Automatic lung segmentation of magnetic resonance images: A new approach applied to healthy volunteers undergoing enhanced Deep-Inspiration-Breath-Hold for motion-mitigated 4D proton therapy of lung tumors.

Autor:	Missimer JH; Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland., Emert F; Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland., Lomax AJ; Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland.; Department of Physics, ETH Zurich, Zurich, Switzerland., Weber DC; Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland.; Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland.; Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
Jazyk:	angličtina
Zdroj:	Physics and imaging in radiation oncology [Phys Imaging Radiat Oncol] 2024 Jan 04; Vol. 29, pp. 100531. Date of Electronic Publication: 2024 Jan 04 (Print Publication: 2024).
DOI:	10.1016/j.phro.2024.100531
Abstrakt:	Background and Purpose: Respiratory suppression techniques represent an effective motion mitigation strategy for 4D-irradiation of lung tumors with protons. A magnetic resonance imaging (MRI)-based study applied and analyzed methods for this purpose, including enhanced Deep-Inspiration-Breath-Hold (eDIBH). Twenty-one healthy volunteers (41-58 years) underwent thoracic MR scans in four imaging sessions containing two eDIBH-guided MRIs per session to simulate motion-dependent irradiation conditions. The automated MRI segmentation algorithm presented here was critical in determining the lung volumes (LVs) achieved during eDIBH. Materials and Methods: The study included 168 MRIs acquired under eDIBH conditions. The lung segmentation algorithm consisted of four analysis steps: (i) image preprocessing, (ii) MRI histogram analysis with thresholding, (iii) automatic segmentation, (iv) 3D-clustering. To validate the algorithm, 46 eDIBH-MRIs were manually contoured. Sørensen-Dice similarity coefficients (DSCs) and relative deviations of LVs were determined as similarity measures. Assessment of intrasessional and intersessional LV variations and their differences provided estimates of statistical and systematic errors. Results: Lung segmentation time for 100 2D-MRI planes was ∼ 10 s. Compared to manual lung contouring, the median DSC was 0.94 with a lower 95 % confidence level (CL) of 0.92. The relative volume deviations yielded a median value of 0.059 and 95 % CLs of -0.013 and 0.13. Artifact-based volume errors, mainly of the trachea, were estimated. Estimated statistical and systematic errors ranged between 6 and 8 %. Conclusions: The presented analytical algorithm is fast, precise, and readily available. The results are comparable to time-consuming, manual segmentations and other automatic segmentation approaches. Post-processing to remove image artifacts is under development. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2024 The Authors.)
Databáze:	MEDLINE
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