Quantifying the spatial distribution of the accumulated dose uncertainty using the novel delta index

<i>Objective.</i>Inter- and intra-fractional anatomical changes during a radiotherapy treatment can cause differences between the initially planned dose and the delivered dose. The total delivered dose can be accumulated over all fractions by using deformable image registration (DIR). However, there is uncertainty in this process which should be accounted for. The aim of this study is to propose a novel metric estimating the spatial distribution of the accumulated dose uncertainty and to evaluate its performance for multi-fraction online adaptive treatments.<i>Approach.</i>We postulate a new metric, the delta (<i>δ</i>) index, to estimate the uncertainties associated with the dose accumulation process. This metric is calculated for each voxel and takes into account the spatial uncertainty in DIR and local dose differences. For the spatial uncertainty of the DIR, the distance discordance metric was used. The accumulated dose and the<i>δ</i>index were determined for ten lung stereotactic body radiation therapy patients. The<i>δ</i>index was complemented by a more understandable metric, the<i>δ</i>index passing rate, which is the percentage of points satisfying the passing criteria in a region.<i>Main results.</i>The spatial distribution of the<i>δ</i>index and the<i>δ</i>index passing rates showed that voxels failing the criteria were predominantly in lower-dose regions. The mean percentage of voxels passing the criterion increased from 65% to 78%, for threshold doses of 20% and 90% of the prescription doses, respectively.<i>Significance.</i>The<i>δ</i>index was postulated to quantify the spatial distribution of the uncertainties associated with the dose accumulation process. The metric gives an intuitive understanding of the reliability of accumulated dose distributions and derived DVH metrics. The performance of the<i>δ</i>index was evaluated for multi-fraction online adaptive treatments, where a case of sub-optimal image registration was identified by the metric.