Magnetisation transfer (MT) imaging of the central nervous system has provided further insight into the pathophysiology of neurological disease. However, the use of this method to study the lower ...spinal cord has been technically challenging, despite the important role of this region, not only for motor control of the lower limbs, but also for the neural control of lower urinary tract, sexual and bowel functions. In this study, the feasibility of obtaining reliable grey matter (GM) and white matter (WM) magnetisation transfer ratio (MTR) measurements within the lumbosacral enlargement (LSE) was investigated in ten healthy volunteers using a clinical 3T MRI system. The mean cross-sectional area of the LSE (LSE-CSA) and the mean GM area (LSE-GM-CSA) were first obtained by means of image segmentation and tissue-specific (i.e. WM and GM) MTR measurements within the LSE were subsequently obtained. The reproducibility of the segmentation method and MTR measurements was assessed from repeated measurements and their % coefficient of variation (%COV). Mean ( plus or minus SD) LSE-CSA across 10 healthy subjects was 59.3 ( plus or minus 8.4) mm2 and LSE-GM-CSA was 17.0 ( plus or minus 3.1) mm2. The mean intra- and inter-rater % COV for measuring the LSE-CSA were 0.8% and 2.3%, respectively and for the LSE-GM-CSA were 3.8% and 5.4%, respectively. Mean ( plus or minus SD) WM-MTR was 43.2 ( plus or minus 4.4) and GM-MTR was 40.9 ( plus or minus 4.3). The mean scan-rescan % COV for measuring WM-MTR was 4.6% and for GM-MTR was 3.8%. Using a paired t-test, a statistically significant difference was identified between WM-MTR and GM-MTR in the LSE (p<0.0001). This pilot study has shown that it is possible to obtain reliable tissue-specific MTR measurements within the LSE using a clinical MR system at 3T. The MTR acquisition and analysis protocol presented in this study can be used in future investigations of intrinsic spinal cord diseases that affect the LSE.
Lung ultrasound imaging has been shown effective in detecting typical patterns for interstitial pneumonia, as a point-of-care tool for both patients with COVID-19 and other community-acquired ...pneumonia (CAP). In this work, we focus on the hyperechoic B-line segmentation task. Using deep neural networks, we automatically outline the regions that are indicative of pathology-sensitive artifacts and their associated sonographic patterns. With a real-world data-scarce scenario, we investigate approaches to utilize both COVID-19 and CAP lung ultrasound data to train the networks; comparing fine-tuning and unsupervised domain adaptation. Segmenting either type of lung condition at inference may support a range of clinical applications during evolving epidemic stages, but also demonstrates value in resource-constrained clinical scenarios. Adapting real clinical data acquired from COVID-19 patients to those from CAP patients significantly improved Dice scores from 0.60 to 0.87 (p < 0.001) and from 0.43 to 0.71 (p < 0.001), on independent COVID-19 and CAP test cases, respectively. It is of practical value that the improvement was demonstrated with only a small amount of data in both training and adaptation data sets, a common constraint for deploying machine learning models in clinical practice. Interestingly, we also report that the inverse adaptation, from labelled CAP data to unlabeled COVID-19 data, did not demonstrate an improvement when tested on either condition. Furthermore, we offer a possible explanation that correlates the segmentation performance to label consistency and data domain diversity in this point-of-care lung ultrasound application.
We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification ...during coronavirus pandemics. The proposed system consists of two deep-learning-based models: a quality assessment module that automates predictions of image quality, and a diagnosis assistance module that determines the likelihood-oh-anomaly in ultrasound images of sufficient quality. Our two-stage strategy uses a novelty detection algorithm to address the lack of control cases available for training the quality assessment classifier. The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module. Using more than 25000 ultrasound images from 37 COVID-19-positive patients scanned at two hospitals, plus 12 control cases, this study demonstrates the feasibility of using the proposed machine learning approach. We report an accuracy of 86% when classifying between sufficient and insufficient quality images by the quality assessment module. For data of sufficient quality - as determined by the quality assessment module - the mean classification accuracy, sensitivity, and specificity in detecting COVID-19-positive cases were 0.95, 0.91, and 0.97, respectively, across five holdout test data sets unseen during the training of any networks within the proposed system. Overall, the integration of the two modules yields accurate, fast, and practical acquisition guidance and diagnostic assistance for patients with suspected respiratory conditions at point-of-care.
A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI) and infer properties about the white matter ...microstructure. However, a head-to-head comparison of DW-MRI models is critically missing in the field. To address this deficiency, we organized the "White Matter Modeling Challenge" during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed at identifying the DW-MRI models that best predict unseen DW data. in vivo DW-MRI data was acquired on the Connectom scanner at the A.A.Martinos Center (Massachusetts General Hospital) using gradients strength of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the whole dataset, and their models were ranked on their ability to predict the remaining unseen quarter of data. In this paper we provide both an overview and a more in-depth description of each evaluated model, report the challenge results, and infer trends about the model characteristics that were associated with high model ranking. This work provides a much needed benchmark for DW-MRI models. The acquired data and model details for signal prediction evaluation are provided online to encourage a larger scale assessment of diffusion models in the future.
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