Volumetric parcellation of the cardiac right ventricle for regional geometric and functional assessment

Le Cœur en Sabot: shape associations with adverse events in repaired tetralogy of Fallot

BACKGROUND

Maladaptive remodelling mechanisms occur in patients with repaired tetralogy of Fallot (rToF) resulting in a cycle of metabolic and structural changes. Biventricular shape analysis may indicate mechanisms associated with adverse events independent of pulmonary regurgitant volume index (PRVI). We aimed to determine novel remodelling patterns associated with adverse events in patients with rToF using shape and function analysis.

METHODS

Biventricular shape and function were studied in 192 patients with rToF (median time from TOF repair to baseline evaluation 13.5 years). Linear discriminant analysis (LDA) and principal component analysis (PCA) were used to identify shape differences between patients with and without adverse events. Adverse events included death, arrhythmias, and cardiac arrest with median follow-up of 10 years.

RESULTS

LDA and PCA showed that shape characteristics pertaining to adverse events included a more circular left ventricle (LV) (decreased eccentricity), dilated (increased sphericity) LV base, increased right ventricular (RV) apical sphericity, and decreased RV basal sphericity. Multivariate LDA showed that the optimal discriminative model included only RV apical ejection fraction and one PCA mode associated with a more circular and dilated LV base (AUC = 0.77). PRVI did not add value, and shape changes associated with increased PRVI were not predictive of adverse outcomes.

CONCLUSION

Pathological remodelling patterns in patients with rToF are significantly associated with adverse events, independent of PRVI. Mechanisms related to incident events include LV basal dilation with a reduced RV apical ejection fraction.

Weakly supervised attention model for RV strain classification from volumetric CTPA scans

BACKGROUND AND OBJECTIVE

Evaluation of the right ventricle (RV) is a key component of the clinical assessment of many cardiovascular and pulmonary disorders. In this work, we focus on RV strain classification from patients who were diagnosed with pulmonary embolism (PE) in computed tomography pulmonary angiography (CTPA) scans. PE is a life-threatening condition, often without warning signs or symptoms. Early diagnosis and accurate risk stratification are critical for decreasing mortality rates. High-risk PE relies on the presence of RV dysfunction resulting from acute pressure overload. PE severity classification and specifically, high-risk PE diagnosis are crucial for appropriate therapy. CTPA is the golden standard in the diagnostic workup of suspected PE. Therefore, it can link between diagnosis and risk stratification strategies.

METHODS

We retrieved data of consecutive patients who underwent CTPA and were diagnosed with PE and extracted a single binary label of "RV strain biomarker" from the CTPA scan report. This label was used as a weak label for classification. Our solution applies a 3D DenseNet network architecture, further improved by integrating residual attention blocks into the network's layers.

RESULTS

This model achieved an area under the receiver operating characteristic curve (AUC) of 0.88 for classifying RV strain. For Youden's index, the model showed a sensitivity of 87% and specificity of 83.7%. Our solution outperforms state-of-the-art 3D CNN networks. The proposed design allows for a fully automated network that can be trained easily in an end-to-end manner without requiring computationally intensive and time-consuming preprocessing or strenuous labeling of the data.

CONCLUSIONS

This current solution demonstrates that a small dataset of readily available unmarked CTPAs can be used for effective RV strain classification. To our knowledge, this is the first work that attempts to solve the problem of RV strain classification from CTPA scans and this is the first work where medical images are used in such an architecture. Our generalized self-attention blocks can be incorporated into various existing classification architectures making this a general methodology that can be applied to 3D medical datasets.