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Improvement of Four Dimensional Cone Beam CT Image Quality with Iterative Reconstruction

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S Kida

S Kida1*, M Nakano1, Y Masutani2, K Nakagawa2, A Haga2, (1) The University of Tokyo, Tokyo, , (2) The University of Tokyo Hospital, Tokyo,

SU-E-I-14 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Four-dimensional cone beam CT (4D-CBCT) is well known as powerful modality for image guided radiation therapy. Conventionally, it is reconstructed by sorting the X-ray projection images into each respiratory phase according to a breathing signal using the FDK algorithm. This usually leads to inadequate number of projections in each phase, resulting in low quality 4D-CBCT images with obvious streaking artifacts. We tried to improve the image quality for 4D CBCT using iterative reconstruction method (ML-convex) and compared with the images by FDK in SNR of the reconstructed images.

We used ML-convex algorithm, which has the ability to reconstruct CBCT images from a few number of noisy x-ray projections by taking knowledge of x-ray photon statistics into account. We compared the image quality of 4D CBCT by ML-convex with that by FDK using projection images acquired with different rotational speeds by XVI 5.0 (Elekta). Projection images were sorted by 10 phases for both reconstruction algorithms. A phantom study and patient study were performed. The convergence rate and reconstruction accuracy were evaluated using Catphan-600 physical phantom.

Image quality of 4D-CBCT is substantially enhanced by the ML-convex algorithm. Streaking artifacts, commonly presented in the image reconstructed by FDK algorithm, are almost suppressed in spite of the small number of projections. Quantitative evaluations indicated that, compared with the FDK results, ML-convex method improves signal-to-noise- ratio (SNR) and reconstructed more accurate CT value.

The ML-convex algorithm yielded images with higher SNR and more accurate CT value than those produced by FDK algorithm. This result will enable us to apply 4D CBCT by ML-convex algorithm to Image Guided Adaptive Radiation Therapy and patient daily CBCT-based treatment localization in real clinical environments with the higher computational efficiency.

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