bibtext: @InProceedings{ Raj_Death_MICCAI2024,
                   author = { Rajput, Junaid R. and Weinmueller, Simon and Endres, Jonathan and Dawood, Peter and Knoll, Florian and Maier, Andreas and Zaiss, Moritz },
                   title = { { Death by Retrospective Undersampling - Caveats and Solutions for Learning-Based MRI Reconstructions } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
abstract: This study challenges the validity of retrospective undersampling
  in MRI data science by analysis via an MRI physics simulation. We
  demonstrate that retrospective undersampling, a method often used to
  create training data for reconstruction models, can inherently alter MRI
  signals from their prospective counterparts. This arises from the sequential
  nature of MRI acquisition, where undersampling post-acquisition
  effectively alters the MR sequence and the magnetization dynamic in a
  non-linear fashion. We show that even in common sequences, this effect
  can make learning-based reconstructions unreliable. Our simulation provides
  both, (i) a tool for generating accurate prospective undersampled
  datasets for analysis of such effects, or for MRI training data augmentation,
  and (ii) a differentiable reconstruction operator that models undersampling
  correctly. The provided insights are crucial for the development
  and evaluation of AI-driven acceleration of diagnostic MRI tools.
title: Death by Retrospective Undersampling - Caveats and Solutions for Learning-Based MRI Reconstructions
authors: Rajput, Junaid R. Weinmueller, Simon Endres, Jonathan Dawood, Peter Knoll, Florian Maier, Andreas Zaiss, Moritz