Fractal Analysis: Prognostic Value of Left Ventricular Trabecular Complexity Cardiovascular MRI in Participants with Hypertrophic Cardiomyopathy

Fractal analysis of left ventricular trabeculae in heart failure with preserved ejection fraction patients with multivessel coronary artery disease

OBJECTIVES

Endocardial trabeculae undergo varicose changes and hyperplasia in response to hemodynamic influences and are a variable phenotype reflecting changes in disease. Fractal analysis has been used to analyze the complexity of endocardial trabeculae in a variety of cardiomyopathies. The aim of this paper was to quantify the myocardial trabecular complexity through fractal analysis and to investigate its predictive value for the diagnosis of heart failure with preserved ejection fraction (HFpEF) in patients with multivessel coronary artery disease (CAD).

METHODS

The retrospective study population consisted of 97 patients with multivessel CAD, 39 of them were diagnosed with HFpEF, while 46 healthy volunteers were recruited as controls. Fractal dimension (FD) was obtained through fractal analysis of endocardial trabeculae on LV short-axis cine images. Logistic regression analyses were used to confirm the predictors and compare different prediction models.

RESULTS

Mean basal FD was significantly higher in patients with HFpEF than in patients without HFpEF or in the healthy group (median: 1.289; IQR: 0.078; p < 0.05). Mean basal FD was also a significant independent predictor in univariate and multivariate logistic regression (OR: 1.107 and 1.043, p < 0.05). Furthermore, adding FD to the prediction model improved the calibration and accuracy of the model (c-index: 0.806).

CONCLUSION

The left ventricular FD obtained with fractal analysis can reflect the complexity of myocardial trabeculae and has an independent predictive value for the diagnosis of HFpEF in patients with multivessel CAD. Including FD into the diagnostic model can help improve the diagnosis.

CRITICAL RELEVANCE STATEMENT

Differences show in the complexity of endocardial trabeculae in multivessel coronary artery disease patients, and obtaining fractal dimensions (FD) by fractal analysis can help identify heart failure with preserved ejection fraction (HFpEF) patients.

KEY POINTS

The complexity of myocardial trabeculae differs among patients with multivessel coronary artery disease. Left ventricular fractal dimensions can reflect the complexity of the myocardial trabecular. Fractal dimensions have predictive value for the diagnosis of heart failure with preserved ejection fraction.

The Prognostic Value of Left Ventricular Entropy From T1 Mapping in Patients With Hypertrophic Cardiomyopathy

Background

The prognostic value of left ventricular (LV) entropy in hypertrophic cardiomyopathy (HCM) is unclear.

Objectives

This study aimed to assess the prognostic value of LV entropy from T1 mapping in HCM.

Methods

A total of 748 participants with HCM, who underwent cardiovascular magnetic resonance (CMR), were consecutively enrolled. LV entropy was quantified by native T1 mapping. A competing risk analysis and a Cox proportional hazards regression analysis were performed to identify potential associations of LV entropy with sudden cardiac death (SCD) and cardiovascular death (CVD), respectively.

Results

A total of 40 patients with HCM experienced SCD, and 65 experienced CVD during a median follow-up of 43 months. Participants with increased LV entropy (≥4.06) were more likely to experience SCD and CVD (all P < 0.05) in the entire study cohort or the subgroup with low late gadolinium enhancement (LGE) extent (<15%). After adjustment for the European Society of Cardiology predictors and the presence of high LGE extent (≥15%), LV mean entropy was an independent predictor for SCD (HR: 1.03; all P < 0.05) by the multivariable competing risk analysis and CVD (HR: 1.06; 95% CI: 1.03-1.09; P < 0.001) by multivariable Cox regression analysis.

Conclusions

LV mean entropy derived from native T1 mapping, reflecting myocardial tissue heterogeneity, was an independent predictor of SCD and CVD in participants with HCM. (Cardiac Magnetic Resonance Imaging Clinical Application Registration Study; ChiCTR1900024094).

Fractal analysis of left ventricular trabeculae in post-STEMI: from acute to chronic phase

PURPOSE

The temporal evolution of ventricular trabecular complexity and its correlation with major adverse cardiovascular events (MACE) remain indeterminate in patients presenting with acute ST elevation myocardial infarction (STEMI).

METHODS

This retrospective analysis enrolled patients undergoing primary percutaneous coronary intervention (pPCI) for acute STEMI, possessing cardiac magnetic resonance (CMR) data in the acute (within 7 days), subacute (1 month after pPCI), and chronic phases (6 months after pPCI) from January 2015 to January 2020 at the three participating sites. Fractal dimensions (FD) were measured for the global, infarct, and remote regions of left ventricular trabeculae during each phase. The potential association of FD with MACE was analyzed using multivariate Cox regression.

RESULTS

Among the 200 analyzed patients (182 men; median age, 61 years; age range, 50-66 years), 37 (18.5%) encountered MACE during a median follow-up of 31.2 months. FD exhibited a gradual decrement (global FD at acute, subacute, and chronic phases: 1.253 ± 0.049, 1.239 ± 0.046, 1.230 ± 0.045, p < 0.0001), with a more pronounced decrease observed in patients subsequently experiencing MACE (p < 0.001). The global FD at the subacute phase correlated with MACE (hazard ratio 0.89 (0.82, 0.97), p = 0.01), and a global FD value below 1.26 was associated with a heightened risk.

CONCLUSION

In patients post-STEMI, the global FD, serving as an indicator of left ventricular trabeculae complexity, independently demonstrated an association with subsequent major adverse cardiovascular events, beyond factors encompassing left ventricular ejection fraction, indexed left ventricular end-diastolic volume, infarct size, heart rate, NYHA class, and post-pPCI TIMI flow.

CRITICAL RELEVANCE STATEMENT

In patients who have had an ST-segment elevation myocardial infarction, global fractal dimension, as a measure of left ventricular trabeculae complexity, provided independent association with subsequent major adverse cardiovascular event.

KEY POINTS

• Global and regional FD decreased after STEMI, and more so in patients with subsequent MACE. • Lower global FD at the subacute phase and Δglobal FD from acute to subacute phase were associated with subsequent MACE besides clinical and CMR factors. • Global FD at the subacute phase independently correlated with MACE and global FD value below 1.26 was associated with higher risk.

Current perspectives of sudden cardiac death management in hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant disorder characterized by left ventricular hypertrophy. Sudden cardiac death (SCD) is a rare but the most catastrophic complication in patients with HCM. Implantable cardioverter-defibrillators (ICDs) are widely recognized as effective preventive measures for SCD. Individualized risk stratification and early intervention in HCM can significantly improve patient prognosis. In this study, we review the latest findings regarding pathogenesis, risk stratification, and prevention of SCD in HCM patients, highlighting the clinic practice of cardiovascular magnetic resonance imaging for SCD management.

Prognostic value of left ventricular trabeculae fractal analysis in patients with dilated cardiomyopathy

BACKGROUND

The prognostic value of left ventricular (LV) myocardial trabecular complexity on cardiovascular magnetic resonance (CMR) in dilated cardiomyopathy (DCM) remains unknown. This study aimed to evaluate the prognostic value of LV myocardial trabecular complexity using fractal analysis in patients with DCM.

METHODS

Consecutive patients with DCM who underwent CMR between March 2017 and November 2021 at two hospitals were prospectively enrolled. The primary endpoints were defined as the combination of all-cause death and heart failure hospitalization. The events of cardiac death alone were defined as the secondary endpoints.LV trabeculae complexity was quantified by measuring the fractal dimension (FD) of the endocardial border based on fractal geometry on CMR. Cox proportional hazards regression and Kaplan-Meier survival analysis were used to examine the association between variables and outcomes. The incremental prognostic value of FD was assessed in nested models.

RESULTS

A total of 403 patients with DCM (49.31 ± 14.68 years, 69% male) were recruited. After a median follow-up of 43 months (interquartile range, 28-55 months), 87 and 24 patients reached the primary and secondary endpoints, respectively. Age, heart rate, New York Heart Association functional class >II, N-terminal pro-B-type natriuretic peptide, LV ejection fraction, LV end-diastolic volume index, LV end-systolic volume index, LV mass index, presence of late gadolinium enhancement, global FD, LV mean apical FD, and LV maximal apical FD were univariably associated with the outcomes (all P < 0.05). After multivariate adjustment, LV maximal apical FD remained a significant independent predictor of outcome [hazard ratio = 1.179 (1.116, 1.246), P < 0.001]. The addition of LV maximal apical FD in the nested models added incremental prognostic value to other common clinical and imaging risk factors (all <0.001; C-statistic: 0.84-0.88, P < 0.001).

CONCLUSION

LV maximal apical FD was an independent predictor of the adverse clinical outcomes in patients with DCM and provided incremental prognostic value over conventional clinical and imaging risk factors.

Novel Magnetic Resonance Imaging Tools for Hypertrophic Cardiomyopathy Risk Stratification

Hypertrophic cardiomyopathy (HCM) is a common genetic disorder with a well described risk of sudden cardiac death; however, risk stratification has remained a challenge. Recently, novel parameters in cardiac magnetic resonance imaging (CMR) have shown promise in helping to improve upon current risk stratification paradigms. In this manuscript, we have reviewed novel CMR risk markers and their utility in HCM. The results of the review showed that T1, extracellular volume, CMR feature tracking, and other miscellaneous novel CMR variables have the potential to improve sudden death risk stratification and may have additional roles in diagnosis and prognosis. The strengths and weaknesses of these imaging techniques, and their potential utility and implementation in HCM risk stratification are discussed.

Fractal Analysis of Left Ventricular Trabeculae in Patients with End-Stage Renal Disease: A Random Survival Tree Analysis

BACKGROUND

The complexity of left ventricular (LV) trabeculae is related to the prognosis of several cardiovascular diseases.

PURPOSE

To evaluate the prognostic value of LV trabecular complexity in patients with end-stage renal disease (ESRD).

STUDY TYPE

Prospective outcome study.

POPULATION

207 participants on maintenance dialysis, divided into development (160 patients from 2 centers) and external validation (47 patients from a third center) cohorts, and 72 healthy controls.

FIELD STRENGTH

3.0T, steady-state free precession (SSFP) and modified Look-Locker imaging sequences.

ASSESSMENT

All participants had their trabecular complexity quantified by fractal analysis using cine SSFP images. Patients were followed up every 2 weeks until April 2023, or endpoint events happened. Random Forest (RF) and Cox regression models including age, diabetes, LV mass index, mean basal fractal dimension (FD), and left atrial volume index, were developed to predict major adverse cardiac events (MACE). Patients were divided into low- and high-risk groups based on scores derived from the RF model and survival compared.

STATISTICAL TESTS

Receiver operating characteristic curve analysis; Kaplan-Meier survival analysis with log rank tests; Harrel's C-index to assess model performance. A P value <0.05 was considered statistically significant.

RESULTS

Fifty-five patients (26.57%) experienced MACE during a median follow-up time of 21.83 months. An increased mean basal FD (≥1.324) was associated with a significantly higher risk of MACE. The RF model (C-index: 0.81) had significantly better discrimination than the Cox regression model (C-index: 0.74). Participants of the external validation dataset classified into the high-risk group had a hazard of experiencing MACE increased by 12.29 times compared to those in the low-risk group.

DATA CONCLUSION

LV basal FD was an independent predictor for MACE in patients with ESRD. Reliable risk stratification models could be generated based on LV basal FD and other MRI variables using RF analysis.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

3D Fractal Dimension Analysis: Prognostic Value of Right Ventricular Trabecular Complexity in Participants with Arrhythmogenic Cardiomyopathy

BACKGROUND

Arrhythmogenic cardiomyopathy (ACM) is characterized by progressive myocardial fibro-fatty infiltration accompanied by trabecular disarray. Traditionally, two-dimensional (2D) instead of 3D fractal dimension (FD) analysis has been used to evaluate trabecular disarray. However, the prognostic value of trabecular disorder assessed by 3D FD measurement remains unclear.

PURPOSE

To investigate the prognostic value of right ventricular trabecular complexity in ACM patients using 3D FD analysis based on cardiac MR cine images.

STUDY TYPE

Retrospective.

POPULATION

85 ACM patients (mean age: 45 ± 17 years, 52 male).

FIELD STRENGTH/SEQUENCE

3.0T/cine imaging, T2-short tau inversion recovery (T2-STIR), and late gadolinium enhancement (LGE).

ASSESSMENT

Using cine images, RV (right ventricular) volumetric and functional parameters were obtained. RV trabecular complexity was measured with 3D fractal analysis by box-counting method to calculate 3D-FD. Cox and logistic regression models were established to evaluate the prognostic value of 3D-FD for major adverse cardiac events (MACE).

STATISTICAL TESTS

Cox regression and logistic regression to explore the prognostic value of 3D-FD. C-index, time-dependent receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) to evaluate the incremental value of 3D-FD. Intraclass correlation coefficient for interobserver variability. P < 0.05 indicated statistical significance.

RESULTS

26 MACE were recorded during the 60 month follow-up (interquartile range: 48-67 months). RV 3D-FD significantly differed between ACM patients with MACE (2.67, interquartile range: 2.51 ~ 2.81) and without (2.52, interquartile range: 2.40 ~ 2.67) and was a significant independent risk factor for MACE (hazard ratio, 1.02; 95% confidence interval: 1.01, 1.04). In addition, prognostic model fitness was significantly improved after adding 3D-FD to RV global longitudinal strain, LV involvement, and 5-year risk score separately.

DATA CONCLUSION

The myocardial trabecular complexity assessed through 3D FD analysis was found associated with MACE and provided incremental prognostic value beyond conventional ACM risk factors.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 1.

Prognostic value of right ventricular trabecular complexity in patients with arrhythmogenic cardiomyopathy

OBJECTIVES

The present study aimed to investigate the incremental prognostic value of the right ventricular fractal dimension (FD), a novel marker of myocardial trabecular complexity by cardiac magnetic resonance (CMR) in patients with arrhythmogenic cardiomyopathy (ACM).

METHODS

Consecutive patients with ACM undergoing CMR were followed up for major cardiac events, including sudden cardiac death, aborted cardiac arrest, and appropriate implantable cardioverter defibrillator intervention. Prognosis prediction was compared by Cox regression analysis. We established a multivariable model supplemented with RV FD and evaluated its discrimination by Harrell's C-statistic. We compared the category-free, continuous net reclassification improvement (cNRI) and integrated discrimination index (IDI) before and after the addition of FD.

RESULTS

A total of 105 patients were prospectively included from three centers and followed up for a median of 60 (48, 66) months; experienced 36 major cardiac events were recorded. Trabecular FD displayed a strong unadjusted association with major cardiac events (p < 0.05). In the multivariable Cox regression analysis, RV maximal apical FD maintained an independent association with major cardiac events (hazard ratio, 1.31 (1.11-1.55), p < 0.002). The Hosmer-Lemeshow goodness of fit test displayed good fit (X2 = 0.68, p = 0.99). Diagnostic performance was significantly improved after the addition of RV maximal apical FD to the multivariable baseline model, and the continuous net reclassification improvement increased 21% (p = 0.001), and the integrated discrimination index improved 16% (p = 0.045).

CONCLUSIONS

In patients with ACM, CMR-assessed myocardial trabecular complexity was independently correlated with adverse cardiovascular events and provided incremental prognostic value.

CLINICAL RELEVANCE STATEMENT

The application of FD values for assessing RV myocardial trabeculae may become an accessible and promising parameter in monitoring and early diagnosis of risk factors for adverse cardiovascular events in patients with ACM.

KEY POINTS

• Ventricular trabecular morphology, a novel quantitative marker by CMR, has been explored for the first time to determine the severity of ACM. • Patients with higher maximal apical fractal dimension of RV displayed significantly higher cumulative incidence of major cardiac events. • RV maximal apical FD was independently associated with major cardiac events and provided incremental prognostic value in patients with ACM.

Fractal analysis: Left ventricular trabecular complexity cardiac MRI adds independent risks for heart failure with preserved ejection fraction in participants with end-stage renal disease

PURPOSE

To measure left ventricular (LV) trabecular complexity by fractal dimension (FD) in patients with end-stage renal disease (ESRD), and assess whether FD was an independent risk factor for heart failure with preserved ejection fraction (HFpEF), or a significant predictor for adverse outcome in this population.

METHODS

The study retrospectively enrolled 104 participants with ESRD who underwent 3.0 T cardiac magnetic resonance imaging (MRI) from June 2018 to November 2020. LV trabeculation was quantified with fractal analysis of short-axis cine slices to estimate the FD. Logistic regression analyses were used to evaluate FD and cardiac MRI parameters and to find independent risk predictors. Cox proportional hazard regression was used to investigate the association between FD and MACE.

RESULTS

LV FD was higher in in the HFpEF group than those in the non-HFpEF group, with the greatest difference near the base of the ventricle. Age, minimum left atrial volume index, and LV mean basal FD were independent predictors for HFpEF in patients with ESRD. Combining the mean basal FD with typical predictive factors resulted in a C-index (0.902 vs 0.921), which was not significantly higher. Same improvements were found for net reclassification improvement [0.642; 95% confidence interval (CI), 0.254-1.029] and integrated discrimination index (0.026; 95% CI, 0.008-0.061). Participants with a LV global FD above the cutoff value (1.278) had higher risks of MACE in ESRD patients.

CONCLUSIONS

LV trabecular complexity measured by FD was an independent risk factor for HFpEF, and a significant predictor for MACE among patients with ESRD.

Late gadolinium enhancement entropy as a new measure of myocardial tissue heterogeneity for prediction of adverse cardiac events in patients with hypertrophic cardiomyopathy

OBJECTIVES

Entropy is a new late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR)-derived parameter that is independent of signal intensity thresholds. Entropy can be used to measure myocardial tissue heterogeneity by comparing full pixel points of tissue images. This study investigated the incremental prognostic value of left ventricular (LV) entropy in patients with hypertrophic cardiomyopathy (HCM).

METHODS

This study enrolled 337 participants with HCM who underwent 3.0-T CMR. The LV entropy was obtained by calculating the probability distribution of the LV myocardial pixel signal intensities of the LGE sequence. Patients who underwent CMR imaging were followed up for endpoints. The primary endpoint was defined as readmission to the hospital owing to heart failure. The secondary endpoint was the composite of the primary endpoint, sudden cardiac death and non-cardiovascular death.

RESULTS

During the median follow-up of 24 months ± 13 (standard deviation), 43 patients who reached the primary and secondary endpoints had a higher entropy (6.20 ± 0.45, p < 0.001). The patients with increased entropy (≥ 5.587) had a higher risk of the primary and secondary endpoints, compared with HCM patients with low entropy (p < 0.001 for both). In addition, Cox analysis showed that LV entropy provided significant prognostic value for predicting both primary and secondary endpoints (HR: 1.291 and 1.273, all p < 0.001). Addition of LV entropy to the multivariable model improved model performance and risk reclassification (p < 0.05).

CONCLUSION

LV entropy assessed by CMR was an independent predictor of primary and secondary endpoints. LV entropy assessment contributes to improved risk stratification in patients with HCM.

CRITICAL RELEVANCE STATEMENT

Myocardial heterogeneity reflected by entropy the derived parameter of LGE has prognostic value for adverse events in HCM. The measurement of LV entropy helped to identify patients with HCM who were at risk for heart failure and sudden cardiac death.

KEY POINTS

• Left ventricular entropy can reflect myocardial heterogeneity in HCM patients. • Left ventricular entropy was significantly higher in HCM patients who reached endpoint events. • Left ventricular entropy helps to predict the occurrence of heart failure and death in HCM patients.

Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study

PURPOSE

In the past decade, there has been a rapid increase in the development of automatic cardiac segmentation methods. However, the automatic quality control (QC) of these segmentation methods has received less attention. This study aims to address this gap by developing an automatic pipeline that incorporates DL-based cardiac segmentation and radiomics-based quality control.

METHODS

In the DL-based localization and segmentation part, the entire heart was first located and cropped. Then, the cropped images were further utilized for the segmentation of the right ventricle cavity (RVC), myocardium (MYO), and left ventricle cavity (LVC). As for the radiomics-based QC part, a training radiomics dataset was created with segmentation tasks of various quality. This dataset was used for feature extraction, selection, and QC model development. The model performance was then evaluated using both internal and external testing datasets.

RESULTS

In the internal testing dataset, the segmentation model demonstrated a great performance with a dice similarity coefficient (DSC) of 0.954 for whole heart segmentations. Images were then appropriately cropped to 160 × 160 pixels. The models also performed well for cardiac substructure segmentations. The DSC values were 0.863, 0.872, and 0.940 for RVC, MYO, and LVC for 2D masks and 0.928, 0.886, and 0.962 for RVC, MYO, and LVC for 3D masks with an attention-UNet. After feature selection with the radiomics dataset, we developed a series of models to predict the automatic segmentation quality and its DSC value for the RVC, MYO, and LVC structures. The mean absolute values for our best prediction models were 0.060, 0.032, and 0.021 for 2D segmentations and 0.027, 0.017, and 0.011 for 3D segmentations, respectively. Additionally, the radiomics-based classification models demonstrated a high negative detection rate of >0.85 in all 2D groups. In the external dataset, models showed similar results.

CONCLUSIONS

We developed a pipeline including cardiac substructure segmentation and QC at both the slice (2D) and subject (3D) levels. Our results demonstrate that the radiomics method possesses great potential for the automatic QC of cardiac segmentation.

Papillary-Muscle-Derived Radiomic Features for Hypertrophic Cardiomyopathy versus Hypertensive Heart Disease Classification

Purpose: This study aimed to assess the value of radiomic features derived from the myocardium (MYO) and papillary muscle (PM) for left ventricular hypertrophy (LVH) detection and hypertrophic cardiomyopathy (HCM) versus hypertensive heart disease (HHD) differentiation. Methods: There were 345 subjects who underwent cardiovascular magnetic resonance (CMR) examinations that were analyzed. After quality control and manual segmentation, the 3D radiomic features were extracted from the MYO and PM. The data were randomly split into training (70%) and testing (30%) datasets. Feature selection was performed on the training dataset. Five machine learning models were evaluated using the MYO, PM, and MYO+PM features in the detection and differentiation tasks. The optimal differentiation model was further evaluated using CMR parameters and combined features. Results: Six features were selected for the MYO, PM, and MYO+PM groups. The support vector machine models performed best in both the detection and differentiation tasks. For LVH detection, the highest area under the curve (AUC) was 0.966 in the MYO group. For HCM vs. HHD differentiation, the best AUC was 0.935 in the MYO+PM group. Comparing the radiomics models to the CMR parameter models for the differentiation tasks, the radiomics models achieved significantly improved the performance (p = 0.002). Conclusions: The radiomics model with the MYO+PM features showed similar performance to the models developed from the MYO features in the detection task, but outperformed the models developed from the MYO or PM features in the differentiation task. In addition, the radiomic models performed better than the CMR parameters' models.

Fractal dimension of the aortic annulus: a novel predictor of paravalvular leak after transcatheter aortic valve implantation

To evaluate the prognostic relevance of aortic annulus (AA) and left ventricular outflow tract (LVOT) Fractal dimension (FD). FD is a mathematical concept that describes geometric complexity of a structure and has been shown to predict adverse outcomes in several contexts. Computed tomography (CT) scans from the SOLVE-TAVI trial, which, in a 2 × 2 factorial design, randomized 447 patients to TAVI with the balloon-expandable Edwards Sapien 3 or the self-expanding Medtronic Evolut R, and conscious sedation or general anesthesia, were analyzed semi-automatically with a custom-built software to determine border of AA and LVOT. FD was measured by box counting using grid calibers between 0.8 and 6.75 mm and was compared between patients with none/trivial and mild/moderate paravalvular regurgitation (PVR). Overall, 122 patients had CT scans sufficient for semi-automatic PVR in 30-day echocardiography. PVR was none in 65(53.3%) patients, trace in 9(7.4%), mild in 46(37.7%), moderate in 2(1.6%) and severe in 0 patients. FD determined in diastolic images was significantly higher in patients with mild/moderate PVR (1.0558 ± 0.0289 vs. 1.0401 ± 0.0284, p = 0.017). Annulus eccentricity was the only conventional measure of AA and LVOT geometry significantly correlated to FD (R = 0.337, p < 0.01). Area under the curve (AUC) of diastolic annular FD for prediction of mild/moderate PVR in ROC analysis was 0.661 (0.542-0.779, p = 0.014). FD shows promise in prediction of PVR after TAVI. Further evaluation using larger patient numbers and refined algorithms to better understand its predictive performance is warranted.Trial Registration: www.clinicaltrials.gov , identifier: NCT02737150, date of registration: 13.04.2016.

Correlation between left ventricular fractal dimension and impaired strain assessed by cardiac MRI feature tracking in patients with left ventricular noncompaction and normal left ventricular ejection fraction

OBJECTIVES

To investigate the correlation between the extent of excessive trabeculation assessed by fractal dimension (FD) and myocardial contractility assessed by cardiac MRI feature tracking in patients with left ventricular noncompaction (LVNC) and normal left ventricular ejection fraction (LVEF).

METHODS

Forty-one LVNC patients with normal LVEF (≥ 50%) and 41 healthy controls were retrospectively included. All patients fulfilled three available diagnostic criteria on MRI. Cardiac MRI feature tracking was performed on cine images to determine left ventricular (LV) peak strains in three directions: global radial strain (GRS), global circumferential strain (GCS), and global longitudinal strain (GLS). The complexity of excessive trabeculation was quantified by fractal analysis on short-axis cine stacks.

RESULTS

Compared with controls, patients with LVNC had impaired GRS, GCS, and GLS (all p < 0.05). The global, maximal, and regional FD values of the LVNC population were all significantly higher than those of the controls (all p < 0.05). Global FD was positively correlated with the end-diastolic volume index, end-systolic volume index, and stroke volume index (r = 0.483, 0.505, and 0.335, respectively, all p < 0.05), but negatively correlated with GRS and GCS (r =  - 0.458 and 0.508, respectively, both p < 0.001). Moreover, apical FD was also weakly associated with LVEF and GLS (r =  - 0.249 and 0.252, respectively, both p < 0.05).

CONCLUSION

In patients with LVNC, LV systolic dysfunction was detected early by cardiac MRI feature tracking despite the presence of normal LVEF and was associated with excessive trabecular complexity assessed by FD.

KEY POINTS

• Left ventricular global strain was already impaired in patients with extremely prominent excessive trabeculation but normal left ventricular ejection fraction. • An increased fractal dimension was associated with impaired deformation in left ventricular noncompaction.

Cardiovascular CT and MRI in 2020: Review of Key Articles

Despite the global coronavirus pandemic, cardiovascular imaging continued to evolve throughout 2020. It was an important year for cardiac CT and MRI, with increasing prominence in cardiovascular research, use in clinical decision making, and in guidelines. This review summarizes key publications in 2020 relevant to current and future clinical practice. In cardiac CT, these have again predominated in assessment of patients with chest pain and structural heart diseases, although more refined CT techniques, such as quantitative plaque analysis and CT perfusion, are also maturing. In cardiac MRI, the major developments have been in patients with cardiomyopathy and myocarditis, although coronary artery disease applications remain well represented. Deep learning applications in cardiovascular imaging have continued to advance in both CT and MRI, and these are now closer than ever to routine clinical adoption. Perhaps most important has been the rapid deployment of MRI in enhancing understanding of the impact of COVID-19 infection on the heart. Although this review focuses primarily on articles published in Radiology, attention is paid to other leading journals where published CT and MRI studies will have the most clinical and scientific value to the practicing cardiovascular imaging specialist.

Left ventricular noncompaction in pediatric population: could cardiovascular magnetic resonance derived fractal analysis aid diagnosis?

BACKGROUND

Cardiovascular magnetic resonance (CMR) derived fractal analysis of the left ventricle (LV) has been shown in adults to be a useful quantitative measure of trabeculation with high reproducibility and accuracy for the diagnosis of LV non-compaction (LVNC). The aim of this study was to investigate the utility and feasibility of fractal analysis in children.

METHODS

Eighty-four subjects underwent CMR: (1) 28 patients with LVNC (as defined by the Petersen criteria with NC/C ratio [Formula: see text] 2.3); (2) 28 patients referred by clinicians for assessment of hyper-trabeculation and found not to qualify as LVNC (NC/C [Formula: see text] 1.8 and < 2.3); (3) 28 controls. The fractal scores for each group were presented as global and maximal fractal dimension as well as for 3 segments of the LV: basal, mid, and apical. Statistical comparison of the fractal scores between the 3 groups was performed.

RESULTS

Global fractal dimension (FD) was higher in the LVNC group than in the hyper-trabeculated group: 1.345 (SEM 0.053) vs 1.252 (SEM 0.034), p < 0.001 and higher in hyper-trabeculated group than in controls: 1.252 (SEM 0.034) vs 1.158 (SEM 0.038), p < 0.001. The highest maximum FD was in the apical portion of the LV in the LVNC group, (1.467; SEM 0.035) whereas it was in the mid ventricle in the hyper-trabeculated (1.327; SEM 0.025) and healthy groups (1.251; SEM 0.042). Fractal analysis showed lower intra- and interobserver variability than the Petersen and Jacquier methods.

CONCLUSIONS

It is technically feasible to perform fractal analysis in children using CMR and that it is quick, accurate and reproducible. Fractal scoring accurately distinguishes between LVNC, hyper-trabeculation and healthy controls as defined by the Petersen criteria.