mlsanityData to 7 Oct 2026

Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

arXiv 2102.02711 ↗First posted 4 Feb 2021eess.IV cs.CVcited by 11

Not on the map. Why?

Abstract (arXiv)

Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised. To overcome this spatio-temporal trade-off, this research presents a super-resolution (SR) MRI reconstruction with prior knowledge based fine-tuning to maximise spatial information while reducing the required scan-time for dynamic MRIs. An U-Net based network with perceptual loss is trained on a benchmark dataset and fine-tuned using one subject-specific static high resolution MRI as prior knowledge to obtain high resolution dynamic images during the inference stage. 3D dynamic data for three subjects were acquired with different parameters to test the generalisation capabilities of the network. The method was tested for different levels of in-plane undersampling for dynamic MRI. The reconstructed dynamic SR results after fine-tuning showed higher similarity with the high resolution ground-truth, while quantitatively achieving statistically significant improvement. The average SSIM of the lowest resolution experimented during this research (6.25~% of the k-space) before and after fine-tuning were 0.939 ± 0.008 and 0.957 ± 0.006 respectively. This could theoretically result in an acceleration factor of 16, which can potentially be acquired in less than half a second. The proposed approach shows that the super-resolution MRI reconstruction with prior-information can alleviate the spatio-temporal trade-off in dynamic MRI, even for high acceleration factors.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 25 of its 77 references that are in the map. First posted on arXiv 4 Feb 2021.

Builds on

25 of 77 references.

References · 25

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  4. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  5. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  6. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  7. 2018Deep Visual Domain Adaptation: A Surveycited by 634
  8. 2016Accelerating the Super-Resolution Convolutional Neural Networkcited by 393
  9. 2017The Effectiveness of Data Augmentation in Image Classification using Deep Learningcited by 368
  10. 2019Deep Learning for Image Super-resolution: A Surveycited by 368
  11. 2018A Survey of Unsupervised Deep Domain Adaptationcited by 361
  12. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
13 more
  1. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 307
  2. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  3. 2017CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noisecited by 181
  4. 2016EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesiscited by 177
  5. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  6. 2017Deep learning for undersampled MRI reconstructioncited by 91
  7. 2018Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Networkcited by 64
  8. 2017Transfer learning for music classification and regression taskscited by 36
  9. 2017Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Codingcited by 33
  10. 2017Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolutioncited by 29
  11. 2020Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Networkcited by 12
  12. 2020DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Datacited by 11
  13. 2017Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNNcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 3; 2025: 0; 2026: 1.

11 papers.

  1. 20245%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Taskscited by 34
  2. 2023Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictionscited by 24
  3. 2023Adapter is All You Need for Tuning Visual Taskscited by 19
  4. 2021Classification of Brain Tumours in MR Images using Deep Spatiospatial Modelscited by 10
  5. 2021ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learningcited by 4
  6. 2024NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty Estimationcited by 4
  7. 2022Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imagingcited by 2
  8. 2022DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRIcited by 1
  9. 2023Reconstruction of Cardiac Cine MRI Using Motion-Guided Deformable Alignment and Multi-Resolution Fusioncited by 1
  10. 2024Segmentation of tibiofemoral joint tissues from knee MRI using MtRA-Unet and incorporating shape information: Data from the Osteoarthritis Initiativecited by 0
  11. 2026Layer Selection in Feature-Based Losses Affects Image Quality and Microstructural Consistency in Deep Learning Super-Resolution of Brain Diffusion MRIcited by 0