Functional cerebral blood volume mapping with simultaneous multi-slice acquisition

The aim of this study is to overcome the current limits of brain coverage available with multi-slice echo planar imaging (EPI) for vascular space occupancy (VASO) mapping. By incorporating simultaneous multi-slice (SMS) EPI image acquisition into slice-saturation slab-inversion VASO (SS-SI VASO), many more slices can be acquired for non-invasive functional measurements of blood volume responses. Blood-volume-weighted VASO and gradient echo blood oxygenation level-dependent (GE-BOLD) data were acquired in humans at 7 T with a 32-channel head coil. SMS-VASO was applied in three scenarios: A) high-resolution acquisition of spatially distant brain areas in the visuo-motor network (V1/V5/M1/S1); B) high-resolution acquisition of an imaging slab covering the entire M1/S1 hand regions; and C) low-resolution acquisition with near whole-brain coverage. The results show that the SMS-VASO sequence provided images enabling robust detection of blood volume changes in up to 20 slices with signal readout durations shorter than 150 ms. High-resolution application of SMS-VASO revealed improved specificity of VASO to GM tissue without contamination from large draining veins compared to GE-BOLD in the visual cortex and in the sensory-motor cortex. It is concluded that VASO fMRI with SMS-EPI allows obtaining a reasonable three-dimensional coverage not achievable with standard VASO during the short time period when blood magnetization is approximately nulled. Due to the increased brain coverage and better spatial specificity to GM tissue of VASO compared to GE-BOLD signal, the proposed method may play an important role in high-resolution human fMRI at 7 T

New Approaches to Simultaneous Multislice Magnetic Resonance Imaging : Sequence Optimization and Deep Learning based Image Reconstruction

Magnetic resonance imaging (MRI) is a versatile imaging modality in clinical diagnostics. Despite the impressive range of application, a main drawback of MRI is its inherently low acquisition speed. However, scan time is crucial for many applications and also for an efficient utilization of MRI in clinical routine. Two developments have influenced MRI recently: Simultaneous multislice imaging (SMS) and deep learning (DL). Simultaneous multislice imaging is a paradigm shift in MRI which has re-emerged in the early 2010'. It yields improved image quality compared to in-plane parallel imaging, because it benefits from increased signal-to-noise ratio and robustness for higher accelerations. SMS sequences accelerate data acquisition by undersampling along the slice dimension and specific algorithms allow reconstruction of these undersampled data. In the first part, SMS was extended to measure multiple image contrasts in contrast-enhanced dynamic MRI. Therefore, a bespoke MRI sequence was developed to accelerate segmented echo-planar imaging of three echoes. Dynamic in-vivo data with sufficient spatial coverage were acquired in an animal model. Data acquisition were fast enough to sample the arterial input function which is essential for pharmacokinetic modeling. Imperfections in the excitation of multiple slice and their relevance for reconstruction algorithms were closely investigated and evaluated for processing of multi-contrast data. This work connects SMS and deep learning. Today, the application of deep learning in medicine assists decision making in medical diagnosis, analysis of radiologic data or personalized medicine in genomics. In MRI however, deep learning has just entered the stage. With two abstracts matching the search term 'deep learning' at the ISMRM 2016, the number of abstracts rose to 42 in 2017 and to 139 in 2018. Most of the early contributions to DL in MRI concern image processing and data evaluation. Image reconstruction itself is mostly conducted in standard fashioned way. Common algorithmic approaches applying deep neural networks for (some) processing steps have shown impressive results and can often be generalized to similar problems. In the second part, the separation of overlapping slice content after SMS was performed by an artificial neural network. This novel reconstruction technique, termed SMSnet, does not require any reference data for calibration of the MR machine's receiver characteristics. Omitting the need for reference data could extend the use of modern accelerated imaging sequences to a broad spectrum of applications. Potential and limitations of this approach were investigated in various experiments accounting for image quality, robustness, sensitivity and how the network generalizes. The discussion at the end summarizes and relates the results of this work to state-of-the-art techniques and recent developments in MRI and gives an outlook to future work on SMS and DL-based reconstructions

Accelerated Quantitative Mapping and Angiography for Cerebral and Cardiovascular Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) produces images with anatomical and functional information. These images can be obtained without the use of contrast agents, which generally require long scan times. This dissertation investigates existing techniques for accelerating such functional MRI methods, contributes novel fast acquisition and reconstruction techniques, and proposes new ways of analyzing real-time MRI data. First, we aim to determine an advantageous approach for accelerating high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different data undersampling patterns and reconstruction methods over a range of acceleration rates. Quantitative results on healthy and edematous hearts reveal that the relaxometry maps are more sensitive to undersampling than anatomical images. The 3-fold variable density random undersampling with model-based or joint-sparsity sensitivity encoding (SENSE) is recommended. Second, we develop a rapid T2 mapping protocol using spiral acquisition and novel model-based approach joined with compressed sensing (CS) and model-based reconstruction. We also develop a sequence that suppresses cerebrospinal fluid (CSF). Quantitative evaluation on digital phantoms and healthy volunteers demonstrates the feasibility of T2 quantification with 3D high-resolution and whole-brain coverage in 2-3 min. Third, we propose a Golden Angle (GA) rotated Spiral Sparse Parallel imaging (GASSP) method for high spatial (0.8mm) and high temporal (<21ms) resolution for measuring coronary blood flow in a single breath-hold. We reduce k-space gaps using novel binning and triggered GA schemes. Velocity and flow metrics are validated against two existing methods and show high reproducibility. Fourth, we construct an abdominal non-contrast-enhanced magnetic resonance angiography (MRA) protocol with a large spatial coverage at 3.0T. The protocol uses advanced velocity-selective (VS) pulse trains. MRA with a large spatial coverage is slow and accelerated using CS. The VS-MRA sequences generate high-quality angiograms and arteriograms with high blood contrast. Finally, physiological changes in real-time (RT) MRI (30-100 frames/sec) are explored using Fourier transform (FT), principal component analyses (PCA), and perfusion modeling. We detect spectral patterns in pharyngeal images acquired during speaking and obtain T1-weighted, pulsation-weighted, and respiration-weighted images in healthy volunteers and heart patients with wall motion abnormalities with FT and PCA. RT perfusion maps are estimated from a proposed perfusion model in ongoing work in progress

Tissue quantification based on Magnetic Resonance Fingerprinting

Quantification of tissue properties including the relaxation parameters has long been a goal of magnetic resonance imaging (MRI), to provide a basis for inter-patient comparability. However, extended acquisition times have hindered the usage of quantification for clinical applications. Magnetic Resonance Fingerprinting (MRF)was introduced as a promising method for simultaneous and fast quantification of multiple tissue parameters. Most MRF methods rely on spiral k-space trajectories, though they are well known to suffer from detrimental effects on the image quality, caused by gradient inaccuracies. The aim of thisworkwas to develop an implementation of the MRF paradigm for quantitative imaging based on Cartesian k-space readout, potentially increasing its usability and robustness. In a first step, a single slice MRF method based on echo-planar imaging (MRF-EPI) was developed, acquiring 160 gradient-spoiled EPI images with Cartesian readout. By varying the flip angle, echo times and including an inversion pulse, fluctuating signal paths were created. T1 and T2* were quantified through matching the fingerprints with a precomputed dictionary. The quantification accuracy was validated in phantom scans showing good agreement of MRF-EPI with reference measurements, with average deviations of 2+-3% and 2+-3% for T1 and T2*, respectively. In vivo maps were of high visual quality and comparable to in vivo reference measurements, despite the substantially shortened scan times of 10 s per slice. In a second step, MRF-EPI was modified for improved volumetric coverage by using a slice-interleaved acquisition scheme. In addition to the T1 and T2* maps, proton density (PD) maps could be created without the need of additional measurements. In vivo whole-brain coverage of T1, T2* and PD with 32 slices were acquired within 3:36 minutes, resulting in parameter maps of high visual quality and comparable performance with single-slice MRF-EPI at 4-fold scan-time reduction. In a final step the motion sensitivity of MRF methods was studied. Simulations demonstrated that MRF sequences based on spiral and Cartesian readout exert sensitivity to motion. To correct for motion, the individual measurements of MRF-EPI were corrected by co-registering them with an intensity-based coregistration method. Phantom and in vivo measurements demonstrated that motion artefacts were successfully mitigated with intensity-based co-registration, leading to motion-robust artefact-free T1 and T2* maps. Combining the developments of this work resulted in a fast and robust method for multi-parametric whole brain quantification in clinically acceptable scan time

Simultaneous multi-slice Turbo-FLASH imaging with CAIPIRINHA for whole brain distortion-free pseudo-continuous arterial spin labeling at 3 and 7 T.

Simultaneous multi-slice (SMS) or multiband (MB) imaging has recently been attempted for arterial spin labeled (ASL) perfusion MRI in conjunction with echo-planar imaging (EPI) readout. It was found that SMS-EPI can reduce the T1 relaxation effect of the label and improve image coverage and resolution with little penalty in signal-to-noise ratio (SNR). However, EPI still suffers from geometric distortion and signal dropout from field inhomogeneity effects especially at high and ultrahigh magnetic fields. Here we present a novel scheme for achieving high fidelity distortion-free quantitative perfusion imaging by combining pseudo-continuous ASL (pCASL) with SMS Turbo-FLASH (TFL) readout at both 3 and 7 T. Bloch equation simulation was performed to characterize and optimize the TFL-based pCASL perfusion signal. Two MB factors (3 and 5) were implemented in SMS-TFL pCASL and compared with standard 2D TFL and EPI pCASL sequences. The temporal SNR of SMS-TFL pCASL relative to that of standard TFL pCASL was 0.76 ± 0.10 and 0.74 ± 0.11 at 7 T and 0.70 ± 0.05 and 0.65 ± 0.05 at 3T for MB factor of 3 and 5, respectively. By implementing background suppression in conjunction with SMS-TFL at 3T, the relative temporal SNR improved to 0.84 ± 0.09 and 0.79 ± 0.10 for MB factor of 3 and 5, respectively. Compared to EPI pCASL, significantly increased temporal SNR (p&lt;0.001) and improved visualization of orbitofrontal cortex were achieved using SMS-TFL pCASL. By combining SMS acceleration with TFL pCASL, we demonstrated the feasibility for whole brain distortion-free quantitative mapping of cerebral blood flow at high and ultrahigh magnetic fields