1. Correction of bias field artifact in T1w MR images of the thigh and calf muscles Correction de contraste d’images IRM Projet A

2. Context – Bioclinica The leader in clinical trial management solutions Clinical trial service provider, worldwide The company offers industry-leading: Medical imaging services, Medical imaging Enterprise eClinical technologies,eClinical Clinical research centers and cardiovascular safety cardiovascular safety Solutions that bring quality and efficiency to every phase of clinical development. BioClinica's experience spans three decades and includes thousands of studies in all therapeutic areas. The company serves more than 400 pharmaceutical, biotechnology, and device organizations – including all of the top 20 – through a network of offices in the U.S., Europe and Asia. (www.bioclinica.com)

3. Context – Medical endpoint Muscular dystrophy is characterized by bilateral, progressive muscle weakness, muscle fiber necrosis and muscle infiltration by fatty tissue. Magnetic Resonance Imaging (MRI) is an ideal method for identifying areas of muscle atrophy and fatty infiltration. Medical endpoints Skeletal muscle volume (SM) Subcutaneous adipose tissue (SAT) volume Intermuscular adipose tissue (IMAT) volume Normal subject Subject with fatty infiltration of peroneal muscles SAT Bone and marrow IMATSM

4. Context – Current limitations Automatic assessment of muscle and fat volumes is corrupted with Bias field artifact Smooth variation of intensities Could corrupt segmentation quality

5. Proposed work Implement a technique To correct for bias field artifact in T1w MRI images of the thigh and the calf AND to improve segmentation accuracy (if enough time)

6. Validation Data 5 T1w MRI images of the thigh Raw, N3 corrected image and related manual segmentation (from 1 slice) 5 T1w MRI images of the calf Raw, N3 corrected image and related manual segmentation (from 1 slice) Statistical analysis Bias field correction: Coefficient of variation per tissue based on manual segmentation Segmentation accuracy: Dice value per tissue based on manual segmentation Acceptance criteria Bias field correction is improved with the new technique Segmentation accuracy is improved with the new technique

7. References Purushwalkam et al., Automatic Segmentation of Adipose Tissue from Thigh Magnetic Resonance Images, ICIAR 2013 Orgiu et al., Automatic muscle and fat segmentation in the thigh from T1-Weighted MRI, JMRI 2015