MICCAI 2022 Challenge (Quad22)
The LPi is organizing an official Challenge inside MICCAI 2022: "Quality augmentation in diffusion MRI for clinical studies: Validation in migraine" (QuaD22). More information in the official webpage [here].
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 will 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 (dMRI) 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.
We have selected migraine considering that it is a pathology in which differences between groups are subtle and very dependent on the number of gradient directions.