|Topic:||Kalibrace metody MRI "Arterial spin labeling" z M0 skenů s potlačením pozadí|
|Supervisor:||mgr. Jan Petr, Ph.D.|
|Announce as:||Diplomová práce, Semestrální projekt|
|Description:||Arterial spin labeling (ASL) is an MRI technique that uses magnetically labeled blood as an endogenous tracer to image cerebral perfusion (Williams et al. 1992). Compared to the other techniques for measuring perfusion, like contrast-agent-based MRI or PET and CT, it completely refutes the use of gadolinium-based contrast-agents or radioactive tracers. At the same time, it delivers fully quantitative measurements with accuracy comparable to 15O-H2O PET measurements (Heijtel et al. 2014). Its complete non-invasiveness and the simplicity of its use makes it an ideal candidate for longitudinal perfusion studies or for use in pediatric and healthy populations (Hernandez-Garcia, Lahiri, and Schollenberger 2018). The key to its successful clinical application is to follow the basic requirements for acquisition (Alsop et al. 2015) and postprocessing (Mutsaerts et al. 2020).
In a typical ASL setup, a perfusion-weighted image is obtained by subtraction of two images obtained with (labeled image) and without (control image) prior blood labeling. The quantified perfusion image is then calculated by normalization by the equilibrium magnetization of blood that can be estimated from the control image. The advent of the use of background suppression, a technique aiming at improving SNR by reducing the static tissue signal (Garcia, Duhamel, and Alsop 2005), has made the use of control images for quantification impossible because control scans with background suppression lack the information about the tissue equilibrium magnetization. Therefore, acquisition of a separate M0-scan for calibration is recommended. Despite that most vendor product sequences have implemented a built-in M0-scan, there are still some clinical studies lacking this reference scan.
The background-suppression pulses are timed to achieve close to maximal suppression of static tissue signal. However, for practical reasons in 3D sequences, due to interslice T1-relaxation in 2D multi-slice acquisitions, a full tissue signal suppression is not always reached in the complete volume. With the knowledge of the saturation timings and the tissue relaxation times and distribution, it might be possible to reconstruct the original signal intensities in a control image in a reasonable quality for the quantification calibration.
The goal of this project is to test a method for correction of intensities in a background suppressed control image and compare its use for quantification with a use of an M0-scan acquired in the standard way. A trivial version of the above-mentioned approach was already designed and implemented as part of the software package for ASL data processing called ExploreASL (Mutsaerts et al. 2020). The approach now needs to be tested and validated to determine its potential for replacing the standard M0-scans. This will be done in two datasets of thirty subjects each containing pseudo-continuous ASL scans from 2D EPI and 3D GRASE. The use of a corrected M0 scan and a standard M0 scan for perfusion quantification will be compared to evaluate the regional and between-subject difference. Secondary goal of the project will be to determine the quantitative accuracy of the proposed method separately in gray and white matter regions, and also across different slices. The outcome of the project will be a report summarizing the usability of the method for both scanners and across different regions. These findings will lay the foundation for further improvement of the method to reach a reasonable quantitative accuracy of perfusion maps across the whole image volume, different tissues and different scanners and sequences.
The technical supervision of the project will be guaranteed by Dr. J. Petr (Helmholtz-Zentrum Dresden-Rossendorf). The project will be co-supervised by Dr. H. Mutsaerts (Amsterdam UMC) who will grant the student access to data. The student will work with the ExploreASL software for the ASL data analysis and write custom Matlab scripts for the data evaluation.
|Bibliography:||Alsop, David C., John A. Detre, Xavier Golay, Matthias Günther, Jeroen Hendrikse, Luis Hernandez-Garcia, Hanzhang Lu, et al. 2015. “Recommended Implementation of Arterial Spin-Labeled Perfusion MRI for Clinical Applications: A Consensus of the ISMRM Perfusion Study Group and the European Consortium for ASL in Dementia.” Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 73 (1): 102–16.
Garcia, Dairon M., Guillaume Duhamel, and David C. Alsop. 2005. “Efficiency of Inversion Pulses for Background Suppressed Arterial Spin Labeling.” Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 54 (2): 366–72.
Heijtel, D. F. R., H. J. M. M. Mutsaerts, E. Bakker, P. Schober, M. F. Stevens, E. T. Petersen, B. N. M. van Berckel, et al. 2014. “Accuracy and Precision of Pseudo-Continuous Arterial Spin Labeling Perfusion during Baseline and Hypercapnia: A Head-to-Head Comparison with 15O H2O Positron Emission Tomography.” NeuroImage 92 (-): 182–92.
Hernandez-Garcia, Luis, Anish Lahiri, and Jonas Schollenberger. 2018. “Recent Progress in ASL.” NeuroImage, no. December 2017 (January): 1–14.
Mutsaerts, Henk J. M. M., Jan Petr, Paul Groot, Pieter Vandemaele, Silvia Ingala, Andrew D. Robertson, Lena Václavů, et al. 2020. “ExploreASL: An Image Processing Pipeline for Multi-Center ASL Perfusion MRI Studies.” NeuroImage, June, 117031.
Williams, D. S., J. A. Detre, J. S. Leigh, and A. P. Koretsky. 1992. “Magnetic Resonance Imaging of Perfusion Using Spin Inversion of Arterial Water.” Proceedings of the National Academy of Sciences of the United States of America 89 (1): 212–16.