Challenge Abstract

Deep Learning (DL) techniques have been used in medical imaging to improve quality and generate new images from reduced medical imaging acquisitions. They have implied a true revolution in the medical field, with myriads of new applications rising every year. We cannot deny the excellent outcomes these applications produce, with high-quality images and compelling results. However, when applied to medical images, most of the validation of these techniques has been done visually and/or qualitatively, not necessarily adequately assessed in clinical studies. There is a key question that may affect many of the DL applications in medical studies: “are we losing relevant quantitative clinical information when generating high-quality images with artificial intelligence techniques?”. The question is related to the validity of traditional quality measures such as the Peak Signal-to- Noise Ratio (PSNR), Structural Similarity Index (SSIM) or Normalized Root Mean Squared Error (NRMSE), commonly used in medical image analysis. Strictly speaking, it is not enough that the images look alike as they must also preserve all the relevant clinical information.

In this challenge, we try to answer the question about the validity of reconstructed images in a real clinical study. To that end, we will focus on a real diffusion magnetic resonance imaging (MRI) study on migraine. Data were acquired for a clinical study carried out in a local hospital (Hospital Clinico Universitario, Valladolid, Spain) by a group of neurologists.

Migraine is a primary disabling disorder characterized by recurrent episodes of headache that usually last 4-72 hours. It is more widespread among young and middle-aged women. Despite the high prevalence of migraine, its pathophysiological mechanisms are not well known, and there are no biomarkers currently. Two types of migraine are currently distinguished: episodic migraine (EM) and chronic migraine (CM). This classification criterion is based exclusively on the number of headache days per month (15 or more days with headache per month for chronic migraine patients). The unique, relevant radiological findings in migraine are white matter hyperintensities observed through T2-weighted images, and their role is unclear. The advantage of migraine in a challenge like the present one is that MRI findings related to diffusion MRI are subtle compared to healthy controls, according to previous studies. In severe disorders such as Alzheimer’s disease or schizophrenia, it is relatively easy to find statistically significant results with classic methods (i.e. Diffusion Tensor Imaging, T1-, T2-weighted MR imaging), and thus it is challenging to appreciate techniques or parameters that can better define pathophysiological properties. There are some diffusion MRI studies assessing migraine. Diffusion Tensor Imaging (DTI) has been the most employed technique to evaluate microstructural properties with differences found between controls and migraine patients (MP) and between EM and CM patients for DTI-related scalars like fractional anisotropy (FA), mean diffusion (MD) and Axial Diffusivity (AD).

With these features in mind, the principal purpose of our challenge is to validate if those DTI-based parameters generated from low-quality data directly or DL-based augmented data are able to replicate the statistical findings that appear when using standard quality data (i.e., if part of the relevant quantitative information is missing). Thus, we can validate the usefulness of DL-based reconstruction techniques in real clinical studies.

We have selected the migraine problem due to the following reasons:

  1. We have been collecting a large and unique database of migraine patients with a proper acquisition scheme.
  2. We have already confirmed the brain regions where the statistically significant differences occur with the fully-sampled data, and we keep these regions as the "silver standard" for reduced acquisitions.
  3. We collaborate with neurologists, specialized in headache disorders that assist in interpreting the data.
  4. The findings in migraine are subtle. If we reduce the number of subjects in the study or the number or gradient directions in the acquisition protocol, the differences are deeply reduced. Thus, this database is excellent to test the importance of deep learning methods. Since some of the differences are missing, the question arises: can powerful deep learning methods recover them?

Currently, we are working with 160 volumes, all including a unique q-space coverage scheme that enables us to easily subsample the data by merely selecting appropriate 21 gradient directions out of 61 without the need of applying interpolation algorithms. With this database, we can control the preprocessing pipeline and compare the quantitative measures obtained from the DTI under a reduced acquisition scheme to those estimated from fully- sampled data.

Challenge keywords

diffusion-weighted MRI, diffusion tensor MRI, data augmentation, deep learning, migraine, neurology, neuroimaging

Publication and future plans

We plan to publish a paper in a top-tier journal (e.g. Medical Image Analysis, Neuroimage, or Neuroimage: Clinical) presenting the results from the Challenge and further recommendations in diffusion MRI data augmentation techniques for clinical studies.

Up to three members per team indicated by the team leader qualify as the paper's authors from the challenge. The top 6 ranked teams qualify as the authors following the criteria:

  1. The results show a minimum of quality.
  2. 100% of the data is properly processed.
  3. The method follows a scientific procedure to obtain the results



Laboratorio de Procesado de Imagen
E.T.S.I. Telecomunicación
Campus Miguel Delibes
Universidad de Valladolid
47011, Valladolid, Spain