1
|
Kofler A, Kerkering KM, Goschel L, Fillmer A, Kolbitsch C. Quantitative MR Image Reconstruction Using Parameter-Specific Dictionary Learning With Adaptive Dictionary-Size and Sparsity-Level Choice. IEEE Trans Biomed Eng 2024; 71:388-399. [PMID: 37540614 DOI: 10.1109/tbme.2023.3300090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2023]
Abstract
OBJECTIVE We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI). METHODS Because different quantitative parameter-maps differ from each other in terms of local features, we propose a method where the employed dictionary learning (DL) and sparse coding (SC) algorithms automatically estimate the optimal dictionary-size and sparsity level separately for each parameter-map. We evaluated the method on a T1-mapping QMRI problem in the brain using the BrainWeb data as well as in-vivo brain images acquired on an ultra-high field 7 T scanner. We compared it to a model-based acceleration for parameter mapping (MAP) approach, other sparsity-based methods using total variation (TV), Wavelets (Wl), and Shearlets (Sh) to a method which uses DL and SC to reconstruct qualitative images, followed by a non-linear (DL+Fit). RESULTS Our algorithm surpasses MAP, TV, Wl, and Sh in terms of RMSE and PSNR. It yields better or comparable results to DL+Fit by additionally significantly accelerating the reconstruction by a factor of approximately seven. CONCLUSION The proposed method outperforms the reported methods of comparison and yields accurate T1-maps. Although presented for T1-mapping in the brain, our method's structure is general and thus most probably also applicable for the the reconstruction of other quantitative parameters in other organs. SIGNIFICANCE From a clinical perspective, the obtained T1-maps could be utilized to differentiate between healthy subjects and patients with Alzheimer's disease. From a technical perspective, the proposed unsupervised method could be employed to obtain ground-truth data for the development of data-driven methods based on supervised learning.
Collapse
|
2
|
Zhu Y, Wang C, Su S, Qiu Z, Yan Z, Liang D, Wang Y, Wang H. High resolution single-shot myocardial imaging using bSSFP with wave encoding. Med Phys 2023; 50:7039-7048. [PMID: 37219842 DOI: 10.1002/mp.16471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 04/03/2023] [Accepted: 04/03/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Single-shot balanced steady-state free precession (bSSFP) sequence is widely used in cardiac imaging. However, the limited scan time in one heartbeat greatly hinders its spatial resolution compared to the segmented acquisition mode. Therefore, a highly accelerated single-shot bSSFP imaging technology is needed for clinical use. PURPOSE To develop and evaluate a wave-encoded bSSFP sequence with high acceleration rates for single-shot myocardial imaging. METHODS The proposed Wave-bSSFP method is implemented by adding a sinusoidal wave gradient in the phase encoding direction during the readout of bSSFP sequence. Uniform undersampling is used for acceleration. Its performance was first validated via phantom studies by comparison with conventional bSSFP. Then it was evaluated in volunteer studies via anatomical imaging, T2 -prepared bSSFP, and T1 mapping in in-vivo cardiac imaging. All methods were compared with accelerated conventional bSSFP reconstructed using iterative SENSE and compressed sensing (CS), to demonstrate the advantage of wave encoding in suppressing the noise amplification and artifacts induced by acceleration. RESULTS The proposed Wave-bSSFP method achieved a high acceleration factor of 4 for single-shot acquisitions. The proposed method showed lower average g-factors than bSSFP, and fewer blurring artifacts than CS reconstruction. The Wave-bSSFP with R = 4 achieved higher spatial and temporal resolutions compared with the conventional bSSFP with R = 2 in several applications such as T2 -prepared bSSFP and T1 mapping, and could be applied in systolic imaging. CONCLUSION Wave encoding can be used to highly accelerate 2D bSSFP imaging with single-shot acquisitions. Compared with the conventional bSSFP sequence, the proposed Wave-bSSFP method can effectively reduce the g-factor and aliasing artifacts in cardiac imaging.
Collapse
Affiliation(s)
- Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Che Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Department of Biomedical Engineering, Chongqing University of Technology, Chongqing, China
| | - Shi Su
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhilang Qiu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhonghong Yan
- Department of Biomedical Engineering, Chongqing University of Technology, Chongqing, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yining Wang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haifeng Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| |
Collapse
|
3
|
Sheagren CD, Cao T, Patel JH, Chen Z, Lee HL, Wang N, Christodoulou AG, Wright GA. Motion-compensated T 1 mapping in cardiovascular magnetic resonance imaging: a technical review. Front Cardiovasc Med 2023; 10:1160183. [PMID: 37790594 PMCID: PMC10542904 DOI: 10.3389/fcvm.2023.1160183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/22/2023] [Indexed: 10/05/2023] Open
Abstract
T 1 mapping is becoming a staple magnetic resonance imaging method for diagnosing myocardial diseases such as ischemic cardiomyopathy, hypertrophic cardiomyopathy, myocarditis, and more. Clinically, most T 1 mapping sequences acquire a single slice at a single cardiac phase across a 10 to 15-heartbeat breath-hold, with one to three slices acquired in total. This leaves opportunities for improving patient comfort and information density by acquiring data across multiple cardiac phases in free-running acquisitions and across multiple respiratory phases in free-breathing acquisitions. Scanning in the presence of cardiac and respiratory motion requires more complex motion characterization and compensation. Most clinical mapping sequences use 2D single-slice acquisitions; however newer techniques allow for motion-compensated reconstructions in three dimensions and beyond. To further address confounding factors and improve measurement accuracy, T 1 maps can be acquired jointly with other quantitative parameters such as T 2 , T 2 ∗ , fat fraction, and more. These multiparametric acquisitions allow for constrained reconstruction approaches that isolate contributions to T 1 from other motion and relaxation mechanisms. In this review, we examine the state of the literature in motion-corrected and motion-resolved T 1 mapping, with potential future directions for further technical development and clinical translation.
Collapse
Affiliation(s)
- Calder D. Sheagren
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Tianle Cao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, CA, United States
| | - Jaykumar H. Patel
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Zihao Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, CA, United States
| | - Hsu-Lei Lee
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, CA, United States
| | - Graham A. Wright
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| |
Collapse
|
4
|
Li Y, Wu C, Qi H, Si D, Ding H, Chen H. Motion correction for native myocardial T 1 mapping using self-supervised deep learning registration with contrast separation. NMR IN BIOMEDICINE 2022; 35:e4775. [PMID: 35599351 DOI: 10.1002/nbm.4775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/15/2022] [Accepted: 05/18/2022] [Indexed: 06/15/2023]
Abstract
In myocardial T1 mapping, undesirable motion poses significant challenges because uncorrected motion can affect T1 estimation accuracy and cause incorrect diagnosis. In this study, we propose and evaluate a motion correction method for myocardial T1 mapping using self-supervised deep learning based registration with contrast separation (SDRAP). A sparse coding based method was first proposed to separate the contrast component from T1 -weighted (T1w) images. Then, a self-supervised deep neural network with cross-correlation (SDRAP-CC) or mutual information as the registration similarity measurement was developed to register contrast separated images, after which signal fitting was performed on the motion corrected T1w images to generate motion corrected T1 maps. The registration network was trained and tested in 80 healthy volunteers with images acquired using the modified Look-Locker inversion recovery (MOLLI) sequence. The proposed SDRAP was compared with the free form deformation (FFD) registration method regarding (1) Dice similarity coefficient (DSC) and mean boundary error (MBE) of myocardium contours, (2) T1 value and standard deviation (SD) of T1 fitting, (3) subjective evaluation score for overall image quality and motion artifact level, and (4) computation time. Results showed that SDRAP-CC achieved the highest DSC of 85.0 ± 3.9% and the lowest MBE of 0.92 ± 0.25 mm among the methods compared. Additionally, SDRAP-CC performed the best by resulting in lower SD value (28.1 ± 17.6 ms) and higher subjective image quality scores (3.30 ± 0.79 for overall quality and 3.53 ± 0.68 for motion artifact) evaluated by a cardiologist. The proposed SDRAP took only 0.52 s to register one slice of MOLLI images, achieving about sevenfold acceleration over FFD (3.7 s/slice).
Collapse
Affiliation(s)
- Yuze Li
- Center for Biomedical Imaging Research (CBIR), School of Medicine, Tsinghua University, Beijing, China
| | - Chunyan Wu
- Center for Biomedical Imaging Research (CBIR), School of Medicine, Tsinghua University, Beijing, China
| | - Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Dongyue Si
- Center for Biomedical Imaging Research (CBIR), School of Medicine, Tsinghua University, Beijing, China
| | - Haiyan Ding
- Center for Biomedical Imaging Research (CBIR), School of Medicine, Tsinghua University, Beijing, China
| | - Huijun Chen
- Center for Biomedical Imaging Research (CBIR), School of Medicine, Tsinghua University, Beijing, China
| |
Collapse
|
5
|
Guo R, El-Rewaidy H, Assana S, Cai X, Amyar A, Chow K, Bi X, Yankama T, Cirillo J, Pierce P, Goddu B, Ngo L, Nezafat R. Accelerated cardiac T 1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T 1 estimation approach. J Cardiovasc Magn Reson 2022; 24:6. [PMID: 34986850 PMCID: PMC8734349 DOI: 10.1186/s12968-021-00834-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/30/2021] [Indexed: 11/24/2022] Open
Abstract
PURPOSE To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses fully connected neural networks (FCNN) to estimate T1 values from four T1-weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4). METHOD We implemented an FCNN for MyoMapNet to estimate T1 values from a reduced number of T1-weighted images and corresponding inversion-recovery times. We studied MyoMapNet performance when trained using native, post-contrast T1, or a combination of both. We also explored the effects of number of T1-weighted images (four and five) for native T1. After rigorous training using in-vivo modified Look-Locker inversion recovery (MOLLI) T1 mapping data of 607 patients, MyoMapNet performance was evaluated using MOLLI T1 data from 61 patients by discarding the additional T1-weighted images. Subsequently, we implemented a prototype MyoMapNet and LL4 on a 3 T scanner. LL4 was used to collect T1 mapping data in 27 subjects with inline T1 map reconstruction by MyoMapNet. The resulting T1 values were compared to MOLLI. RESULTS MyoMapNet trained using a combination of native and post-contrast T1-weighted images had excellent native and post-contrast T1 accuracy compared to MOLLI. The FCNN model using four T1-weighted images yields similar performance compared to five T1-weighted images, suggesting that four T1 weighted images may be sufficient. The inline implementation of LL4 and MyoMapNet enables successful acquisition and reconstruction of T1 maps on the scanner. Native and post-contrast myocardium T1 by MOLLI and MyoMapNet was 1170 ± 55 ms vs. 1183 ± 57 ms (P = 0.03), and 645 ± 26 ms vs. 630 ± 30 ms (P = 0.60), and native and post-contrast blood T1 was 1820 ± 29 ms vs. 1854 ± 34 ms (P = 0.14), and 508 ± 9 ms vs. 514 ± 15 ms (P = 0.02), respectively. CONCLUSION A FCNN, trained using MOLLI data, can estimate T1 values from only four T1-weighted images. MyoMapNet enables myocardial T1 mapping in four heartbeats with similar accuracy as MOLLI with inline map reconstruction.
Collapse
Affiliation(s)
- Rui Guo
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Hossam El-Rewaidy
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Salah Assana
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Xiaoying Cai
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
- Siemens Medical Solutions USA, Inc, Boston, MA, USA
| | - Amine Amyar
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Kelvin Chow
- Siemens Medical Solutions USA, Inc, Chicago, IL, USA
| | - Xiaoming Bi
- Siemens Medical Solutions USA, Inc, Chicago, IL, USA
| | - Tuyen Yankama
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Julia Cirillo
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Patrick Pierce
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Beth Goddu
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Long Ngo
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA.
| |
Collapse
|
6
|
Zhou R, Weller DS, Yang Y, Wang J, Jeelani H, Mugler JP, Salerno M. Dual-excitation flip-angle simultaneous cine and T 1 mapping using spiral acquisition with respiratory and cardiac self-gating. Magn Reson Med 2021; 86:82-96. [PMID: 33590591 PMCID: PMC8849625 DOI: 10.1002/mrm.28675] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 12/18/2020] [Accepted: 12/19/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE To develop a free-breathing cardiac self-gated technique that provides cine images and B1+ slice profile-corrected T1 maps from a single acquisition. METHODS Without breath-holding or electrocardiogram gating, data were acquired continuously on a 3T scanner using a golden-angle gradient-echo spiral pulse sequence, with an inversion RF pulse applied every 4 seconds. Flip angles of 3° and 15° were used for readouts after the first four and second four inversions. Self-gating cardiac triggers were extracted from heart image navigators, and respiratory motion was corrected by rigid registration on each heartbeat. Cine images were reconstructed from the steady-state portion of 15° readouts using a low-rank plus sparse reconstruction. The T1 maps were fit using a projection onto convex sets approach from images reconstructed using slice profile-corrected dictionary learning. This strategy was evaluated in a phantom and 14 human subjects. RESULTS The self-gated signal demonstrated close agreement with the acquired electrocardiogram signal. The image quality for the proposed cine images and standard clinical balanced SSFP images were 4.31 (±0.50) and 4.65 (±0.30), respectively. The slice profile-corrected T1 values were similar to those of the inversion-recovery spin-echo technique in phantom, and had a higher global T1 value than that of MOLLI in human subjects. CONCLUSIONS Cine and T1 mapping using spiral acquisition with respiratory and cardiac self-gating successfully acquired T1 maps and cine images in a single acquisition without the need for electrocardiogram gating or breath-holding. This dual-excitation flip-angle approach provides a novel approach for measuring T1 while accounting for B1+ and slice profile effect on the apparent T1∗ .
Collapse
Affiliation(s)
- Ruixi Zhou
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
| | - Daniel S. Weller
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Junyu Wang
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
| | - Haris Jeelani
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - John P. Mugler
- Radiology & Medical Imaging, Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
| | - Michael Salerno
- Cardiology, Radiology & Medical Imaging, Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
| |
Collapse
|
7
|
Su S, Qiu Z, Luo C, Shi C, Wan L, Zhu Y, Li Y, Liu X, Zheng H, Liang D, Wang H. Accelerated 3D bSSFP Using a Modified Wave-CAIPI Technique With Truncated Wave Gradients. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:48-58. [PMID: 32886608 DOI: 10.1109/tmi.2020.3021737] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The Wave Controlled Aliasing In Parallel Imaging (Wave-CAIPI) technique manifests great potential to highly accelerate three-dimensional (3D) balanced steady-state free precession (bSSFP) through substantially reducing the geometric factor (g-factor) and aliasing artifacts of image reconstruction. However, severe banding artifacts appear in bSSFP imaging due to unbalanced gradients with nonzero 0th moment applied by the conventional Wave-CAIPI technique. In this study, we propose a 3D Wave-bSSFP scheme that adopts truncated wave gradients with zero 0th moment to avoid introducing additional banding artifacts and to maintain the advantages of wave encoding. The simulation results indicate that the number of wave cycles that are truncated and different options of applying wave gradients affect both the g-factor reduction and image quality, but the influence is limited. In phantom experiments, the proposed technique shows similar acceleration performance as the conventional Wave-CAIPI technique and effectively eliminates its introduced banding artifacts. Additionally, Wave-bSSFP obtains up to 12× retrospective acceleration at 0.8 mm isotropic resolution in in vivo 3D brain experiments and is superior to the state-of-the-art Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration (CAIPIRINHA) technique, according to both visual validation and quantitative analysis. Moreover, in vivo 3D spine and abdomen imaging demonstrate the potential clinical applications of Wave-bSSFP with fast acquisition speed, improved isotropic resolution and fine image quality.
Collapse
|
8
|
Kucukseymen S, Neisius U, Rodriguez J, Tsao CW, Nezafat R. Negative synergism of diabetes mellitus and obesity in patients with heart failure with preserved ejection fraction: a cardiovascular magnetic resonance study. Int J Cardiovasc Imaging 2020; 36:2027-2038. [PMID: 32533279 DOI: 10.1007/s10554-020-01915-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 06/05/2020] [Indexed: 12/11/2022]
Abstract
In patients with heart failure with preserved ejection fraction (HFpEF), diabetes mellitus (DM) and obesity are important comorbidities as well as major risk factors. Their conjoint impact on the myocardium provides insight into the HFpEF aetiology. We sought to investigate the association between obesity, DM, and their combined effect on alterations in the myocardial tissue in HFpEF patients. One hundred and sixty-two HFpEF patients (55 ± 12 years, 95 men) and 45 healthy subjects (53 ± 12 years, 27 men) were included. Patients were classified according to comorbidity prevalence (36 obese patients without DM, 53 diabetic patients without obesity, and 73 patients with both). Myocardial remodeling, fibrosis, and longitudinal contractility were quantified with cardiovascular magnetic resonance imaging using cine and myocardial native T1 images. Patients with DM and obesity had impaired global longitudinal strain (GLS) and increased myocardial native T1 compared to patients with only one comorbidity (DM + Obesity vs. DM and Obesity; GLS, - 15 ± 2.1 vs - 16.5 ± 2.4 and - 16.7 ± 2.2%; native T1, 1162 ± 37 vs 1129 ± 25 and 1069 ± 29 ms; P < 0.0001 for all). A negative synergistic effect of combined obesity and DM prevalence was observed for native T1 (np2 = 0.273, p = 0.002) and GLS (np2 = 0.288, p < 0.0001). Additionally, severity of insulin resistance was associated with GLS (R = 0.590, P < 0.0001), and native T1 (R = 0.349, P < 0.0001). The conjoint effect of obesity and DM in HFpEF patients is associated with diffuse myocardial fibrosis and deterioration in GLS. The negative synergistic effects observed on the myocardium may be related to severity of insulin resistance.
Collapse
Affiliation(s)
- Selcuk Kucukseymen
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA
| | - Ulf Neisius
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA
| | - Jennifer Rodriguez
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA
| | - Connie W Tsao
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
| |
Collapse
|
9
|
Becker KM, Blaszczyk E, Funk S, Nuesslein A, Schulz‐Menger J, Schaeffter T, Kolbitsch C. Fast myocardial T
1
mapping using cardiac motion correction. Magn Reson Med 2019; 83:438-451. [DOI: 10.1002/mrm.27935] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 07/11/2019] [Accepted: 07/16/2019] [Indexed: 01/05/2023]
Affiliation(s)
- Kirsten M. Becker
- Physikalisch‐Technische Bundesanstalt (PTB) Braunschweig and Berlin Germany
| | - Edyta Blaszczyk
- Charité Medical Faculty University Medicine Berlin Germany
- Working Group on Cardiovascular Magnetic Resonance Experimental and Clinical Research Center (ECRC) Charité Humboldt University Berlin, DZHK partner site Berlin Berlin Germany
- Department of Cardiology and Nephrology HELIOS Klinikum Berlin Buch Berlin Germany
| | - Stephanie Funk
- Charité Medical Faculty University Medicine Berlin Germany
- Working Group on Cardiovascular Magnetic Resonance Experimental and Clinical Research Center (ECRC) Charité Humboldt University Berlin, DZHK partner site Berlin Berlin Germany
- Department of Cardiology and Nephrology HELIOS Klinikum Berlin Buch Berlin Germany
| | - André Nuesslein
- Physikalisch‐Technische Bundesanstalt (PTB) Braunschweig and Berlin Germany
| | - Jeanette Schulz‐Menger
- Charité Medical Faculty University Medicine Berlin Germany
- Working Group on Cardiovascular Magnetic Resonance Experimental and Clinical Research Center (ECRC) Charité Humboldt University Berlin, DZHK partner site Berlin Berlin Germany
- Department of Cardiology and Nephrology HELIOS Klinikum Berlin Buch Berlin Germany
| | - Tobias Schaeffter
- Physikalisch‐Technische Bundesanstalt (PTB) Braunschweig and Berlin Germany
- School of Biomedical Engineering and Imaging Sciences King's College London London United Kingdom
| | - Christoph Kolbitsch
- Physikalisch‐Technische Bundesanstalt (PTB) Braunschweig and Berlin Germany
- School of Biomedical Engineering and Imaging Sciences King's College London London United Kingdom
| |
Collapse
|