Comparative study of algorithms for synthetic CT generation from MRI : Consequences for MRI ‐guided radiation planning in the pelvic region

Autor: Nikolaos Koutsouvelis, Jason Dowling, Xiao Han, Habib Zaidi, Peter B. Greer, Hossein Arabi, Ninon Burgos
Přispěvatelé: Geneva University Hospital (HUG), CSIRO Information and Commuciation Technologies (CSIRO ICT Centre), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Elekta Inc. [Maryland Heights], Calvary Mater Newcastle Hospital, University of Newcastle [Australia] (UoN), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Newcastle [Callaghan, Australia] (UoN), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Rok vydání: 2018
Předmět:
IMAGE
Image Processing
Computer-Assisted/methods
Weighted voting
ECHO-TIME
THERAPY
ddc:616.0757
Pelvis/diagnostic imaging
Radiation planning
Pelvis
030218 nuclear medicine & medical imaging
CT synthesis
Machine Learning
03 medical and health sciences
Segmentation
0302 clinical medicine
Atlas (anatomy)
Machine learning
Image Processing
Computer-Assisted
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging
atlas-based
medicine
COMPUTED-TOMOGRAPHY GENERATION
ONLY RADIOTHERAPY
PROSTATE
Humans
HEAD
Radiation treatment planning
ComputingMilieux_MISCELLANEOUS
Mathematics
Atlas-based
PSEUDO-CT
medicine.diagnostic_test
segmentation
Magnetic resonance imaging
General Medicine
Neural Networks (Computer)
Magnetic Resonance Imaging
ATTENUATION CORRECTION
3. Good health
medicine.anatomical_structure
030220 oncology & carcinogenesis
MRI-guided radiotherapy planning
Neural Networks
Computer
Tomography
X-Ray Computed
Algorithm
Correction for attenuation
Algorithms
Zdroj: Arabi, H, Dowling, J A, Burgos, N, Han, X, Greer, P B, Koutsouvelis, N & Zaidi, H 2018, ' Comparative study of algorithms for synthetic CT generation from MRI : Consequences for MRI-guided radiation planning in the pelvic region ', Medical Physics, vol. 45, no. 11, pp. 5218-5233 . https://doi.org/10.1002/mp.13187
Medical Physics, Vol. 45, No 11 (2018) pp. 5218-5224
Medical Physics
Medical Physics, American Association of Physicists in Medicine, 2018, ⟨10.1002/mp.13187⟩
Medical Physics, 2018, ⟨10.1002/mp.13187⟩
Medical Physics, 45(11), 5218-5233. Wiley
ISSN: 2473-4209
0094-2405
DOI: 10.1002/mp.13187
Popis: Purpose Methods Magnetic resonance imaging (MRI)-guided radiation therapy (RT) treatment planning is limited by the fact that the electron density distribution required for dose calculation is not readily provided by MR imaging. We compare a selection of novel synthetic CT generation algorithms recently reported in the literature, including segmentation-based, atlas-based and machine learning techniques, using the same cohort of patients and quantitative evaluation metrics. Six MRI-guided synthetic CT generation algorithms were evaluated: one segmentation technique into a single tissue class (water-only), four atlas-based techniques, namely, median value of atlas images (ALMedian), atlas-based local weighted voting (ALWV), bone enhanced atlas-based local weighted voting (ALWV-Bone), iterative atlas-based local weighted voting (ALWV-Iter), and a machine learning technique using deep convolution neural network (DCNN). Results Conclusions Organ auto-contouring from MR images was evaluated for bladder, rectum, bones, and body boundary. Overall, DCNN exhibited higher segmentation accuracy resulting in Dice indices (DSC) of 0.93 +/- 0.17, 0.90 +/- 0.04, and 0.93 +/- 0.02 for bladder, rectum, and bones, respectively. On the other hand, ALMedian showed the lowest accuracy with DSC of 0.82 +/- 0.20, 0.81 +/- 0.08, and 0.88 +/- 0.04, respectively. DCNN reached the best performance in terms of accurate derivation of synthetic CT values within each organ, with a mean absolute error within the body contour of 32.7 +/- 7.9 HU, followed by the advanced atlas-based methods (ALWV: 40.5 +/- 8.2 HU, ALWV-Iter: 42.4 +/- 8.1 HU, ALWV-Bone: 44.0 +/- 8.9 HU). ALMedian led to the highest error (52.1 +/- 11.1 HU). Considering the dosimetric evaluation results, ALWV-Iter, ALWV, DCNN and ALWV-Bone led to similar mean dose estimation within each organ at risk and target volume with less than 1% dose discrepancy. However, the two-dimensional gamma analysis demonstrated higher pass rates for ALWV-Bone, DCNN, ALMedian and ALWV-Iter at 1%/1 mm criterion with 94.99 +/- 5.15%, 94.59 +/- 5.65%, 93.68 +/- 5.53% and 93.10 +/- 5.99% success, respectively, while ALWV and water-only resulted in 86.91 +/- 13.50% and 80.77 +/- 12.10%, respectively. Overall, machine learning and advanced atlas-based methods exhibited promising performance by achieving reliable organ segmentation and synthetic CT generation. DCNN appears to have slightly better performance by achieving accurate automated organ segmentation and relatively small dosimetric errors (followed closely by advanced atlas-based methods, which in some cases achieved similar performance). However, the DCNN approach showed higher vulnerability to anatomical variation, where a greater number of outliers was observed with this method. Considering the dosimetric results obtained from the evaluated methods, the challenge of electron density estimation from MR images can be resolved with a clinically tolerable error.
Databáze: OpenAIRE
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