Volumetric Modulated Arc Radiotherapy is an innovative technique currently utilized to efficiently deliver complex
treatments. Dose rate, speed of rotation, and field shape are continuously varied as the radiation source rotates about the
patient. Patient specific quality assurance is performed to verify that the delivered dose distribution is consistent with the
plan formulated in a treatment planning system. The purpose of this work is to present novel methodology using a
Gafchromic EBT3 film image of a patient plan in a cylindrical phantom and calculating the delivered MU per control
point. Images of two dimensional plan dose matrices and film scans are analyzed using MATLAB with the imaging
toolbox. Dose profiles in a ring corresponding to the film position are extracted from the plan matrices for comparison
with the corresponding measured film dose. The plan is made up of a series of individual static Control Points. If we
consider these Control Points a set of basis vectors, then variations in the plan can be represented as the weighted sum of
the basis. The weighing coefficients representing the actual delivered MU can be determined by any available
optimization tool, such as downhill simplex or non-linear programming. In essence we reconstruct an image of the
delivered dose. Clinical quality assurance is performed with this technique by computing a patient plan with the
measured monitor units and standard plan evaluation tools such as Dose Volume Histograms. Testing of the algorithm
with known changes in the reference images indicated a correlation coefficient greater than 0.99.
Our work presents a rapid and robust process that can analytically evaluate and correct patient setup error for head and neck radiotherapy by comparing orthogonal megavoltage portal images with digitally reconstructed radiographs. For robust data Photoshop is used to interactively segment images and registering reference contours to the transformed PI. MatLab is used for matrix computations and image analysis. The closest point distance for each PI point to a DRR point forms a set of homologous points. The translation that aligns the PI to the DRR is equal to the difference in centers of mass. The original PI points are transformed and the process repeated with an Iterative Closest Point algorithm until the transformation change becomes negligible. Using a 3.00 GHz processor the calculation of the 2500x1750 CPD matrix takes about 150 sec per iteration. Standard down sampling to about 1000 DRR and 250 PI points significantly reduces that time. We introduce a local neighborhood matrix consisting of a small subset of the DRR points in the vicinity of each PI point to further reduce the CPD matrix size. Our results demonstrate the effects of down sampling on accuracy. For validation, analytical detailed results are displayed as a histogram.