|Time-resolved magnetic resonance fingerprinting for motion measurement in radiotherapy
|Cai, Jing (HTI)
|FHSS Faculty Distinguished Thesis Award (2020/21)
|Magnetic resonance imaging
Radiotherapy -- Data processing
Hong Kong Polytechnic University -- Dissertations
|Department of Health Technology and Informatics
|xxvii, 156 pages : color illustrations
|Background: Recently, quantitative magnetic resonance fingerprinting (MRF) technique has drawn increasing attention because of its ability to acquire different quantitative parameter maps in a single scan. However, the applications of MRF only focus on static images nowadays, mostly brain image. Therefore, further application on moving subject is warranted. Purpose: This study aims to develop a novel time-resolved magnetic resonance fingerprinting (TR-MRF) technique for respiratory motion imaging applications and investigate different acquisition schemes for TR-MRF. Methods and Materials: Our proposed TR-MRF technique consists of repeated MRF acquisitions using an unbalanced steady-state free precession sequence with spiral-in-spiral-out trajectory. The acquired MRF data are then retrospectively binned into different respiratory phases using a phase-based sorting method. The TR-MRF technique was first simulated in MATLAB (MathWorks, Natick, MA) using a four dimensional extended cardiac-torso (XCAT) phantom for both regular and irregular breathing profiles and was tested in three healthy volunteers. MRF images were simulated with different number of repetitions from 1 to 15 and the simulation was repeated 200 times for each number of repetitions. Three different acquisition methods were used to generate TR-MRF images: 1) continuous acquisition without delay between MRF repetitions; 2) continuous acquisition with 5 seconds delay between MRF repetitions; 3) triggered acquisition with variable delay between MRF repetitions to allow the next acquisition to start at different respiration phase. Parametric MRF maps at different respiratory phases were subsequently estimated using our TR-MRF sorting and reconstruction techniques. The resulting TR-MRF maps were evaluated using a set of metrices related to radiotherapy applications, including absolute difference in parametric maps, error in the amplitude of diaphragm motion (ADM), tumor volume error (TVE), signal-to-noise ratio (SNR), and tumor contrast.
Results: TR-MRF maps using three different acquisition methods were successfully generated using XCAT phantom. The overall and liver T1 value error, liver SNR in T1 and T2 maps, and tumor SNR from T1 maps from triggered method is significantly better compared to the other two methods (p-value < 0.05). The other image quality indexes have no significant difference between the triggered method and the other two continuous acquisition methods. All image quality indexes exhibit no significant difference between the acquisition methods with 0 second and 5 seconds delay. Numerical simulations showed that the TVE were 1.6±2.7% and 1.3±2.2%, the average absolute differences in tumor motion amplitude were 0.3±0.7 mm and 0.3±0.6 mm, and the ADM were 4.1±0.9% and 3.5±0.9% for irregular and regular breathing respectively. The SNR of the T1 and T2 maps of the liver and the tumor were generally higher for regular breathing compared to irregular breathing, whereas tumor-to-liver contrast is similar between the two breathing patterns. The proposed technique was successfully implemented on the healthy volunteers. Conclusion: We have successfully demonstrated in both digital phantom and health subjects a novel TR-MRF technique capable of imaging respiratory motions with simultaneous quantification of MR multi-parametric maps.
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