Background Quantitative assessment of dynamic lung water accumulation is of interest to unmask latent heart failure. We develop and validate a free-breathing 3D ultrashort echo time (UTE) sequence with automated inline image processing to image changes in lung water density (LWD) using high-performance 0.55 T cardiovascular magnetic resonance (CMR). Methods Quantitative lung water CMR was performed on 15 healthy subjects using free-breathing 3D stack-of-spirals proton density weighted UTE at 0.55 T. Inline image reconstruction and automated image processing was performed using the Gadgetron framework. A gravity-induced redistribution of LWD was provoked by sequentially acquiring images in the supine, prone, and again supine position. Quantitative validation was performed in a phantom array of vials containing mixtures of water and deuterium oxide. Results The phantom experiment validated the capability of the sequence in quantifying water density (bias ± SD 4.3 ± 4.8%, intraclass correlation coefficient, ICC = 0.97). The average global LWD was comparable between imaging positions (supine 24.7 ± 3.4%, prone 22.7 ± 3.1%, second supine 25.3 ± 3.6%), with small differences between imaging phases (first supine vs prone 2.0%, p < 0.001; first supine vs second supine − 0.6%, p = 0.001; prone vs second supine − 2.7%, p < 0.001). In vivo test–retest repeatability in LWD was excellent (− 0.17 ± 0.91%, ICC = 0.97). A regional LWD redistribution was observed in all subjects when repositioning, with a predominant posterior LWD accumulation when supine, and anterior accumulation when prone (difference in anterior–posterior LWD: supine − 11.6 ± 2.7%, prone 5.5 ± 2.7%, second supine − 11.4 ± 2.9%). Global LWD maps were calculated inline within 23.2 ± 0.3 s following the image reconstruction using the automated pipeline. Conclusions Redistribution of LWD due to gravitational forces can be depicted and quantified using a validated free-breathing 3D proton density weighted UTE sequence and inline automated image processing pipeline on a high-performance 0.55 T CMR system.

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Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5–40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.