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- Palmér, Emilia, 1993, et al.
(author)
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Estimation and visualization of geometric fidelity using geometric offset maps for improved guidance in H&N radiation therapy
- 2022
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In: Annual Meeting ISMRM (International Society of Magnetic Resonance in Medicine).
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Conference paper (peer-reviewed)abstract
- n an MRI-only workflow, high geometric fidelity of the MRI data is required. Head and neck (H&N) cancer patients, however, frequently have implants, e.g., dental restorations, causing distortions of the MRI data. Geometric offset maps were computed using B0-map calculated from the Dixon-sequence included in the standard clinical protocol. Even though the implants included in this pre-study did not contribute with a significant geometric offset in the delineated target volume, visualization of the geometric offset maps as such bring additional important information when delineating structures in an MRI-only H&N workflow and could thereby become a promising tool in clinical practice.
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- Palmér, Emilia, 1993, et al.
(author)
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Head and neck cancer patient positioning using synthetic CT data in MRI-only radiation therapy.
- 2022
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In: Journal of applied clinical medical physics. - : Wiley. - 1526-9914. ; 23:4
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Journal article (peer-reviewed)abstract
- The accuracy and precision of patient positioning is crucial in radiotherapy; however, there are no publications available using synthetic computed tomography (sCT) that evaluate rotations in head and neck (H&N) patients positioning or the effect of translation and rotation combined. The aim of this work was to evaluate the differences between using sCT with the CT for 2D- and 3D-patient positioning in a magnetic resonance imaging (MRI)-only workflow.This study included 14 H&N cancer patients, with generated sCT data (MRI Planner v2.2) and the CT deformably registered to the MRI. Patient positioning was evaluated by comparing sCT against CT data: 3D cone beam CT (CBCT) was registered to the deformed CT (dCT) and sCT in six degrees of freedom (DoF) with a rigid auto-registration algorithm and bone threshold, and 2D deformed digital reconstructed radiographs (dDRR) and synthetic DRRs (sDRR) were manually registered to orthogonal projections in five DoF by six blinded observers. The difference in displacement in all DoF were calculated for dCT and sCT, as well as for dDRR and sDRR. The interobserver variation was evaluated by separate application of the paired dDRR and sDRR registration matrices to the original coordinates of the planning target volume (PTV) structures and calculation of the Euclidean distance between the corresponding points. The Dice similarity coefficient (DSC) was calculated between dDRR/sDRR-registered PTVs.The mean difference in patient positioning using CBCT was<0.7mm and<0.3° and using orthogonal projections<0.4mm and<0.2° in all directions. The maximum Euclidean distance was 5.1mm, the corresponding mean (1SD) Euclidean distance and mean DSC were 3.5 ± 0.7mm and 0.93, respectively.This study shows that the sCT-based patient positioning gives a comparable result with that based on CT images, allowing sCT to replace CT as reference for patient treatment positioning.
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- Palmér, Emilia, 1993, et al.
(author)
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Synthetic computed tomography data allows for accurate absorbed dose calculations in a magnetic resonance imaging only workflow for head and neck radiotherapy
- 2021
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In: Physics and Imaging in Radiation Oncology. - : Elsevier BV. - 2405-6316. ; 17, s. 36-42
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Journal article (peer-reviewed)abstract
- Background and purpose: Few studies on magnetic resonance imaging (MRI) only head and neck radiation treatment planning exist, and none using a generally available software. The aim of this study was to evaluate the accuracy of absorbed dose for head and neck synthetic computed tomography data (sCT) generated by a commercial convolutional neural network-based algorithm. Materials and methods: For 44 head and neck cancer patients, sCT were generated and the geometry was validated against computed tomography data (CT). The clinical CT based treatment plan was transferred to the sCT and recalculated without re-optimization, and differences in relative absorbed dose were determined for dose-volume-histogram (DVH) parameters and the 3D volume. Results: For overall body, the results of the geometric validation were (Mean ± 1sd): Mean error −5 ± 10 HU, mean absolute error 67 ± 14 HU, Dice similarity coefficient 0.98 ± 0.05, and Hausdorff distance difference 4.2 ± 1.7 mm. Water equivalent depth difference for region Th1-C7, mid mandible and mid nose were −0.3 ± 3.4, 1.1 ± 2.0 and 0.7 ± 3.8 mm respectively. The maximum mean deviation in absorbed dose for all DVH parameters was 0.30% (0.12 Gy). The absorbed doses were considered equivalent (p-value < 0.001) and the mean 3D gamma passing rate was 99.4 (range: 95.7–99.9%). Conclusions: The convolutional neural network-based algorithm generates sCT which allows for accurate absorbed dose calculations for MRI-only head and neck radiation treatment planning. The sCT allows for statistically equivalent absorbed dose calculations compared to CT based radiotherapy.
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