Motion Correction in Magnetic Resonance Imaging Using the Signal of Free-Induction-Decay

Maryna Volodimirivna Waszak
EPFL, 2016
EPFL thesis

Abstract

Magnetic resonance imaging (MRI) is highly susceptible to subject's motion and can significantly degrade image quality. In brain MRI exams, involuntary head movements can affect the sampled k-space data. Such unintended alterations may result in visible image artifacts such as blurring, ghosting and others, and therefore potentially disqualify the image from diagnostic purposes. Methods to characterize motion in order to mitigate its impact on image quality exist and range from MR sequence based techniques to scanner independent tracking systems. Although, many motion detection and correction strategies have been proposed in the past, a universal solution to the problem does not exist yet. The work of this thesis was focused on the exploitation of the motion information from a multi-channel Free Induction Decay Navigator (FID) to develop and to optimize motion detection and correction methods in structural brain MRI. After a short introduction to the motion problem in MRI the fundamental methodology behind FID based motion detection is presented and used in this thesis. Considerable work has already been done in the field of motion correction for MRI that is summarized by reviewing the most recent literature, which allowed to reveal some pitfalls in the present approaches and to demonstrate the motivation behind an FID-based method for motion correction in MRI. The first study was conducted to demonstrate that substantial motion information is contained in the multi-channel FID signal, whereby the FID signal is correlated with motion parameters that were obtained from a highly accurate optical tracking system. This work was able to confirm the theoretical foundations from the Biot-Savart law, however, also revealed that a pure FID-based method is not sufficient to exactly calculate the underlying motion trajectory. It is speculated that scanner and subject related information might lead to a closed form solution, yet it was not possible to derive one due to a high dimensionality of the motion problem. Therefore, two alternative approaches were developed to utilize the FID signal for motion detection and correction in MRI. First, a prospective motion correction strategy for an MP-RAGE sequence is demonstrated, whereby the FID signal is used to trigger a prospective motion correction mechanism. The second alternative approach describes how the FID signal can be used to evaluate the quality of an already acquired image and how the FID signal can be used as an optimizer for an autofocusing based retrospective motion correction technique.

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Maryna Volodimirivna Waszak
EPFL, 2016
EPFL thesis

Abstract

Official source

https://infoscience.epfl.ch/record/218525?ln=en

About this result

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Motivation. The study of human brain development in its early stage is today possible thanks to in vivo fetal magnetic resonance imaging (MRI) techniques. A quantitative analysis of fetal cortical surface represents a new approach which can be used as a marker of the cerebral maturation (as gyration) and also for studying central nervous system pathologies [1]. However, this quantitative approach is a major challenge for several reasons. First, movement of the fetus inside the amniotic cavity requires very fast MRI sequences to minimize motion artifacts, resulting in a poor spatial resolution and/or lower SNR. Second, due to the ongoing myelination and cortical maturation, the appearance of the developing brain differs very much from the homogenous tissue types found in adults. Third, due to low resolution, fetal MR images considerably suffer of partial volume (PV) effect, sometimes in large areas. Today extensive efforts are made to deal with the reconstruction of high resolution 3D fetal volumes [2,3,4] to cope with intra-volume motion and low SNR. However, few studies exist related to the automated segmentation of MR fetal imaging. [5] and [6] work on the segmentation of specific areas of the fetal brain such as posterior fossa, brainstem or germinal matrix. First attempt for automated brain tissue segmentation has been presented in [7] and in our previous work [8]. Both methods apply the Expectation-Maximization Markov Random Field (EM-MRF) framework but contrary to [7] we do not need from any anatomical atlas prior. Data set & Methods. Prenatal MR imaging was performed with a 1-T system (GE Medical Systems, Milwaukee) using single shot fast spin echo (ssFSE) sequences (TR 7000 ms, TE 180 ms, FOV 40 x 40 cm, slice thickness 5.4mm, in plane spatial resolution 1.09mm). Each fetus has 6 axial volumes (around 15 slices per volume), each of them acquired in about 1 min. Each volume is shifted by 1 mm with respect to the previous one. Gestational age (GA) ranges from 29 to 32 weeks. Mother is under sedation. Each volume is manually segmented to extract fetal brain from surrounding maternal tissues. Then, in-homogeneity intensity correction is performed using [9] and linear intensity normalization is performed to have intensity values that range from 0 to 255. Note that due to intra-tissue variability of developing brain some intensity variability still remains. For each fetus, a high spatial resolution image of isotropic voxel size of 1.09 mm is created applying [2] and using B-splines for the scattered data interpolation [10] (see Fig. 1). Then, basal ganglia (BS) segmentation is performed on this super reconstructed volume. Active contour framework with a Level Set (LS) implementation is used. Our LS follows a slightly different formulation from well-known Chan-Vese [11] formulation. In our case, the LS evolves forcing the mean of the inside of the curve to be the mean intensity of basal ganglia. Moreover, we add local spatial prior through a probabilistic map created by fitting an ellipsoid onto the basal ganglia region. Some user interaction is needed to set the mean intensity of BG (green dots in Fig. 2) and the initial fitting points for the probabilistic prior map (blue points in Fig. 2). Once basal ganglia are removed from the image, brain tissue segmentation is performed as described in [8]. Results. The case study presented here has 29 weeks of GA. The high resolution reconstructed volume is presented in Fig. 1. The steps of BG segmentation are shown in Fig. 2. Overlap in comparison with manual segmentation is quantified by the Dice similarity index (DSI) equal to 0.829 (values above 0.7 are considered a very good agreement). Such BG segmentation has been applied on 3 other subjects ranging for 29 to 32 GA and the DSI has been of 0.856, 0.794 and 0.785. Our segmentation of the inner (red and blue contours) and outer cortical surface (green contour) is presented in Fig. 3. Finally, to refine the results we include our WM segmentation in the Freesurfer software [12] and some manual corrections to obtain Fig.4. Discussion. Precise cortical surface extraction of fetal brain is needed for quantitative studies of early human brain development. Our work combines the well known statistical classification framework with the active contour segmentation for central gray mater extraction. A main advantage of the presented procedure for fetal brain surface extraction is that we do not include any spatial prior coming from anatomical atlases. The results presented here are preliminary but promising. Our efforts are now in testing such approach on a wider range of gestational ages that we will include in the final version of this work and studying as well its generalization to different scanners and different type of MRI sequences. References. [1] Guibaud, Prenatal Diagnosis 29(4) (2009). [2] Rousseau, Acad. Rad. 13(9), 2006, [3] Jiang, IEEE TMI 2007. [4] Warfield IADB, MICCAI 2009. [5] Claude, IEEE Trans. Bio. Eng. 51(4) (2004). [6] Habas, MICCAI (Pt. 1) 2008. [7] Bertelsen, ISMRM 2009 [8] Bach Cuadra, IADB, MICCAI 2009. [9] Styner, IEEE TMI 19(39 (2000). [10] Lee, IEEE Trans. Visual. And Comp. Graph. 3(3), 1997, [11] Chan, IEEE Trans. Img. Proc, 10(2), 2001 [12] Freesurfer, http://surfer.nmr.mgh.harvard.edu.

2010

Fat navigators based retrospective motion correction strategies for brain magnetic resonance imaging

Frédéric Gretsch

Magnetic resonance resonance (MRI) is a widely used modality to obtain in vivo tissue information. Clinical applications are near countless, and almost all body parts can be examined using an MR scanner. As the method is non invasive, does not use ionizing radiation and provides excellent soft tissue contrast, it also appears as an excellent tool for neuroscience research. The major drawback of MRI remains the relatively long acquisition times, of the order of several minutes. During the measurement, the subject must stay still and avoid moving at all costs, as otherwise image artefacts will appear and potentially render the acquired data (partially) unusable. As higher image resolution imply longer acquisition time, probing finer anatomical details imply ultimately requires dealing with said motion. While some research goes in the way of reducing the acquisition time, it necessarily comes at the price of lower sensitivity and hence inherently diminishes the achievable gain for high- resolution imaging as the signal is weaker to start with. In this work, the focus is to try and compensate for motion during brain imaging using a navigator method. This amounts to measure not only the desired image, but also other MR based information, called navigator, at regular intervals during the scan. A modeling step then establishes a link between the navigators samples and the head position change. Incorporating the motion information into the main image reconstruction framework helps to retrospectively reduce the impact of said motion and the associated incoherences which would appear during the standard reconstruction. Brain imaging is probably the easiest case of motion correction in MRI, as the motion can readily be well approximated as rigid. The navigator methods developed and investigated in this work, called FatNavs, are based on the fat signal, which in head imaging is very sparse in space and therefore can be imaged rapidly. They also present the advantage of reduced impact on the main image water signal. Several implementation strategies were tested as, due to the versatility of MRI, all image contrasts cannot be ideally navigated using a single general implementation. Applications to inversion recovery based sequences (MP2RAGE) used a well separated navigator and image acquisition scheme. This method being routinely acquired, comparison to Moir Ìe Phase Tracking, the current gold standard for motion tracking and correction, was also performed in collaboration with Hendrik Mattern from the Magdeburg University. For gradient-echo imaging sequences (GRE), on which time-of-flight angiography and susceptibil- ity induced contrasts are based, both separate and mixed acquisition schemes were tested. Further- more, for imaging protocols using long echo time, the fluctuation of the magnetic field during the scan can also induce severe artefacts. Therefore, extension of the FatNavs to a dual-echo field-mapping version was also explored. Finally, combination of FatNavs with FID navigators, which lack spatial information but have much higher temporal resolution, was investigated for both motion and field fluctuation retrospective correction.

EPFL2020

Real-time shimming with FID navigators

Tobias Kober

Purpose: To implement a method for real-time field control using rapid FID navigator (FIDnav) measurements and evaluate the efficacy of the proposed approach for mitigating dynamic field perturbations and improving T-2*-weighted image quality. Methods: FIDnavs were embedded in a gradient echo sequence and a subject-specific linear calibration model was generated on the scanner to facilitate rapid shim updates in response to measured FIDnav signals. To confirm the accuracy of FID-navigated field updates, phantom and volunteer scans were performed with online updates of the scanner B-0 shim settings. To evaluate improvement inT(2)-weighted image quality with real-time shimming, 10 volunteers were scanned at 3T while performing deep-breathing and nose-touching tasks designed to modulate the B-0 field. Quantitative image quality metrics were compared with and without FID-navigated field control. An additional volunteer was scanned at 7T to evaluate performance at ultra-high field. Results: Applying measured FIDnav shim updates successfully compensated for applied global and linear field offsets in phantoms and across all volunteers. FID-navigated real-time shimming led to a substantial reduction in field fluctuations and a consequent improvement in T-2-weighted image quality in volunteers performing deep-breathing and nose-touching tasks, with 7.57% +/- 6.01% and 8.21% +/- 10.90% improvement in peak SNR and structural similarity, respectively. Conclusion: FIDnavs facilitate rapid measurement and application of field coefficients for slice-wise B-0 shimming. The proposed approach can successfully counteract spatiotemporal field perturbations and substantially improves T-2*-weighted image quality, which is important for a variety of clinical and research applications, particularly at ultra-high field.

WILEY2022

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2010

Fat navigators based retrospective motion correction strategies for brain magnetic resonance imaging

Frédéric Gretsch

EPFL2020