Program Information

Ion Imaging to Better Estimate In-Vivo Relative Stopping Powers Using X-Ray CT Prior-Knowledge Information

M Dias^1,2*, C Collins-Fekete^2,3 , M Riboldi¹ , P Doolan ^2,4,D Hansen⁵ , G Baroni¹ , J Seco² , (1) Dipartamento di Elettronica, Informazione e Bioingegneria - DEIB, Politecnico di Milano, Italy, (2) Massachusetts General Hospital, Boston, MA, (3) Departement de physique, de genie physique et d'optique et Centre de recherche sur le cancer, Universite Laval, Quebec, (4) University College London, London, U.K (5) Experimental Clinical Oncology, Aarhus University, 8000 Aarhus C, Denmark

Presentations

SU-E-J-83 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose:
To reduce uncertainties in relative stopping power (RSP) estimates for ions (alpha and carbon) by using Ion radiographic-imaging and X-ray CT prior-knowledge.

Methods:
A 36x36 phantom matrix composed of 9 materials with different thicknesses and randomly placed is generated. Theoretical RSPs are calculated using stopping power (SP) data from three references (Janni, ICRU49 and Bischel). We introduced an artificial systematic error (1.5%, 2.5% or 3.5%) and a random error (<0.5%) to the SP to simulated patient ion-range errors present in clinic environment. Carbon/alpha final energy for each RSPs set (theoretical and from CT images) is obtained with a ray-tracing algorithm. A gradient descent (GD) method is used to minimize the difference in exit particle energy, between theory and X-ray CT RSP maps, by iteratively correcting the RSP map from X-ray CT. Once a new set of RSPs is obtained for a direction a new optimization is done over other direction using the RSPs from the previous optimization. Theoretical RSPs are compared with experimental RSPs obtained with Gammex Phantom.

Results:
Preliminary results show that optimized RSP values can be obtained with smaller uncertainties (<1%) than clinical RSPs (1.5% to 3.5%). Theoretical values from three different references show uncertainties, up to 3% from experimental values. Further investigation will consider prior-knowledge from RSP obtained with CT images and ion radiographies from Monte Carlo Simulations.

Conclusion:
GD and ray-tracing methods have been implemented to reduce RSP uncertainties from values obtained for clinical treatment. Experimental RSPs will be obtained using carbon/alpha beams to consider the existence of material dependent systematic errors. Based on the results, it is hoped to show that using ray-tracing optimization with ion radiography and prior knowledge on RPSs, treatment planning accuracy and cost-effectiveness can be improved.

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