentioning

confidence: 99%

SparseCT: System concept and design of multislit collimators

et al. 2019

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Purpose SparseCT, an undersampling scheme for compressed sensing (CS) computed tomography (CT), has been proposed to reduce radiation dose by acquiring undersampled projection data from clinical CT scanners (Koesters et al. in, SparseCT: Interrupted‐Beam Acquisition and Sparse Reconstruction for Radiation Dose Reduction; 2017). SparseCT partially blocks the x‐ray beam with a multislit collimator (MSC) to perform a multidimensional undersampling along the view and detector row dimensions. SparseCT undersamples the projection data within each view and moves the MSC along the z‐direction during gantry rotation to change the undersampling pattern. It enables reconstruction of images from undersampled data using CS algorithms. The purpose of this work is to design the spacing and width of the MSC slits and the MSC motion patterns based on beam separation, undersampling efficiency, and image quality. The development and testing of a SparseCT prototype with the designed MSC will be described in a following paper. Methods We chose a few initial MSC designs based on the guidance from two metrics: beam separation and undersampling efficiency. Both beam separation and undersampling efficiency were measured from numerically simulated photon distribution with MSC taken into consideration. Beam separation measures the separation between x‐ray beams from consecutive slits, taking into account penumbra effects on both sides of each slit. Undersampling efficiency measures the dose‐weighted similarity between penumbra undersampling and binary undersampling, in other words, the effective contribution of the incident dose to the signal to noise ratio of the projection data. We then compared the initially chosen MSC designs in terms of their reconstruction image quality. SparseCT projections were simulated from fully sampled patient projection data according to the MSC design and motion pattern, reconstructed iteratively using a sparsity‐enforcing penalized weighted least squares cost function with ordered subsets/momentum algorithm, and compared visually and quantitatively. Results Simulated photon distributions indicate that the size of the penumbra is dominated by the size of the focal spot. Therefore, a wider MSC slit and a smaller focal spot lead to increased beam separation and undersampling efficiency. For fourfold undersampling with a 1.2 mm focal spot, a minimum MSC slit width of three detector rows (projected to the detector surface) is needed for beam separation; for threefold undersampling, a minimum slit width of four detector rows is needed. Simulations of SparseCT projection and reconstruction indicate that the motion pattern of the MSC does not have a visible impact on image quality. An MSC slit width of three or four detector rows yields similar image quality. Conclusion The MSC is the key component of the SparseCT method. Simulations of MSC designs incorporating x‐ray beam penumbra effects showed that for threefold and fourfold dose reductions, an MSC slit width of four detector rows provided reasonable beam separat...

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“…An alternative to view-based undersampling are interrupted-beam sampling approaches in which the source is partially blocked using a multislit collimator (MSC). Previous studies have investigated beam blocking for applications such as scatter correction (Zhu et al 2009) and dose reduction (Abbas et al 2013, Dong et al 2013, Koesters et al 2017). These have indicated that MSC designs may be able to outperform other sparse sampling schemes (Abbas et al 2013) or even current commercial low-dose imaging methods (Koesters et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have investigated beam blocking for applications such as scatter correction (Zhu et al 2009) and dose reduction (Abbas et al 2013, Dong et al 2013, Koesters et al 2017). These have indicated that MSC designs may be able to outperform other sparse sampling schemes (Abbas et al 2013) or even current commercial low-dose imaging methods (Koesters et al 2017). MSC-based designs introduce new challenges that must be addressed: penumbra effects from the finite size of the source (Chen et al 2017) and corresponding coherence drop-offs related to collimator design parameters (Muckley et al 2017) have been modeled and simulated.…”

Section: Introductionmentioning

confidence: 99%

Image reconstruction for interrupted-beam x-ray CT on diagnostic clinical scanners

et al. 2019

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Low-dose x-ray CT is a major research area with high clinical impact. Compressed sensing using view-based sparse sampling and sparsity-promoting regularization has shown promise in simulations, but these methods can be difficult to implement on diagnostic clinical CT scanners since the x-ray beam cannot be switched on and off rapidly enough. An alternative to view-based sparse sampling is interrupted-beam sparse sampling. SparseCT is a recently-proposed interrupted-beam scheme that achieves sparse sampling by blocking a portion of the beam using a multislit collimator (MSC). The use of an MSC necessitates a number of modifications to the standard compressed sensing reconstruction pipeline. In particular, we find that SparseCT reconstruction is feasible within a model-based image reconstruction framework that incorporates data fidelity weighting to consider penumbra effects and source jittering to consider the effect of partial source obstruction. Here, we present these modifications and demonstrate their application in simulations and real-world prototype scans. In simulations compared to conventional low-dose acquisitions, SparseCT is able to achieve smaller normalized root-mean square differences and higher structural similarity measures on two reduction factors. In prototype experiments, we successfully apply our reconstruction modifications and maintain image resolution at quarter-dose reduction level. The SparseCT design requires only small hardware modifications to current diagnostic clinical scanners, opening up new possibilities for CT dose reduction.

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SparseCT: interrupted-beam acquisition and sparse reconstruction for radiation dose reduction

Cited by 15 publications

References 0 publications

Optimized spatial-spectral CT for multi-material decomposition

Optimized spatial-spectral CT for multi-material decomposition

SparseCT: System concept and design of multislit collimators

Image reconstruction for interrupted-beam x-ray CT on diagnostic clinical scanners

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