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Clinical Evaluation of the Iterative Metal Artifact Reduction Algorithm for CT Simulation in Radiotherapy


M Axente

M Axente1*, K Lee1, A Paidi2, A Bani-Hashemi2, D Hristov1, (1) Stanford Hospitals and Clinics, Stanford, CA, (2) Siemens Healthcare USA, Martinez, CA

TU-E-141-7 Tuesday 2:00PM - 3:50PM Room: 141

Purpose: The iterative metal artifact reduction (IMAR) algorithm has been proposed for commercial implementation in upcoming Siemens platforms. The purpose of this study is to evaluate the performance of this algorithm in radiation oncology settings.

Methods:Mean CT numbers and noise (standard-deviation) within delineated regions of interest were compared before/after IMAR correction on standard electron-density phantom images. Patient IMAR-corrected images were evaluated by 4 observers and ranked based on conspicuity of structures near artifacts (0-5 scale, 5 best score). The dosimetric impact of utilizing IMAR-corrected patient images for planning was analyzed by comparing original dose distributions and those recalculated on IMAR-corrected images. All images were acquired on a Siemens Definition scanner. In order to reference the observations herein, all analyses were also conducted on images corrected with a second algorithm: metal deletion technique (MDT), available for public use.

Results:IMAR accurately recovers CT numbers. CT number percent differences were reduced on average from 62% to 18%, while average noise percent differences were minimally reduced (146% before, 140% after). MDT performed worse retrieving mean CT numbers (62% to 27%), and better at reducing noise (146% to 24%). After visually inspecting the images, physicians agreed that IMAR-corrected images offered better confidence at reading patient anatomy than original images. The MDT-corrected images scored 4.3 on average while IMAR-corrected images scored 4 with reviewing physicians (p = 0.052). Local dose differences up to ±20-30cGy were noted, but γ-analysis (3%/3mm) did not indicate major overall differences between plans calculated on original images and those calculated on IMAR-corrected images.

Conclusion:The IMAR algorithm accurately recovered CT numbers (better than MDT), while minimally reducing noise values (worse than MDT). No clinically significant differences were detected between dose distributions calculated on original CT images and those planned on IMAR-corrected images. Initial analysis indicates that IMAR images could be used for treatment planning.

Funding Support, Disclosures, and Conflict of Interest: Siemens Healthcare

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