scholarly journals Feasibility and Implementation of a Deep Learning MR Reconstruction for TSE Sequences in Musculoskeletal Imaging

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1484
Author(s):  
Judith Herrmann ◽  
Gregor Koerzdoerfer ◽  
Dominik Nickel ◽  
Mahmoud Mostapha ◽  
Mariappan Nadar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Spin Echo ◽  
Musculoskeletal Imaging ◽  
Acquisition Time ◽  
Diagnostic Confidence ◽  
Clinical Routine ◽  
Time Saving ◽  
Anatomical Structures ◽  
Edge Sharpness

Magnetic Resonance Imaging (MRI) of the musculoskeletal system is one of the most common examinations in clinical routine. The application of Deep Learning (DL) reconstruction for MRI is increasingly gaining attention due to its potential to improve the image quality and reduce the acquisition time simultaneously. However, the technology has not yet been implemented in clinical routine for turbo spin echo (TSE) sequences in musculoskeletal imaging. The aim of this study was therefore to assess the technical feasibility and evaluate the image quality. Sixty examinations of knee, hip, ankle, shoulder, hand, and lumbar spine in healthy volunteers at 3 T were included in this prospective, internal-review-board-approved study. Conventional (TSES) and DL-based TSE sequences (TSEDL) were compared regarding image quality, anatomical structures, and diagnostic confidence. Overall image quality was rated to be excellent, with a significant improvement in edge sharpness and reduced noise compared to TSES (p < 0.001). No difference was found concerning the extent of artifacts, the delineation of anatomical structures, and the diagnostic confidence comparing TSES and TSEDL (p > 0.05). Therefore, DL image reconstruction for TSE sequences in MSK imaging is feasible, enabling a remarkable time saving (up to 75%), whilst maintaining excellent image quality and diagnostic confidence.

Clinical Implementation of Deep Learning MR reconstruction for TSE Sequences: Reduction of Acquisition Time and Maintenance

2021 ◽  
Author(s):  
Judith Herrmann ◽  
Sebastian Gassenmaier ◽  
Thomas Kuestner ◽  
Matthias Kuendel ◽  
Dominik Nickel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Acquisition Time ◽  
Clinical Implementation ◽  
Clinical Workflow ◽  
Diagnostic Confidence ◽  
Clinical Routine ◽  
Time Saving ◽  
Reconstruction Process

Abstract Background: The application of Deep Learning (DL) in MR image reconstruction is increasingly gaining attention due to its potential of increasing image quality and reducing acquisition time. However, the technology hasn’t been yet implemented in clinical routine. The aim of this study was therefore to describe the implementation of this novel DL image reconstruction for turbo spin echo (TSE) sequences in clinical workflow including a thorough explanation of the required steps and an evaluation of the obtainable image quality compared to conventional TSE.Methods: DL image reconstruction using a variational network was clinically implemented to enable acquisition of accelerated TSE sequences. After internal review board’s approval and informed consent, 30 examinations for knee, shoulder, and lumbar spine in 15 volunteers at 3 T were included in this prospective study. Conventional TSE sequences (TSE) and TSE with deep learning reconstruction (TSEDL) were compared regarding overall image quality, noise, sharpness, and subjective signal-to-noise-ratio (SNR), as well diagnostic confidence and image impression. Comparative analyses were conducted to assess the differences between the sequences. A survey on technologists’ acceptance was performed for DL image reconstruction. Results: DL image reconstruction was successfully implemented in a clinical workflow and TSEDL allowed a remarkable time saving of more than 50%. Overall image quality, diagnostic confidence and image impression for TSEDL were rated as excellent (median 4, IQR 4-4) and comparable to TSE (image quality: p=0.059, diagnostic confidence: p=0.157, image impression: p=0.102). Noise, sharpness, artifacts, and subjective SNR for TSEDL reached significantly superior levels to TSE (noise: p<0.001, sharpness: p=0.001, artifacts: p=0.014, subjective SNR: p<0.001). Technologists reported high levels of acceptance for DL image reconstruction. Required time for the reconstruction process was rated moderate and longer than standard sequences (median 2, IQR 2-3). Required time and effort for the implementation in daily workflow was rated as low effort (median 4, IQR 3-4). General applicability of DL reconstruction as well as acceptance of DL sequences in clinical routine were rated excellent (median 4, IQR 3-4). Conclusion: DL image reconstruction for TSE sequences can be implemented in clinical workflow and enables a remarkable time saving (>50%) in image acquisition while maintaining excellent image quality.Trial registration: Your clinical trial is officially registered at the German DRKS with the registration number: DRKS00023278.

scholarly journals Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3593
Author(s):  
Sebastian Gassenmaier ◽  
Saif Afat ◽  
Marcel Dominik Nickel ◽  
Mahmoud Mostapha ◽  
Judith Herrmann ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Acquisition Time ◽  
Noise Levels ◽  
Diagnostic Confidence ◽  
Prospective Comparison ◽  
The Impact ◽  
Lesion Conspicuity

Multiparametric MRI (mpMRI) of the prostate has become the standard of care in prostate cancer evaluation. Recently, deep learning image reconstruction (DLR) methods have been introduced with promising results regarding scan acceleration. Therefore, the aim of this study was to investigate the impact of deep learning image reconstruction (DLR) in a shortened acquisition process of T2-weighted TSE imaging, regarding the image quality and diagnostic confidence, as well as PI-RADS and T2 scoring, as compared to standard T2 TSE imaging. Sixty patients undergoing 3T mpMRI for the evaluation of prostate cancer were prospectively enrolled in this institutional review board-approved study between October 2020 and March 2021. After the acquisition of standard T2 TSE imaging (T2S), the novel T2 TSE sequence with DLR (T2DLR) was applied in three planes. Overall, the acquisition time for T2S resulted in 10:21 min versus 3:50 min for T2DLR. The image evaluation was performed by two radiologists independently using a Likert scale ranging from 1–4 (4 best) applying the following criteria: noise levels, artifacts, overall image quality, diagnostic confidence, and lesion conspicuity. Additionally, T2 and PI-RADS scoring were performed. The mean patient age was 69 ± 9 years (range, 49–85 years). The noise levels and the extent of the artifacts were evaluated to be significantly improved in T2DLR versus T2S by both readers (p < 0.05). Overall image quality was also evaluated to be superior in T2DLR versus T2S in all three acquisition planes (p = 0.005–<0.001). Both readers evaluated the item lesion conspicuity to be superior in T2DLR with a median of 4 versus a median of 3 in T2S (p = 0.001 and <0.001, respectively). T2-weighted TSE imaging of the prostate in three planes with an acquisition time reduction of more than 60% including DLR is feasible with a significant improvement of image quality.

scholarly journals Comparison of Compressed Sensing and Gradient and Spin-Echo in Breath-Hold 3D MR Cholangiopancreatography: Qualitative and Quantitative Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 634
Author(s):  
Weon Jang ◽  
Ji Soo Song ◽  
Sang Heon Kim ◽  
Jae Do Yang
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Spin Echo ◽  
Contrast Ratio ◽  
Rank Test ◽  
Breath Holding ◽  
Breath Hold ◽  
Background Suppression ◽  
Time Saving ◽  
Noise Ratio

While magnetic resonance cholangiopancreatography (MRCP) is routinely used, compressed sensing MRCP (CS-MRCP) and gradient and spin-echo MRCP (GRASE-MRCP) with breath-holding (BH) may allow sufficient image quality with shorter acquisition times. This study qualitatively and quantitatively compared BH-CS-MRCP and BH-GRASE-MRCP and evaluated their clinical effectiveness. Data from 59 consecutive patients who underwent both BH-CS-MRCP and BH-GRASE-MRCP were qualitatively analyzed using a five-point Likert-type scale. The signal-to-noise ratio (SNR) of the common bile duct (CBD), contrast-to-noise ratio (CNR) of the CBD and liver, and contrast ratio between periductal tissue and the CBD were measured. Paired t-test, Wilcoxon signed-rank test, and McNemar’s test were used for statistical analysis. No significant differences were found in overall image quality or duct visualization of the CBD, right and left 1st level intrahepatic duct (IHD), cystic duct, and proximal pancreatic duct (PD). BH-CS-MRCP demonstrated higher background suppression and better visualization of right (p = 0.004) and left 2nd level IHD (p < 0.001), mid PD (p = 0.003), and distal PD (p = 0.041). Image quality degradation was less with BH-GRASE-MRCP than BH-CS-MRCP (p = 0.025). Of 24 patients with communication between a cyst and the PD, 21 (87.5%) and 15 patients (62.5%) demonstrated such communication on BH-CS-MRCP and BH-GRASE-MRCP, respectively. SNR, contrast ratio, and CNR of BH-CS-MRCP were higher than BH-GRASE-MRCP (p < 0.001). Both BH-CS-MRCP and BH-GRASE-MRCP are useful imaging methods with sufficient image quality. Each method has advantages, such as better visualization of small ducts with BH-CS-MRCP and greater time saving with BH-GRASE-MRCP. These differences allow diverse choices for visualization of the pancreaticobiliary tree in clinical practice.

Deep learning–accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality

2021 ◽  
Vol 137 ◽  
pp. 109600
Author(s):  
Sebastian Gassenmaier ◽  
Saif Afat ◽  
Dominik Nickel ◽  
Mahmoud Mostapha ◽  
Judith Herrmann ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Acquisition Time

Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis

2021 ◽  
pp. 1-8
Author(s):  
Jihang Sun ◽  
Haoyan Li ◽  
Haiyun Li ◽  
Michelle Li ◽  
Yingzi Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Ct Angiography ◽  
Takayasu Arteritis ◽  
Image Noise ◽  
Diagnostic Confidence ◽  
Ct Number ◽  
Laboratory Test Results ◽  
Significant Difference

BACKGROUND: The inflammatory indexes of children with Takayasu arteritis (TAK) usually tend to be normal immediately after treatment, therefore, CT angiography (CTA) has become an important method to evaluate the status of TAK and sometime is even more sensitive than laboratory test results. OBJECTIVE: To evaluate image quality improvement in CTA of children diagnosed with TAK using a deep learning image reconstruction (DLIR) in comparison to other image reconstruction algorithms. METHODS: hirty-two TAK patients (9.14±4.51 years old) underwent neck, chest and abdominal CTA using 100 kVp were enrolled. Images were reconstructed at 0.625 mm slice thickness using Filtered Back-Projection (FBP), 50%adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers. RESULTS: There was no significant difference in CT number across images reconstructed using different algorithms. Image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 using FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P >  0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P <  0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, however, only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). CONCLUSION: DLIR-H improves CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.

Clinical routine use of virtual monochromatic datasets based on spectral CT in patients with hypervascularized abdominal tumors - evaluation of effectiveness and efficiency

2018 ◽  
Vol 60 (4) ◽  
pp. 425-432 ◽  
Author(s):  
Georg Böning ◽  
Felix Feldhaus ◽  
Sebastian Adelt ◽  
Johannes Kahn ◽  
Uli Fehrenbach ◽  
...  
Keyword(s):  
Image Quality ◽  
Reading Time ◽  
Signal To Noise Ratio ◽  
Low Energy ◽  
Arterial Phase ◽  
Diagnostic Confidence ◽  
Clinical Routine ◽  
Portal Venous Phase ◽  
Abdominal Tumors ◽  
Oncologic Patients

Background Virtual monochromatic images (VMI) generated using spectral computed tomography (CT) are promising recently available tools to improve diagnostic performance in oncologic patients. Purpose To investigate if virtual monochromatic datasets are suitable for clinical routine use in patients with hypervascularized abdominal tumors. Material and Methods A total of 41 patients with hypervascularized hepatocellular carcinoma (HCC), renal cell carcinoma (RCC), or neuroendocrine tumors (NET) were enrolled in the study; 451 CT series were analyzed. In an intra-individual study design, virtual monochromatic datasets of the arterial phase of each scan were computed. Image quality was assessed objectively by determining signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNR) and subjectively by using five-point Likert-scales. The volume CT dose index (CTDIvol) was taken from each radiation dose report. The increase in reading time was estimated from the increase in the number of images. Results Intra-individual comparison of the spectral mode in the arterial phase with the portal venous phase revealed no significant increase in the applied dose. SNR, CNRtumor-to-liver , and CNRtumor-to-muscle were significantly increased by lowering virtual monochromatic energy. Subjective image quality scores revealed an increase of contrast in low energy datasets, resulting in significantly higher diagnostic confidence, but an increased image noise at low energies. While diagnostic confidence improved, taking all datasets into account resulted in a significantly longer estimated reading time. Conclusion In clinical practice, the use of low energy VMI improved diagnostic confidence without a significant increase in dose. The main disadvantage is a decrease in efficiency due to longer reading times.

scholarly journals Non-Linear Realignment Improves Hippocampus Subfield Segmentation

2019 ◽  
Author(s):  
Thomas B Shaw ◽  
Steffen Bollmann ◽  
Nicole T Atcheson ◽  
Christine Guo ◽  
Jurgen Fripp ◽  
...  
Keyword(s):  
Image Quality ◽  
Signal To Noise Ratio ◽  
Spin Echo ◽  
Mr Image ◽  
Anatomical Structures ◽  
Improve Image Quality ◽  
Non Linear ◽  
Friedman's Test ◽  
Noise Ratio ◽  
Healthy Participants

AbstractParticipant movement can deleteriously affect MR image quality. Further, for the visualization and segmentation of small anatomical structures, there is a need to improve image quality, specifically signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), by acquiring multiple anatomical scans consecutively. We aimed to ameliorate movement artefacts and increase SNR in a high-resolution turbo spin-echo (TSE) sequence acquired thrice using non-linear realignment in order to improve segmentation consistency of the hippocampus subfields. We assessed the method in young healthy participants, Motor Neurone Disease patients, and age matched controls. Results show improved image segmentation of the hippocampus subfields when comparing template-based segmentations with individual segmentations with Dice overlaps N=51; ps < 0.001 (Friedman’s test) and higher sharpness ps < 0.001 in non-linearly realigned scans as compared to linearly, and arithmetically averaged scans.

BLADE turbo gradient- and spin-echo in the assessment of sinonasal lesions: a comprehensive comparison of image quality in readout-segmented echo-planar imaging

2021 ◽  
pp. 028418512110418
Author(s):  
Yue Geng ◽  
Yiqian Shi ◽  
Wei Chen ◽  
Zuohua Tang ◽  
Zhongshuai Zhang ◽  
...  
Keyword(s):  
Image Quality ◽  
Pulse Sequence ◽  
Spin Echo ◽  
Qualitative Assessment ◽  
Geometric Distortion ◽  
Acquisition Time ◽  
Planar Imaging ◽  
Adc Value ◽  
Diffusion Weighted ◽  
Noise Ratio

Background A two-dimensional turbo gradient-echo and spin-echo diffusion-weighted pulse sequence with a non-Cartesian BLADE trajectory (TGSE BLADE) can eliminate image artifacts and distortion with clinically acceptable scan times. This process has the potential to overcome the shortcomings of current diffusion-weighted imaging (DWI) techniques, especially in the sinonasal region. Purpose To investigate the feasibility of TGSE BLADE in the assessment of sinonasal lesions and compare the quality of TGSE BLADE with RESOLVE images both qualitatively and quantitatively. Material and Methods A total of 36 patients with sinonasal lesions were included in this prospective study. DW images acquired using TGSE BLADE and RESOLVE were performed with the same acquisition time. Two independent observers evaluated the qualitative parameters (overall image quality, lesion visibility, and geometric distortion) and quantitative parameters (geometric distortion ratio [GDR], signal-to-noise ratio [SNR], contrast, contrast-to-noise ratio [CNR], and apparent diffusion coefficient [ADC] value) of the two sequences. Results Qualitative assessment revealed that TGSE BLADE exhibited higher overall image quality ( P < 0.001) and lesion visibility ( P < 0.001) and less geometric distortion ( P < 0.001) than RESOLVE. Quantitative assessment showed that TGSE BLADE images exhibited higher contrast ( P < 0.001) and CNR ( P < 0.001) and lower GDR ( P < 0.05) and SNR ( P < 0.001) than RESOLVE images. The ADC value of TGSE BLADE was significantly lower than that of RESOLVE ( P < 0.05). Conclusion TGSE BLADE can reduce susceptibility artifacts and geometric distortion more than RESOLVE and appears to be a promising diffusion imaging sequence for the assessment of sinonasal lesions.

scholarly journals Validation of a deep learning reconstruction framework for 3D delayed myocardial enhancement imaging

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
G Delso ◽  
K Suryanarayanan ◽  
JT Ortiz-Perez ◽  
S Prat ◽  
A Doltra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Blood Pool ◽  
Image Noise ◽  
Delayed Enhancement ◽  
New Method ◽  
Acquisition Time ◽  
Trade Off ◽  
Feature Preservation ◽  
Myocardial Delayed Enhancement

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Myocardial delayed enhancement (MDE) MRI plays an important role in the identification of several cardiac conditions, both ischemic and non-ischemic (e.g. myocarditis, IDC, amyloidosis). 3D imaging offers increased resolution, full heart coverage and better depiction of complex pathologies, but its image quality is limited by long acquisition times. Deep learning (DL) models enable advanced reconstruction algorithms that yield regularized images in practical computation times. In this study we evaluate a novel 3D-DL reconstruction to overcome the trade-off between reconstructed quality and acquisition time on MDE data. Methods A group of 14 subjects referred for CMR (5 F / 9 M, 59 ± 11 y.o., 78 ± 13 kg) were scanned with a 3D MDE sequence prototype: SPGR with IR preparation, fat & spatial saturation, respiratory navigator, ARC 2x, FOV 40x40cm, ST 1.4-2.4mm, matrix 280²-320², FA 20deg, BW 62.5 kHz, TE 2.1 ± 0.1ms, TI based on a CINE IR scout. All were retrospectively reconstructed using a 3D DL algorithm, trained on a database of over 700 datasets to reconstruct high-quality images with adjustable noise reduction. The images were compared with standard 3D Cartesian reconstruction by two experienced cardiologists, to identify alterations in morphology or contrast distribution. Noise was estimated using the intensity standard deviation on a blood pool ROI. Feature preservation was estimated using the structural similarity index (SSI). Results The new method improved perceived image quality without loss of structural information or resolution (fig 1). Quantitative analysis (fig 2) confirmed these results: The average coefficient of variation in the blood was 0.08 ± 0.02 in the reference and 0.05 ± 0.02 with the new method; Given a target image noise level, DL reconstruction yielded up to 10% better SSI, compared to anisotropic filtering. The clinical review didn’t reveal diagnostically significant alterations of structure or uptake pattern. A perceived reduction of sharpness was initially reported but individual examination of landmarks (e.g. pulmonary and coronary arteries) confirmed that no relevant features were being lost with the new reconstruction. Discussion The 3D MDE images obtained with DL reconstruction improved the trade-off between image noise -estimated by the blood pool intensity deviation- and feature preservation -estimated by SSI-. Consistent improvement of image quality without morphological alterations of diagnostic relevance indicates that the new method can be considered for clinical practice. The next step in the validation process will require testing the robustness over a large set of cases with heterogeneous acquisition settings. Conclusion We presented the preliminary evaluation of a deep learning reconstruction method with 3D myocardial delayed enhancement data. The results show systematic improvement of overall image quality without loss of relevant diagnostic information. Abstract Figure.

scholarly journals Efficient non-contrast enhanced 3D Cartesian cardiovascular magnetic resonance angiography of the thoracic aorta in 3 min

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
Anastasia Fotaki ◽  
Camila Munoz ◽  
Yaso Emanuel ◽  
Alina Hua ◽  
Filippo Bosio ◽  
...  
Keyword(s):  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
Image Quality ◽  
Magnetic Resonance Angiography ◽  
Thoracic Aorta ◽  
Aortic Disease ◽  
Acquisition Time ◽  
Diagnostic Confidence ◽  
Thoracic Aortic Disease ◽  
Relative Standard

Abstract Background The application of cardiovascular magnetic resonance angiography (CMRA) for the assessment of thoracic aortic disease is often associated with prolonged and unpredictable acquisition times and residual motion artefacts. To overcome these limitations, we have integrated undersampled acquisition with image-based navigators and inline non-rigid motion correction to enable a free-breathing, contrast-free Cartesian CMRA framework for the visualization of the thoracic aorta in a short and predictable scan of 3 min. Methods 35 patients with thoracic aortic disease (36 ± 13y, 14 female) were prospectively enrolled in this single-center study. The proposed 3D T2-prepared balanced steady state free precession (bSSFP) sequence with image-based navigator (iNAV) was compared to the clinical 3D T2-prepared bSSFP with diaphragmatic-navigator gating (dNAV), in terms of image acquisition time. Three cardiologists blinded to iNAV vs. dNAV acquisition, recorded image quality scores across four aortic segments and their overall diagnostic confidence. Contrast ratio (CR) and relative standard deviation (RSD) of signal intensity (SI) in the corresponding segments were estimated. Co-axial aortic dimensions in six landmarks were measured by two readers to evaluate the agreement between the two methods, along with inter-observer and intra-observer agreement. Kolmogorov–Smirnov test, Mann–Whitney U (MWU), Bland–Altman analysis (BAA), intraclass correlation coefficient (ICC) were used for statistical analysis. Results The scan time for the iNAV-based approach was significantly shorter (3.1 ± 0.5 min vs. 12.0 ± 3.0 min for dNAV, P = 0.005). Reconstruction was performed inline in 3.0 ± 0.3 min. Diagnostic confidence was similar for the proposed iNAV versus dNAV for all three reviewers (Reviewer 1: 3.9 ± 0.3 vs. 3.8 ± 0.4, P = 0.7; Reviewer 2: 4.0 ± 0.2 vs. 3.9 ± 0.3, P = 0.4; Reviewer 3: 3.8 ± 0.4 vs. 3.7 ± 0.6, P = 0.3). The proposed method yielded higher image quality scores in terms of artefacts from respiratory motion, and non-diagnostic images due to signal inhomogeneity were observed less frequently. While the dNAV approach outperformed the iNAV method in the CR assessment, the iNAV sequence showed improved signal homogeneity along the entire thoracic aorta [RSD SI 5.1 (4.4, 6.5) vs. 6.5 (4.6, 8.6), P = 0.002]. BAA showed a mean difference of < 0.05 cm across the 6 landmarks between the two datasets. ICC showed excellent inter- and intra-observer reproducibility. Conclusions Thoracic aortic iNAV-based CMRA with fast acquisition (~ 3 min) and inline reconstruction (3 min) is proposed, resulting in high diagnostic confidence and reproducible aortic measurements.

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