Robustness of T1 and ECV mapping radiomics features: a between-session evaluation in young athletes

Funding Acknowledgements

Type of funding sources: None.

Introduction

Radiomics of cardiac MRI T1, T2 and extracellular volume (ECV) maps has the potential to add biomarkers that can aid in the detection and diagnosis of myocardial diseases. Recently, the feasibility of CMR mapping based radiomics to classify various myocardial diseases was demonstrated [1-6]. However, reproducibility studies have reported sensitivity of radiomics to acquisition parameters and processing steps involved concluding that only a limited number of features may be reproducible [7-8]. As CMR mapping guidelines recommend to use site-specific normal values [9], radiomics features derived likely also need careful site-specific evaluation to benchmark disease-related feature alterations.

Purpose

We aimed to assess the between-session reproducibility of radiomics features in a longitudinal dataset of MOLLI T1 and ECV maps obtained in young athletes at 1.5T.

Materials and methods

This study included data from 17 healthy subjects (15-20y; informed consent obtained) with data acquired two years apart [10] considered for this purpose as test-retest data since a prior standard analysis showed near identical average T1 (t1: 977±16 ms, t2: 982±20ms) and ECV (t1: 23.4±1.3%, t2: 23.4±1.5%). T1 mapping data was acquired on a 1.5T system (Ingenia, Philips) using MOLLI 5s(3s)3s. After motion correction and T1 and ECV map calculation [11], the left ventricular myocardium was manually delineated by two readers independently (3D Slicer [12]). In total 44 images (short and long axis) were included for each time point. The radiomics analysis resulted in 96 features per image (7 feature families, ‘shape’ excluded; no filters applied; Pyradiomics, [13]). The concordance correlation coefficient (CCC) was calculated to assess reproducibility, and features with CCCs ≥ 0.7 were considered reproducible. A coefficient of variation (CV) below 15% was considered low.

Results

Only a limited number of radiomics features had high CCC (T1: 6/96 ECV 0/96) or a low CV (T1: 32/96, ECV:30/96) in the between-session analysis. The inter-reader evaluation showed that the effect of the delineation on the results was limited. Features that were most robust in the between-session analysis were ‘first order (total)energy’ for T1 maps and ‘glcm_Autocorrelation’ for ECV maps (table 1). These results in young healthy subjects confirm previous test-retest reports [9-10]. Features with low CCC levels or high CV may however still be useful when discriminating between patient with myocardial diseases if the difference is larger than the confidence interval assessed via this reproducibility analysis.

Conclusion

In these healthy subjects, a strong variability in reproducibility of radiomics features of T1 and ECV mapping can be noted. Nonetheless, these variability measures are informative to determine features that are likely most robust when discriminating between health and disease and can be used as a benchmark towards radiomics AI-based diagnostic approaches. Top ranked features for either T1 or ECV

T1 and ECV mapping texture analysis distinguishing hypertrophic cardiomyopathy from athletes heart better than median values

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Ph.D fellowship of the Research Foundation Flanders (FWO). The Master@Heart trial is funded by the FWO.

Introduction

Differentiating intensive training induced hypertrophy from hyperthropic cardiomyopathy (HCM) is important to identify those young athletes at risk of sudden cardiac death. Swoboda and colleagues demonstrated that T1 and ECV mapping can aid such a differentiation between athletic and pathological hypertrophy, particularly in subjects with indeterminate wall thickness (1).

Recently texture analysis (TA) methods of CMR data have demonstrated improved diagnostic accuracy over conventional qualitative analysis in various heart diseases. Only few studies have applied TA to T1 and ECV mapping data (2-4). Here we aimed to demonstrate that a TA approach provides superior capacity to distinguish HCM from athlete’s heart over average native T1 and ECV values.

Purpose

It was our hypothesis that a texture analysis of T1 and ECV mapping images would identify features that could discriminate between a HCM and athlete’s heart with a higher classification accuracy (CA) than average T1 and ECV values.

Methods

This study included data from 97 subjects diagnosed with HCM (acc. to guidelines; 5) and 28 athletes that took part in the Master@Heart trial (an ongoing study assessing the beneficial effects of long-term endurance exercise for the prevention of coronary artery disease, 6).  Long and short axis T1 mapping data was acquired on a 1.5T Philips Ingenia system using MOLLI (seconds scheme). After offline motion correction and T1 and ECV map calculation (7), the left ventricular myocardium was manually delineated (3D Slicer; 8). Texture analysis of the masked images resulted in 194 features (Pyradiomics, standard settings; 9). The dataset was then split (75/25%) for training and testing purposes keeping images from the same subject within the same set. A fast correlation based filter rank was applied to the training data to derive relevant features. A further reduction to only two features was based on the CA of a support vector machine (SVM) learning method (linear kernel; cost 0.9 regression loss epsilon 0.1; leave-one-out). Finally, ROC analysis on the test data was used to determine the diagnostic accuracy for the following predictors: (1) median T1 and ECV (2) two most relevant features (training) (3) combination of (1) and (2) (ROC AUC statistics (10)).

Results

The two most relevant features were the histogram feature ECV energy and the gray level size zone matrix (GLSZM) feature native T1 zone entropy, a measure of heterogeneity in the texture pattern.

A model to distinguish HCM from athletes based on these features outperformed the model using only median T1 and ECV values with both higher sensitivity and specificity (table 1) and a significantly  higher AUC in the ROC analysis (p < 0.05, figure 1). Combining these two features with median values did not improve the CA further. 

Conclusion

Texture analysis of motion-corrected T1 and ECV mapping images out-performs classical analysis based on average values in distinguishing HCM from athlete"s heart.

Towards optimized MR thermometry of the human heart at 3T

Catheter ablation using radio frequency (RF) has been used increasingly for the treatment of cardiac arrhythmias and may be combined with proton resonance frequency shift (PRFS) -based MR thermometry to determine the therapy endpoint. We evaluated the suitability of two different MR thermometry sequences (TFE and TFE-EPI) and three blood suppression techniques. Experiments were performed without heating, using an optimized imaging protocol including navigator respiratory compensation, cardiac triggering, and image processing for the compensation of motion and susceptibility artefacts. Blood suppression performance and its effect on temperature stability were evaluated in the ventricular septum of eight healthy volunteers using multislice double inversion recovery (MDIR), motion sensitized driven equilibrium (MSDE), and inflow saturation by saturation slabs (IS). It was shown that blood suppression during MR thermometry improves the contrast-to-noise ratio (CNR), the robustness of the applied motion correction algorithm as well as the temperature stability. A gradient echo sequence accelerated by an EPI readout and parallel imaging (SENSE) and using inflow saturation blood suppression was shown to achieve the best results. Temperature stabilities of 2 °C or better in the ventricular septum with a spatial resolution of 3.5 × 3.5 × 8mm(3) and a temporal resolution corresponding to the heart rate of the volunteer, were observed. Our results indicate that blood suppression improves the temperature stability when performing cardiac MR thermometry. The proposed MR thermometry protocol, which optimizes temperature stability in the ventricular septum, represents a step towards PRFS-based MR thermometry of the heart at 3 T.

[Diffusion-weighted MR imaging of the spine and cord]

Due to its excellent sensitivity, MR imaging is invaluable for the evaluation of lesions of the cord and spine. Several studies dedicated to diffusion-weighted MR evaluation of the cord and spine have been published. While diffusion-weighted MR imaging of the brain is routinely performed, it is seldom performed when imaging the spine due to serious limitations. While anatomical limitations may not be changed, the voxel size, phase-encoding direction, mode of k-space filling, and acceleration factor are all parameters that can be optimized in order to routinely obtain diffusion-weighted imaging of the spine on 1.5T and 3T scanners.