École Polytechnique Fédérale de Lausanne

Author Of 1 Presentation

Machine Learning/Network Science Poster Presentation

P0017 - Translating MPRAGE to MP2RAGE improves the automatic tissue and lesion segmentation in Multiple Sclerosis patients (ID 766)

Speakers
Presentation Number
P0017
Presentation Topic
Machine Learning/Network Science

Abstract

Background

Compared to the conventional magnetization-prepared rapid gradient-echo imaging (MPRAGE) MRI, magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) (Marques, J. P., 2010) shows a higher brain tissue and lesion contrast in multiple sclerosis (MS) patients. This specialized sequence is, however, mainly limited to research settings and not widely acquired in clinical routine.

Objectives

To synthesize realistic-looking MP2RAGE images from MPRAGE acquisitions via generative adversarial network (GAN) and verify if these improve the performance of automatic MS lesion and brain tissue segmentation tools.

Methods

We propose a GAN inspired by the pix2pix framework (Isola, P., 2018) which takes as input 2D slices of 3D MPRAGE images and generates a synthetic MP2RAGE (synMP2RAGE). Differently from pix2pix, the generator of the GAN combines three loss functions: a pixel-wise L1 loss, an adversarial loss, and a perceptual loss. Our framework is trained on 12 healthy controls and 8 MS patients and tested on 36 MS patients, for which an expert manually delineated cortical and white matter lesions. Imaging was performed with a 3T MRI scanner (Siemens Healthcare, Erlangen, Germany) with a 1x1x1.2 mm resolution. Evaluation is performed with reference-based metrics and through automatic segmentation of MS lesions (La Rosa, F., 2020) and brain tissue (Avants, B.B., 2011).

Results

Considering as reference the acquired MP2RAGE, synMP2RAGE achieves a peak signal-to-noise ratio of 31.39, normalized root mean square error of 0.13, and structural similarity index of 0.98, overperforming the MPRAGE (29.49, 0.17, 0.97, respectively) for all metrics. Compared to the initial MPRAGE it also significantly improves the lesion and tissue segmentation masks in terms of the Dice coefficient and volume difference (p-values < 0.001). On the contrary, no significant differences between the real and synMP2RAGE are found in the patient-wise comparison of the lesions’ segmentation (p-values > 0.05), whereas they are significant between MPRAGE and MP2RAGE (p-value < 0.001).

Conclusions

Our proposed framework successfully translates MPRAGE to MP2RAGE, synthesizing realistic-looking images which improve the performance of automatic segmentation tools tested on MS patients. In accordance with previous claims (Finck, T., 2020), these results confirm that GANs can be helpful in the automatic analysis of MRI images.

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