Self-Supervised Joint Reconstruction and Denoising of T2-Weighted PROPELLER MRI of the Lung at 0.55T

Abstract

Purpose: To improve 0.55T T2-weighted PROPELLER lung MRI by developing a self-supervised framework for joint reconstruction and denoising. Methods: T2-weighted 0.55T lung MRI datasets from 44 patients with prior COVID-19 infection were used. Each PROPELLER blade was split along the readout direction into two disjoint subsets: one subset for training an unrolled network, and the other for loss calculation. Following the Noise2Noise paradigm, this framework split k-space into two subsets with independent, matched noise but identical underlying signal, enabling joint reconstruction and denoising without external training references. For comparison, coil-wise Marchenko-Pastur Principal Component Analysis (MPPCA) denoising followed by parallel imaging reconstruction was performed. The reconstructed images were evaluated by two experienced chest radiologists. Results: The self-supervised model generated lung images with improved clarity, better delineation of parenchymal and airway structures, and maintained high fidelity in cases with available CT references. In addition, the proposed framework also enabled further reduction of scan time by reconstructing images with adequate diagnostic quality from only half the number of blades. The reader study confirmed that the proposed method outperformed MPPCA across all categories (Wilcoxon signed-rank test, p < 0.001), with moderate inter-reader agreement (weighted Cohen's kappa = 0.55; percentage of exact and within ±1 point agreement = 91%). Conclusion: By leveraging the intrinsic data redundancy in PROPELLER sampling and extending the Noise2Noise concept, the proposed self-supervised framework enabled simultaneous reconstruction and denoising of lung images at 0.55T to address the low-SNR challenge at low-field. It holds great potential for broad use in other low-field MRI applications.