Robust and time-effcient determination of perfusion parameters using time-encoded Arterial Spin Labeling MRI

In clinical routine, arterial spin labeling (ASL) faces many challenges, such as time pressure, patient- and disease-specific artifacts, e.g., in steno-occlusive and Moya-Moya disease. In addition, individually tailored parametrization of the MR pulse-sequence is frequently required. Time-encoded ASL-techniques like Hadamard time-encoded pseudocontinuous ASL (H-pCASL) offers a time and signal efficient way to measure accurately both perfusion and arterial transit-times. However, it relies on the decoding of a series of volumes. If even a single volume is corrupted this might, via the decoding process, lead to artifacts in the entire dataset and in the worst case result in the loss of the data. In this thesis a general introduction to time encoded ASL is given and three methods are introduced to increase the robustness of time-encoded ASL against image artifacts and to detect corrupted images. The first method is Walsh-ordered time-encoded H-pCASL (WH-pCASL). It proposes the Walsh-ordering of Hadamard encoding-matrices. In contrast to conventional H-pCASL, this makes perfusion-weighted images accessible during a running experiment and even from incomplete sets of encoded images. An optional additional averaging strategy is based on a mirrored matrix and results in more perfusion-weighted images without any penalty in time. The feasibility of the method is shown using five volunteer datasets. As a second method non-decoded time-encoded ASL is introduced. This novel model-based approach to quantification avoids the decoding step altogether. It models the non-decoded time encoded signal. Therefore it uses the convolution of the tissue response function with a model of the true encoded arterial input function, which is determined by the employed encoding matrix. The model was implemented in a Bayesian model-based ASL analysis framework to fit maps for hemodynamic parameters. The feasibility of the method is demonstrated in a study with five volunteers. The last method is an algorithm for the automated detection of outliers and corrupted images, which is based on variational Bayesian inference (VB). Using the variance of the posterior normal distributions, the algorithm measures the quality of a fit directly and without the need for a separate reference dataset. Its performance and feasibility is demonstrated using volunteer data and a clinical dataset

Ultrahigh-Field MRI in Human Ischemic Stroke – a 7 Tesla Study

INTRODUCTION: Magnetic resonance imaging (MRI) using field strengths up to 3 Tesla (T) has proven to be a powerful tool for stroke diagnosis. Recently, ultrahigh-field (UHF) MRI at 7 T has shown relevant diagnostic benefits in imaging of neurological diseases, but its value for stroke imaging has not been investigated yet. We present the first evaluation of a clinically feasible stroke imaging protocol at 7 T. For comparison an established stroke imaging protocol was applied at 3 T. METHODS: In a prospective imaging study seven patients with subacute and chronic stroke were included. Imaging at 3 T was immediately followed by 7 T imaging. Both protocols included T1-weighted 3D Magnetization-Prepared Rapid-Acquired Gradient-Echo (3D-MPRAGE), T2-weighted 2D Fluid Attenuated Inversion Recovery (2D-FLAIR), T2-weighted 2D Fluid Attenuated Inversion Recovery (2D-T2-TSE), T2* weighted 2D Fast Low Angle Shot Gradient Echo (2D-HemoFLASH) and 3D Time-of-Flight angiography (3D-TOF). RESULTS: The diagnostic information relevant for clinical stroke imaging obtained at 3 T was equally available at 7 T. Higher spatial resolution at 7 T revealed more anatomical details precisely depicting ischemic lesions and periinfarct alterations. A clear benefit in anatomical resolution was also demonstrated for vessel imaging at 7 T. RF power deposition constraints induced scan time prolongation and reduced brain coverage for 2D-FLAIR, 2D-T2-TSE and 3D-TOF at 7 T versus 3 T. CONCLUSIONS: The potential of 7 T MRI for human stroke imaging is shown. Our pilot study encourages a further evaluation of the diagnostic benefit of stroke imaging at 7 T in a larger study

Robuste und zeiteffiziente Bestimmung von Perfusionsparametern mit Hilfe von zeitkodierter Arterial Spin Labeling MRT

In clinical routine, arterial spin labeling (ASL) faces many challenges, such as time pressure, patient- and disease-specific artifacts, e.g., in steno-occlusive and Moya-Moya disease. In addition, individually tailored parametrization of the MR pulse-sequence is frequently required. Time-encoded ASL-techniques like Hadamard time-encoded pseudocontinuous ASL (H-pCASL) offers a time and signal efficient way to measure accurately both perfusion and arterial transit-times. However, it relies on the decoding of a series of volumes. If even a single volume is corrupted this might, via the decoding process, lead to artifacts in the entire dataset and in the worst case result in the loss of the data. In this thesis a general introduction to time encoded ASL is given and three methods are introduced to increase the robustness of time-encoded ASL against image artifacts and to detect corrupted images. The first method is Walsh-ordered time-encoded H-pCASL (WH-pCASL). It proposes the Walsh-ordering of Hadamard encoding-matrices. In contrast to conventional H-pCASL, this makes perfusion-weighted images accessible during a running experiment and even from incomplete sets of encoded images. An optional additional averaging strategy is based on a mirrored matrix and results in more perfusion-weighted images without any penalty in time. The feasibility of the method is shown using five volunteer datasets. As a second method non-decoded time-encoded ASL is introduced. This novel model-based approach to quantification avoids the decoding step altogether. It models the non-decoded time encoded signal. Therefore it uses the convolution of the tissue response function with a model of the true encoded arterial input function, which is determined by the employed encoding matrix. The model was implemented in a Bayesian model-based ASL analysis framework to fit maps for hemodynamic parameters. The feasibility of the method is demonstrated in a study with five volunteers. The last method is an algorithm for the automated detection of outliers and corrupted images, which is based on variational Bayesian inference (VB). Using the variance of the posterior normal distributions, the algorithm measures the quality of a fit directly and without the need for a separate reference dataset. Its performance and feasibility is demonstrated using volunteer data and a clinical dataset

Characterization of phase-based methods used for transmission field uniformity mapping: a magnetic resonance study at 3.0 T and 7.0 T.

Knowledge of the transmission field (B1(+)) of radio-frequency coils is crucial for high field (B0  = 3.0 T) and ultrahigh field (B0 ≥7.0 T) magnetic resonance applications to overcome constraints dictated by electrodynamics in the short wavelength regime with the ultimate goal to improve the image quality. For this purpose B1(+) mapping methods are used, which are commonly magnitude-based. In this study an analysis of five phase-based methods for three-dimensional mapping of the B1(+) field is presented. The five methods are implemented in a 3D gradient-echo technique. Each method makes use of different RF-pulses (composite or off-resonance pulses) to encode the effective intensity of the B1(+) field into the phase of the magnetization. The different RF-pulses result in different trajectories of the magnetization, different use of the transverse magnetization and different sensitivities to B1(+) inhomogeneities and frequency offsets, as demonstrated by numerical simulations. The characterization of the five methods also includes phantom experiments and in vivo studies of the human brain at 3.0 T and at 7.0 T. It is shown how the characteristics of each method affect the quality of the B1(+) maps. Implications for in vivo B1(+) mapping at 3.0 T and 7.0 T are discussed

Prospective motion correction in functional MRI using simultaneous multislice imaging and multislice-to-volume image registration.

The sensitivity to subject motion is one of the major challenges in functional MRI (fMRI) studies in which a precise alignment of images from different time points is required to allow reliable quantification of brain activation throughout the scan. Especially the long measurement times and laborious fMRI tasks add to the amount of subject motion found in typical fMRI measurements, even when head restraints are used. In case of moving subjects, prospective motion correction can maintain the relationship between spatial image information and subject anatomy by constantly adapting the image slice positioning to follow the subject in real time. Image-based prospective motion correction is well-established in fMRI studies and typically computes the motion estimates based on a volume-to-volume image registration, resulting in low temporal resolution. This study combines fMRI using simultaneous multislice imaging with multislice-to-volume-based image registration to allow sub-TR motion detection with subsequent real-time adaption of the imaging system. Simultaneous multislice imaging is widely used in fMRI studies and, together with multislice-to-volume-based image registration algorithms, enables computing suitable motion states after only a single readout by registering the simultaneously excited slices to a reference volume acquired at the start of the measurement. The technique is evaluated in three human BOLD fMRI studies (n = 1, 5, and 1) to explore different aspects of the method. It is compared to conventional, volume-to-volume-based prospective motion correction as well as retrospective motion correction methods. Results show a strong reduction in retrospectively computed residual motion parameters of up to 50% when comparing the two prospective motion correction techniques. An analysis of temporal signal-to-noise ratio as well as brain activation results shows high consistency between the results before and after additional retrospective motion correction when using the proposed technique, indicating successful prospective motion correction. The comparison of absolute tSNR values does not show an improvement compared to using retrospective motion correction alone. However, the improved temporal resolution may provide improved tSNR in the presence of more exaggerated intra-volume motion

Nominal values of the initial excitation angle (<i>p</i>0), flip angle of each sub-pulse (α), total flip angle (<i>TotalFA</i>), <i>B₁</i>+ intensity, total duration of the RF-pulses, echo times (TE) and repetition times (TR) for the experiments performed in phantom and <i>in vivo</i>, at 3.0 T and 7.0 T, with methods A–E.

<p>Also the parameters for the comparison with the DAM are listed. The echo times are calculated relatively to the center of the first sub-pulse for each method.</p

Crossed cerebellar diaschisis after stroke: can perfusion-weighted MRI show functional inactivation?

In this study, we aimed to assess the detection of crossed cerebellar diaschisis (CCD) following stroke by perfusion-weighted magnetic resonance imaging (PW-MRI) in comparison with positron emission tomography (PET). Both PW-MRI and 15O-water-PET were performed in acute and subacute hemispheric stroke patients. The degree of CCD was defined by regions of interest placed in the cerebellar hemispheres ipsilateral (I) and contralateral (C) to the supratentorial lesion. An asymmetry index (AI=C/I) was calculated for PET-cerebral blood flow (CBF) and MRI-based maps of CBF, cerebral blood volume (CBV), mean transit time (MTT), and time to peak (TTP). The resulting AI values were compared by Bland–Altman (BA) plots and receiver operating characteristic analysis to detect the degree and presence of CCD. A total of 26 imaging procedures were performed (median age 57 years, 20/26 imaged within 48 hours after stroke). In BA plots, all four PW-MRI maps could not reliably reflect the degree of CCD. In receiver operating characteristic analysis for detection of CCD, PW-CBF performed poorly (accuracy 0.61), whereas CBV, MTT, and TTP failed (accuracy <0.60). On the basis of our findings, PW-MRI at 1.5 T is not suited to depict CCD after stroke