PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
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Abstract (arXiv)
In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Nov 2016.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2; 2025: 2.
9 papers.
- 2023Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRIcited by 10
- 2022CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRIcited by 6
- 2022AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRIcited by 5
- 2024SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Datacited by 5
- 2025From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstructioncited by 1
- 2021Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age predictioncited by 0
- 2023Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Modelcited by 0
- 2024A Unified Deep Learning Framework for Motion Correction in Medical Imagingcited by 0
- 2025SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstructioncited by 0
- 2025From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction19 Jun 2025
- 2025SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction23 May 2025
- 2024A Unified Deep Learning Framework for Motion Correction in Medical Imaging21 Sep 2024
- 2024SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data23 Aug 2024
- 2023Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI5 Dec 2023
- 2023Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model16 Oct 2023
- 2022CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI17 May 2022
- 2022AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI12 May 2022
- 2021Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction29 Oct 2021