SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data
<|reference_start|>SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data: Magnetic resonance imaging (MRI) is crucial in diagnosing various abdominal conditions and anomalies. Traditional MRI scans often yield anisotropic data due to technical constraints, resulting in varying resolutions across spatial dimensions, which limits diagnostic accuracy and volumetric analysis. Super-resolution (SR) techniques aim to address these limitations by reconstructing isotropic high-resolution images from anisotropic data. However, current SR methods often rely on indirect mappings and limited training data, focusing mainly on two-dimensional improvements rather than achieving true three-dimensional isotropy. We introduce SIMPLE, a Simultaneous Multi-Plane Self-Supervised Learning approach for isotropic MRI restoration from anisotropic data. Our method leverages existing anisotropic clinical data acquired in different planes, bypassing the need for simulated downsampling processes. By considering the inherent three-dimensional nature of MRI data, SIMPLE ensures realistic isotropic data generation rather than solely improving through-plane slices. This approach flexibility allows it to be extended to multiple contrast types and acquisition methods commonly used in clinical settings. Our experiments show that SIMPLE outperforms state-of-the-art methods both quantitatively using the Kernel Inception Distance (KID) and semi-quantitatively through radiologist evaluations. The generated isotropic volume facilitates more accurate volumetric analysis and 3D reconstructions, promising significant improvements in clinical diagnostic capabilities.<|reference_end|>
@article{benisty2024simple:,
    title={SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic
  MRI Restoration from Anisotropic Data},
    author={Rotem Benisty, Yevgenia Shteynman, Moshe Porat, Anat Illivitzki, Moti
  Freiman},
    journal={arXiv preprint arXiv:2408.13065},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13065},
    primaryClass={eess.IV cs.CV}
}