Abstract

Background and objectiveCompared to the conventional magnetization-prepared rapid gradient-echo imaging (MPRAGE) MRI sequence, the specialized magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) shows a higher brain tissue and lesion contrast in multiple sclerosis (MS) patients. The goal of this work is to retrospectively generate realistic-looking MP2RAGE uniform images (UNI) from already acquired MPRAGE images in order to improve the automatic lesion and tissue segmentation. MethodsFor this task we propose a generative adversarial network (GAN). Multi-contrast MRI data of 12 healthy controls and 44 patients diagnosed with MS was retrospectively analyzed. Imaging was acquired at 3T using a SIEMENS scanner with MPRAGE, MP2RAGE, FLAIR, and DIR sequences. We train the GAN with both healthy controls and MS patients to generate synthetic MP2RAGE UNI images. These images were then compared to the real MP2RAGE UNI (considered as ground truth) analyzing the output of automatic brain tissue and lesion segmentation tools. Reference-based metrics as well as the lesion-wise true and false positives, Dice coefficient, and volume difference were considered for the evaluation. Statistical differences were assessed with the Wilcoxon signed-rank test. ResultsThe synthetic MP2RAGE UNI significantly improves the lesion and tissue segmentation masks in terms of Dice coefficient and volume difference (p-values < 0.001) compared to the MPRAGE. For the segmentation metrics analyzed no statistically significant differences are found between the synthetic and acquired MP2RAGE UNI. ConclusionSynthesized MP2RAGE UNI images are visually realistic and improve the output of automatic segmentation tools.

Highlights

  • Magnetic resonance imaging (MRI) plays a crucial role in multiple sclerosis (MS) as it is the imaging technique of choice for diagnosing the disease and monitoring its progression [1]

  • Our generative approach synthetizes images that are consistent in the three planes and exhibit a visually evident increase in tissue contrast, with only a slight over-smoothing compared to the real magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE)

  • In this work we present a tailored generative adversarial network for translating magnetization-prepared rapid gradient-echo imaging (MPRAGE) images to realistic-looking MP2RAGE ones

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Summary

Introduction

Magnetic resonance imaging (MRI) plays a crucial role in multiple sclerosis (MS) as it is the imaging technique of choice for diagnosing the disease and monitoring its progression [1]. An extension of the MPRAGE is the so-called magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) [4] This specialized sequence combines two images acquired at different inversion times, creating T1-weighted uniform images (UNI) with excellent tissue contrast and self-correction for B1- bias field. Compared to the conventional magnetization-prepared rapid gradient-echo imaging (MPRAGE) MRI sequence, the specialized magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) shows a higher brain tissue and lesion contrast in multiple sclerosis (MS) patients. We train the GAN with both healthy controls and MS patients to generate synthetic MP2RAGE UNI images These images were compared to the real MP2RAGE UNI (considered as ground truth) analyzing the output of automatic brain tissue and lesion segmentation tools. Conclusion: Synthesized MP2RAGE UNI images are visually realistic and improve the output of automatic seg­ mentation tools

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