No abstract

A new Monte Carlo (MC) algorithm, the "Dose Planning Method" (DPM), and its associated computer program for simulating the transport of electrons and photons in radiotherapy class problems employing primary electron beams is presented. DPM is intended to be a high accuracy Monte Carlo alternative to the current generation of treatment planning codes which rely on analytical algorithms based on approximate solution of the photon/electron Boltzmann transport equation. For primary electron beams, DPM is capable of computing 3D dose distributions (in 1 mm 3 voxels) which agree to within 1% in dose maximum with widely used and exhaustively benchmarked general purpose, public domain MC codes in only a fraction of the CPU time. A representative problem, the simulation of 1 million 10 MeV electrons impinging upon a water phantom of 128 3 voxels of 1 mm on a side, can be performed by DPM in roughly 3 minutes on a modern desktop workstation. DPM achieves this performance by employing transport mechanics and electron multiple scattering distribution functions which have been derived to permit long transport steps (on the order of 5 mm) which can cross heterogeneity boundaries. The underlying algorithm is a "mixed" class simulation scheme, with differential cross sections for hard inelastic collisions and Bremsstrahlung events described in an approximate manner to simplify their sampling. The continuous energy loss approximation is employed for energy losses below some predefined thresholds, and photon transport (including Compton, photoelectric absorption and pair production) is simulated in an analog manner. The δ-scattering method (Woodcock tracking) is adopted to minimize the computational costs of transporting photons across voxels.

For optimal treatment planning in radionuclide therapy, robust tumor dose–response correlations must be established. Here, fully 3-dimensional (3D) dosimetry was performed coupling SPECT/CT at multiple time points with Monte Carlo–based voxel-by-voxel dosimetry to examine such correlations. Methods Twenty patients undergoing 131I-tositumomab for the treatment of refractory B-cell lymphoma volunteered for the study. Sixty tumors were imaged. Activity quantification and dosimetry were performed using previously developed 3D algorithms for SPECT reconstruction and absorbed dose estimation. Tumors were outlined on CT at multiple time points to obtain absorbed dose distributions in the presence of tumor deformation and regression. Equivalent uniform dose (EUD) was calculated to assess the biologic effects of the nonuniform absorbed dose, including the cold antibody effect. Response for correlation analysis was determined on the basis of the percentage reduction in the product of the largest perpendicular tumor diameters on CT at 2 mo. Overall response classification (as complete response, partial response, stable disease, or progressive disease) used for prediction analysis was based on criteria that included findings on PET. Results Of the evaluated tumor-absorbed dose summary measures (mean absorbed dose, EUD, and other measures from dose-volume histogram analysis), a statistically significant correlation with response was seen only with EUD (r = 0.36 and P = 0.006 at the individual tumor level; r = 0.46 and P = 0.048 at the patient level). The median value of mean absorbed dose for stable disease, partial response, and complete response patients was 196, 346, and 342 cGy, respectively, whereas the median value of EUD for each of these categories was 170, 363, and 406 cGy, respectively. At a threshold of 200 cGy, both mean absorbed dose and EUD had a positive predictive value for responders (partial response + complete response) of 0.875 (14/16) and a negative predictive value of 1.0 (3/3). Conclusion Improved dose–response correlations were demonstrated when EUD incorporating the cold antibody effect was used instead of the conventionally used mean tumor-absorbed dose. This work demonstrates the importance of 3D calculation and radiobiologic modeling when estimating absorbed dose for correlation with outcome.

A Maximum Likelihood (ML) image reconstruction technique using list-mode data has been applied to Compton scattering camera imaging. List-mode methods are appealing in Compton camera image reconstruction because the total number of data elements in the list (the number of detected photons) is significantly smaller than the number of possible combinations of position and energy measurements, leading to a much smaller problem than that faced by traditional iterative reconstruction techniques. For a realistic size device, the number of possible detector bins can be as large as 10 billion per pixel of the image space, while the number of counted photons would typically be a very small fraction of that. The primary difficulty in applying the list-mode technique is in determining the parameters which describe the response of the imaging system. In this work, a simple method for determining the required system matrix coefficients is employed, in which a back-projection is performed in list-mode, and response coefficients determined for only tallied pixels. Projection data has been generated for a representative Compton camera system by Monte Carlo simulation for disk sources with hot and cold spots and energies of 141, 364, and 511 keV, and reconstructions performed.