Assessment of left atrial (LA) fibrosis from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) adds to the management of patients with atrial fibrillation (AF). However, accurate assessment of fibrosis in the LA wall remains challenging. Excluding anatomical structures in the LA proximity using clipping techniques can reduce misclassification of LA fibrosis. A novel FK-means approach for combined automatic clipping and automatic fibrosis segmentation was developed. This approach combines a feature-based Voronoi diagram with a hierarchical 3D K-means fractal-based method. The proposed automatic Voronoi clipping method was applied on LGE MRI data and achieved a Dice score of 0.75, similar as the score obtained by a deep learning method (3D UNet) for clipping (0.74). The automatic fibrosis segmentation method, which utilizes the Voronoi clipping method, achieved a Dice score of 0.76. This outperformed a 3D U-Net method for clipping and fibrosis classification, which had a Dice score of 0.69. Moreover, the proposed automatic fibrosis segmentation method achieved a Dice score of 0.90, using manual clipping of anatomical structures. The findings suggest that the automatic FK-means analysis approach enables reliable LA fibrosis segmentation and that clipping of anatomical structures in the atrial proximity can add to the assessment of atrial fibrosis. 

Purpose To implement and evaluate a new dictionary-based technique for native myocardial T-1 and T-2 mapping using Cartesian sampling. Methods The proposed technique (Multimapping) consisted of single-shot Cartesian image acquisitions in 10 consecutive cardiac cycles, with inversion pulses in cycle 1 and 5, and T-2 preparation (TE: 30 ms, 50 ms, and 70 ms) in cycles 8-10. Multimapping was simulated for different T-1 and T-2, where entries corresponding to the k-space centers were matched to acquired data. Experiments were performed in a phantom, 16 healthy subjects, and 3 patients with cardiovascular disease. Results Multimapping phantom measurements showed good agreement with reference values for both T-1 and T-2, with no discernable heart-rate dependency for T-1 and T-2 within the range of myocardium. In vivo mean T-1 in healthy subjects was significantly higher using Multimapping (T-1 = 1114 +/- 14 ms) compared to the reference (T-1 = 991 +/- 26 ms) (p < 0.01). Mean Multimapping T-2 (47.1 +/- 1.3 ms) and T-2 spatial variability (5.8 +/- 1.0 ms) was significantly lower compared to the reference (T-2 = 54.7 +/- 2.2 ms, p < 0.001; spatial variability = 8.4 +/- 2.0 ms, p < 0.01). Increased T-1 and T-2 was detected in all patients using Multimapping. Conclusions Multimapping allows for simultaneous native myocardial T-1 and T-2 mapping with a conventional Cartesian trajectory, demonstrating promising in vivo image quality and parameter quantification results.