Deformable Groupwise Registration Using a Locally Low-Rank Dissimilarity
Metric for Myocardial Strain Estimation from Cardiac Cine MRI Images
- URL: http://arxiv.org/abs/2311.07348v1
- Date: Mon, 13 Nov 2023 14:06:44 GMT
- Title: Deformable Groupwise Registration Using a Locally Low-Rank Dissimilarity
Metric for Myocardial Strain Estimation from Cardiac Cine MRI Images
- Authors: Haiyang Chen, Juan Gao, and Chenxi Hu
- Abstract summary: The proposed method tracks the feature points by a groupwise registration-based two-step strategy.
Groupwise-LLR achieved more accurate tracking and strain estimation compared with other methods.
- Score: 1.1938237087895653
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Objective: Cardiovascular magnetic resonance-feature tracking (CMR-FT)
represents a group of methods for myocardial strain estimation from cardiac
cine MRI images. Established CMR-FT methods are mainly based on optical flow or
pairwise registration. However, these methods suffer from either inaccurate
estimation of large motion or drift effect caused by accumulative tracking
errors. In this work, we propose a deformable groupwise registration method
using a locally low-rank (LLR) dissimilarity metric for CMR-FT. Methods: The
proposed method (Groupwise-LLR) tracks the feature points by a groupwise
registration-based two-step strategy. Unlike the globally low-rank (GLR)
dissimilarity metric, the proposed LLR metric imposes low-rankness on local
image patches rather than the whole image. We quantitatively compared
Groupwise-LLR with the Farneback optical flow, a pairwise registration method,
and a GLR-based groupwise registration method on simulated and in vivo
datasets. Results: Results from the simulated dataset showed that Groupwise-LLR
achieved more accurate tracking and strain estimation compared with the other
methods. Results from the in vivo dataset showed that Groupwise-LLR achieved
more accurate tracking and elimination of the drift effect in late-diastole.
Inter-observer reproducibility of strain estimates was similar between all
studied methods. Conclusion: The proposed method estimates myocardial strains
more accurately due to the application of a groupwise registration-based
tracking strategy and an LLR-based dissimilarity metric. Significance: The
proposed CMR-FT method may facilitate more accurate estimation of myocardial
strains, especially in diastole, for clinical assessments of cardiac
dysfunction.
Related papers
- Robust CNN-based Respiration Rate Estimation for Smartwatch PPG and IMU [2.206233030459147]
Respiratory rate (RR) serves as an indicator of various medical conditions, such as cardiovascular diseases and sleep disorders.
Existing RR estimation methods struggle to accurately extract RR when PPG data are collected from wrist area under free-living conditions.
We propose a convolutional neural network-based approach to extract RR from PPG, accelerometer, and gyroscope signals captured via smartwatches.
arXiv Detail & Related papers (2024-01-10T15:15:46Z) - Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative
Cardiac MRI [15.778560241913674]
Co-registration of all baseline images within a quantitative cardiac MRI sequence is essential for the accuracy and precision of maps.
We propose a novel motion correction framework that decomposes quantitative cardiac MRI into low-rank and sparse components.
We show that our method effectively improved registration performance over baseline methods without introducing rPCA.
arXiv Detail & Related papers (2023-11-03T13:48:13Z) - Learning to diagnose cirrhosis from radiological and histological labels
with joint self and weakly-supervised pretraining strategies [62.840338941861134]
We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset.
We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis.
This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
arXiv Detail & Related papers (2023-02-16T17:06:23Z) - Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian
Shape Framework [65.19784967388934]
Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy.
We propose a knowledge-driven framework for RLN localization, mimicking the standard approach surgeons take to identify the RLN according to its surrounding organs.
Experimental results indicate that the proposed method achieves superior hit rates and substantially smaller distance errors compared with state-of-the-art methods.
arXiv Detail & Related papers (2022-06-30T13:04:42Z) - Domain Adaptation of Automated Treatment Planning from Computed
Tomography to Magnetic Resonance [0.5599792629509229]
We created highly acceptable Magnetic resonance only treatment plans using a CT-trained machine learning model.
clinically significant dose deviations from the CT based plans were observed.
arXiv Detail & Related papers (2022-03-07T18:18:00Z) - DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on
Cardiac Tagging Magnetic Resonance Images [10.434681088538866]
We propose a novel deep learning-based fully unsupervised method for in vivo motion tracking on t-MRI images.
Our method has been validated on a representative clinical t-MRI dataset.
arXiv Detail & Related papers (2021-03-04T00:42:11Z) - fMRI-Kernel Regression: A Kernel-based Method for Pointwise Statistical
Analysis of rs-fMRI for Population Studies [0.5459797813771498]
Cross-subject comparison is challenging due to the spontaneous nature of resting-state fMRI (rs-fMRI) signals.
Here we describe an approach that measures pairwise distances between the synchronized rs-fMRI signals of pairs of subjects.
We also present a method for fMRI data comparison that leverages this generated pairwise feature to establish a radial basis function kernel matrix.
arXiv Detail & Related papers (2020-12-13T05:53:53Z) - Statistical control for spatio-temporal MEG/EEG source imaging with
desparsified multi-task Lasso [102.84915019938413]
Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques.
The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge.
We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
arXiv Detail & Related papers (2020-09-29T21:17:16Z) - Improved Slice-wise Tumour Detection in Brain MRIs by Computing
Dissimilarities between Latent Representations [68.8204255655161]
Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods.
We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder.
We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
arXiv Detail & Related papers (2020-07-24T14:02:09Z) - Residual-driven Fuzzy C-Means Clustering for Image Segmentation [152.609322951917]
We elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation.
Built on this framework, we present a weighted $ell_2$-norm fidelity term by weighting mixed noise distribution.
The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over existing FCM-related algorithms.
arXiv Detail & Related papers (2020-04-15T15:46:09Z) - Modal Regression based Structured Low-rank Matrix Recovery for
Multi-view Learning [70.57193072829288]
Low-rank Multi-view Subspace Learning has shown great potential in cross-view classification in recent years.
Existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously.
We propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy.
arXiv Detail & Related papers (2020-03-22T03:57:38Z)
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.