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Published on: June 7, 2015
Clinical validation of a high-definition mid-position magnetic resonance imaging approach for lung radiotherapy
Katrinus Keijnemans1, Astrid L H M W van Lier1, Pim T S Borman1
1Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.
Background:
Respiratory-correlated four-dimensional (4D) magnetic resonance imaging (4D-MRI) is useful to estimate breathing induced motion for MRI-guided radiotherapy. Based on 4D-MR image sets, a three-dimensional mid-position (MidP) MRI can be generated using deformable image registration (DIR) for radiotherapy planning. However, the desired spatial resolution and image contrast of the MidP MRI may differ from the original 4D-MRI.
Purpose:
This retrospective study validates a high-definition (HD)-MidP MRI approach that combines 4D-MRI motion information with a high-resolution MRI to enhance the spatial resolution of the MidP image.
Methods:
Computed tomography (CT) and MR image sets of 25 lung cancer patients were eligible, of whom 17 were complete and suitable for analysis. Standard-definition (SD)-MidP images were derived by applying DIR to warp the ten respiratory phases of a 4D-CT or 4D-MRI, whereas the HD-MidP MRI was derived by warping a high-resolution respiratory-triggered MRI to the MidP. The MidP image quality was assessed with a 4-point Likert scale on tumor and organ at risk (OAR) distinctiveness by three readers. Additionally, the gross tumor volume (GTV) was delineated by the readers, from which a consensus contour was derived for each MidP image. Reader contours were evaluated using the Dice similarity coefficient (DSC) and mean distance to agreement (DTA). Anatomical accuracy was evaluated by comparing MidP tumor locations to manually determined tumor displacements, while DIR precision was analyzed using the distance to discordance metric (DDM). Moreover, deformation vector fields (DVFs) from the DIR were used to automatically calculate MidP-based treatment margins.
Results:
Eighteen targets were identified in seventeen patients. All HD-MidP MR image sets were delineated, while 98% (53/54) of the SD-MidP CT and 87% (47/54) of the SD-MidP MR image sets were of adequate quality for delineation. The SD-MidP MRI was positively scored in 13 out of 47 assessments for tumor distinctiveness and in 6 out of 47 assessments for OAR distinctiveness. In contrast, the HD-MidP MRI showed a substantial improvement, with positive scores in 45 out of 54 assessments for tumor distinctiveness and 51 out of 54 assessments for OAR distinctiveness. Contour analyses revealed that the HD-MidP MRI achieved the highest average DSC value (0.83) and, simultaneously, the lowest mean DTA value (0.96 mm). Compared to the manually determined tumor displacements, subvoxel differences in MidP tumor location were observed in 96% (52/54) of the registrations. The distribution of DDM values (median: 1.1 mm) for the HD-MidP MRI was found to be significantly higher than the distributions for the SD-MidP CT (median: 0.2 mm) and SD-MidP MRI (median: 0.7 mm), indicating a lower, but still subvoxel, precision for the HD-MidP MRI approach. The DVF variability was higher for the HD-MidP MRI (median: 2.7 mm) than for the SD-MidP MRI (median: 2.3 mm). However, when used to derive treatment margins, these margins were identical.
Conclusions:
The presented HD-MidP MRI methodology scored highest on both tumor and OAR distinctiveness, with GTV contours demonstrating the best alignment. Combined with its high anatomical accuracy, these findings support its potential for lung radiotherapy planning.
