A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis

Figure 1. Illustration of multilinear principal component analysis (MPCA). PCA is a traditional linear dimensionality reduction method that extracts low-dimensional features from high-dimensional input. MPCA extends PCA to tensor representations of data. In this article, the input to MPCA is N samples of CMR sequences with size I × J × K, with a spatial dimension of I × J and a time dimension of K (frames). MPCA maps each I × J × K tensor to a low-dimensional P × Q × R tensor (P < I, Q < J, and R < K) using three projection matrices of size I × P, J × Q, and K × R. During training, these three matrices are optimized to maximize the variation captured in the N mapped P × Q × R tensors and these optimized matrices are the output of the MPCA learning algorithm. During testing, the learnt three matrices map a new I × J × K tensor input of I × J × K into a P × Q × R tensor as its low-dimensional representation.

Figure 2. Proposed machine learning workflow: manual landmarking, pre-processing steps with registration, masking, and rescaling, machine learning steps, visualization of learnt factors and features, and feature maps. The learnt factors in the fourth row are highly interpretable: the column (size I × 1) and row (size J × 1) factors capture spatial variations closely resemble wavelets popular in representing fundamental patterns in natural images, and the time factor (size K × 1) shows the cardiac phase.

Figure 3. Patient flow diagram.

Figure 4. Representative feature map images: (i) short-axis (A) and four-chamber (B) CMR images in a patient with PAH, taken from Phase 11, early diastole. Notably, features are identified at the level of the interventricular septum and close to the right ventricular outflow tract within the blood pool. (ii) Short-axis (C) and four-chamber (D) CMR images in a patient without PAH, taken from Phase 1, end-diastole. Features are noted at the level of the mid chamber/apex septum and basal LV lateral wall. Red features are more indicative of PAH and green indicative of no PAH. The colour bars show the amplitude of the features detected, with a higher absolution value indicating a higher importance.

Figure 5. Receiver operating characteristic curve analysis showing diagnostic accuracy of the proposed machine learning approach in identification of PAH and IPAH, using short-axis (scale: 64 × 64) and four-chamber images (scale: 128 × 128) and small ellipse mask.