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High Quality and Artifact-Free 4D Cone Beam CT and Its Application in Adaptive Treatment Planning

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K Niu

K Niu*, K Li , J Smilowitz , G Chen , University of Wisconsin, Madison, WI


TH-E-17A-9 Thursday 1:00PM - 2:50PM Room: 17A

Purpose: To develop a high quality 4D cone beam CT (4DCBCT) method that is immune to patient/couch truncations and to investigate its application in adaptive replanning of lung XRT.

Methods: In this study, IRB-approved human subject CBCT data was acquired using a Varian on-board imager with 1 minute rotation time. The acquired projection data was retrospectively sorted into 20 respiratory phase bins, from which 4DCBCT images with high SNR and high temporal resolution were generated using Prior Image Constrained Compressed Sensing (PICCS). Couch and patient truncations generate strong data inconsistency in the projection data and artifacts in the 4DCBCT image. They were addressed using an adaptive PICCS method. The artifact-free PICCS-4DCBCT images were used to generate adaptive treatment plans for the same patient at the 10th (day 21) and 30th (day 47) fractions. Dosimetric impacts with and without PICCS-4DCBCT were evaluated by isodose distributions, DVHs, and other dosimetric factors.

Results: The adaptive PICCS-4DCBCT method improves image quality by removing residue truncation artifacts; measured universal image quality increased 37%. The isodose lines and DVHs with PICCS-4DCBCT-based adaptive replanning were significantly more conformal to PTV than without replanning due to changes in patient anatomy caused by progress of the treatment. The mean dose to PTV at the 10th fraction was 63.1Gy with replanning and 64.2Gy without replanning, where the prescribed dose was 60Gy, in 2Gy x 30 fractions. The mean dose to PTV at the 30th fraction was 61.6Gy with replanning and 64.9Gy without replanning. Lung V20 was 37.1%, 41.9% and 43.3% for original plan, 10th fraction plan and 30th fraction plan; with re-planning, Lung V20 was 37.1%, 32%, 27.8%.

Conclusion: 4DCBCT imaging using adaptive PICCS is able to generate high quality, artifact-free images that potentially can be used to create replanning for improving radiotherapy of the lung.

Funding Support, Disclosures, and Conflict of Interest: K Niu, K Li, J Smilowitz: Nothing to Disclose. G Chen: General Electric Company Research funded, Siemens AG Research funded, Varian Medical Systems Research funded, Hologic Research funded.

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