Exterior 투영데이터를 이용한 Region-of-Interest CT의 반복적 영상재구성 방법
An Iterative Image Reconstruction Method for the Region-of-Interest CT Assisted from Exterior Projection Data
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초록
In an ordinary CT scan, a large number of projections with full field-of-view (FFOV) are necessary toreconstruct high resolution images. However, excessive x-ray dosage is a great concern in FFOV scan. Region-ofinterest(ROI) CT or sparse-view CT is considered to be a solution to reduce x-ray dosage in CT scanning, but itsuffers from bright-band artifacts or streak artifacts giving contrast anomaly in the reconstructed image. In this study,we propose an image reconstruction method to eliminate the bright-band artifacts and the streak artifacts simultaneously. In addition to the ROI scan for the interior projection data with relatively high sampling rate in the viewdirection, we get sparse-view exterior projection data with much lower sampling rate. Then, we reconstruct imagesby solving a constrained total variation (TV) minimization problem for the interior projection data, which is assistedby the exterior projection data in the compressed sensing (CS) framework. For the interior image reconstructionassisted by the exterior projection data, we implemented the proposed method which enforces dual data fidelity termsand a TV term. The proposed method has effectively suppressed the bright-band artifacts around the ROI boundaryand the streak artifacts in the ROI image. We expect the proposed method can be used for low-dose CT scans basedon limited x-ray exposure to a small ROI in the human body.
Abstract
In an ordinary CT scan, a large number of projections with full field-of-view (FFOV) are necessary toreconstruct high resolution images. However, excessive x-ray dosage is a great concern in FFOV scan. Region-ofinterest(ROI) CT or sparse-view CT is considered to be a solution to reduce x-ray dosage in CT scanning, but itsuffers from bright-band artifacts or streak artifacts giving contrast anomaly in the reconstructed image. In this study,we propose an image reconstruction method to eliminate the bright-band artifacts and the streak artifacts simultaneously. In addition to the ROI scan for the interior projection data with relatively high sampling rate in the viewdirection, we get sparse-view exterior projection data with much lower sampling rate. Then, we reconstruct imagesby solving a constrained total variation (TV) minimization problem for the interior projection data, which is assistedby the exterior projection data in the compressed sensing (CS) framework. For the interior image reconstructionassisted by the exterior projection data, we implemented the proposed method which enforces dual data fidelity termsand a TV term. The proposed method has effectively suppressed the bright-band artifacts around the ROI boundaryand the streak artifacts in the ROI image. We expect the proposed method can be used for low-dose CT scans basedon limited x-ray exposure to a small ROI in the human body.
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