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Self-Supervised Super-Resolution for Anisotropic MR Images with and Without Slice Gap. | LitMetric

AI Article Synopsis

  • Magnetic resonance (MR) imaging often uses thicker slices and gaps between them to decrease scan time and improve quality, but this can affect the accuracy of volumetric analyses and 3D methods.
  • Many existing super-resolution techniques haven't effectively tackled the issue of slice gaps, and data-driven approaches can struggle with inconsistencies in imaging conditions.
  • This study presents a self-supervised super-resolution method that works with both isotropic and anisotropic MR images, demonstrating enhanced performance in signal recovery and related tasks across two open-source datasets, with code available online.

Article Abstract

Magnetic resonance (MR) images are often acquired as multi-slice volumes to reduce scan time and motion artifacts while improving signal-to-noise ratio. These slices often are thicker than their in-plane resolution and sometimes are acquired with gaps between slices. Such thick-slice image volumes (possibly with gaps) can impact the accuracy of volumetric analysis and 3D methods. While many super-resolution (SR) methods have been proposed to address thick slices, few have directly addressed the slice gap scenario. Furthermore, data-driven methods are sensitive to domain shift due to the variability of resolution, contrast in acquisition, pathology, and differences in anatomy. In this work, we propose a self-supervised SR technique to address anisotropic MR images with and without slice gap. We compare against competing methods and validate in both signal recovery and downstream task performance on two open-source datasets and show improvements in all respects. Our code publicly available at https://gitlab.com/iacl/smore.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11613142PMC
http://dx.doi.org/10.1007/978-3-031-44689-4_12DOI Listing

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