Successive iterative restoration applied to streak artifact reduction in X-ray CT image of dento-alveolar region

Usefulness of the superior orbitomeatal line without the lens included in the scan range at different tube voltage during pediatric head CT

INTRODUCTION AND OBJECTIVES

To compare the real time skin dosimeter values at lens between with the lens included in the scan range (orbitomeatal base line [OML]) or without the lens included in the scan range (superior orbitomeatal line [SOML]) at different tube voltages.

MATERIALS AND METHODS

We used three pediatric anthropomorphic phantoms with a 64 detector-row computed tomography (CT) scanner with the OML- or SOML-protocol at different tube voltages during the head CT. A real time skin dosimeter was inserted into the phantom center of the head, and surfaces of the lens. We compared the real time skin dosimeter values at lens between the OML- and SOML-protocol.

RESULTS

There were no significant differences in the real time skin dosimeter values for the head in the scan area for each phantom at different tube voltages between the OML- and SOML-protocol (p > 0.05 for all phantom). Compared with the OML protocol, it is possible to reduce the real time skin dosimeter values at lens by approximately 80% by using the SOML protocol (p < 0.05) at all tube voltages. Compared with the OML protocol, it is possible to reduce the real time skin dosimeter values at mammary gland by approximately 20% by using the SOML protocol (p < 0.05) at all tube voltages.

CONCLUSIONS

During the pediatric head CT examination, SOML protocol was possible to reduce the real time skin dosimeter values at lens by approximately 80% compared with OML protocol at all tube voltages.

Impact of statistical reconstruction and compressed sensing algorithms on projection data elimination during X-ray CT image reconstruction

OBJECTIVES

To examine the effect of incomplete, or total elimination of, projection data on computed tomography (CT) images subjected to statistical reconstruction and/or compressed sensing algorithms.

METHODS

Multidetector row CT images were used. The algebraic reconstruction technique (ART) and the maximum likelihood-expectation maximization (ML-EM) method were compared with filtered back-projection (FBP). Effects on reconstructed images were studied when the projection data of 360° (360 projections) were decreased to 180 or 90 projections by reducing the collection angle or thinning the image data. The total variation (TV) regularization method using compressed sensing was applied to images processed by the ART. Image noise was subjectively evaluated using the root-mean-square error and signal-to-noise ratio.

RESULTS

When projection data were reduced by one-half or three-quarters, ART and ML-EM produced better image quality than FBP. Both ART and ML-EM resulted in high quality at a spread of 90 projections over 180° rotation. Computational loading was high for statistical reconstruction, but not for ML-EM, compared with the ART. TV regularization made it possible to use only 36 projections while still achieving acceptable image quality.

CONCLUSIONS

Incomplete projection data-accomplished by reducing the angle to collect image data or thinning the projection data without reducing the angle of rotation over which it is collected-made it possible to reduce the radiation dose while retaining image quality with statistical reconstruction algorithms and/or compressed sensing. Despite heavier computational calculation loading, these methods should be considered for reducing radiation doses.

Statistical iterative reconstruction for streak artefact reduction when using multidetector CT to image the dento-alveolar structures

OBJECTIVES

When metallic prosthetic appliances and dental fillings exist in the oral cavity, the appearance of metal-induced streak artefacts is not avoidable in CT images. The aim of this study was to develop a method for artefact reduction using the statistical reconstruction on multidetector row CT images.

METHODS

Adjacent CT images often depict similar anatomical structures. Therefore, reconstructed images with weak artefacts were attempted using projection data of an artefact-free image in a neighbouring thin slice. Images with moderate and strong artefacts were continuously processed in sequence by successive iterative restoration where the projection data was generated from the adjacent reconstructed slice. First, the basic maximum likelihood-expectation maximization algorithm was applied. Next, the ordered subset-expectation maximization algorithm was examined. Alternatively, a small region of interest setting was designated. Finally, the general purpose graphic processing unit machine was applied in both situations.

RESULTS

The algorithms reduced the metal-induced streak artefacts on multidetector row CT images when the sequential processing method was applied. The ordered subset-expectation maximization and small region of interest reduced the processing duration without apparent detriments. A general-purpose graphic processing unit realized the high performance.

CONCLUSIONS

A statistical reconstruction method was applied for the streak artefact reduction. The alternative algorithms applied were effective. Both software and hardware tools, such as ordered subset-expectation maximization, small region of interest and general-purpose graphic processing unit achieved fast artefact correction.

CT image sequence restoration based on sparse and low-rank decomposition

Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT) image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA), Linearized Alternating Direction Method with Adaptive Penalty (LADMAP) and Go Decomposition (GoDec). Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images.

Metal-induced streak artifact reduction using iterative reconstruction algorithms in x-ray computed tomography image of the dentoalveolar region

OBJECTIVE

The objective of this study was to reduce metal-induced streak artifact on oral and maxillofacial x-ray computed tomography (CT) images by developing the fast statistical image reconstruction system using iterative reconstruction algorithms.

STUDY DESIGN

Adjacent CT images often depict similar anatomical structures in thin slices. So, first, images were reconstructed using the same projection data of an artifact-free image. Second, images were processed by the successive iterative restoration method where projection data were generated from reconstructed image in sequence. Besides the maximum likelihood-expectation maximization algorithm, the ordered subset-expectation maximization algorithm (OS-EM) was examined. Also, small region of interest (ROI) setting and reverse processing were applied for improving performance.

RESULTS

Both algorithms reduced artifacts instead of slightly decreasing gray levels. The OS-EM and small ROI reduced the processing duration without apparent detriments. Sequential and reverse processing did not show apparent effects.

CONCLUSIONS

Two alternatives in iterative reconstruction methods were effective for artifact reduction. The OS-EM algorithm and small ROI setting improved the performance.