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Single-Subject Multi-View MRI Super-Resolution via Implicit Neural Representations

Heejong Kim, Abhishek Thanki, Roel van Herten, Daniel Margolis, Mert R Sabuncu

Abstract

Clinical MRI frequently acquires anisotropic volumes with high in-plane resolution and low through-plane resolution to reduce acquisition time. Multiple orientations are therefore acquired to provide complementary anatomical information. Conventional integration of these views relies on registration followed by interpolation, which can degrade fine structural details. Recent deep learning-based super-resolution (SR) approaches have demonstrated strong performance in enhancing single-view images. However, their clinical reliability is often limited by the need for large-scale training datasets, resulting in increased dependence on cohort-level priors. Self-supervised strategies offer an alternative by learning directly from the target scans. Prior work either neglects the existence of multi-view information or assumes that in-plane information can supervise through-plane reconstruction under the assumption of pre-alignment between images. However, this assumption is rarely satisfied in clinical settings. In this work, we introduce Single-Subject Implicit Multi-View Super-Resolution for MRI (SIMS-MRI), a framework that operates solely on anisotropic multi-view scans from a single patient without requiring pre- or post-processing. Our method combines a multi-resolution hash-encoded implicit representation with learned inter-view alignment to generate a spatially consistent isotropic reconstruction. We validate the SIMS-MRI pipeline on both simulated brain and clinical prostate MRI datasets. Code will be made publicly available for reproducibility: https://github.com/abhshkt/SIMS-MRI

Single-Subject Multi-View MRI Super-Resolution via Implicit Neural Representations

Abstract

Clinical MRI frequently acquires anisotropic volumes with high in-plane resolution and low through-plane resolution to reduce acquisition time. Multiple orientations are therefore acquired to provide complementary anatomical information. Conventional integration of these views relies on registration followed by interpolation, which can degrade fine structural details. Recent deep learning-based super-resolution (SR) approaches have demonstrated strong performance in enhancing single-view images. However, their clinical reliability is often limited by the need for large-scale training datasets, resulting in increased dependence on cohort-level priors. Self-supervised strategies offer an alternative by learning directly from the target scans. Prior work either neglects the existence of multi-view information or assumes that in-plane information can supervise through-plane reconstruction under the assumption of pre-alignment between images. However, this assumption is rarely satisfied in clinical settings. In this work, we introduce Single-Subject Implicit Multi-View Super-Resolution for MRI (SIMS-MRI), a framework that operates solely on anisotropic multi-view scans from a single patient without requiring pre- or post-processing. Our method combines a multi-resolution hash-encoded implicit representation with learned inter-view alignment to generate a spatially consistent isotropic reconstruction. We validate the SIMS-MRI pipeline on both simulated brain and clinical prostate MRI datasets. Code will be made publicly available for reproducibility: https://github.com/abhshkt/SIMS-MRI
Paper Structure (13 sections, 3 figures, 2 tables)

This paper contains 13 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Method
  3. SIMS-MRI
  4. Phase 1: Initial Implicit Image Representation.
  5. Phase 2: Implicit Image Registration.
  6. Phase 3: Multi-view Fusion using Both Views.
  7. Inference: Isotropic Reconstruction.
  8. Implementation Details.
  9. Experiments
  10. Simulated Two-View Brain MRI
  11. Real-world Clinical Prostate MRI
  12. Discussion and Conclusion

Figures (3)

  • Figure 1: Overview of the proposed three-phase coordinate-based multi-view MRI fusion framework. Phase 1: A continuous implicit image representation model with multi-resolution hash encoding, $f_\theta$, is trained on a reference (axial) view to map 3D world coordinates to voxel intensities. Phase 2: A SIREN-based registration networkwolterink2022implicit$g_\phi$ learns a dense displacement field that aligns the second (coronal) view to the reference coordinate space while keeping $f_\theta$ fixed. Phase 3: The implicit image representation is fine-tuned using both the reference and registered views for information fusion. Inference: The learned continuous representation is queried on an isotropic 3D grid to generate a super-resolved volume, enabling reconstruction of unseen slice orientations.
  • Figure 2: Qualitative results for the unseen view in simulated two-view brain MRI. The first row presents a comparison across methods, and the second row shows a zoomed-in view of the highlighted region. Yellow and green arrows indicate areas where SIMS-MRI achieves superior structural detail reconstruction.
  • Figure 3: Results for prostate MRI. The violin plots show the MONAI metric (MONAI: Medical Open Network for Artificial Intelligence model-based perceptual metric). Each row corresponds to a representative subject, selected from slices near the median of the distribution shown in the violin plots (color-coded and marked in the plots).