A stochastic approach to estimate the uncertainty of dose mapping caused by uncertainties in b-spline registration.

PURPOSE: In fractionated radiation therapy, image guidance with daily tomographic imaging becomes more and more clinical routine. In principle, this allows for daily computation of the delivered dose and for accumulation of these daily dose distributions to determine the actually delivered total dose to the patient. However, uncertainties in the mapping of the images can translate into errors of the accumulated total dose, depending on the dose gradient. In this work, an approach to estimate the uncertainty of mapping between medical images is proposed that identifies areas bearing a significant risk of inaccurate dose accumulation. METHODS: This method accounts for the geometric uncertainty of image registration and the heterogeneity of the dose distribution, which is to be mapped. Its performance is demonstrated in context of dose mapping based on b-spline registration. It is based on evaluation of the sensitivity of dose mapping to variations of the b-spline coefficients combined with evaluation of the sensitivity of the registration metric with respect to the variations of the coefficients. It was evaluated based on patient data that was deformed based on a breathing model, where the ground truth of the deformation, and hence the actual true dose mapping error, is known. RESULTS: The proposed approach has the potential to distinguish areas of the image where dose mapping is likely to be accurate from other areas of the same image, where a larger uncertainty must be expected. CONCLUSIONS: An approach to identify areas where dose mapping is likely to be inaccurate was developed and implemented. This method was tested for dose mapping, but it may be applied in context of other mapping tasks as well.

Objective assessment of deformable image registration in radiotherapy: a multi-institution study.

The looming potential of deformable alignment tools to play an integral role in adaptive radiotherapy suggests a need for objective assessment of these complex algorithms. Previous studies in this area are based on the ability of alignment to reproduce analytically generated deformations applied to sample image data, or use of contours or bifurcations as ground truth for evaluation of alignment accuracy. In this study, a deformable phantom was embedded with 48 small plastic markers, placed in regions varying from high contrast to roughly uniform regional intensity, and small to large regional discontinuities in movement. CT volumes of this phantom were acquired at different deformation states. After manual localization of marker coordinates, images were edited to remove the markers. The resulting image volumes were sent to five collaborating institutions, each of which has developed previously published deformable alignment tools routinely in use. Alignments were done, and applied to the list of reference coordinates at the inhale state. The transformed coordinates were compared to the actual marker locations at exhale. A total of eight alignment techniques were tested from the six institutions. All algorithms performed generally well, as compared to previous publications. Average errors in predicted location ranged from 1.5 to 3.9 mm, depending on technique. No algorithm was uniformly accurate across all regions of the phantom, with maximum errors ranging from 5.1 to 15.4 mm. Larger errors were seen in regions near significant shape changes, as well as areas with uniform contrast but large local motion discontinuity. Although reasonable accuracy was achieved overall, the variation of error in different regions suggests caution in globally accepting the results from deformable alignment.

Technical note: a physical phantom for assessment of accuracy of deformable alignment algorithms.

The purpose of this study was to investigate the feasibility of a simple deformable phantom as a QA tool for testing and validation of deformable image registration algorithms. A diagnostic thoracic imaging phantom with a deformable foam insert was used in this study. Small plastic markers were distributed through the foam to create a lattice with a measurable deformation as the ground truth data for all comparisons. The foam was compressed in the superior-inferior direction using a one-dimensional drive stage pushing a flat "diaphragm" to create deformations similar to those from inhale and exhale states. Images were acquired at different compressions of the foam and the location of every marker was manually identified on each image volume to establish a known deformation field with a known accuracy. The markers were removed digitally from corresponding images prior to registration. Different image registration algorithms were tested using this method. Repeat measurement of marker positions showed an accuracy of better than 1 mm in identification of the reference marks. Testing the method on several image registration algorithms showed that the system is capable of evaluating errors quantitatively. This phantom is able to quantitatively assess the accuracy of deformable image registration, using a measure of accuracy that is independent of the signals that drive the deformation parameters.

[Evaluation and optimization of an image-subtraction method for the analysis of the repositioning accuracy of female patients with breast cancer]. / Evaluierung und Optimierung eines Differenzbildverfahrens zur Analyse der Repositioniergenauigkeit von Patientinnen mit Mammakarzinom.

To analyze the repositioning accuracy in female patients with breast carcinoma, two different setups of an image-subtraction system (Positioning System FIVE) were devised using different numbers and alignments of lasers. The applicability of the system was tested for repositioning of the breast in normal volunteers. Horizontal translations as well as breathing-related movements in the vertical direction were measured. The mean repositioning accuracy was found to be 2.9 mm for the first setup and 1.5 mm for a second, optimized setup. For this second setup, a gating function was implemented which evaluates the position of the breast twelve times per second. The simulation of a gated treatment showed that the breathing-related displacement of the breast can be reduced to 45-70% of the displacement without gating. This implies a significant improvement of the positioning accuracy.

A method to visualize the uncertainty of the prediction of radiobiological models.

A method for quantitative visualization of the uncertainty in the predicted tumor control probability (TCP) and normal tissue complication probability (NTCP) in radiotherapy has been developed. Uncertainties of TCP and NTCP due to inter-individual variation of the underlying radiosensitivity parameters was simulated by sampling the prescribed dose from a uniform distribution and the radiosensitivity-parameters from a Gaussian distribution. The result is visualized as a scatter-plot superimposed to the population-based dose response curves using the prescribed dose as the common dosimetric variable. In addition, probability histograms are derived quantifying the probability of specific TCP- or NTCP-values for individual patients from the underlying population. The method is exemplified with a pleural mesothelioma case with the lung as organ at risk. A prescribed dose of 54 Gy together with radiosensitivity variations of 6% (tumor) and 10% (normal tissue) results in a TCP of 85% (range 68-94%, 90% confidence interval, CI) and an NTCP of 4% (range 3-6%, 90% CI), respectively. Increasing the radiosensitivity variation of the tumor to 15% and reducing the lung tolerance dose by 25% results in values of 84% (range 51-97%, 90% CI) for TCP and 9% (range 6-12%, 90% CI) for NTCP. Increasing the dose to 60 Gy leads to TCP- and NTCP-values of 93% (range 69-100%, 90% CI) and 12% (range 8-17%, 90% CI), respectively. The new method visualizes the uncertainty of TCP- and NTCP-values and hence of the therapeutic window. This can help the clinician to assess the treatment plan for the individual patient.

Software for quantitative analysis of radiotherapy: overview, requirement analysis and design solutions.

Radiotherapy is a fast-developing discipline which plays a major role in cancer care. Quantitative analysis of radiotherapy data can improve the success of the treatment and support the prediction of outcome. In this paper, we first identify functional, conceptional and general requirements on a software system for quantitative analysis of radiotherapy. Further we present an overview of existing radiotherapy analysis software tools and check them against the stated requirements. As none of them could meet all of the demands presented herein, we analyzed possible conceptional problems and present software design solutions and recommendations to meet the stated requirements (e.g. algorithmic decoupling via dose iterator pattern; analysis database design). As a proof of concept we developed a software library "RTToolbox" following the presented design principles. The RTToolbox is available as open source library and has already been tested in a larger-scale software system for different use cases. These examples demonstrate the benefit of the presented design principles.