Deep Learning for MRI Slice Interpolation: The Critical Role of Problem Formulation

• URL: http://arxiv.org/abs/2605.16476v1
• Date: Fri, 15 May 2026 16:04:13 GMT
• Title: Deep Learning for MRI Slice Interpolation: The Critical Role of Problem Formulation
• Abstract summary: Through-plane resolution in clinical MRI is typically much coarser than in-plane resolution, limiting diagnostic utility.<n>This work investigates deep learning approaches to interpolate intermediate MRI slices in prostate imaging, effectively doubling through-plane resolution.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Through-plane resolution in clinical MRI is typically much coarser than in-plane resolution, limiting diagnostic utility. This work investigates deep learning approaches to interpolate intermediate MRI slices in prostate imaging, effectively doubling through-plane resolution. I evaluated five architectures (CNN, U-Net, two GAN variants, and DDPM) and discovered that problem formulation has dramatically more impact than architectural complexity. By reformulating the interpolation task to use adjacent slices (i-1, i+1) rather than distant slices (i-2, i+2), I achieved a 58% improvement in SSIM performance across all deterministic architectures. The U-Net model achieved the best results with PSNR of 30.08 dB and SSIM of 0.898, representing a 10.1% improvement over linear interpolation baseline. A DDPM was also evaluated but showed poor reconstruction quality due to fundamental mismatch between stochastic generation and deterministic reconstruction requirements. These findings demonstrate that problem formulation can have 290x more impact than architectural sophistication in medical imaging tasks.

Related papers

• On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation [0.6303112417588329]
  This study systematically evaluates how individual MRI sequences influence model robustness across two well-known datasets.<n>A ResUNet-based framework is employed, where each modality is trained independently to isolate its effect.<n>Results show that the T2f/FLAIR sequence achieves the best cross-dataset performance, with Dice scores exceeding 75%.
  arXiv  Detail & Related papers  (2026-08-30T18:17:18Z)
• Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts [56.74856091904914]
  We propose a unified framework combining parameter-informed contrast disentanglement with severity-aware adaptive correction.<n>ScanCLIP derives contrast embeddings from acquisition parameters to disentangle contrast style from anatomical content, yielding contrast-free features.<n>A Vision Transformer then estimates motion severity and routes features through a Mixture-of-Experts network, enabling targeted artifact correction.
  arXiv  Detail & Related papers  (2026-05-29T02:48:23Z)
• D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities [54.87947751720332]
  We propose a novel segmentation model D3Seg to maintain stable performance under missing-modality settings.<n>The proposed model achieves approximately 1.5-2.0% Dice improvement on enhancing tumor (ET) and around 1.0% on tumor core (TC) across multiple missing modality configurations.
  arXiv  Detail & Related papers  (2026-05-21T09:55:42Z)
• A Systematic Benchmark of GAN Architectures for MRI-to-CT Synthesis [38.31064949197421]
  Generative Adversarial Network (GAN) architectures have been proposed for MRI-to-CT translation.<n>We present a benchmark of ten GAN evaluated on the SynthRAD2025 dataset across three anatomical districts.
  arXiv  Detail & Related papers  (2026-03-13T18:54:20Z)
• Revisiting Global Token Mixing in Task-Dependent MRI Restoration: Insights from Minimal Gated CNN Baselines [43.505945728449774]
  Global token mixing has become a popular model design choice for MRI restoration.<n>We ask whether global token mixing is actually beneficial in each individual task across three representative settings.<n>For accelerated MRI reconstruction, the minimal unrolled gated-CNN baseline is already highly competitive.<n>For super-resolution, where low-frequency k-space data are largely preserved by the controlled low-pass degradation, local gated models remain competitive.<n>For denoising with pronounced spatially heteroscedastic noise, token-mixing models achieve the strongest overall performance.
  arXiv  Detail & Related papers  (2026-03-02T04:57:52Z)
• Cancer-Net PCa-MultiSeg: Multimodal Enhancement of Prostate Cancer Lesion Segmentation Using Synthetic Correlated Diffusion Imaging [55.62977326180104]
  Current deep learning approaches for prostate cancer lesion segmentation achieve limited performance.<n>We investigate synthetic correlated diffusion imaging (CDI$s$) as an enhancement to standard diffusion-based protocols.<n>Our results establish validated integration pathways for CDI$s$ as a practical drop-in enhancement for PCa lesion segmentation tasks.
  arXiv  Detail & Related papers  (2025-11-11T04:16:12Z)
• A Novel Multi-branch ConvNeXt Architecture for Identifying Subtle Pathological Features in CT Scans [1.2461503242570642]
  This paper introduces a novel multi-branch ConvNeXt architecture designed specifically for the nuanced challenges of medical image analysis.<n>The proposed model incorporates a rigorous end-to-end pipeline, from meticulous data preprocessing to augmentation to a disciplined two-phase training strategy.<n> Experimental results demonstrate a superior performance on the validation set, achieving a final ROC-AUC of 0.9937, a validation accuracy of 0.9757, and an F1-score of 0.9825 for COVID-19 cases.
  arXiv  Detail & Related papers  (2025-10-10T08:00:46Z)
• Improved mmFormer for Liver Fibrosis Staging via Missing-Modality Compensation [8.687370165870613]
  We propose a multimodal MRI classification model based on the mmFormer architecture with an adaptive module for handling arbitrary combinations of missing modalities.<n>This method is evaluated on the test set of Comprehensive Analysis & Computing of REal-world medical images (CARE 2025 challenge)<n>For Cirrhosis Detection and Substantial Fibrosis Detection on in-distribution vendors, our model obtains accuracies of 66.67%, and 74.17%, and corresponding area under the curve (AUC) scores of 71.73% and 68.48%, respectively.
  arXiv  Detail & Related papers  (2025-09-19T21:31:05Z)
• Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge [20.058385954540082]
  The PENGWIN challenge aimed to advance automated fracture segmentation.<n>The top-performing CT algorithm achieved an average fragment-wise intersection over union (IoU) of 0.930, demonstrating satisfactory accuracy.<n>The best algorithm attained an IoU of 0.774, highlighting the greater challenges posed by overlapping anatomical structures.
  arXiv  Detail & Related papers  (2025-04-03T08:19:36Z)
• DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction [45.00528216648563]
  Diffusion Prior Driven Neural Representation (DPER) is an unsupervised framework designed to address the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into data fidelity and distribution prior sub-problems. We conduct comprehensive experiments to evaluate the performance of DPER on LACT and ultra-SVCT reconstruction with two public datasets.
  arXiv  Detail & Related papers  (2024-04-27T12:55:13Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.