Comparison of 3D phase-sensitive inversion-recovery and 2D inversion-recovery MRI at 3.0 T for the assessment of late gadolinium enhancement in patients with hypertrophic cardiomyopathy

Fast acquisition of left and right ventricular function parameters applying cardiovascular magnetic resonance in clinical routine - validation of a 2-shot compressed sensing cine sequence

Objectives. To evaluate if cine sequences accelerated by compressed sensing (CS) are feasible in clinical routine and yield equivalent cardiac morphology in less time. Design. We evaluated 155 consecutive patients with various cardiac diseases scanned during our clinical routine. LV and RV short axis (SAX) cine images were acquired by conventional and prototype 2-shot CS sequences on a 1.5 T CMR. The 2-shot prototype captures the entire heart over a period of 3 beats making the acquisition potentially even faster. Both scans were performed with identical slice parameters and positions. We compared LV and RV morphology with Bland-Altmann plots and weighted the results in relation to pre-defined tolerance intervals. Subjective and objective image quality was evaluated using a 4-point score and adapted standardized criteria. Scan times were evaluated for each sequence. Results. In total, no acquisitions were lost due to non-diagnostic image quality in the subjective image score. Objective image quality analysis showed no statistically significant differences. The scan time of the CS cines was significantly shorter (p < .001) with mean scan times of 178 ± 36 s compared to 313 ± 65 s for the conventional cine. All cardiac function parameters showed excellent correlation (r 0.978-0.996). Both sequences were considered equivalent for the assessment of LV and RV morphology. Conclusions. The 2-shot CS SAX cines can be used in clinical routine to acquire cardiac morphology in less time compared to the conventional method, with no total loss of acquisitions due to nondiagnostic quality.

TRIAL REGISTRATION

ISRCTN12344380. Registered 20 November 2020, retrospectively registered.

Classification of myocardial fibrosis in DE-MRI based on semi-supervised semantic segmentation and dual attention mechanism

OBJECTIVE

It is essential to utilize cardiac delayed-enhanced magnetic resonance imaging (DE-MRI) to diagnose cardiovascular disease. By segmenting myocardium DE-MRI images, it provides critical information for the evaluation and treatment of myocardial infarction. As a consequence, it is vital to investigate the segmentation and classification technique of myocardial DE-MRI.

METHODS

Firstly, an end-to-end minimally supervised and semi-supervised semantic DE-MRI myocardial fibrosis segmentation framework is proposed, which combines image classification and semantic segmentation branches based on the self-attention mechanism. Following that, a residual hole network fused with the dual attention mechanism was built, and a double attention metabolic pathway classification method for cardiac fibrosis in DE-MRI images was developed.

RESULTS

By adding pixel-level labels to an extra 40 training images, the segmentation model may enhance semantic segmentation performance by 2.6 percent (from 61.2 percent to 63.8 percent). When the number of pixel-level labels is increased to 80, semi-supervised feature extraction increases by 4.7 percent when compared to weakly guided semantic segmentation. Adding an attention mechanism to the critical network DRN (Deep Residual Network) can increase the classifier's performance by a small amount. Experiments revealed that the models worked effectively.

CONCLUSION

This paper investigates the segmentation and classification of cardiac fibrosis in DE-MRI data using a semi-supervised semantic segmentation and dual attention mechanism, dealing with the issue that existing segmentation algorithms have difficulty segmenting myocardial fibrosis tissue. In the future, we can consider optimizing the design of the attention module to reduce the module computation.

Whole-Heart High-Resolution Late Gadolinium Enhancement: Techniques and Clinical Applications

In cardiovascular magnetic resonance, late gadolinium enhancement (LGE) has become the cornerstone of myocardial tissue characterization. It is widely used in clinical routine to diagnose and characterize the myocardial tissue in a wide range of ischemic and nonischemic cardiomyopathies. The recent growing interest in imaging left atrial fibrosis has led to the development of novel whole‐heart high‐resolution late gadolinium enhancement (HR‐LGE) techniques. Indeed, conventional LGE is acquired in multiple breath‐holds with limited spatial resolution: ~1.4–1.8 mm in plane and 6–8 mm slice thickness, according to the Society for Cardiovascular Magnetic Resonance standardized guidelines. Such large voxel size prevents its use in thin structures such as the atrial or right ventricular walls. Whole‐heart 3D HR‐LGE images are acquired in free breathing to increase the spatial resolution (up to 1.3 × 1.3 × 1.3 mm³) and offer a better detection and depiction of focal atrial fibrosis. The downside of this increased resolution is the extended scan time of around 10 min, which hampers the spread of HR‐LGE in clinical practice. Initially introduced for atrial fibrosis imaging, HR‐LGE interest has evolved to be a tool to detect small scars in the ventricles and guide ablation procedures. Indeed, the detection of scars, nonvisible with conventional LGE, can be crucial in the diagnosis of myocardial infarction with nonobstructed coronary arteries, in the detection of the arrhythmogenic substrate triggering ventricular arrhythmia, and improve the confidence of clinicians in the challenging diagnoses such as the arrhythmogenic right ventricular cardiomyopathy. HR‐LGE also offers a precise visualization of left ventricular scar morphology that is particularly useful in planning ablation procedures and guiding them through the fusion of HR‐LGE images with electroanatomical mapping systems. In this narrative review, we attempt to summarize the technical particularities of whole‐heart HR‐LGE acquisition and provide an overview of its clinical applications with a particular focus on the ventricles.

Comparison of free breathing 3D mDIXON with single breath-hold 3D inversion recovery sequences for the assessment of Late Gadolinium Enhancement

PURPOSE

To evaluate the technical and diagnostic performance of three dimensional (3D) mDIXON versus 3D inversion recovery (3D VIAB) and 3D spectral presaturation with inversion recovery (3D SPIR) late gadolinium enhancement (LGE) sequences.

METHODS

A total of 78 patients (50 males and 28 females, age 49 ± 18 years) with 1.5 T CMR examination including three different 3D LGE sequences (3D mDIXON, 3D VIAB, and 3D SPIR) were evaluated for technical and diagnostic performance by two readers. Qualitative scores and quantitative signal and contrast-to-noise ratios were compared among sequences. Qualitative comparisons were made using Friedman and Wilcoxon signed rank tests. Quantitative comparisons were made using one way ANOVA. Reader agreements were tested using Cohen's Kappa. Any p-value <0.05 was significant.

RESULTS

19 out of 78 patients (24 %) were excluded due to poor (grade 4) image quality and 29 patients were excluded due to absence of LGE. For the remaining 30 patients, free breathing 3D mDIXON showed higher confidence in diagnosis of subepicardial LGE (p-value < 0.05). 3D mDIXON outperformed 3D SPIR in both visualization of LGE (p = 0.02) and quality of fat suppression (p = 0.001). Nevertheless, 3D mDIXON showed lower image quality compared to the other two sequences.

CONCLUSION

Free breathing 3D mDIXON is a diagnostic problem-solving tool, especially when making a diagnosis of subepicardial enhancement and/or fat suppression is needed, owing to its high spatial resolution and robust fat suppression. Choice of 3D LGE sequence should be based on patient's breath-hold ability, diagnostic needs, and institutional availability considering the strengths and limitations of each sequence.

Comparison of 3D and 2D late gadolinium enhancement magnetic resonance imaging in patients with acute and chronic myocarditis

We compared a fast, single breath-hold three dimensional LGE sequence (3D LGE) with an established two dimensional multi breath-hold sequence (2D LGE) and evaluated image quality and the amount of myocardial fibrosis in patients with acute and chronic myocarditis. 3D LGE and 2D LGE (both spatial resolution 1.5 × 1.5 mm², slice-thickness 8 mm, field of view 350 × 350 mm²) were acquired in 25 patients with acute myocarditis (mean age 40 ± 18 years, 7 female) and 27 patients with chronic myocarditis (mean age 44 ± 22 years, 9 female) on a 1.5 T MR system. Image quality was evaluated by two independent, blinded readers using a 5-point Likert scale. Total myocardial mass, fibrotic mass and total fibrotic tissue percentage were quantified for both sequences in both groups. There was no significant difference in image quality between 3D und 2D acquisitions in patients with acute (p = 0.8) and chronic (p = 0.5) myocarditis. No significant differences between 3D and 2D acquisitions could be shown for myocardial mass (acute p = 0.2; chronic p = 0.3), fibrous tissue mass (acute p = 0.7; chronic p = 0.1) and total fibrous percentage (acute p = 0.4 and chronic p = 0.2). Inter-observer agreement was substantial to almost perfect. Acquisition time was significantly shorter for 3D LGE (24 ± 5 s) as compared to 2D LGE (350 ± 58 s, p < 0.001). In patients with acute and chronic myocarditis 3D LGE imaging shows equal diagnostic quality compared to standard 2D LGE imaging but with significantly reduced acquisition time.

Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging

PURPOSE OF REVIEW

Myocardial fibrosis (MF) arises due to myocardial infarction and numerous cardiac diseases. MF may lead to several heart disorders, such as heart failure, arrhythmias, and ischemia. Cardiac magnetic resonance (CMR) imaging techniques, such as late gadolinium enhancement (LGE) CMR, enable non-invasive assessment of MF in the left ventricle (LV). Manual assessment of MF on CMR is a tedious and time-consuming task that is subject to high observer variability. Automated segmentation and quantification of MF is important for risk stratification and treatment planning in patients with heart disorders. This article aims to review the machine learning (ML)-based methodologies developed for MF quantification in the LV using CMR images.

RECENT FINDINGS

With the availability of relatively large labeled datasets supervised learning methods based on both conventional ML and state-of-the-art deep learning (DL) methods have been successfully applied for automated segmentation of MF. The incorporation of ML algorithms into imaging techniques such as 3D LGE CMR permits fast characterization of MF on CMR imaging and may enhance the diagnosis and prognosis of patients with heart disorders. Concurrently, the studies using cine CMR images have revealed that accurate segmentation of MF on non-contrast CMR imaging might be possible. The application of ML/DL tools in CMR image interpretation is likely to result in accurate and efficient quantification of MF.

Comparison of fast multi-slice and standard segmented techniques for detection of late gadolinium enhancement in ischemic and non-ischemic cardiomyopathy - a prospective clinical cardiovascular magnetic resonance trial

BACKGROUND

Segmented phase-sensitive inversion recovery (PSIR) cardiovascular magnetic resonance (CMR) sequences are reference standard for non-invasive evaluation of myocardial fibrosis using late gadolinium enhancement (LGE). Several multi-slice LGE sequences have been introduced for faster acquisition in patients with arrhythmia and insufficient breathhold capability. The aim of this study was to assess the accuracy of several multi-slice LGE sequences to detect and quantify myocardial fibrosis in patients with ischemic and non-ischemic myocardial disease.

METHODS

Patients with known or suspected LGE due to chronic infarction, inflammatory myocardial disease and hypertrophic cardiomyopathy (HCM) were prospectively recruited. LGE images were acquired 10-20 min after administration of 0.2 mmol/kg gadolinium-based contrast agent. Three different LGE sequences were acquired: a segmented, single-slice/single-breath-hold fast low angle shot PSIR sequence (FLASH-PSIR), a multi-slice balanced steady-state free precession inversion recovery sequence (bSSFP-IR) and a multi-slice bSSFP-PSIR sequence during breathhold and free breathing. Image quality was evaluated with a 4-point scoring system. Contrast-to-noise ratios (CNR) and acquisition time were evaluated. LGE was quantitatively assessed using a semi-automated threshold method. Differences in size of fibrosis were analyzed using Bland-Altman analysis.

RESULTS

Three hundred twelve patients were enrolled (n = 212 chronic infarction, n = 47 inflammatory myocardial disease, n = 53 HCM) Of which 201 patients (67,4%) had detectable LGE (n = 143 with chronic infarction, n = 27 with inflammatory heart disease and n = 31 with HCM). Image quality and CNR were best on multi-slice bSSFP-PSIR. Acquisition times were significantly shorter for all multi-slice sequences (bSSFP-IR: 23.4 ± 7.2 s; bSSFP-PSIR: 21.9 ± 6.4 s) as compared to FLASH-PSIR (361.5 ± 95.33 s). There was no significant difference of mean LGE size for all sequences in all study groups (FLASH-PSIR: 8.96 ± 10.64 g; bSSFP-IR: 8.69 ± 10.75 g; bSSFP-PSIR: 9.05 ± 10.84 g; bSSFP-PSIR free breathing: 8.85 ± 10.71 g, p > 0.05). LGE size was not affected by arrhythmia or absence of breathhold on multi-slice LGE sequences.

CONCLUSIONS

Fast multi-slice and standard segmented LGE sequences are equivalent techniques for the assessment of myocardial fibrosis, independent of an ischemic or non-ischemic etiology. Even in patients with arrhythmia and insufficient breathhold capability, multi-slice sequences yield excellent image quality at significantly reduced scan time and may be used as standard LGE approach.

TRIAL REGISTRATION

ISRCTN48802295 (retrospectively registered).

Cardiovascular magnetic resonance myocardial T1 mapping to detect and quantify cardiac involvement in familial amyloid polyneuropathy

OBJECTIVES

This study sought to explore the potential role of non-contrast T1 mapping for the detection and quantification of cardiac involvement in familial amyloid polyneuropathy (FAP).

METHODS

Japanese patients with FAP [n = 41, age 53.2 ± 13.9 years, genotype Val30Met (n = 25), non-Val30Met (n = 16)] underwent cardiac magnetic resonance imaging that included T1 mapping (saturation-recovery method) and late gadolinium-enhanced (LGE) imaging on a 3.0-T MR scanner. Their native T1 was measured on mid-ventricular short-axis images and compared with 30 controls.

RESULTS

Of the 41 FAP patients 29 were LGE positive. The native T1 was significantly higher in FAP patients than in the controls (1,634.1 ± 126.3 ms vs. 1,432.4 ± 69.0 ms, p < 0.01), significantly higher in LGE-positive- than LGE-negative FAP patients (1,687.1 ± 104.4 ms vs. 1,505.4 ± 68.5 ms, p < 0.01), and significantly higher in LGE-negative FAP patients than the controls (p < 0.01). A native T1 cutoff value of 1,610 ms yielded 85.4% accuracy for identifying LGE-positive FAP. The native T1 significantly correlated with the interventricular septum wall thickness, the left ventricular mass, the LGE volume, the plasma B-type natriuretic peptide level, and the E/e' ratio (all p < 0.01).

CONCLUSION

T1 mapping is of high diagnostic accuracy for the detection of LGE-positive FAP. The native myocardial T1 may be correlated with the severity of cardiac amyloid deposition.

KEY POINTS

• The native T1 was higher in FAP patients than the controls. • The native T1 was higher in LGE-positive- than LGE-negative FAP patients. • The native T1 was higher in LGE-negative FAP patients than the controls. • The native T1 correlated with clinical markers of systolic and diastolic dysfunction. • Myocardial T1 mapping is of high diagnostic accuracy for detecting LGE-positive FAP.

Late gadolinium enhancement cardiac imaging on a 3T scanner with parallel RF transmission technique: prospective comparison of 3D-PSIR and 3D-IR

OBJECTIVE

To qualitatively and quantitatively compare different late gadolinium enhancement (LGE) sequences acquired at 3T with a parallel RF transmission technique.

METHODS

One hundred and sixty participants prospectively enrolled underwent a 3T cardiac MRI with 3 different LGE sequences: 3D Phase-Sensitive Inversion-Recovery (3D-PSIR) acquired 5 minutes after injection, 3D Inversion-Recovery (3D-IR) at 9 minutes and 3D-PSIR at 13 minutes. All LGE-positive patients were qualitatively evaluated both independently and blindly by two radiologists using a 4-level scale, and quantitatively assessed with measurement of contrast-to-noise ratio and LGE maximal surface. Statistical analyses were calculated under a Bayesian paradigm using MCMC methods.

RESULTS

Fifty patients (70 % men, 56yo ± 19) exhibited LGE (62 % were post-ischemic, 30 % related to cardiomyopathy and 8 % post-myocarditis). Early and late 3D-PSIR were superior to 3D-IR sequences (global quality, estimated coefficient IR > early-PSIR : -2.37 CI = [-3.46 ; -1.38], prob(coef > 0) = 0 % and late-PSIR > IR : 3.12 CI = [0.62 ; 4.41], prob(coef > 0) = 100 %), LGE surface estimated coefficient IR > early-PSIR: -0.09 CI = [-1.11; -0.74], prob(coef > 0) = 0 % and late-PSIR > IR : 0.96 CI = [0.77; 1.15], prob(coef > 0) = 100 %). Probabilities for late PSIR being superior to early PSIR concerning global quality and CNR were over 90 %, regardless of the aetiological subgroup.

CONCLUSIONS

In 3T cardiac MRI acquired with parallel RF transmission technique, 3D-PSIR is qualitatively and quantitatively superior to 3D-IR.

KEY POINTS

• Late gadolinium enhancement is an essential part of a cardiac MRI examination • PSIR and IR sequences are the two possible options for LGE imaging • At 3T with parallel RF transmission, PSIR sequences are significantly better • One LGE sequence is sufficient, allowing an optimization of the acquisition time.

Comparison of semi-automated scar quantification techniques using high-resolution, 3-dimensional late-gadolinium-enhancement magnetic resonance imaging

The quantification and modeling of myocardial scar is of expanding interest for image-guided therapy, particularly in the field of arrhythmia management. Migration towards high-resolution, three-dimensional (3D) MRI techniques for spatial mapping of myocardial scar provides superior spatial registration. However, to date no systematic comparison of available approaches to 3D scar quantification have been performed. In this study we compare the reproducibility of six 3D scar segmentation algorithms for determination of left ventricular scar volume. Additionally, comparison to two-dimensional (2D) scar quantification and 3D manual segmentation is performed. Thirty-five consecutive patients with ischemic cardiomyopathy were recruited and underwent conventional 2D late gadolinium enhancement (LGE) and 3D isotropic LGE imaging (voxel size 1.3 mm(3)) using a 3 T scanner. 3D LGE datasets were analyzed using six semi-automated segmentation techniques, including the signal threshold versus reference mean (STRM) technique at >2, >3, >5 and >6 standard deviations (SD) above reference myocardium, the full width at half maximum (FWHM) technique, and an optimization-based technique called hierarchical max flow (HMF). The mean ejection fraction was 32.1 ± 12.7 %. Reproducibility was greatest for HMF and FWHM techniques with intra-class correlation coefficient values ≥0.95. 3D scar quantification and modeling is clinically feasible in patients with ischemic cardiomyopathy. While several approaches show acceptable reproducibility, HMF appears superior due to maintenance of accuracy towards manual segmentations.

Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images

We propose a novel multi-region image segmentation approach to extract myocardial scar tissue from 3-D whole-heart cardiac late-enhancement magnetic resonance images in an interactive manner. For this purpose, we developed a graphical user interface to initialize a fast max-flow-based segmentation algorithm and segment scar accurately with progressive interaction. We propose a partially-ordered Potts (POP) model to multi-region segmentation to properly encode the known spatial consistency of cardiac regions. Its generalization introduces a custom label/region order constraint to Potts model to multi-region segmentation. The combinatorial optimization problem associated with the proposed POP model is solved by means of convex relaxation, for which a novel multi-level continuous max-flow formulation, i.e., the hierarchical continuous max-flow (HMF) model, is proposed and studied. We demonstrate that the proposed HMF model is dual or equivalent to the convex relaxed POP model and introduces a new and efficient hierarchical continuous max-flow based algorithm by modern convex optimization theory. In practice, the introduced hierarchical continuous max-flow based algorithm can be implemented on the parallel GPU to achieve significant acceleration in numerics. Experiments are performed in 50 whole heart 3-D LE datasets, 35 with left-ventricular and 15 with right-ventricular scar. The experimental results are compared to full-width-at-half-maximum and Signal-threshold to reference-mean methods using manual expert myocardial segmentations and operator variabilities and the effect of user interaction are assessed. The results indicate a substantial reduction in image processing time with robust accuracy for detection of myocardial scar. This is achieved without the need for additional region constraints and using a single optimization procedure, substantially reducing the potential for error.