Chiara Paganelli1, Matteo Seregni2, Giovanni Fattori2, Paul Summers3, Massimo Bellomi4, Guido Baroni5, Marco Riboldi5. 1. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy. Electronic address: chiara.paganelli@polimi.it. 2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy. 3. Division of Radiology, Istituto Europeo di Oncologia, Milano, Italy. 4. Division of Radiology, Istituto Europeo di Oncologia, Milano, Italy; Department of Health Sciences, Università degli Studi di Milano, Milano, Italy. 5. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy; Bioengineering Unit, CNAO Foundation, Pavia, Italy.
Abstract
PURPOSE: This study applied automatic feature detection on cine-magnetic resonance imaging (MRI) liver images in order to provide a prospective comparison between MRI-guided and surrogate-based tracking methods for motion-compensated liver radiation therapy. METHODS AND MATERIALS: In a population of 30 subjects (5 volunteers plus 25 patients), 2 oblique sagittal slices were acquired across the liver at high temporal resolution. An algorithm based on scale invariant feature transform (SIFT) was used to extract and track multiple features throughout the image sequence. The position of abdominal markers was also measured directly from the image series, and the internal motion of each feature was quantified through multiparametric analysis. Surrogate-based tumor tracking with a state-of-the-art external/internal correlation model was simulated. The geometrical tracking error was measured, and its correlation with external motion parameters was also investigated. Finally, the potential gain in tracking accuracy relying on MRI guidance was quantified as a function of the maximum allowed tracking error. RESULTS: An average of 45 features was extracted for each subject across the whole liver. The multi-parametric motion analysis reported relevant inter- and intrasubject variability, highlighting the value of patient-specific and spatially-distributed measurements. Surrogate-based tracking errors (relative to the motion amplitude) were were in the range 7% to 23% (1.02-3.57 mm) and were significantly influenced by external motion parameters. The gain of MRI guidance compared to surrogate-based motion tracking was larger than 30% in 50% of the subjects when considering a 1.5-mm tracking error tolerance. CONCLUSIONS: Automatic feature detection applied to cine-MRI allows detailed liver motion description to be obtained. Such information was used to quantify the performance of surrogate-based tracking methods and to provide a prospective comparison with respect to MRI-guided radiation therapy, which could support the definition of patient-specific optimal treatment strategies.
PURPOSE: This study applied automatic feature detection on cine-magnetic resonance imaging (MRI) liver images in order to provide a prospective comparison between MRI-guided and surrogate-based tracking methods for motion-compensated liver radiation therapy. METHODS AND MATERIALS: In a population of 30 subjects (5 volunteers plus 25 patients), 2 oblique sagittal slices were acquired across the liver at high temporal resolution. An algorithm based on scale invariant feature transform (SIFT) was used to extract and track multiple features throughout the image sequence. The position of abdominal markers was also measured directly from the image series, and the internal motion of each feature was quantified through multiparametric analysis. Surrogate-based tumor tracking with a state-of-the-art external/internal correlation model was simulated. The geometrical tracking error was measured, and its correlation with external motion parameters was also investigated. Finally, the potential gain in tracking accuracy relying on MRI guidance was quantified as a function of the maximum allowed tracking error. RESULTS: An average of 45 features was extracted for each subject across the whole liver. The multi-parametric motion analysis reported relevant inter- and intrasubject variability, highlighting the value of patient-specific and spatially-distributed measurements. Surrogate-based tracking errors (relative to the motion amplitude) were were in the range 7% to 23% (1.02-3.57 mm) and were significantly influenced by external motion parameters. The gain of MRI guidance compared to surrogate-based motion tracking was larger than 30% in 50% of the subjects when considering a 1.5-mm tracking error tolerance. CONCLUSIONS: Automatic feature detection applied to cine-MRI allows detailed liver motion description to be obtained. Such information was used to quantify the performance of surrogate-based tracking methods and to provide a prospective comparison with respect to MRI-guided radiation therapy, which could support the definition of patient-specific optimal treatment strategies.
Authors: Seyoun Park; Rana Farah; Steven M Shea; Erik Tryggestad; Russell Hales; Junghoon Lee Journal: Phys Med Biol Date: 2018-01-11 Impact factor: 3.609
Authors: Naoyuki Shono; Brian Ninni; Franklin King; Takahisa Kato; Junichi Tokuda; Takahiro Fujimoto; Kemal Tuncali; Nobuhiko Hata Journal: Med Phys Date: 2020-03-28 Impact factor: 4.071
Authors: Bryan P Bednarz; Sydney Jupitz; Warren Lee; David Mills; Heather Chan; Timothy Fiorillo; James Sabitini; David Shoudy; Aqsa Patel; Jhimli Mitra; Shourya Sarcar; Bo Wang; Andrew Shepard; Charles Matrosic; James Holmes; Wesley Culberson; Michael Bassetti; Patrick Hill; Alan McMillan; James Zagzebski; L Scott Smith; Thomas K Foo Journal: Phys Med Date: 2021-07-01 Impact factor: 3.119