Abstract

Magnetic resonance imaging radio frequency arrays are composed of multiple receive coils that have their signals combined to form an image. Combination requires an estimate of the radio frequency coil sensitivities to align signal phases and prevent destructive interference. At lower fields this can be accomplished using a uniform physical reference coil. However, at higher fields, uniform volume coils are lacking and, when available, suffer from regions of low receive sensitivity that result in poor sensitivity estimation and combination. Several approaches exist that do not require a physical reference coil but require manual intervention, specific prescans, or must be completed post-acquisition. This makes these methods impractical for large multi-volume datasets such as those collected for novel types of functional MRI or quantitative susceptibility mapping, where magnitude and phase are important. This pilot study proposes a fitted SVD method which utilizes existing combination methods to create a phase sensitive combination method targeted at large multi-volume datasets. This method uses any multi-image prescan to calculate the relative receive sensitivities using voxel-wise singular value decomposition. These relative sensitivities are fitted to the solid harmonics using an iterative least squares fitting algorithm. Fits of the relative sensitivities are used to align the phases of the receive coils and improve combination in subsequent acquisitions during the imaging session. This method is compared against existing approaches in the human brain at 7 Tesla by examining the combined data for the presence of singularities and changes in phase signal-to-noise ratio. Two additional applications of the method are also explored, using the fitted SVD method in an asymmetrical coil and in a case with subject motion. The fitted SVD method produces singularity-free images and recovers between 95–100% of the phase signal-to-noise ratio depending on the prescan data resolution. Using solid harmonic fitting to interpolate singular value decomposition derived receive sensitivities from existing prescans allows the fitted SVD method to be used on all acquisitions within a session without increasing exam duration. Our fitted SVD method is able to combine imaging datasets accurately without supervision during online reconstruction.

Highlights

  • Using phase as a contrast has been a subject of interest since the development of magnetic resonance imaging (MRI)

  • The fitted SVD method relies on three hyperparameters: the thresholds for the signal-to-noise ratio (SNR)-based masks during minimax phase correction and solid harmonic fitting as well as the order of the solid harmonic basis

  • In large multi-volume imaging datasets, such as those acquired for functional MRI (fMRI) or fQSM, inline combination becomes vital as the computational load for exporting uncombined data can be prohibitive

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Summary

Introduction

Using phase as a contrast has been a subject of interest since the development of magnetic resonance imaging (MRI). Improvements in MRI technology and techniques has led to increased popularity of these applications, and has resulted in the development of many novel techniques that use complex data, such as functional MRI (fMRI) analysis [6,7,8] and the development of functional QSM [9] These novel functional applications require the collection of large time series datasets where both the magnitude and phase data are analyzed. The current defaults provided by MRI systems are not always optimized for phase datasets and additional coil combination methods may be required [10] One such example is the default combination for phase fMRI images which is complex sum on many systems, such as the CMRR Multiband EPI sequence on Siemens systems prior to 2017 [11]. This is true for functional MRI data sets which are routinely large due to their multivolume nature

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