Abstract

We present a method to efficiently separate signal in magnetic resonance imaging (MRI) into a base signal S0, representing the mainly T1-weighted component without T2*-relaxation, and its T2*-weighted counterpart by the rapid acquisition of multiple contrasts for advanced pharmacokinetic modelling. This is achieved by incorporating simultaneous multislice (SMS) imaging into a multi-contrast, segmented echo planar imaging (EPI) sequence to allow extended spatial coverage, which covers larger body regions without time penalty. Simultaneous acquisition of four slices was combined with segmented EPI for fast imaging with three gradient echo times in a preclinical perfusion study. Six female domestic pigs, German-landrace or hybrid-form, were scanned for 11 minutes respectively during administration of gadolinium-based contrast agent. Influences of reconstruction methods and training data were investigated. The separation into T1- and T2*-dependent signal contributions was achieved by fitting a standard analytical model to the acquired multi-echo data. The application of SMS yielded sufficient temporal resolution for the detection of the arterial input function in major vessels, while anatomical coverage allowed perfusion analysis of muscle tissue. The separation of the MR signal into T1- and T2*-dependent components allowed the correction of susceptibility related changes. We demonstrate a novel sequence for dynamic contrast-enhanced MRI that meets the requirements of temporal resolution (Δt < 1.5 s) and image quality. The incorporation of SMS into multi-contrast, segmented EPI can overcome existing limitations of dynamic contrast enhancement and dynamic susceptibility contrast methods, when applied separately. The new approach allows both techniques to be combined in a single acquisition with a large spatial coverage.

Highlights

  • Dynamic magnetic resonance imaging (MRI) has become an established imaging modality to assess a variety of physiological characteristics, such as tissue perfusion or metabolism in diseases [1]

  • The focus of this work is on the general method and, only the results of a single experiment are presented in detail

  • The SG kernel was based on separate Autocalibration signals (ACS) source (MB = 4) and target (SB) data with identical image contrast parameters which were acquired before contrast agent (CA) administration

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Summary

Introduction

Dynamic magnetic resonance imaging (MRI) has become an established imaging modality to assess a variety of physiological characteristics, such as tissue perfusion or metabolism in diseases [1]. Its potential to characterize tumor tissue with high image quality and reliability has made MRI the leading modality in modern radiology for this purpose [2]. Dynamic MRI techniques allow assessment of tumor perfusion and vascularization. Various methods for dynamic MRI have been proposed and have coexisted for perfusion imaging over the last decades. The two most commonly used approaches for dynamic MRI with contrast agent (CA) enhancement are: dynamic contrast enhanced imaging (DCE), which relies on the T1-weighted signal, and the dynamic susceptibility contrast (DSC) method, which measures the T2- or T2Ã-weighted signal changes over time [5]

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