Evaluation of image quality of wideband single‐shot late gadolinium‐enhancement MRI in patients with a cardiac implantable electronic device

Introduction: Wideband late gadolinium enhancement (LGE) CMR is capable of suppressing image artifacts induced by cardiac implanted electronic devices (CIEDs). We implemented our own wideband segmented (seg) breath-hold and wideband single-shot (SS) free-breathing LGE pulse sequences and used them clinically since 2016. The purpose of this study was to evaluate image quality and CMR safety of wideband LGE compared to standard LGE. Methods: We retrospectively identified 54 consecutive patients (mean age: 61±15 years; 31% females) with CIED (33 t-ICD, 4 s-ICD, 15 pacemaker, 1 CRT-D, 1 CRT-P) who underwent CMR at 1.5T (Avanto, Siemens). Standard seg, wideband seg, and wideband SS LGE used standard imaging parameters. 16 myocardial segments were scored for scar/myocardial conspicuity and presence of any visual artifact on a 5-point Likert scale (1:worst; 3:acceptable; 5:best). Distance between center of the heart and CIED (CXR D) was measured on chest X-ray. Whole-body specific absorption rate (SAR) was read from DICOM metadata. Device changes were calculated from pre- and post- device interrogation measurements. Results: Both wideband seg and SS LGE consistently produced better image quality than standard LGE (Figure 1A). Median conspicuity and artifact scores were significantly better for wideband seg (F=20.6, p<0.001) and wideband SS (F=24.2, p<0.001) LGE compared to standard LGE. There was a trend in conspicuity and artifact scores with CIED distance for standard LGE (rho=0.476, p=0.02), but not wideband LGE scans (Figure 1B, 1C). Whole-body SAR averaged for both wideband scans (0.15±0.04 W/kg) was one order of magnitude below the 2.0 W/kg FDA limit. Device parameters (sensing, impedance, threshold, battery level) did not differ before and after CMR including wideband LGE. Conclusions: Both wideband seg and SS LGE scans produced improved image quality compared to standard LGE while maintaining CMR safety. *The first two authors (SS and AP) contributed equally

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Late gadolinium enhancement magnetic resonance imaging for the assessment of myocardial infarction: comparison of image quality between single and double doses of contrast agents

The International Journal of Cardiovascular Imaging ◽

10.1007/s10554-014-0505-x ◽

2014 ◽

Vol 30 (S2) ◽

pp. 129-135 ◽

Cited By ~ 6

Author(s):

Yeo Koon Kim ◽

Eun-Ah Park ◽

Whal Lee ◽

Sang Yoon Kim ◽

Jin Wook Chung

Keyword(s):

Magnetic Resonance Imaging ◽

Myocardial Infarction ◽

Late Gadolinium Enhancement ◽

Magnetic Resonance ◽

Image Quality ◽

Contrast Agents ◽

Resonance Imaging ◽

Gadolinium Enhancement

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Improvement of late gadolinium enhancement image quality using a deep learning–based reconstruction algorithm and its influence on myocardial scar quantification

European Radiology ◽

10.1007/s00330-020-07461-w ◽

2020 ◽

Author(s):

Nikki van der Velde ◽

H. Carlijne Hassing ◽

Brendan J. Bakker ◽

Piotr A. Wielopolski ◽

R. Marc Lebel ◽

...

Keyword(s):

Deep Learning ◽

Late Gadolinium Enhancement ◽

Image Quality ◽

Noise Reduction ◽

Reconstruction Algorithm ◽

Remote Myocardium ◽

Full Width ◽

Half Maximum ◽

Gadolinium Enhancement ◽

Scar Size

Abstract Objectives The aim of this study was to assess the effect of a deep learning (DL)–based reconstruction algorithm on late gadolinium enhancement (LGE) image quality and to evaluate its influence on scar quantification. Methods Sixty patients (46 ± 17 years, 50% male) with suspected or known cardiomyopathy underwent CMR. Short-axis LGE images were reconstructed using the conventional reconstruction and a DL network (DLRecon) with tunable noise reduction (NR) levels from 0 to 100%. Image quality of standard LGE images and DLRecon images with 75% NR was scored using a 5-point scale (poor to excellent). In 30 patients with LGE, scar size was quantified using thresholding techniques with different standard deviations (SD) above remote myocardium, and using full width at half maximum (FWHM) technique in images with varying NR levels. Results DLRecon images were of higher quality than standard LGE images (subjective quality score 3.3 ± 0.5 vs. 3.6 ± 0.7, p < 0.001). Scar size increased with increasing NR levels using the SD methods. With 100% NR level, scar size increased 36%, 87%, and 138% using 2SD, 4SD, and 6SD quantification method, respectively, compared to standard LGE images (all p values < 0.001). However, with the FWHM method, no differences in scar size were found (p = 0.06). Conclusions LGE image quality improved significantly using a DL-based reconstruction algorithm. However, this algorithm has an important impact on scar quantification depending on which quantification technique is used. The FWHM method is preferred because of its independency of NR. Clinicians should be aware of this impact on scar quantification, as DL-based reconstruction algorithms are being used. Key Points • The image quality based on (subjective) visual assessment and image sharpness of late gadolinium enhancement images improved significantly using a deep learning–based reconstruction algorithm that aims to reconstruct high signal-to-noise images using a denoising technique. • Special care should be taken when scar size is quantified using thresholding techniques with different standard deviations above remote myocardium because of the large impact of these advanced image enhancement algorithms. • The full width at half maximum method is recommended to quantify scar size when deep learning algorithms based on noise reduction are used, as this method is the least sensitive to the level of noise and showed the best agreement with visual late gadolinium enhancement assessment.

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Towards Replacing Late Gadolinium Enhancement with Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy

Circulation ◽

10.1161/circulationaha.121.054432 ◽

2021 ◽

Author(s):

Qiang Zhang ◽

Matthew K. Burrage ◽

Elena Lukaschuk ◽

Mayooran Shanmuganathan ◽

Iulia A. Popescu ◽

...

Keyword(s):

Artificial Intelligence ◽

Cardiovascular Magnetic Resonance ◽

Deep Learning ◽

Late Gadolinium Enhancement ◽

Hypertrophic Cardiomyopathy ◽

Magnetic Resonance ◽

Image Quality ◽

Tissue Characterization ◽

Wilcoxon Test ◽

Gadolinium Enhancement

Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for non-invasive myocardial tissue characterization, but requires intravenous contrast agent administration. It is highly desired to develop a contrast-agent-free technology to replace LGE for faster and cheaper CMR scans. Methods: A CMR Virtual Native Enhancement (VNE) imaging technology was developed using artificial intelligence. The deep learning model for generating VNE uses multiple streams of convolutional neural networks to exploit and enhance the existing signals in native T1-maps (pixel-wise maps of tissue T1 relaxation times) and cine imaging of cardiac structure and function, presenting them as LGE-equivalent images. The VNE generator was trained using generative adversarial networks. This technology was first developed on CMR datasets from the multi-center Hypertrophic Cardiomyopathy Registry (HCMR), using HCM as an exemplar. The datasets were randomized into two independent groups for deep learning training and testing. The test data of VNE and LGE were scored and contoured by experienced human operators to assess image quality, visuospatial agreement and myocardial lesion burden quantification. Image quality was compared using nonparametric Wilcoxon test. Intra- and inter-observer agreement was analyzed using intraclass correlation coefficients (ICC). Lesion quantification by VNE and LGE were compared using linear regression and ICC. Results: 1348 HCM patients provided 4093 triplets of matched T1-maps, cines, and LGE datasets. After randomization and data quality control, 2695 datasets were used for VNE method development, and 345 for independent testing. VNE had significantly better image quality than LGE, as assessed by 4 operators (n=345 datasets, p<0.001, Wilcoxon test). VNE revealed characteristic HCM lesions in high visuospatial agreement with LGE. In 121 patients (n=326 datasets), VNE correlated with LGE in detecting and quantifying both hyper-intensity myocardial lesions (r=0.77-0.79, ICC=0.77-0.87; p<0.001) and intermediate-intensity lesions (r=0.70-0.76, ICC=0.82-0.85; p<0.001). The native CMR images (cine plus T1-map) required for VNE can be acquired within 15 minutes. Producing a VNE image takes less than one second. Conclusions: VNE is a new CMR technology that resembles conventional LGE, without the need for contrast administration. VNE achieved high agreement with LGE in the distribution and quantification of lesions, with significantly better image quality.

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