DUAL ENERGY COMPUTED TOMOGRAPHY FOR PROTON THERAPY TREATMENT PLANNING

Proton radiation therapy is gaining popularity because of the unique characteristics of its dose distribution, e.g., high dose-gradient at the distal end of the percentage-depth-dose curve (known as the Bragg peak). The high dose-gradient offers the possibility of delivering high dose to the target while still sparing critical organs distal to the target. However, the high dose-gradient is a double-edged sword: a small shift of the highly conformal high-dose area can cause the target to be substantially under-dosed or the critical organs to be substantially over-dosed. Because of that, large margins are required in treatment planning to ensure adequate dose coverage of the target, which prevents us from realizing the full potential of proton beams. Therefore, it is critical to reduce uncertainties in the proton radiation therapy. One major uncertainty in a proton treatment is the range uncertainty related to the estimation of proton stopping power ratio (SPR) distribution inside a patient. The SPR distribution inside a patient is required to account for tissue heterogeneities when calculating dose distribution inside the patient. In current clinical practice, the SPR distribution inside a patient is estimated from the patient’s treatment planning computed tomography (CT) images based on the CT number-to-SPR calibration curve. The SPR derived from a single CT number carries large uncertainties in the presence of human tissue composition variations, which is the major drawback of the current SPR estimation method. We propose to solve this problem by using dual energy CT (DECT) and hypothesize that the range uncertainty can be reduced by a factor of two from currently used value of 3.5%. A MATLAB program was developed to calculate the electron density ratio (EDR) and effective atomic number (EAN) from two CT measurements of the same object. An empirical relationship was discovered between mean excitation energies and EANs existing in human body tissues. With the MATLAB program and the empirical relationship, a DECT-based method was successfully developed to derive SPRs for human body tissues (the DECT method). The DECT method is more robust against the uncertainties in human tissues compositions than the current single-CT-based method, because the DECT method incorporated both density and elemental composition information in the SPR estimation. Furthermore, we studied practical limitations of the DECT method. We found that the accuracy of the DECT method using conventional kV-kV x-ray pair is susceptible to CT number variations, which compromises the theoretical advantage of the DECT method. Our solution to this problem is to use a different x-ray pair for the DECT. The accuracy of the DECT method using different combinations of x-ray energies, i.e., the kV-kV, kV-MV and MV-MV pair, was compared using the measured imaging uncertainties for each case. The kV-MV DECT was found to be the most robust against CT number variations. In addition, we studied how uncertainties propagate through the DECT calculation, and found general principles of selecting x-ray pairs for the DECT method to minimize its sensitivity to CT number variations. The uncertainties in SPRs estimated using the kV-MV DECT were analyzed further and compared to those using the stoichiometric method. The uncertainties in SPR estimation can be divided into five categories according to their origins: the inherent uncertainty, the DECT modeling uncertainty, the CT imaging uncertainty, the uncertainty in the mean excitation energy, and SPR variation with proton energy. Additionally, human body tissues were divided into three tissue groups – low density (lung) tissues, soft tissues and bone tissues. The uncertainties were estimated separately because their uncertainties were different under each condition. An estimate of the composite range uncertainty (2s) was determined for three tumor sites – prostate, lung, and head-and-neck, by combining the uncertainty estimates of all three tissue groups, weighted by their proportions along typical beam path for each treatment site. In conclusion, the DECT method holds theoretical advantages in estimating SPRs for human tissues over the current single-CT-based method. Using existing imaging techniques, the kV-MV DECT approach was capable of reducing the range uncertainty from the currently used value of 3.5% to 1.9%-2.3%, but it is short to reach our original goal of reducing the range uncertainty by a factor of two. The dominant source of uncertainties in the kV-MV DECT was the uncertainties in CT imaging, especially in MV CT imaging. Further reduction in beam hardening effect, the impact of scatter, out-of-field object etc. would reduce the Hounsfeld Unit variations in CT imaging. The kV-MV DECT still has the potential to reduce the range uncertainty further.

An investigation of the pica practices of pregnant women in Houston and Prairie View, Texas

Pregnant African American women are at higher risk of having a preterm delivery and/or a low birthweight infant. Many factors are associated with adverse pregnancy outcomes but a food habit that deserves further study in the causal process is pica, a craving for, and ingestion of, nonnutritive substances such as laundry starch, clay, dirt, or ice. This food habit is more common in the African American population but has not been adequately studied in relation to preterm and/or low birth weight infants.^ Mothers (n = 281) with infants less than one year of age who participated in the Special Supplementary Food Program for Women, Infants, and Children (WIC) at clinics in Houston and Prairie View, Texas were interviewed regarding pica practices during pregnancy, dietary practices, and some demographic indices. Hospital records were abstracted for health information on the mothers and infants, including birthweight and gestational age at birth of the infant.^ The subjects were 88.6% African American, 6.8% Hispanic, and 4.6% Caucasian. Overall prevalence of pica was 76.5%. Pica prevalence by substance(s) was as follows: ice 53.7%; ice and freezer frost 14.6%; other substances such as baking soda, baking powder, cornstarch, laundry starch, and clay or dirt 8.2%; and 23.5% reported no pica. The women who reported ice/freezer frost pica had a higher percentage of illegal drug use and alcohol use during pregnancy. The women who reported other pica substances had the lowest mean educational level, highest gravidity, and a higher percentage smoked during pregnancy.^ There were no significant differences in nutrient intakes measured by the mean 24-hour dietary recalls between women who reported ice pica (n = 103) and women who denied pica (n = 50). The women who reported ice/freezer frost pica or other pica substances had more food cravings and food dislikes during pregnancy than those who reported ice pica or no pica.^ There were no differences in mean birthweight or mean gestational age at birth of infants born to mothers from the three pica groups and the no pica group but regression analyses revealed a possible relationship between pica, low maternal hemoglobin at delivery, and preterm birth. ^