# Semi-automated approach for quality control and harmonization of large multiparametric and multicenter MRI databases: methodology and application

Marc Saghiah¹, Jean-Côme Douteau², Alice Bressand³, Sophie Ancelet³, Michel Dojat¹,², Benjamin Lemasson¹

¹ Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
² Université Grenoble Alpes, Inria, Grenoble, France
³ PSE-SANTE-SESANE/LEPID, Autorité de Sureté Nucléaire et de Radioprotection, Fontenay-Aux-Roses, France

<figure><img src="image_1.png" /></figure>

anr®
21-CE45-0038

## RADI AIDE

Gliomas are rare brain tumors, but the most common in adults. Every year, hundreds of thousands of new cases occur worldwide, and many die. Recently, the survival rate has been increasing thanks to a combination of surgery, chemotherapy and radiotherapy(1). But radiotherapy can cause damage to healthy tissue, as well as neurocognitive disorders(2). To study this, the EPIBRAINRAD multicentric cohort has been set up, since 2015, by IRSN, AP-HP and ICANS, with 261 glioma patients, containing thousands of images. For each patient, follow-up includes MRIs, CTs, clinical evaluation and neuropsychological assessments.

RADIO-AIDE(3) (Radiation-induced neurotoxicity assessed by spatio-temporal modelling combined with AI after brain radiotherapy) studies the neurotoxic mechanisms involved in the initiation and evolution of cognitive disorders after radiotherapy. Within this project, we are involved in the development of an automated pipeline for the extraction and quantification of biomarkers relevant to patient monitoring. More specifically, we are exploring efficient methods for segmenting MRI scans acquired at different times and locations, from non-harmonized protocols. In a multi-center context, differences between scanners can lead to differences in the images acquired, known as the "center effect". Harmonization helps to correct this center effect.

### Challenge: clinical data curation and determine if there is a center effect on our data and correct it through harmonization

<figure><img src="image_2.png" /><figcaption>### Results: MR image quality control</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 1: Quality control results</figcaption></figure>

The analysis was carried out for the 4 contrasts separately, for the images available on Shanoir. In total, we have ~95% of images that are correct and usable, and ~5% of images to be deleted.

<figure><img src="image_4.png" /><figcaption>Fig. 2: Segmentation results for whole tumor label</figcaption></figure>

For each contrast, from left to right: image, tumor ROI, healthy brain ROI, contour overlay. Example for 4 different images in our dataset. We trained 4 different segmentation models for each contrast separately, due to missing contrasts in multiple patients. As the healthy brain tissue is more homogeneous within images than tumor tissue, this is the ROI from which radiomics are extracted.

<figure><img src="image_5.png" /><figcaption>Fig. 4: Example of harmonization for one feature</figcaption></figure>

Box plots and density graphs of an example feature before and after harmonization. Before harmonization, we can see a center effect with the large variability for our feature example for the different machines. Harmonization has corrected this center effect.

<figure><img src="image_6.png" /><figcaption>Fig. 5: PCA of the different machines before and after harmonization</figcaption></figure>

One point corresponds to one machine. Harmonization has corrected the center effect observed in our data. There are no longer many differences, making the data more homogeneous.

### Next steps

- Full automatization of our pipeline

- Feature selection: which are the most significant ?

- Predict neurological disorders by exploring radiomics in a given brain area

- Harmonize images (not only radiomic features) to improve automatic deep learning classification and segmentation (9)

<footnote>(1) Ricard, D., et al. (2012). Primary brain tumours in adults. *The Lancet*, 379(9830), 1984-1996.</footnote>

<footnote>(2) Source 2 Glioma. (2024). Nature Reviews Disease Primers, 10(1).</footnote>

<footnote>(3) RADIO-AIDE: projet ANR-21-CE45-0038</footnote>

<footnote>(4) Barillot, C., et al. (2016). Shanoir: Applying the Software as a Service Distribution Model to Manage Brain Imaging Research Repositories. Frontiers in ICT, 3.</footnote>

<footnote>(5) Sadri, A. R., et al. (2020). Technical Note: MRQy - An open-source tool for quality control of MR imaging data. *Medical physics*, 47(12), 6029-6038.</footnote>

<footnote>(6) Myronenko, A. (2018). 3D MRI brain tumor segmentation using autoencoder regularization. arXiv.org.</footnote>

<footnote>(7) Van Griethuysen, J. J., et al. (2017). Computational Radiomics System to Decode the Radiographic Phenotype. *Cancer Research*, 77(21), e104-e107.</footnote>

<footnote>(8) Fortin, J., et al. (2017). Harmonization of multi-site diffusion tensor imaging data. *NeuroImage*, 161, 149-170.</footnote>

<footnote>(9) Cackowski, S., et al. (2023). ImUnity: A generalizable VAE-GAN solution for multicenter MR image harmonization. *Medical Image Analysis*, 88, 102799.</footnote>