Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

Santiago Aja-Fernández*, Carmen Martín-Martín, Álvaro Planchuelo-Gómez, Abrar Faiyaz, Md Nasir Uddin, Giovanni Schifitto, Abhishek Tiwari, Saurabh J. Shigwan, Rajeev Kumar Singh, Tianshu Zheng, Zuozhen Cao, Dan Wu, Stefano B. Blumberg, Snigdha Sen, Tobias Goodwin-Allcock, Paddy J. Slator, Mehmet Yigit Avci, Zihan Li, Berkin Bilgic, Qiyuan TianXinyi Wang, Zihao Tang, Mariano Cabezas, Amelie Rauland, Dorit Merhof, Renata Manzano Maria, Vinícius Paraníba Campos, Tales Santini, Marcelo Andrade da Costa Vieira, Seyyed Kazem HashemizadehKolowri, Edward DiBella, Chenxu Peng, Zhimin Shen, Zan Chen, Irfan Ullah, Merry Mani, Hesam Abdolmotalleby, Samuel Eckstrom, Steven H. Baete, Patryk Filipiak, Tanxin Dong, Qiuyun Fan, Rodrigo de Luis-García, Antonio Tristán-Vega, Tomasz Pieciak

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
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Abstract

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

Original languageEnglish
Article number103483
Pages (from-to)1-17
Number of pages17
JournalNeuroImage: Clinical
Volume39
DOIs
Publication statusPublished - Jan 2023
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • angular resolution
  • artificial intelligence
  • deep learning
  • diffusion MRI
  • diffusion tensor
  • machine learning

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