1. Feasibility of Genetic Programming for the Optimization of Tissue-Type-Segmented Maps in the Generation of Synthetic CT in Radiation Therapy Treatment Planning 2. Matthew Witten, PhD 8 Barstow Rd Apt 2J Great Neck, NY 11021 USA +1(516) 521 - 9341 matthew.witten@nyulangone.org Owen Clancey, PhD 716 Canopy Cv Charleston, SC 29412 +1(360) 708-6005 owen.clancey@nyulangone.org 3. Matthew Witten, PhD matthew.witten@nyulangone.org 4. Abstract—Modern radiation therapy treatment planning has traditionally relied upon computed tomography (CT) in modeling the interaction of megavoltage (MV) photons with the various tissues and organs of the body. CT image data provide both detailed information about individual patient anatomy, as well as a voxel-by-voxel three-dimensional grid of Hounsfield units (HU), which specifies, at all points within the patient, essentially the difference between the attenuation coefficient of the tissue within that voxel from the attenuation coefficient of water, normalized to that of water. From the HU value, the relative electron density can be inferred, and as the relative electron density is the major determinant of the interaction of the tissue with MV photons, the radiation dose distribution can then be calculated. Recently, there has been interest in using magnetic resonance imaging (MR) in lieu of CT, as MR provides superior soft-tissue contrast; however, MR does not provide any electron density information. Various approaches have been essayed to create a synthetic CT (synCT) from the MR data. In the present study, genetic programming was used to construct mappings of MR data to HU data for seven tissue types: bladder, cancellous bone, cortical bone, fat, muscle, prostate, and rectum. These maps were then applied to randomly chosen points in five patient data sets to calculate the synCT HU values, which were then compared with the actual HU values from CT images of those same patients. The method produced mean absolute errors (MAE) of 9.28 HU, 33.24 HU, 75.32 HU, 18.64 HU, 17.12 HU, 11.76 HU, and 18.40 HU for the respective tissue types, and these MAE values are less than those of previous approaches, indicating superior performance. Although the method of the present study does require more manual input, the superior performance is compensatory. Further study is necessary to confirm accuracy on entire MR data sets, and to ensure there is no sample variance effect on the current results. 5. B, D, G 6. The results produced by GP-optimized tissue-type segmented maps were superior to all other approaches in the literature for predicting Hounsfield units in the generation of synthetic CT data for use in radiotherapy treatment planning. Producing synthetic CT data sets from MR data is a challenging problem in contemporary radiation therapy, as one may acertain by reviewing the many attempts to do so in the medcial physics literature. 7. M. Witten and O. Clancey, "Feasibility of Genetic Programming for the Optimization of Tissue-Type-Segmented Maps in the Generation of Synthetic CT in Radiation Therapy Treatment Planning," 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1-6, doi: 10.1109/CEC48606.2020.9185768. 8. Any prize money, if any, is to be divided equally among the co-authors. 9. This submission would be best because it addresses a problem central to radiation therapy treatment planning-- namely: how to create a CT data set from an MR data set acquired for a specific patient. By implementing the method clinically, there is the potential for positively impacting the treatment planning process for cancer patients undegoing radiation therapy, improving the accuracy of the treatment plans and conseuqently, the clinical outcomes for these patients. 10. GP 11. 2020