Magnetic resonance imaging-guided versus surrogate-based motion tracking in liver radiation therapy: a prospective comparative study.
OBJECTIVE
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
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.
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy. Electronic address: chiara.paganelli@polimi.it.
Chiara
C
Paganelli
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.
Matteo
M
Seregni
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.
Giovanni
G
Fattori
Division of Radiology, Istituto Europeo di Oncologia, Milano, Italy.
Paul
P
Summers
Division of Radiology, Istituto Europeo di Oncologia, Milano, Italy; Department of Health Sciences, Università degli Studi di Milano, Milano, Italy.
Massimo
M
Bellomi
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy; Bioengineering Unit, CNAO Foundation, Pavia, Italy.
Guido
G
Baroni
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy; Bioengineering Unit, CNAO Foundation, Pavia, Italy.
Marco
M
Riboldi
D000465:Algorithms; D057918:Fiducial Markers; D006801:Humans; D008099:Liver / Q000033:anatomy & histology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D009068:Movement; D011446:Prospective Studies