Particle Filter-Based Target Tracking Algorithm for Magnetic Resonance-Guided Respiratory Compensation: Robustness and Accuracy Assessment.

PURPOSE: To assess overall robustness and accuracy of a modified particle filter-based tracking algorithm for magnetic resonance (MR)-guided radiation therapy treatments. METHODS AND MATERIALS: An improved particle filter-based tracking algorithm was implemented, which used a normalized cross-correlation function as the likelihood calculation. With a total of 5 healthy volunteers and 8 patients, the robustness of the algorithm was tested on 24 dynamic magnetic resonance imaging (MRI) time series with varying resolution, contrast, and signal-to-noise ratio. The complete data set included data acquired with different scan parameters on a number of MRI scanners with varying field strengths, including the 1.5T MR linear accelerator. Tracking errors were computed by comparing the results obtained by the particle filter algorithm with experts' delineations. RESULTS: The ameliorated tracking algorithm was able to accurately track abdominal as well as thoracic tumors, whereas the previous Bhattacharyya distance-based implementation failed in more than 50% of the cases. The tracking error, combined over all MRI acquisitions, is 1.1 ± 0.4 mm, which demonstrated high robustness against variations in contrast, noise, and image resolution. Finally, the effect of the input/control parameters of the model was very similar across all cases, suggesting a class-based optimization is possible. CONCLUSIONS: The modified particle filter tracking algorithm is highly accurate and robust against varying image quality. This makes the algorithm a promising candidate for automated tracking on the MR linear accelerator.

A particle filter based autocontouring algorithm for lung tumor tracking using dynamic magnetic resonance imaging.

PURPOSE: This study introduces a novel autocontouring algorithm based on particle filter for lung tumors. It is validated on dynamic magnetic resonance (MR) images and is developed in the context of MR-linac treatments. METHODS: A sequential Monte Carlo method called particle filter is used as the main structure of the algorithm and is combined with Otsu's thresholding technique to contour lung tumors on dynamic MR images. Four non-small cell lung cancer (NSCLC) patients were imaged with a 1.5 T MR for 60 s at a rate of 4 images/s and were asked to breathe normally. Prior to treatment, some image processing is required by the proposed algorithm, which includes a manual contour of the tumor, the tumor's displacement, and its descriptive statistics. During treatment, the contours are automatically generated by thresholding around the center of mass of the particles. A comparison with the expert's contours is obtained by calculating the Dice similarity coefficient (DSC), the precision, the recall, the Hausdorff distance, and the difference in centroid positions (Δd). RESULTS: This autocontouring algorithm is independent of pretreatment training and presents continuous adaptability as provided by the nature of particle filters. The number of particles is proportional to the area of the tumor and increases the computational time at a rate of 2 ms for every 500 particles, whereas the contouring step adds a constant 14 ms. The contours' comparison is obtained with a mean DSC of 0.89-0.91, mean precision of 0.88-0.91, mean recall of 0.89-0.95, and mean Δd of 0.6-2.0 mm. CONCLUSIONS: This work presents a proof of concept of a new autocontouring algorithm for NSCLC patients on dynamic MR images. The contours were generated in good agreement with the expert's contours.

A stoichiometric calibration method for dual energy computed tomography.

The accuracy of radiotherapy dose calculation relies crucially on patient composition data. The computed tomography (CT) calibration methods based on the stoichiometric calibration of Schneider et al (1996 Phys. Med. Biol. 41 111-24) are the most reliable to determine electron density (ED) with commercial single energy CT scanners. Along with the recent developments in dual energy CT (DECT) commercial scanners, several methods were published to determine ED and the effective atomic number (EAN) for polyenergetic beams without the need for CT calibration curves. This paper intends to show that with a rigorous definition of the EAN, the stoichiometric calibration method can be successfully adapted to DECT with significant accuracy improvements with respect to the literature without the need for spectrum measurements or empirical beam hardening corrections. Using a theoretical framework of ICRP human tissue compositions and the XCOM photon cross sections database, the revised stoichiometric calibration method yields Hounsfield unit (HU) predictions within less than ±1.3 HU of the theoretical HU calculated from XCOM data averaged over the spectra used (e.g., 80 kVp, 100 kVp, 140 kVp and 140/Sn kVp). A fit of mean excitation energy (I-value) data as a function of EAN is provided in order to determine the ion stopping power of human tissues from ED-EAN measurements. Analysis of the calibration phantom measurements with the Siemens SOMATOM Definition Flash dual source CT scanner shows that the present formalism yields mean absolute errors of (0.3 ± 0.4)% and (1.6 ± 2.0)% on ED and EAN, respectively. For ion therapy, the mean absolute errors for calibrated I-values and proton stopping powers (216 MeV) are (4.1 ± 2.7)% and (0.5 ± 0.4)%, respectively. In all clinical situations studied, the uncertainties in ion ranges in water for therapeutic energies are found to be less than 1.3 mm, 0.7 mm and 0.5 mm for protons, helium and carbon ions respectively, using a generic reconstruction algorithm (filtered back projection). With a more advanced method (sinogram affirmed iterative technique), the values become 1.0 mm, 0.5 mm and 0.4 mm for protons, helium and carbon ions, respectively. These results allow one to conclude that the present adaptation of the stoichiometric calibration yields a highly accurate method for characterizing tissue with DECT for ion beam therapy and potentially for photon beam therapy.