Another step to bring artificial intelligence closer to clinical application – noise reduction for late gadolinium enhancement

2021 ◽  
Author(s):  
Akos Varga-Szemes ◽  
Tilman Emrich
Keyword(s):  
Artificial Intelligence ◽  
Late Gadolinium Enhancement ◽  
Noise Reduction ◽  
Clinical Application ◽  
Gadolinium Enhancement

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

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.

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

Circulation ◽  
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.

scholarly journals Risk stratification of cardiac metastases using late gadolinium enhancement cardiovascular magnetic resonance: prognostic impact of hypo-enhancement evidenced tumor avascularity

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Angel T. Chan ◽  
William Dinsfriend ◽  
Jiwon Kim ◽  
Brian Yum ◽  
Razia Sultana ◽  
...  
Keyword(s):  
Cardiovascular Magnetic Resonance ◽  
Late Gadolinium Enhancement ◽  
Magnetic Resonance ◽  
Contrast Enhancement ◽  
Cancer Etiology ◽  
Enhancement Pattern ◽  
Gadolinium Enhancement ◽  
Lesion Location ◽  
Heterogeneous Enhancement ◽  
Lge Cmr

Abstract Background Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is widely used to identify cardiac neoplasms, for which diagnosis is predicated on enhancement stemming from lesion vascularity: Impact of contrast-enhancement pattern on clinical outcomes is unknown. The objective of this study was to determine whether cardiac metastasis (CMET) enhancement pattern on LGE-CMR impacts prognosis, with focus on heterogeneous lesion enhancement as a marker of tumor avascularity. Methods Advanced (stage IV) systemic cancer patients with and without CMET matched (1:1) by cancer etiology underwent a standardized CMR protocol. CMET was identified via established LGE-CMR criteria based on lesion enhancement; enhancement pattern was further classified as heterogeneous (enhancing and non-enhancing components) or diffuse and assessed via quantitative (contrast-to-noise ratio (CNR); signal-to-noise ratio (SNR)) analyses. Embolic events and mortality were tested in relation to lesion location and contrast-enhancement pattern. Results 224 patients were studied, including 112 patients with CMET and unaffected (CMET -) controls matched for systemic cancer etiology/stage. CMET enhancement pattern varied (53% heterogeneous, 47% diffuse). Quantitative analyses were consistent with lesion classification; CNR was higher and SNR lower in heterogeneously enhancing CMET (p < 0.001)—paralleled by larger size based on linear dimensions (p < 0.05). Contrast-enhancement pattern did not vary based on lesion location (p = NS). Embolic events were similar between patients with diffuse and heterogeneous lesions (p = NS) but varied by location: Patients with right-sided lesions had threefold more pulmonary emboli (20% vs. 6%, p = 0.02); those with left-sided lesions had lower rates equivalent to controls (4% vs. 5%, p = 1.00). Mortality was higher among patients with CMET (hazard ratio [HR] = 1.64 [CI 1.17–2.29], p = 0.004) compared to controls, but varied by contrast-enhancement pattern: Diffusely enhancing CMET had equivalent mortality to controls (p = 0.21) whereas prognosis was worse with heterogeneous CMET (p = 0.005) and more strongly predicted by heterogeneous enhancement (HR = 1.97 [CI 1.23–3.15], p = 0.005) than lesion size (HR = 1.11 per 10 cm [CI 0.53–2.33], p = 0.79). Conclusions Contrast-enhancement pattern and location of CMET on CMR impacts prognosis. Embolic events vary by CMET location, with likelihood of PE greatest with right-sided lesions. Heterogeneous enhancement—a marker of tumor avascularity on LGE-CMR—is a novel marker of increased mortality risk.

Apical hypertrophic cardiomyopathy with subendocardial late gadolinium enhancement in an adolescent

2020 ◽  
pp. 1-3
Author(s):  
Simona Boroni Grazioli ◽  
Marc-Philip Hitz ◽  
Inga Voges
Keyword(s):  
Late Gadolinium Enhancement ◽  
Hypertrophic Cardiomyopathy ◽  
Resonance Imaging ◽  
Gadolinium Enhancement ◽  
Apical Hypertrophic Cardiomyopathy ◽  
T Wave ◽  
Wave Inversion ◽  
Cardiology Department ◽  
History Of ◽  
Rare Diagnosis

Abstract A 17-year-old boy with a history of dyspnea attacks and chest pain was referred to our paediatric cardiology department. Electrocardiogram at presentation showed T-wave inversion in the inferior leads. Cardiovascular magnetic resonance imaging revealed the rare diagnosis of apical hypertrophic cardiomyopathy with subendocardial late gadolinium enhancement, missed by echocardiography.

Aktueller Stellenwert der Kardio-MRT in Diagnostik und Therapie

2021 ◽  
Vol 146 (07) ◽  
pp. 461-465
Author(s):  
Julia M. Vietheer ◽  
Christian W. Hamm ◽  
Andreas Rolf
Keyword(s):  
Late Gadolinium Enhancement ◽  
T2 Mapping ◽  
Strain Imaging ◽  
Gadolinium Enhancement ◽  
Quantitative Parameter ◽  
Diagnostik Und Therapie ◽  
Die Stress

Quantifizierung der links- und rechtsventrikulären Funktion Strain Imaging erweitert die klassische Volumetrie der MRT um einen sehr subtilen globalen und regionalen Funktionsparameter. Strain detektiert sehr frühe Funktionseinbußen beider Ventrikel, die visuell noch nicht erkennbar sind. Insbesondere der longitudinale Strain ist bei ischämischen und nichtischämischen Erkrankungen frühzeitig reduziert. Strain hat über die EF hinaus zusätzliche prognostische Bedeutung. Gewebecharakterisierung T1- und T2-Mapping erweitern die klassische Gewebecharakterisierung mit Late Gadolinium Enhancement (LGE) um sehr subtile quantitative Parameter, die diffuse Fibrose (T1) und Ödem (T1 und T2) widerspiegeln. Ischämiediagnostik Die Perfusions-MRT unter Vasodilatatorstress ist das genaueste Verfahren zur Ischämiediagnostik. Die MR-INFORM-Studie zeigt, dass mithilfe der Stress-MRT fast die Hälfte der Katheteruntersuchungen in einem Kollektiv mit hoher Prätestwahrscheinlichkeit vermieden werden kann. Dabei ist die Stress-MRT der FFR-basierten Strategie nicht unterlegen und genauso sicher.

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