Implantation d’un CT sur rails en radio-oncologie au nouveau CHUM
A new in-room imaging system is installed in radio-oncology at the new CHUM called the CT-Linac system. It combines a CT mounted on rails and a linear accelerator (linac) using a unique treatment table that rotates on his axis to align with each device separately. CT images show good contrast for soft tissues, which makes it easier to visualize certain tumors, thus facilitate patient alignment for the treatment. By improving the accuracy of the treatment, it is theoretically possible to increase the dose administered to the tumor and reduce the toxicity of healthy tissues. On the other hand, the CT-Linac system does not allow the same alignment procedure used for the other linac-mounted CBCT imaging devices since there is no correspondence between an imaging isocenter and the treatment isocenter. Thus, a new positioning procedure must be designed. Therefore, we have created an image registration software that calculates the displacements to apply to correct the position of the patient before treatment. The displacements are based on the location of radiopaque markers placed on the patient’s skin, which identify the treatment isocenter. The registration method compares multiple sets of parameters, associated with the transformation matrix, using a similarity metric based on mutual information to find the best match between the two images. Browsing each set of parameters is done using an evolution algorithm and an optimization strategy in order to reduce the computation time while keeping a clinically acceptable registration quality. To test the software, we simulated the positioning procedure in the treatment room with a QUASAR phantom. The displacement corrections calculated by the software were compared with the actual known displacement corrections values. For an acceptable registration quality, we notice that the number of iterations of the algorithm, the images resolution, the number of resolution levels and the use of a mask applied to our images influence the time required to perform the registration. Thus, the software allows the user to change the value of these parameters before starting the image registration. There are no fixed optimal values for these parameters, but they vary according to the specificities of each image. For a humanoid phantom, we notice that three resolution levels, a decrease by three in voxel resolution in each dimension, a maximum number of iterations of 250 and the use of a mask gives the best results in the pelvic region according to every metrics (DICE, True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV) and Mutual Information (IM)).
Computed tomography on rails radiotherapy volumetric images registration new CHUM