Abstract
ObjectiveTo develop and evaluate a framework for simulating low-field proton-density weighted MRI acquisitions based on high-field acquisitions, which could be used to predict the minimum B0 field strength requirements for MRI techniques. This framework would be particularly useful in the evaluation of de-noising and constrained reconstruction techniques.Materials and MethodsGiven MRI raw data, lower field MRI acquisitions can be simulated based on the signal and noise scaling with field strength. Certain assumptions are imposed for the simulation and their validity is discussed. A validation experiment was performed using a standard resolution phantom imaged at 0.35 T, 1.5 T, 3 T, and 7 T. This framework was then applied to two sample proton-density weighted MRI applications that demonstrated estimation of minimum field strength requirements: real-time upper airway imaging and liver proton-density fat fraction measurement.ResultsThe phantom experiment showed good agreement between simulated and measured images. The SNR difference between simulated and measured was ≤ 8% for the 1.5T, 3T, and 7T cases which utilized scanners with the same geometry and from the same vendor. The measured SNR at 0.35T was 1.8- to 2.5-fold less than predicted likely due to unaccounted differences in the RF receive chain. The predicted minimum field strength requirements for the two sample applications were 0.2 T and 0.3 T, respectively.ConclusionsUnder certain assumptions, low-field MRI acquisitions can be simulated from high-field MRI data. This enables prediction of the minimum field strength requirements for a broad range of MRI techniques.
Highlights
Magnetic resonance imaging (MRI) is one of the most powerful imaging modalities, and has had a significant impact on healthcare [1]
Low-field MRI acquisitions can be simulated from high-field MRI data
We provide phantom validation of this framework, and provide two illustrative examples of how to predict minimum field strength requirements
Summary
Magnetic resonance imaging (MRI) is one of the most powerful imaging modalities, and has had a significant impact on healthcare [1]. MRI has two notable limitations, cost and speed. These are highly relevant in an era where rising healthcare costs [2] have placed greater pressure on determining and optimizing the cost-effectiveness of imaging for specific clinical questions. Standard clinical MRI (1.5 T/3 T) has proven to be cost-prohibitive for many potential screening and preventative medicine applications. Even for diagnostic applications, achieving better image quality without improving outcomes, at the expense of reducing access due to high cost, can be only counterproductive [3]. Low-field MRI ( 0.5 T) can be much less expensive while still maintaining equivalent diagnostic values for certain applications, as demonstrated by Rutt et al [4]
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