mlsanityData to 7 Oct 2026

A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI

arXiv 2410.21602 ↗First posted 28 Oct 2024physics.med-ph cs.LG eess.IVcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

We present the first acquisition-agnostic diffusion generative model for Magnetic Resonance Imaging (MRI) in the neonatal intensive care unit (NICU) to solve a range of inverse problems for shortening scan time and improving motion robustness. In-NICU MRI scanners leverage permanent magnets at lower field-strengths (i.e., below 1.5 Tesla) for non-invasive assessment of potential brain abnormalities during the critical phase of early live development, but suffer from long scan times and motion artifacts. In this setting, training data sizes are small and intrinsically suffer from low signal-to-noise ratio (SNR). This work trains a diffusion probabilistic generative model using such a real-world training dataset of clinical neonatal MRI by applying several novel signal processing and machine learning methods to handle the low SNR and low quantity of data. The model is then used as a statistical image prior to solve various inverse problems at inference time without requiring any retraining. Experiments demonstrate the generative model's utility for three real-world applications of neonatal MRI: accelerated reconstruction, motion correction, and super-resolution.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 8 of its 21 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

8 of 21 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Accelerated Motion Correction with Deep Generative Diffusion Models1 Nov
  2. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 6

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  4. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  5. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  6. 2019Noisier2Noise: Learning to Denoise from Unpaired Noisy Datacited by 125

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Citing papers per year: 2025: 1.

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  1. 2025Deep learning of personalized priors from past MRI scans enables fast, quality-enhanced point-of-care MRI with low-cost systemscited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2025Diffusion Probabilistic Generative Models for Accelerated, in-NICU Permanent Magnet Neonatal MRI
  2. 2025MRIQT: Physics-Aware Diffusion Model for Image Quality Transfer in Neonatal Ultra-Low-Field MRI
  3. 2025Deep generative priors for 3D brain analysis
  4. 2026Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuning
  5. 2026Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models
  6. 2024Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI
  7. 2022Brain Imaging Generation with Latent Diffusion Models
  8. 2025Pathological MRI Segmentation by Synthetic Pathological Data Generation in Fetuses and Neonates