Compressed Sensing Accelerated Magnetic Resonance Spectroscopic Imaging

abstract: Magnetic resonance spectroscopic imaging (MRSI) is a valuable technique for assessing the in vivo spatial profiles of metabolites like N-acetylaspartate (NAA), creatine, choline, and lactate. Changes in metabolite concentrations can help identify tissue heterogeneity, providing prognostic and diagnostic information to the clinician. The increased uptake of glucose by solid tumors as compared to normal tissues and its conversion to lactate can be exploited for tumor diagnostics, anti-cancer therapy, and in the detection of metastasis. Lactate levels in cancer cells are suggestive of altered metabolism, tumor recurrence, and poor outcome. A dedicated technique like MRSI could contribute to an improved assessment of metabolic abnormalities in the clinical setting, and introduce the possibility of employing non-invasive lactate imaging as a powerful prognostic marker. However, the long acquisition time in MRSI is a deterrent to its inclusion in clinical protocols due to associated costs, patient discomfort (especially in pediatric patients under anesthesia), and higher susceptibility to motion artifacts. Acceleration strategies like compressed sensing (CS) permit faithful reconstructions even when the k-space is undersampled well below the Nyquist limit. CS is apt for MRSI as spectroscopic data are inherently sparse in multiple dimensions of space and frequency in an appropriate transform domain, for e.g. the wavelet domain. The objective of this research was three-fold: firstly on the preclinical front, to prospectively speed-up spectrally-edited MRSI using CS for rapid mapping of lactate and capture associated changes in response to therapy. Secondly, to retrospectively evaluate CS-MRSI in pediatric patients scanned for various brain-related concerns. Thirdly, to implement prospective CS-MRSI acquisitions on a clinical magnetic resonance imaging (MRI) scanner for fast spectroscopic imaging studies. Both phantom and in vivo results demonstrated a reduction in the scan time by up to 80%, with the accelerated CS-MRSI reconstructions maintaining high spectral fidelity and statistically insignificant errors as compared to the fully sampled reference dataset. Optimization of CS parameters involved identifying an optimal sampling mask for CS-MRSI at each acceleration factor. It is envisioned that time-efficient MRSI realized with optimized CS acceleration would facilitate the clinical acceptance of routine MRSI exams for a quantitative mapping of important biomarkers.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

Adaption in Dynamic Contrast-Enhanced MRI

In breast DCE MRI, dynamic data are acquired to assess signal changes caused by contrast agent injection in order to classify lesions. Two approaches are used for data analysis. One is to fit a pharmacokinetic model, such as the Tofts model, to the data, providing physiological information. For accurate model fitting, fast sampling is needed. Another approach is to evaluate architectural features of the contrast agent distribution, for which high spatial resolution is indispensable. However, high temporal and spatial resolution are opposing aims and a compromise has to be found. A new area of research are adaptive schemes, which sample data at combined resolutions to yield both, accurate model fitting and high spatial resolution morphological information. In this work, adaptive sampling schemes were investigated with the objective to optimize fitting accuracy, whilst providing high spatial resolution images. First, optimal sampling design was applied to the Tofts model. By that it could be determined, based on an assumed parameter distribution, that time points during the onset and the initial fast kinetics, lasting for approximately two minutes, are most relevant for fitting. During this interval, fast sampling is required. Later time points during wash-out can be exploited for high spatial resolution images. To achieve fast sampling during the initial kinetics, data acquisition has to be accelerated. A common way to increase imaging speed is to use view-sharing methods, which omit certain k-space data and interpolate the missing data from neighboring time frames. In this work, based on phantom simulations, the influence of different view-sharing techniques during the initial kinetics on fitting accuracy was investigated. It was found that all view-sharing methods imposed characteristic systematic errors on the fitting results of Ktrans. The best fitting performance was achieved by the scheme ``modTRICKS'', which is a combination of the often used schemes keyhole and TRICKS. It is not known prior to imaging, when the contrast agent will arrive in the lesion or when the wash-out begins. Currently used adaptive sequences change resolutions a fixed time points. However, missing time points on the upslope may cause fitting errors and missing the signal peak may lead to a loss in morphological information. This problem was addressed with a new automatic resolution adaption (AURA) sequence. Acquired dynamic data were analyzed in real-time to find the onset and the beginning of the wash-out and consequently the temporal resolution was automatically adapted. Using a perfusion phantom it could be shown that AURA provides both, high fitting accuracy and reliably high spatial resolution images close to the signal peak. As alternative approach to AURA, a sequence which allows for retrospective resolution adaption, was assesses. Advantages are that adaption does not have to be a global process, and can be tailored regionally to local sampling requirements. This can be useful for heterogeneous lesions. For that, a 3D golden angle radial sequence was used, which acquires contrast information with each line and the golden angles allow arbitrary resolutions at arbitrary time points. Using a perfusion phantom, it could be shown that retrospective resolution adaption yields high fitting accuracy and relatively high spatial resolution maps

Adaption in Dynamic Contrast-Enhanced MRI

In breast DCE MRI, dynamic data are acquired to assess signal changes caused by contrast agent injection in order to classify lesions. Two approaches are used for data analysis. One is to fit a pharmacokinetic model, such as the Tofts model, to the data, providing physiological information. For accurate model fitting, fast sampling is needed. Another approach is to evaluate architectural features of the contrast agent distribution, for which high spatial resolution is indispensable. However, high temporal and spatial resolution are opposing aims and a compromise has to be found. A new area of research are adaptive schemes, which sample data at combined resolutions to yield both, accurate model fitting and high spatial resolution morphological information. In this work, adaptive sampling schemes were investigated with the objective to optimize fitting accuracy, whilst providing high spatial resolution images. First, optimal sampling design was applied to the Tofts model. By that it could be determined, based on an assumed parameter distribution, that time points during the onset and the initial fast kinetics, lasting for approximately two minutes, are most relevant for fitting. During this interval, fast sampling is required. Later time points during wash-out can be exploited for high spatial resolution images. To achieve fast sampling during the initial kinetics, data acquisition has to be accelerated. A common way to increase imaging speed is to use view-sharing methods, which omit certain k-space data and interpolate the missing data from neighboring time frames. In this work, based on phantom simulations, the influence of different view-sharing techniques during the initial kinetics on fitting accuracy was investigated. It was found that all view-sharing methods imposed characteristic systematic errors on the fitting results of Ktrans. The best fitting performance was achieved by the scheme ``modTRICKS'', which is a combination of the often used schemes keyhole and TRICKS. It is not known prior to imaging, when the contrast agent will arrive in the lesion or when the wash-out begins. Currently used adaptive sequences change resolutions a fixed time points. However, missing time points on the upslope may cause fitting errors and missing the signal peak may lead to a loss in morphological information. This problem was addressed with a new automatic resolution adaption (AURA) sequence. Acquired dynamic data were analyzed in real-time to find the onset and the beginning of the wash-out and consequently the temporal resolution was automatically adapted. Using a perfusion phantom it could be shown that AURA provides both, high fitting accuracy and reliably high spatial resolution images close to the signal peak. As alternative approach to AURA, a sequence which allows for retrospective resolution adaption, was assesses. Advantages are that adaption does not have to be a global process, and can be tailored regionally to local sampling requirements. This can be useful for heterogeneous lesions. For that, a 3D golden angle radial sequence was used, which acquires contrast information with each line and the golden angles allow arbitrary resolutions at arbitrary time points. Using a perfusion phantom, it could be shown that retrospective resolution adaption yields high fitting accuracy and relatively high spatial resolution maps

Joint multi-field T1 quantification for fast field-cycling MRI

Acknowledgment This article is based upon work from COST Action CA15209, supported by COST (European Cooperation in Science and Technology). Oliver Maier is a Recipient of a DOC Fellowship (24966) of the Austrian Academy of Sciences at the Institute of Medical Engineering at TU Graz. The authors would like to acknowledge the NVIDIA Corporation Hardware grant support.Peer reviewedPublisher PD

Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, K(trans), plasma volume fraction, v(p), and extravascular, extracellular space, v(e), directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, v(p), K(trans), and v(e), respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches

Measurement of the vascular input function in mice for DCE-MRI

DCE-MRI is an important technique in the study of small animal cancer models because its sensitivity to vascular changes opens the possibility of quantitative assessment of early therapeutic response. However, extraction of physiologically descriptive parameters from DCE-MRI data relies upon measurement of the vascular input function (VIF), which represents the contrast agent concentration time course in the blood plasma. This is difficult in small animal models due to artifacts associated with partial volume, inflow enhancement, and the limited temporal resolution achievable with MR imaging. In this work, the development of a suite of techniques for high temporal resolution, artifact resistant measurement of the VIF in mice is described. One obstacle in VIF measurement is inflow enhancement, which decreases the sensitivity of the MR signal to the presence of contrast agent. Because the traditional techniques used to suppress inflow enhancement degrade the achievable spatiotemporal resolution of the pulse sequence, improvements can be achieved by reducing the time required for the suppression. Thus, a novel RF pulse which provides spatial presaturation contemporaneously with the RF excitation was implemented and evaluated. This maximizes the achievable temporal resolution by removing the additional RF and gradient pulses typically required for suppression of inflow enhancement. A second challenge is achieving the temporal resolution required for accurate characterization of the VIF, which exceeds what can be achieved with conventional imaging techniques while maintaining adequate spatial resolution and tumor coverage. Thus, an anatomically constrained reconstruction strategy was developed that allows for sampling of the VIF at extremely high acceleration factors, permitting capture of the initial pass of the contrast agent in mice. Simulation, phantom, and in vivo validation of all components were performed. Finally, the two components were used to perform VIF measurement in the murine heart. An in vivo study of the VIF reproducibility was performed, and an improvement in the measured injection-to-injection variation was observed. This will lead to improvements in the reliability of quantitative DCE-MRI measurements and increase their sensitivity

Analysis and Mitigation of the Effect of Magnetic Field Inhomogeneities and Undersampling Artifacts on Magnetic Resonance Fingerprinting

Magnetic resonance imaging (MRI) is largely limited to producing qualitative contrast images instead of quantitative maps of tissue characteristics. A novel framework for quantitative MRI termed Magnetic Resonance Fingerprinting (MRF) to map tissue parameters such as the relaxation times T1 and T2 has recently been introduced. In MRF, tissue signals are generated by applying a pseudo-randomly varying MRI acquisition, acquired using highly undersampled trajectories and matched to a database of simulated tissue signals. The aim of this thesis is to investigate hypotheses underlying MRF regarding its susceptibility to undersampling artifacts and magnetic field inhomogeneities and develop countermeasures. Since MRF can be implemented in various ways, one of the most popular implementations based on the FISP (Fast Imaging with Steady State Precession) sequence was chosen for analysis and as a basis for further developments. The single shot spiral trajectories employed lead to substantial undersampling artifacts. In this work, the temporal variation of the spiral sampling patterns was examined and optimized. The results show that the originally proposed temporal order yields artifacts of similar frequencies as the signal responses from tissues, which leads to spatially dependent misestimations of parameters. To resolve those, an optimized temporal order was developed in simulations and proven in in-vivo experiments. The following chapter is dedicated to the influence of magnetic field inhomogeneities on MRF. Here it is shown that different local amplitudes of the radio frequency (RF) field B1+ can lead to misestimations of parameters by up to 50%, which can be resolved by measuring a B1+ map and integrating the information in the pattern match. Another newly developed strategy in this work is to mitigate the influence of B1+ by the introduction of acquisition segments that are particularly sensitive to B1+. Two approaches were developed and evaluated, one including FLASH (Fast Low-Angle Shot) and one using two 90° phase shifted pulses. Here, tissue parameter maps and B1+ maps were simultaneously generated, thereby resolving interdependencies. Furthermore, in this work it was found that the static magnetic field B0 can also have an impact on FISP-MRF. The dependency was analyzed and related to the relative phase difference between spin ensembles and RF pulses. A technique to mitigate the dependency by additionally dephasing spins before RF pulses was developed. The chapter is concluded with the presentation of the novel development of MRFF (Magnetic Resonance Field Fingerprinting). By replacing some FISP segments with TrueFISP and FLASH segments, B0 and B1+ dependent information was added, which enabled the simultaneous generation of T1, T2, B0, B1+ and intravoxel phase dispersion maps. In the last chapter, the in-vivo reproducibility of FISP-MRF with the newly developed improvements described in the previous chapters was evaluated by scanning ten volunteers on ten scanners. T1 and T2 values varied less than 8.0% in brain compartments across scanners.Die Magnetresonanztomographie (MRT) beschränkt sich weitgehend auf die Erzeugung qualitativer Kontrastbilder anstelle von quantitativen Karten von Gewebeeigenschaften. Kürzlich wurde ein neuartiges Framework für quantitative MRT, Magnetic Resonance Fingerprinting (MRF) zur direkten Abbildung von Gewebeparametern wie der Relaxationszeiten T1 und T2 präsentiert. Bei MRF werden Gewebesignale mittels einer pseudozufällig variierenden MRT-Sequenz generiert, die unter Verwendung stark unterabgetasteter Trajektorien aufgenommen werden und daraufhin mit einer Datenbank simulierter Gewebesignale zum Zweck der Identifikation von Gewebeparametern verglichen werden. Ziel dieser Arbeit ist es, die Anfälligkeit von MRF für Unterabtastungsartefakte und Magnetfeldinhomogenitäten zu untersuchen und entsprechende Gegenmaßnahmen zu entwickeln. Da MRF auf verschiedene Arten implementiert werden kann, wurde die bis dato am häufigsten verwendete Implementierung basierend auf der FISP (Fast Imaging with Steady State Precession) Sequenz zur Analyse und als Grundlage für weitere Entwicklungen ausgewählt. Die in FISP-MRF verwendeten Einzelschuss-Spiraltrajektorien führen zu erheblichen Unterabtastungsartefakten im Bildraum. In dieser Arbeit wird deren zeitliche Variation untersucht und optimiert. Die Resultate zeigen, dass die ursprünglich vorgeschlagene Abfolge Artefakte mit ähnlichen Frequenzen wie die der Signalantworten von Geweben ergibt, was zu ortsabhängigen Parameterfehlern führt. Eine optimierte Abfolge wurde in Simulationen gefunden, die in in-vivo Experimenten bestätigt wurde. Das folgende Kapitel befasst sich mit dem Einfluss von Magnetfeldinhomogenitäten auf FISP-MRF. Hier wird gezeigt, dass variierende lokale Amplituden des HF-Feldes B1+ zu Parameterfehlern von bis zu 50% führen können, die sich durch die Messung einer B1+ Karte und Integrieren der Informationen in den Musterabgleich beheben lassen können. Eine weitere in dieser Arbeit entwickelte Strategie ist die Einführung von Akquisitionssegmenten, die gegenüber B1+ besonders sensitiv sind. Zwei Ansätze, einer mit FLASH (Fast Low-Angle Shot) und einer mit zwei um 90° phasenverschobenen Hochfrequenz-Pulsen pro TR wurden in dieser Arbeit entwickelt. Hier werden gleichzeitig Gewebeparameter- und B1+ -Karten erzeugt, wodurch gegenseitige Abhängigkeiten aufgelöst werden. In dieser Arbeit wurde auch gezeigt, dass Inhomogenitäten des statischen Magnetfelds B0 sich auf FISP-MRF auswirken können. Diese Abhängigkeit wurde analysiert und mit der relativen Phasendifferenz zwischen Spin-Ensembles und HF-Pulsen in Beziehung gesetzt. Wie in dieser Arbeit gezeigt, kann durch zusätzliches Dephasieren von Spin-Ensembles vor einem HF-Impuls der Einfluss von B0 stark vermindert werden. Im letzten Abschnitt dieses Kapitels wird die neue eigene Entwicklung MRFF (Magnetic Resonance Field Fingerprinting) präsentiert. Durch Ersetzen einiger FISP-Segmente durch TrueFISP- und FLASH-Segmente werden B0 und B1+ abhängige Informationen hinzugefügt, wodurch die gleichzeitige Erzeugung von T1, T2, B0, B1+ sowie Suszeptibilitätskarten möglich wird. Im fünften Kapitel wurde die in-vivo Reproduzierbarkeit und Wiederholbarkeit von FISP-MRF mit den in den vorhergehenden Kapiteln beschriebenen Verbesserungen durch Messungen von zehn Probanden auf insgesamt zehn Scannern evaluiert. Die T1- und T2-Werte variierten zwischen den Scannern in den Gehirnkompartimenten um weniger als 8,0%

Autocalibration Region Extending Through Time: A Novel GRAPPA Reconstruction Algorithm to Accelerate 1H Magnetic Resonance Spectroscopic Imaging

Magnetic resonance spectroscopic imaging (MRSI) has the ability to noninvasively interrogate metabolism in vivo. However, excessively long scan times have thus far prevented its adoption into routine clinical practice. Generalized autocalibrating partially parallel acquisitions (GRAPPA) is a parallel imaging technique that allows one to reduce acquisition duration and use spatial sensitivity correlations to reconstruct the unsampled data points. The coil sensitivity weights are determined implicitly via a fully-sampled autocalibration region in k-space. In this dissertation, a novel GRAPPA-based algorithm is presented for the acceleration of 1H MRSI. Autocalibration Region extending Through Time (ARTT) GRAPPA instead extracts the coil weights from a region in k-t space, allowing for undersampling along each spatial dimension. This technique, by exploiting spatial-spectral correlations present in MRSI data, allows for a more accurate determination of the coil weights and subsequent parallel imaging reconstruction. This improved reconstruction accuracy can then be traded for more aggressive undersampling and a further reduction of acquisition duration. It is shown that the ARTT GRAPPA technique allows for approximately two-fold more aggressive undersampling than the conventional technique while achieving the same reconstruction accuracy. This accelerated protocol is then applied to acquire high-resolution brain metabolite maps in less than twenty minutes in three healthy volunteers at B0 = 7 T