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

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.

Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain

Anouk André, Meritxell Bach Cuadra, Gabriele Vincenzo Bonanno, Marie Schaer, Jean-Philippe Thiran

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

New strategies for highly efficient medical MRI contrast agents

Sabrina Laus

During the last two decades, magnetic resonance imaging (MRI) has evolved into one of the most powerful techniques in medical diagnosis. Among the various medical diagnostic modalities, such as X-ray or ultra-sound imaging, MRI is the technique of choice especially because of its non-invasive character, the absence of ionizing radiations and its high spatial resolution. The principle of an MRI examination relies on the relaxation of water protons in the human body placed in a magnetic field after radiofrequency excitations. MRI is sensitive to the nature of the soft tissues, to the proton density and to the relaxation rate of protons to produce a contrast. The development of MRI is closely related to the successful use of paramagnetic contrast agents, essentially gadolinium(III) complexes. Unlike contrast agents used in other clinical imaging techniques, the MRI contrast agents are not themselves imaged but rather enhance the nuclear relaxation rate of water protons in their vicinity. The use of MRI contrast agents induces not only a better image contrast and a better delineating of diseased tissues but also a shortening of the examination time. Since the discovery of GdIII chelates as MRI contrast agents, a lot of effort has been made to increase their relaxivity, i.e. the gauge of efficiency of the contrast agent. In the work presented in this thesis, two groups of paramagnetic compounds have been studied: GdIII poly(amino carboxylates) and GdIII trapped in carbon cage compounds. For traditional monohydrated GdIII poly(amino carboxylate) complexes, the Solomon- Bloembergen-Morgan equations predict maximum proton relaxivities of 100 mM-1 s-1, instead of 4-5 mM-1 s-1 for commercial agents, when the three most important influencing factors, the rotation, the electron spin relaxation and the water exchange rate are simultaneously optimized. On the basis of structural considerations in the inner sphere of nine-coordinate, monohydrated GdIII poly(amino carboxylate) complexes, we succeeded in accelerating the water exchange by inducing steric compression around the water binding site. We modified the common DTPA5- ligand by replacing one ethylene bridge of the amine backbone by a propylene one (EPTPA5--based ligand), leading to a water exchange rate two orders of magnitude higher than that for the commercial [Gd(DTPA)(H2O)]2-. The replacement of two ethylene bridges (DPTPA5-) results in the elimination of the water molecule from the inner sphere. Although the thermodynamic stability of [Gd(EPTPA-bz-NO2)(H2O)]2- is reduced to a slight extent in comparison to [Gd(DTPA)(H2O)]2-, it is stable enough to be used in medical diagnosis. The next step to obtain high relaxivity contrast agents is the covalent coupling of GdIII EPTPA complexes to three generations (5, 7 and 9) of PAMAM dendrimers to ensure fast water exchange rate and slow rotation. These macromolecular GdIII complexes allow high relaxivities with maximum values at magnetic fields presently used in clinical applications thanks to slow molecular tumbling. The relaxivities show a strong and reversible pH dependency for all three dendrimer generations. This smart behaviour originates from the pHdependent rotational dynamics of the dendrimer skeleton. From the studies on the monomeric and dendrimeric GdIII EPTPA complexes, general considerations have been proposed about the crucial need of more rigid macromolecular assemblies to obtain the highest relaxivity values predicted by the Solomon-Bloembergen-Morgan equations. Water-soluble fullerenes and carbon nanotubes have shown a great potential for various biological and medical applications. Gadofullerenes have been proposed not only as high relaxivity contrast agents thanks to the high number of water molecules close to the paramagnetic centre and their slow tumbling arising from aggregation phenomena but also as smart pH-responsive drugs. Proton relaxivity was proposed as an ideal aggregation-state sensor for the disruption of the water-soluble Gd@C60(OH)x (x ≈ 27) and Gd@C60[C(COOH)2]10 aggregates upon salt addition. This phenomenon has important implications for biological or biomedical applications of fullerene-based materials, since real biological fluids present a rather high salt concentration. With the aim of gaining more insight into the paramagnetic relaxation mechanism induced by water-soluble gadofullerenes, an NMR analysis with the common techniques used for GdIII MRI contrast agents allowed a comparison between the aggregated and the disaggregated systems. In analogy to C60 buckyballs, ultra-short single-walled nanotubes have been loaded with Gd3+ aqua-ions clusters. These Gdn3+@US-tubes, which self-organise into bundles-like structures, exhibit relaxivities up to 90 times higher (depending on the magnetic field) than that of any GdIII-based MRI contrast agent in current clinical use.

EPFL2005

Quantitative morphometry analysis of the fetal brain using clinical MR imaging

Meritxell Bach Cuadra, Gabriele Vincenzo Bonanno, Marie Schaer, Jean-Philippe Thiran

In vivo fetal magnetic resonance imaging provides a unique approach for the study of early human brain development [1]. In utero cerebral morphometry could potentially be used as a marker of the cerebral maturation and help to distinguish between normal and abnormal development in ambiguous situations. However, this quantitative approach is a major challenge because of the movement of the fetus inside the amniotic cavity, the poor spatial resolution provided by very fast MRI sequences and the partial volume effect. Extensive efforts are made to deal with the reconstruction of high-resolution 3D fetal volumes based on several acquisitions with lower resolution [2,3,4]. Frameworks were developed for the segmentation of specific regions of the fetal brain such as posterior fossa, brainstem or germinal matrix [5,6], or for the entire brain tissue [7,8], applying the Expectation-Maximization Markov Random Field (EM-MRF) framework. However, many of these previous works focused on the young fetus (i.e. before 24 weeks) and use anatomical atlas priors to segment the different tissue or regions. As most of the gyral development takes place after the 24th week, a comprehensive and clinically meaningful study of the fetal brain should not dismiss the third trimester of gestation. To cope with the rapidly changing appearance of the developing brain, some authors proposed a dynamic atlas [8]. To our opinion, this approach however faces a risk of circularity: each brain will be analyzed / deformed using the template of its biological age, potentially biasing the effective developmental delay. Here, we expand our previous work [9] to propose post-processing pipeline without prior that allow a comprehensive set of morphometric measurement devoted to clinical application. 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). For each fetus, 6 axial volumes shifted by 1 mm were acquired under mother’s sedation (about 1min per volume). First, each volume is segmented semi-automatically using region-growing algorithms to extract fetal brain from surrounding maternal tissues. Inhomogeneity intensity correction [10] and linear intensity normalization are then performed. Brain tissues (CSF, GM and WM) are then segmented based on the low-resolution volumes as presented in [9]. A high-resolution image with isotropic voxel size of 1.09 mm is created as proposed in [2] and using B-splines for the scattered data interpolation [11]. Basal ganglia segmentation is performed using a levet set implementation on the high-resolution volume [12]. The resulting white matter image is then binarized and given as an input in FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) to provide topologically accurate three-dimensional reconstructions of the fetal brain according to the local intensity gradient. 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 2008. [7] Bertelsen, ISMRM 2009. [8] Habas, Neuroimage 53(2) 2010. [9] Bach Cuadra, IADB, MICCAI 2009. [10] Styner, IEEE TMI 19(39 (2000). [11] Lee, IEEE Trans. Visual. And Comp. Graph. 3(3), 1997. [12] Bach Cuadra, ISMRM 2010.

2011