Hossein Arabi1, Jason A Dowling2, Ninon Burgos3, Xiao Han4, Peter B Greer5,6, Nikolaos Koutsouvelis7, Habib Zaidi1,8,9,10. 1. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, CH-1211, Switzerland. 2. CSIRO Australian e-Health Research Centre, Herston, QLD, Australia. 3. Inria Paris, Aramis Project-Team, Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, F-75013, France. 4. Elekta Inc., Maryland Heights, MO, 63043, USA. 5. Calvary Mater Newcastle Hospital, Waratah, NSW, Australia. 6. University of Newcastle, Callaghan, NSW, Australia. 7. Division of Radiation Oncology, Geneva University Hospital, Geneva, CH-1211, Switzerland. 8. Geneva University Neurocenter, University of Geneva, Geneva, 1205, Switzerland. 9. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, the Netherlands. 10. Department of Nuclear Medicine, University of Southern Denmark, Odense, DK-500, Denmark.
Abstract
PURPOSE: 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. METHODS: 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: 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. CONCLUSIONS: 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.
PURPOSE: 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. METHODS: 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: 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. CONCLUSIONS: 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.
Authors: Samaneh Kazemifar; Ana M Barragán Montero; Kevin Souris; Sara T Rivas; Robert Timmerman; Yang K Park; Steve Jiang; Xavier Geets; Edmond Sterpin; Amir Owrangi Journal: J Appl Clin Med Phys Date: 2020-03-26 Impact factor: 2.102