MPRAGE to MP2RAGE UNI translation via generative adversarial network improves the automatic tissue and lesion segmentation in multiple sclerosis patients

BACKGROUND AND OBJECTIVE 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. 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. METHODS For 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. RESULTS The 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. CONCLUSION Synthesized MP2RAGE UNI images are visually realistic and improve the output of automatic segmentation tools.

[1]  Mário João Fartaria,et al.  Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE , 2020, NeuroImage: Clinical.

[2]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[3]  Bjoern H Menze,et al.  Deep-Learning Generated Synthetic Double Inversion Recovery Images Improve Multiple Sclerosis Lesion Detection , 2020, Investigative radiology.

[4]  Danna Zhou,et al.  d. , 1840, Microbial pathogenesis.

[5]  P. Alam,et al.  H , 1887, High Explosives, Propellants, Pyrotechnics.

[6]  O. Ciccarelli,et al.  MRI CRITERIA FOR THE DIAGNOSIS OF MULTIPLE SCLEROSIS: MAGNIMS CONSENSUS GUIDELINES , 2016, The Lancet Neurology.

[7]  Jean-Philippe Thiran,et al.  Automated detection of white matter and cortical lesions in early stages of multiple sclerosis , 2016, Journal of magnetic resonance imaging : JMRI.

[8]  Guang Yang,et al.  DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction , 2018, IEEE Transactions on Medical Imaging.

[9]  J. Mugler,et al.  Three‐dimensional magnetization‐prepared rapid gradient‐echo imaging (3D MP RAGE) , 1990, Magnetic resonance in medicine.

[10]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[11]  Jiri Matas,et al.  DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[12]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[13]  Yuhua Chen,et al.  MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better , 2020, ArXiv.

[14]  Reto Meuli,et al.  MP2RAGE Multiple Sclerosis Magnetic Resonance Imaging at 3 T , 2012, Investigative radiology.

[15]  P. Alam.  ‘A’ , 2021, Composites Engineering: An A–Z Guide.

[16]  P. Alam.  ‘L’ , 2021, Composites Engineering: An A–Z Guide.

[17]  Reto Meuli,et al.  Accelerated MP2RAGE imaging using Cartesian phyllotaxis readout and compressed sensing reconstruction , 2020, Magnetic resonance in medicine.

[18]  Jeffrey L. Gunter,et al.  Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks , 2018, SASHIMI@MICCAI.

[19]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[21]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[22]  Yang Lei,et al.  MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks. , 2019, Medical physics.

[23]  P. Alam.  ‘T’ , 2021, Composites Engineering: An A–Z Guide.

[24]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[25]  N. Hattori,et al.  Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation , 2019, American Journal of Neuroradiology.

[26]  Tobias Kober,et al.  MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field , 2010, NeuroImage.

[27]  Sandeep Kakde,et al.  A Brief Review on: MRI Images Reconstruction using GAN , 2019, 2019 International Conference on Communication and Signal Processing (ICCSP).

[28]  Bernhard Schölkopf,et al.  EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[29]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[30]  Meritxell Bach Cuadra,et al.  Segmentation of Cortical and Subcortical Multiple Sclerosis Lesions Based on Constrained Partial Volume Modeling , 2017, MICCAI.

[31]  P. Alam.  ‘W’ , 2021, Composites Engineering.

[32]  Alexis Roche,et al.  Classical segmentation methods on novel MR imaging: a study of brain tissue segmentation of MP2RAGE vs MPRAGE , 2013 .

[33]  Tsutomu Okada,et al.  MP2RAGE for deep gray matter measurement of the brain: A comparative study with MPRAGE , 2016, Journal of magnetic resonance imaging : JMRI.

[34]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[35]  Brian B. Avants,et al.  An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data , 2011, Neuroinformatics.

[36]  Jong Chul Ye,et al.  Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks , 2020, Nature Machine Intelligence.

[37]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[38]  David H. Miller,et al.  Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria , 2017, The Lancet Neurology.

[39]  Joaquim Salvi,et al.  Multiple Sclerosis Lesion Synthesis in MRI Using an Encoder-Decoder U-NET , 2019, IEEE Access.