Deep learning motion correction of quantitative stress perfusion cardiovascular magnetic resonance

• URL: http://arxiv.org/abs/2510.00723v1
• Date: Wed, 01 Oct 2025 09:59:48 GMT
• Title: Deep learning motion correction of quantitative stress perfusion cardiovascular magnetic resonance
• Authors: Noortje I. P. Schueler, Nathan C. K. Wong, Richard J. Crawley, Josien P. W. Pluim, Amedeo Chiribiri, Cian M. Scannell,
• Abstract summary: We developed an unsupervised deep learning-based motion correction pipeline.<n>It corrects motion in three steps and uses robust principal component analysis to reduce contrast-related effects.<n>Performance was assessed via temporal alignment and quantitative perfusion values.
• Score: 0.9195911076768547
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Background: Quantitative stress perfusion cardiovascular magnetic resonance (CMR) is a powerful tool for assessing myocardial ischemia. Motion correction is essential for accurate pixel-wise mapping but traditional registration-based methods are slow and sensitive to acquisition variability, limiting robustness and scalability. Methods: We developed an unsupervised deep learning-based motion correction pipeline that replaces iterative registration with efficient one-shot estimation. The method corrects motion in three steps and uses robust principal component analysis to reduce contrast-related effects. It aligns the perfusion series and auxiliary images (arterial input function and proton density-weighted series). Models were trained and validated on multivendor data from 201 patients, with 38 held out for testing. Performance was assessed via temporal alignment and quantitative perfusion values, compared to a previously published registration-based method. Results: The deep learning approach significantly improved temporal smoothness of time-intensity curves (p<0.001). Myocardial alignment (Dice = 0.92 (0.04) and 0.91 (0.05)) was comparable to the baseline and superior to before registration (Dice = 0.80 (0.09), p<0.001). Perfusion maps showed reduced motion, with lower standard deviation in the myocardium (0.52 (0.39) ml/min/g) compared to baseline (0.55 (0.44) ml/min/g). Processing time was reduced 15-fold. Conclusion: This deep learning pipeline enables fast, robust motion correction for stress perfusion CMR, improving accuracy across dynamic and auxiliary images. Trained on multivendor data, it generalizes across sequences and may facilitate broader clinical adoption of quantitative perfusion imaging.

Related papers

• Implicit Neural Representations of Intramyocardial Motion and Strain [5.982605533590515]
  We propose a method to predict continuous left ventricular (LV) displacement without requiring inference-time optimisation.<n> Evaluated on 452 UK Biobank test cases, our method achieved the best tracking accuracy (2.14 mm RMSE) and the lowest combined error in global circumferential (2.86%) and radial (6.42%) strain.
  arXiv  Detail & Related papers  (2025-09-10T21:05:27Z)
• A Deep Learning Based Method for Fast Registration of Cardiac Magnetic Resonance Images [8.41988616568344]
  In this thesis, a fast, volumetric registration model is proposed for the use of quantifying cardiac strain.<n>The proposed Deep Learning Neural Network (DLNN) is designed to utilize an architecture that can compute convolutions incredibly efficiently.
  arXiv  Detail & Related papers  (2025-06-23T22:06:07Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [53.2981100111204]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring.<n>Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors.<n>In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type.<n>Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers [43.17768785084301]
  We train an amortized neural posterior estimator on a newly built large dataset of cardiac simulations.<n>We incorporate elements modeling effects to better align simulated data with real-world measurements.<n>The proposed framework can further integrate in-vivo data sources to refine its predictive capabilities on real-world data.
  arXiv  Detail & Related papers  (2024-12-23T13:05:17Z)
• Attention-aware non-rigid image registration for accelerated MR imaging [10.47044784972188]
  We introduce an attention-aware deep learning-based framework that can perform non-rigid pairwise registration for fully sampled and accelerated MRI. We extract local visual representations to build similarity maps between the registered image pairs at multiple resolution levels. We demonstrate that our model derives reliable and consistent motion fields across different sampling trajectories.
  arXiv  Detail & Related papers  (2024-04-26T14:25:07Z)
• TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction [15.380659401728735]
  We propose a novel method called Temporally and Anatomically Informed Generative Adrial Network (TAI-GAN) to convert early frames into those with tracer distribution similar to the last reference frame. Our proposed method was evaluated on a clinical 82-Rb PET dataset, and the results show that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames.
  arXiv  Detail & Related papers  (2024-02-14T20:39:07Z)
• Machine Learning-based Signal Quality Assessment for Cardiac Volume Monitoring in Electrical Impedance Tomography [0.8541111605978491]
  In clinical applications, a cardiac volume signal is often of low quality, mainly because of the patient's deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient cardiac volume signals. The proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients' conditions.
  arXiv  Detail & Related papers  (2023-01-04T07:13:21Z)
• Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI [58.484353709077034]
  We propose a novel method to generate a realistic numerical phantom of myocardial microstructure. In-silico tissue models enable evaluating quantitative models of magnetic resonance imaging.
  arXiv  Detail & Related papers  (2022-08-22T22:01:44Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification [6.887389908965403]
  We propose a learning-temporal multi-task approach to obtain a complete set of measurements of cardiac left ventricle (LV) morphology. We first segment LVs using an encoder-decoder network and then introduce a framework to regress 11 LV indices and classify the cardiac phase. The proposed model is based on the 3D-temporal convolutions, which extract spatial and features from MR images.
  arXiv  Detail & Related papers  (2020-12-24T17:48:35Z)
• Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation [51.72616167073565]
  We propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a more refined motion field. We then use a novel cyclic teacher-student training strategy to make the inference end-to-end and further improve the tracking performance.
  arXiv  Detail & Related papers  (2020-06-28T21:03:19Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

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.