Program Information
Improving CT Quality for Radiation Therapy of Patients with High Body Mass Index Using Iterative Reconstruction Algorithms
G Noid*, A Tai , X Li , Medical College of Wisconsin, Milwaukee, WI
Presentations
SU-E-I-4 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose: Iterative reconstruction (IR) algorithms are developed to improve CT image quality (IQ) by reducing noise without diminishing spatial resolution or contrast. The CT IQ for patients with a high Body Mass Index (BMI) can suffer from increased noise due to photon starvation. The purpose of this study is to investigate and to quantify the IQ enhancement for high BMI patients through the application of IR algorithms.
Methods: CT raw data collected for 6 radiotherapy (RT) patients with BMI, greater than or equal to 30 were retrospectively analyzed. All CT data were acquired using a CT scanner (Somaton Definition AS Open, Siemens) installed in a linac room (CT-on-rails) using standard imaging protocols. The CT data were reconstructed using the Sinogram Affirmed Iterative Reconstruction (SAFIRE) and Filtered Back Projection (FBP) methods. IQ metrics of the obtained CTs were compared and correlated with patient depth and BMI. The patient depth was defined as the largest distance from anterior to posterior along the bilateral symmetry axis.
Results: IR techniques are demonstrated to preserve contrast and reduce noise in comparison to traditional FBP. Driven by the reduction in noise, the contrast to noise ratio is roughly doubled by adopting the highest SAFIRE strength. A significant correlation was observed between patient depth and IR noise reduction through Pearson’s correlation test (R = 0.9429/P = 0.0167). The mean patient depth was 30.4 cm and the average relative noise reduction for the strongest iterative reconstruction was 55%.
Conclusion: The IR techniques produce a measureable enhancement to CT IQ by reducing the noise. Dramatic noise reduction is evident for the high BMI patients. The improved CT IQ enables more accurate delineation of tumors and organs at risk and more accuarte dose calculations for RT planning and delivery guidance.
Funding Support, Disclosures, and Conflict of Interest: Supported by Siemens
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