22 research outputs found
Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning
Recent development of quantitative myocardial blood flow (MBF) mapping allows
direct evaluation of absolute myocardial perfusion, by computing pixel-wise
flow maps. Clinical studies suggest quantitative evaluation would be more
desirable for objectivity and efficiency. Objective assessment can be further
facilitated by segmenting the myocardium and automatically generating reports
following the AHA model. This will free user interaction for analysis and lead
to a 'one-click' solution to improve workflow. This paper proposes a deep
neural network based computational workflow for inline myocardial perfusion
analysis. Adenosine stress and rest perfusion scans were acquired from three
hospitals. Training set included N=1,825 perfusion series from 1,034 patients.
Independent test set included 200 scans from 105 patients. Data were
consecutively acquired at each site. A convolution neural net (CNN) model was
trained to provide segmentation for LV cavity, myocardium and right ventricular
by processing incoming 2D+T perfusion Gd series. Model outputs were compared to
manual ground-truth for accuracy of segmentation and flow measures derived on
global and per-sector basis. The trained models were integrated onto MR
scanners for effective inference. Segmentation accuracy and myocardial flow
measures were compared between CNN models and manual ground-truth. The mean
Dice ratio of CNN derived myocardium was 0.93 +/- 0.04. Both global flow and
per-sector values showed no significant difference, compared to manual results.
The AHA 16 segment model was automatically generated and reported on the MR
scanner. As a result, the fully automated analysis of perfusion flow mapping
was achieved. This solution was integrated on the MR scanner, enabling
'one-click' analysis and reporting of myocardial blood flow.Comment: This work has been submitted to Radiology: Artificial Intelligence
for possible publicatio
Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study
Purpose: To develop a deep learning–based method for fully automated quantification of left ventricular (LV) function from short-axis cine MR images and to evaluate its performance in a multivendor and multicenter setting.
Materials and Methods: This retrospective study included cine MRI data sets obtained from three major MRI vendors in four medical centers from 2008 to 2016. Three convolutional neural networks (CNNs) with the U-NET architecture were trained on data sets of increasing variability: (a) a single-vendor, single-center, homogeneous cohort of 100 patients (CNN1); (b) a single-vendor, multicenter, heterogeneous cohort of 200 patients (CNN2); and (c) a multivendor, multicenter, heterogeneous cohort of 400 patients (CNN3). All CNNs were tested on an independent multivendor, multicenter data set of 196 patients. CNN performance was evaluated with respect to the manual annotations from three experienced observers in terms of (a) LV detection accuracy, (b) LV segmentation accuracy, and (c) LV functional parameter accuracy. Automatic and manual results were compared with the paired Wilcoxon test, Pearson correlation, and Bland-Altman analysis.
Results: CNN3 achieved the highest performance on the independent testing data set. The average perpendicular distance compared with manual analysis was 1.1 mm ± 0.3 for CNN3, compared with 1.5 mm ± 1.0 for CNN1 (P < .05) and 1.3 mm ± 0.6 for CNN2 (P < .05). The LV function parameters derived from CNN3 showed a high correlation (r2 ≥ 0.98) and agreement with those obtained by experts for data sets from different vendors and centers.
Conclusion: A deep learning–based method trained on a data set with high variability can achieve fully automated and accurate cine MRI analysis on multivendor, multicenter cine MRI data
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Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need.
Cardiac MRI (CMR) allows non-invasive, non-ionizing assessment of cardiac function and anatomy in patients with congenital heart disease (CHD). The utility of CMR as a non-invasive imaging tool for evaluation of CHD have been growing exponentially over the past decade. The algorithms based on artificial intelligence (AI), and in particular, deep learning, have rapidly become a methodology of choice for analyzing CMR. A wide range of applications for AI have been developed to tackle challenges in various aspects of CMR, and significant advances have also been made from image acquisition to image analysis and diagnosis. We include an overview of AI definitions, different architectures, and details on well-known methods. This paper reviews the major deep learning concepts used for analyses of patients with CHD. In the end, we have summarized a list of open challenges and concerns to be considered for future studies