Thoracic target volume delineation using various maximum-intensity projection computed tomography image sets for stereotactic body radiation therapy

The motion of lung tumors during respiration makes the accurate delivery of radiation therapy to the thorax difficult because it increases the uncertainty of target position. The adoption of four-dimensional computed tomography (4D-CT) has allowed us to determine how a tumor moves with respiration for each individual patient. Using information acquired during a 4D-CT scan, we can define the target, visualize motion, and calculate dose during the planning phase of the radiotherapy process. One image data set that can be created from the 4D-CT acquisition is the maximum-intensity projection (MIP). The MIP can be used as a starting point to define the volume that encompasses the motion envelope of the moving gross target volume (GTV). Because of the close relationship that exists between the MIP and the final target volume, we investigated four MIP data sets created with different methodologies (3 using various 4D-CT sorting implementations, and one using all available cine CT images) to compare target delineation. It has been observed that changing the 4D-CT sorting method will lead to the selection of a different collection of images; however, the clinical implications of changing the constituent images on the resultant MIP data set are not clear. There has not been a comprehensive study that compares target delineation based on different 4D-CT sorting methodologies in a patient population. We selected a collection of patients who had previously undergone thoracic 4D-CT scans at our institution, and who had lung tumors that moved at least 1 cm. We then generated the four MIP data sets and automatically contoured the target volumes. In doing so, we identified cases in which the MIP generated from a 4D-CT sorting process under-represented the motion envelope of the target volume by more than 10% than when measured on the MIP generated from all of the cine CT images. The 4D-CT methods suffered from duplicate image selection and might not choose maximum extent images. Based on our results, we suggest utilization of a MIP generated from the full cine CT data set to ensure a representative inclusive tumor extent, and to avoid geometric miss.

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.

Assessment of cone beam computed tomography techniques for imaging lung damage in mice in vivo

Lung damage is a common side effect of chemotherapeutic drugs such as bleomycin. This study used a bleomycin mouse model which simulates the lung damage observed in humans. Noninvasive, in vivo cone-beam computed tomography (CBCT) was used to visualize and quantify fibrotic and inflammatory damage over the entire lung volume of mice. Bleomycin was used to induce pulmonary damage in vivo and the results from two CBCT systems, a micro-CT and flat panel CT (fpCT), were compared to histologic measurements, the standard method of murine lung damage quantification. Twenty C57BL/6 mice were given either 3 U/kg of bleomycin or saline intratracheally. The mice were scanned at baseline, before the administration of bleomycin, and then 10, 14, and 21 days afterward. At each time point, a subset of mice was sacrificed for histologic analysis. The resulting CT images were used to assess lung volume. Percent lung damage (PLD) was calculated for each mouse on both the fpCT (PLDfpcT) and the micro-CT (PLDμCT). Histologic PLD (PLDH) was calculated for each histologic section at each time point (day 10, n = 4; day 14, n = 4; day 21, n = 5; control group, n = 5). A linear regression was applied to the PLDfpCT vs. PLDH, PLDμCT vs. PLDH and PLDfpCT vs. PLDμCT distributions. This study did not demonstrate strong correlations between PLDCT and PLDH. The coefficient of determination, R, was 0.68 for PLDμCT vs. PLDH and 0.75 for the PLD fpCT vs. PLDH. The experimental issues identified from this study were: (1) inconsistent inflation of the lungs from scan to scan, (2) variable distribution of damage (one histologic section not representative of overall lung damage), (3) control mice not scanned with each group of bleomycin mice, (4) two CT systems caused long anesthesia time for the mice, and (5) respiratory gating did not hold the volume of lung constant throughout the scan. Addressing these issues might allow for further improvement of the correlation between PLDCT and PLDH. ^

Prospective gated 64 slice computed tomography in assessment of coronary artery disease: A single center experience and relationship of body mass index to outcomes

Coronary artery disease (CAD) is the most common cause of morbidity and mortality in the United States. While Coronary Angiography (CA) is the gold standard test to investigate coronary artery disease, Prospective gated-64 Slice Computed Tomography (Prosp-64CT) is a new non-invasive technology that uses the 64Slice computed tomography (64CT) with electrocardiographic gating to investigate coronary artery disease. The aim of the current study was to investigate the role of Body Mass Index (BMI) as a factor affecting occurrence of CA after a Prosp-64CT, as well as the quality of the Prosp-64CT. Demographic and clinical characteristics of the study population were described. A secondary analysis of data on patients who underwent a Prosp-64CT for evaluation of coronary artery disease was performed. Seventy seven patients who underwent Prosp-64CT for evaluation for coronary artery disease were included. Fifteen patients were excluded because they had missing data regarding BMI, quality of the Prosp-64CT or CA. Thus, a total of 62 patients were included in the final analysis. The mean age was 56.2 years. The mean BMI was 31.3 kg/m 2. Eight (13%) patients underwent a CA within one month of Prosp-64CT. Eight (13%) patients had a poor quality Prosp-64CT. There was significant association of higher BMI as a factor for occurrence of CA post Prosp-64CT (P<0.05). There was a trend, but no statistical significance was observed for the association of being obese and occurrence of CA (P=0.06). BMI, as well as obesity, were not found to be significantly associated with poor quality of Prosp-64CT (P=0.19 and P=0.76, respectively). In conclusion, BMI was significantly associated with occurrence of CA within one month of Prosp-64CT. Thus, in patients with a higher BMI, diagnostic investigation with both tests could be avoided; rather, only a CA could be performed. However, the relationship of BMI to quality of Prosp-64CT needs to be further investigated since the sample size of the current study was small.^

Design and Optimization of Four-dimensional Cone-beam Computed Tomography in Image-guided Radiation Therapy

The influence of respiratory motion on patient anatomy poses a challenge to accurate radiation therapy, especially in lung cancer treatment. Modern radiation therapy planning uses models of tumor respiratory motion to account for target motion in targeting. The tumor motion model can be verified on a per-treatment session basis with four-dimensional cone-beam computed tomography (4D-CBCT), which acquires an image set of the dynamic target throughout the respiratory cycle during the therapy session. 4D-CBCT is undersampled if the scan time is too short. However, short scan time is desirable in clinical practice to reduce patient setup time. This dissertation presents the design and optimization of 4D-CBCT to reduce the impact of undersampling artifacts with short scan times. This work measures the impact of undersampling artifacts on the accuracy of target motion measurement under different sampling conditions and for various object sizes and motions. The results provide a minimum scan time such that the target tracking error is less than a specified tolerance. This work also presents new image reconstruction algorithms for reducing undersampling artifacts in undersampled datasets by taking advantage of the assumption that the relevant motion of interest is contained within a volume-of-interest (VOI). It is shown that the VOI-based reconstruction provides more accurate image intensity than standard reconstruction. The VOI-based reconstruction produced 43% fewer least-squares error inside the VOI and 84% fewer error throughout the image in a study designed to simulate target motion. The VOI-based reconstruction approach can reduce acquisition time and improve image quality in 4D-CBCT.