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

Water ADC, extracellular space volume, and tortuosity in the rat cortex after traumatic injury

The diffusion parameters in rat cortex were studied 3–35 days following a cortical stab wound, using diffusion-weighted MR to determine the apparent diffusion coefficient of water (ADCW) in the tissue, and the real-time iontophoretic tetramethylammonium (TMA) method to measure the extracellular space (ECS) diffusion parameters: ECS volume fraction and the ADC of TMA+ (ADCTMA). Severe astrogliosis was found close to the wound, and mild astrogliosis was found in the ipsilateral but not the contralateral cortex. Chondroitin sulfate proteoglycan (CSPG) expression was increased throughout the ipsilateral cortex. In the hemisphere contralateral to the wound, , ADCTMA, and ADCW were not significantly different from control values. ECS volume fraction was increased only in the vicinity of the wound, in the region of cell death and severe astrogliosis, at 3 and 7 days after injury. However, both ADCTMA and ADCW were significantly decreased after lesion in the vicinity of the wound as well as in the rest of the ipsilateral hemisphere distant from the wound. Thus, both ADCW and ADCTMA decreased in regions wherein did not change but CSPG increased. An increase in extracellular matrix expression may therefore impose diffusion barriers for water as well as for TMA molecules