The success of hyperthermia treatments is dependent on thermal dose distribution. However, the three-dimensional temperature distribution remains largely unknown. Without this knowledge, the ...relationship between thermal dose and outcome is noisy, and therapy cannot be optimized. Accurate computations of thermal distribution can contribute to an optimized therapy. The hyperthermia modeling group in the Department of Radiotherapy, University Medical Center Utrecht devised a Discrete Vasculature Kotte et al., Phys. Med. Biol. 41, 865–884 (1996) model that accounts for the presence of vessel trees in the computational domain. The vessel tree geometry is tracked using magnetic resonance (MR) angiograms to a minimum diameter between 0.6 and 1 mm. However, smaller vessels (0.2–0.6 mm) are known to account for significant heat transfer. The hyperthermia group at Duke University Medical Center has proposed using perfusion maps derived from dynamic-enhanced magnetic resonance imaging to account for the tissue perfusion heterogeneity Craciunescu et al., Int. J. Hyperthermia 17, 221–239 (2001). In addition, techniques for noninvasive temperature measurements have been devised to measure temperatures in vivo Samulski et al., Int. J. Hypertherminal, 819–829 (1992). In this work, a patient with high-grade sarcoma has been retrospectively modeled to determine the temperature distribution achieved during a hyperthermia treatment. Available for this model were MR depicted geometry, angiograms, perfusion maps, as necessary for accurate thermal modeling, as well as MR thermometry data for validation purposes. The vasculature assembly through modifiable potential program Van Leeuwen et al., IEEE Trans. Biomed. Eng. 45, 596–604 (1998) was used in order to incorporate the traceable large vessels. Temperature simulations were made using different approaches to describe perfusion. The simulated cases were the bioheat equation with constant perfusion rates per tissue type, perfusion maps alone, tracked vessel tree and perfusion maps, and generated vessel tree. The results were compared with MR thermometry data for a single patient data set, concluding that a combination between large traceable vessels and perfusion map yields the best results for this particular patient. The technique has to be repeated on several patients, first with the same type of malignancy, and after that, on patients having malignancies at other different sites.
Accurate spatial dose delivery in radiotherapy is frequently complicated due to changes in the patient's internal anatomy during and in-between therapy segments. The recent introduction of hybrid MRI ...radiotherapy systems allows unequaled soft-tissue visualization during radiation delivery and can be used for dose reconstruction to quantify the impact of motion. To this end, knowledge of anatomical deformations obtained from continuous monitoring during treatment has to be combined with information on the spatio-temporal dose delivery to perform motion-compensated dose accumulation (MCDA). Here, the influence of the choice of deformable image registration algorithm, dose warping strategy, and magnetic resonance image resolution and signal-to-noise-ratio on the resulting MCDA is investigated. For a quantitative investigation, four 4D MRI-datasets representing typical patient observed motion patterns are generated using finite element modeling and serve as a gold standard. Energy delivery is simulated intra-fractionally in the deformed image space and, subsequently, MCDA-processed. Finally, the results are substantiated by comparing MCDA strategies on clinically acquired patient data. It is shown that MCDA is needed for correct quantitative dose reconstruction. For prostate treatments, using the energy per mass transfer dose warping strategy has the largest influence on decreasing dose estimation errors.
For quality assurance and adaptive radiotherapy, validation of the actual delivered dose is crucial.Intrafractional anatomy changes cannot be captured satisfactorily during treatment with hitherto ...available imaging modalitites. Consequently, dose calculations are based on the assumption of static anatomy throughout the treatment. However, intra- and interfraction anatomy is dynamic and changes can be significant.In this paper, we investigate the use of an MR-linac as a dose tracking modality for the validation of treatments in abdominal targets where both respiratory and long-term peristaltic and drift motion occur.The on-line MR imaging capability of the modality provides the means to perform respiratory gating of both delivery and acquisition yielding a model-free respiratory motion management under free breathing conditions.In parallel to the treatment, the volumetric patient anatomy was captured and used to calculate the applied dose. Subsequently, the individual doses were warped back to the planning grid to obtain the actual dose accumulated over the entire treatment duration. Ultimately, the planned dose was validated by comparison with the accumulated dose.Representative for a site subject to breathing modulation, two kidney cases (25 Gy target dose) demonstrated the working principle on volunteer data and simulated delivery. The proposed workflow successfully showed its ability to track local dosimetric changes. Integration of the on-line anatomy information could reveal local dose variations -2.3-1.5 Gy in the target volume of a volunteer dataset. In the adjacent organs at risk, high local dose errors ranging from -2.5 to 1.9 Gy could be traced back.
Image processing such as deformable image registration finds its way into radiotherapy as a means to track non-rigid anatomy. With the advent of magnetic resonance imaging (MRI) guided radiotherapy, ...intrafraction anatomy snapshots become technically feasible. MRI provides the needed tissue signal for high-fidelity image registration. However, acquisitions, especially in 3D, take a considerable amount of time. Pushing towards real-time adaptive radiotherapy, MRI needs to be accelerated without degrading the quality of information. In this paper, we investigate the impact of image resolution on the quality of motion estimations. Potentially, spatially undersampled images yield comparable motion estimations. At the same time, their acquisition times would reduce greatly due to the sparser sampling. In order to substantiate this hypothesis, exemplary 4D datasets of the abdomen were downsampled gradually. Subsequently, spatiotemporal deformations are extracted consistently using the same motion estimation for each downsampled dataset. Errors between the original and the respectively downsampled version of the dataset are then evaluated. Compared to ground-truth, results show high similarity of deformations estimated from downsampled image data. Using a dataset with (2.5 mm)3 voxel size, deformation fields could be recovered well up to a downsampling factor of 2, i.e. (5 mm)3. In a therapy guidance scenario MRI, imaging speed could accordingly increase approximately fourfold, with acceptable loss of estimated motion quality.
In radiotherapy, abdominal and thoracic sites are candidates for performing motion tracking. With real-time control it is possible to adjust the multileaf collimator (MLC) position to the target ...position. However, positions are not perfectly matched and position errors arise from system delays and complicated response of the electromechanic MLC system. Although, it is possible to compensate parts of these errors by using predictors, residual errors remain and need to be compensated to retain target coverage. This work presents a method to statistically describe tracking errors and to automatically derive a patient-specific, per-segment margin to compensate the arising underdosage on-line, i.e. during plan delivery. The statistics of the geometric error between intended and actual machine position are derived using kernel density estimators. Subsequently a margin is calculated on-line according to a selected coverage parameter, which determines the amount of accepted underdosage. The margin is then applied onto the actual segment to accommodate the positioning errors in the enlarged segment. The proof-of-concept was tested in an on-line tracking experiment and showed the ability to recover underdosages for two test cases, increasing V90% in the underdosed area about 47% and 41%, respectively. The used dose model was able to predict the loss of dose due to tracking errors and could be used to infer the necessary margins. The implementation had a running time of 23 ms which is compatible with real-time requirements of MLC tracking systems. The auto-adaptivity to machine and patient characteristics makes the technique a generic yet intuitive candidate to avoid underdosages due to MLC tracking errors.
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