scholarly journals Motion correction for myocardial T1 mapping using image registration with synthetic image estimation

2011 ◽  
Vol 67 (6) ◽  
pp. 1644-1655 ◽  
Author(s):  
Hui Xue ◽  
Saurabh Shah ◽  
Andreas Greiser ◽  
Christoph Guetter ◽  
Arne Littmann ◽  
...  
Keyword(s):  
Image Registration ◽  
Motion Correction ◽  
T1 Mapping ◽  
Synthetic Image ◽  
Image Estimation ◽  
Myocardial T1

Motion correction for myocardial T1 mapping using image registration with synthetic image estimation

2012 ◽  
Vol 67 (6) ◽  
pp. spcone-spcone ◽  
Author(s):  
Hui Xue ◽  
Saurabh Shah ◽  
Andreas Greiser ◽  
Christoph Guetter ◽  
Arne Littmann ◽  
...  
Keyword(s):  
Image Registration ◽  
Motion Correction ◽  
T1 Mapping ◽  
Synthetic Image ◽  
Image Estimation ◽  
Myocardial T1

scholarly journals Deep-Learning based Motion Correction for Myocardial T1 Mapping

2021 ◽  
Author(s):  
Dar Arava ◽  
Mohammad Masarwy ◽  
Samah Khawaled ◽  
Moti Freiman
Keyword(s):  
Deep Learning ◽  
Motion Correction ◽  
T1 Mapping ◽  
Myocardial T1

Robust motion correction for myocardial T1 and extracellular volume mapping by principle component analysis-based groupwise image registration

2017 ◽  
Vol 47 (5) ◽  
pp. 1397-1405 ◽  
Author(s):  
Qian Tao ◽  
Pieternel van der Tol ◽  
Floris F. Berendsen ◽  
Elisabeth H.M. Paiman ◽  
Hildo J. Lamb ◽  
...  
Keyword(s):  
Image Registration ◽  
Principle Component Analysis ◽  
Motion Correction ◽  
Extracellular Volume ◽  
Component Analysis ◽  
Principle Component ◽  
Myocardial T1

Abstract 17448: A Novel Technique for Respiratory Motion Correction in Rapid Left Ventricular Myocardial T1 Mapping and Quantitative Analysis of Myocardial Fibrosis

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Keigo Kawaji ◽  
Marco Marino ◽  
Akiko Tanaka ◽  
Giacomo Tarroni ◽  
Takeyoshi Ota ◽  
...  
Keyword(s):  
Myocardial Fibrosis ◽  
Motion Correction ◽  
T1 Mapping ◽  
Objective Evaluation ◽  
Left Ventricular ◽  
Myocardial Tissue ◽  
Affine Motion ◽  
Noise Characteristics ◽  
T1 Map ◽  
Myocardial T1

Introduction: Quantitative myocardial T1 mapping is increasingly being used to measure myocardial fibrosis, but this approach requires effective breath-holds during MRI. Respiratory artifacts from poor breath-holds result in motion-corrupted pixels and measurement error. We developed and tested the feasibility of an approach that applies motion correction (MC) followed by semi-automated segmentation to obtain motion-free T1 maps of the LV. Methods: Modified Look-Locker Inversion Recovery (MOLLI) data was acquired on 1.5T MRI scanner, where the endo- and epicardial borders were semi-automatically detected using noise characteristics of myocardial tissue [1,2] and followed by fully automated partitioning into AHA-defined segments [1]. Affine motion correction was then applied to each segment to generate MC-T1 maps of the heart. This approach was tested on 24 slices (12 before contrast injection [PRE]; 12 post [POST], 96x2 ROI segments) from 4 swine with no LV abnormality. The same segmented ROIs on T1 maps without MC were also assessed for comparison. Results: The standard deviation of T1 within each ROI became significantly lower after MC: [MC vs non-MC: 94 ± 37 vs 114 ± 51 ms (PRE, p<0.00005); 66 ± 51 vs 89 ± 67 ms (POST, p<0.0005)], suggesting less motion blurring and possibly less error in T1 measurements within each generated ROI (fig). Significant changes were observed in POST T1 values (446 ± 66 vs 435 ± 89ms; p < 0.0005), yielding an average increase of 2.6 ± 1.6% per segment. The inferior (+3.9%) and inferiolateral segments (+4.5%) yielded the most change, corresponding to regions with most motion across MOLLI images as assessed visually. PRE T1 changes were also significant (998 ± 94 vs 1008 ± 114 ms; p < 0.05). Conclusions: Our new semi-automated and motion-corrected T1 map assessment shows promise to improve the accuracy of T1 measurements but needs further validation in a larger dataset. This technique may become useful for objective evaluation of myocardial fibrosis.

scholarly journals MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks

2021 ◽  
Vol 8 ◽  
Author(s):  
Ricardo A. Gonzales ◽  
Qiang Zhang ◽  
Bartłomiej W. Papież ◽  
Konrad Werys ◽  
Elena Lukaschuk ◽  
...  
Keyword(s):  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
Image Registration ◽  
Range Of Motion ◽  
Motion Correction ◽  
T1 Mapping ◽  
Motion Artefacts ◽  
Baseline Method ◽  
Clinical Interpretation ◽  
Wide Range

Background: Quantitative cardiovascular magnetic resonance (CMR) T1 mapping has shown promise for advanced tissue characterisation in routine clinical practise. However, T1 mapping is prone to motion artefacts, which affects its robustness and clinical interpretation. Current methods for motion correction on T1 mapping are model-driven with no guarantee on generalisability, limiting its widespread use. In contrast, emerging data-driven deep learning approaches have shown good performance in general image registration tasks. We propose MOCOnet, a convolutional neural network solution, for generalisable motion artefact correction in T1 maps.Methods: The network architecture employs U-Net for producing distance vector fields and utilises warping layers to apply deformation to the feature maps in a coarse-to-fine manner. Using the UK Biobank imaging dataset scanned at 1.5T, MOCOnet was trained on 1,536 mid-ventricular T1 maps (acquired using the ShMOLLI method) with motion artefacts, generated by a customised deformation procedure, and tested on a different set of 200 samples with a diverse range of motion. MOCOnet was compared to a well-validated baseline multi-modal image registration method. Motion reduction was visually assessed by 3 human experts, with motion scores ranging from 0% (strictly no motion) to 100% (very severe motion).Results: MOCOnet achieved fast image registration (&lt;1 second per T1 map) and successfully suppressed a wide range of motion artefacts. MOCOnet significantly reduced motion scores from 37.1±21.5 to 13.3±10.5 (p &lt; 0.001), whereas the baseline method reduced it to 15.8±15.6 (p &lt; 0.001). MOCOnet was significantly better than the baseline method in suppressing motion artefacts and more consistently (p = 0.007).Conclusion: MOCOnet demonstrated significantly better motion correction performance compared to a traditional image registration approach. Salvaging data affected by motion with robustness and in a time-efficient manner may enable better image quality and reliable images for immediate clinical interpretation.

scholarly journals T1 mapping-derived signature of myocardial involvement in idiopathic inflammatory myopathy compared to acute viral myocarditis: a texture-based analysis

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
E Bollache ◽  
AT Huber ◽  
J Lamy ◽  
E Afari ◽  
TM Bacoyannis ◽  
...  
Keyword(s):  
Contrast Agent ◽  
Texture Analysis ◽  
T1 Mapping ◽  
Inflammatory Myopathy ◽  
Viral Myocarditis ◽  
Texture Features ◽  
Idiopathic Inflammatory Myopathy ◽  
Post Contrast ◽  
Myocardial T1 ◽  
Myocardial Involvement

Abstract Funding Acknowledgements Type of funding sources: None. Background. Recent studies revealed the ability of MRI T1 mapping to characterize myocardial involvement in both idiopathic inflammatory myopathy (IIM) and acute viral myocarditis (AVM), as compared to healthy controls. However, neither myocardial T1 nor T2 maps were able to discriminate between IIM and AVM patients, when considering conventional myocardial mean values and derived indices such as lambda and extracellular volume. Purpose. To investigate the ability of T1 mapping-derived texture analysis to differentiate IIM from AVM. Methods. Forty patients, 20 with IIM (51 ± 17 years, 9 men) and 20 with AVM (34 ± 13 years, 16 men) underwent 1.5T MRI T1 mapping using a modified Look-Locker inversion-recovery sequence before and 15 minutes after injection of a gadolinium contrast agent. After manual delineation of endocardial and epicardial borders and co-registration of all inversion time images, native and post-contrast T1 maps were estimated. Myocardial texture analysis was performed on native T1 maps. Textural features such as: autocorrelation, contrast, dissimilarity, energy and sum entropy were used to build a least squares-based linear regression model. Finally, receiver operating characteristic (ROC) analysis was used to investigate the ability of such texture features score to classify IIM vs. AVM patients, compared to the performance of mean myocardial T1. A Wilcoxon rank-sum test was also used to test difference significance between groups. Results. Both native and post-contrast mean myocardial T1 values were comparable between IIM (native: 1022 ± 43 ms; post-contrast: 319 ± 44 ms) and AVM (1056 ± 59 ms, p = 0.07; 318 ± 35 ms, p = 0.90, respectively) groups. Results of ROC analyses are provided in the Table, indicating that a better discrimination between IIM and AVM patients was obtained when using texture features, with higher AUC and accuracy than mean T1 values (Figure). Conclusion. Texture analysis derived from MRI T1 maps without contrast agent injection was able to discriminate between IIM and AVM with higher accuracy, sensitivity and specificity than conventional T1 indices. Such analysis could provide a useful myocardial signature to help diagnose and manage cardiac alterations associated with IIM in patients presenting with myocarditis and primarily suspected of AVM. Table Area under curve (AUC) Accuracy Sensitivity Specificity Native T1 0.67 0.70 0.65 0.75 Post-contrast T1 0.49 0.60 0.25 0.95 Texture features score 0.85 0.82 0.90 0.75 ROC analyses for classification between IIM and AVM patients Abstract Figure

scholarly journals Imaging sequence for joint myocardial T1 mapping and fat/water separation

2018 ◽  
Vol 81 (1) ◽  
pp. 486-494 ◽  
Author(s):  
Maryam Nezafat ◽  
Shiro Nakamori ◽  
Tamer A. Basha ◽  
Ahmed S. Fahmy ◽  
Thomas Hauser ◽  
...  
Keyword(s):  
T1 Mapping ◽  
Water Separation ◽  
Imaging Sequence ◽  
Myocardial T1

scholarly journals Myocardial T1 Mapping: Techniques and Potential Applications

Radiographics ◽  
2014 ◽  
Vol 34 (2) ◽  
pp. 377-395 ◽  
Author(s):  
Jeremy R. Burt ◽  
Stefan L. Zimmerman ◽  
Ihab R. Kamel ◽  
Marc Halushka ◽  
David A. Bluemke
Keyword(s):  
T1 Mapping ◽  
Potential Applications ◽  
Myocardial T1 ◽  
Mapping Techniques
Sign in / Sign up

Export Citation Format

Share Document